pharmacokinetic challenges in drug discovery

Ernst Schering Research Foundation Workshop 37 Pharmacokinetic Challenges in Drug Discovery Ernst Schering Research Foundation Workshop 37 Pharmacokinetic Challenges in Drug Discovery

Springer-Verlag Berlin Heidelberg GmbH Springer-Verlag Berlin Heidelberg GmbH

Ernst Schering Research Foundation Workshop 37

Pharmacokinetic Challenges in Drug Discovery

o. Pelkonen, A. Baumann, A. Reichel Editors

With 83 Figures and 18 Tables

, Springer

Series Editors: G. Stock and M. Lessl

ISSN 0947-6075 ISBN 978-3-662-04385-1

Die Deutsche Bibliothek - CIP-Einheitsaufnahme Pharmacokinetic Challenges in Drug Discovery / O. Pelkonen. A. Baumann, A. Reichel ed ..

(Erust Schering Research Foundation Workshop; 37) ISBN 978-3-662-04385-1 ISBN 978-3-662-04383-7 (eBook) DOI 10.1007/978-3-662-04383-7

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Preface

The development of new therapeutic agents is an increasingly costly undertaking in which attrition rates at all steps of development are high and the successful outcome uncertain. On the other hand, current advances in in vitro approaches and technologies have created a situation in which much knowledge critical for assessment and extrapolation could be, and in fact is being, produced early in the discovery and development process. However, although data are being produced, the transformation into useful knowledge on which to base decisions about the direction of discovery and development is not always self-evident.

It has become increasingly apparent that the pharmacokinetic properties of a drug, i.e. absorption, distribution, metabolism and excretion (ADME), are of the utmost importance for clinical success. Furthermore, interactions have become crucial for the assessment of drugs. Unwanted properties in pharmacokinetics, be they poor absorption, genetic polymorphism in a metabolic enzyme or a transporter, or a potent interaction, could result in failure during clinical trials or withdrawal after registration. A late failure is extremely costly for the industry. Consequently, the pharmacokinetic properties of a drug, especially keeping in mind intended clinical use and goals, should preferably be, many would say must be, elucidated relatively early. This means that elimination characteristics, half-life or clearance, principal metabolites (also whether there is formation of active metabolites), potential interactions and so on, should be screened and appropriate extrapolations and predictions made as early as possible during the drug discovery and development process. It is also important to bear in mind species differences and to put them through a preliminary screening because

VI Preface

The organisers and speakers of the workshop. From left to right, back: M.K. Bayliss, A. Baumann, C. Wienhold, G. Cruziani, B. Wallmark, J. Dixon, G.S.J. Mannens, H. van de Waterbeemd, G. Fricker, A. Reichel, T. Lave; front: M. Lessl, B. Subramanyam, T.V. Olah, O. Pelkonen, J.H. Lin, A.K. Mandagere

animal toxicology is an integral part of a prec1inical dossier and its assessment regarding anticipated human toxicology is an important part of the overall process.

Some other trends in drug discovery and development create additional challenges for ADME screening of drugs. Through combinatorial chemistry and the use of high-throughput (HT) drug target screening, larger numbers of molecules emerge for toxicity and kinetics screening. The ideal scenario is that through an efficient and reliable optimization and selection process, a few carefully evaluated molecules are launched into further development. Efficiency means HTS and reliability means adequate validation, but in reality these partially competing goals have to be reconciled in a productive way, possibly via extensive in silico approaches and modelling at the molecular, subcellular, cellular, tissue and organism level. All these areas of research

Preface VII

are in such a critical state of development that an integrated overview is needed to develop and apply them optimally in the process of drug discovery.

For these reasons, in early 2000, we decided at Schering AG to arrange a symposium on pharmacokinetic challenges in drug discovery in the Ernst Schering Research Foundation series. The organizers realized that there was a need to bring together experts from both industry and academia to present state-of-the-art information and views on specific aspects of the symposium's topic and to discuss wider implications for the future. The lectures presented during the symposium have now been collected together with the respective discussions as well as the final fornm discussion in this volume, which the editors hope will provide useful reading for scientists in the pharmaceutical industry as well as in research institutions and universities interested in drug discovery and development.

Dr. Andreas Baumann, Schering AG, Berlin Prof Olavi Pelkonen, University of Oulu Dr. Andreas Reichel, Schering AG, Berlin

Contents

1 Accelerating the Process of Drug Discovery A.M. Davis, J. Dixon, C.J. Logan, D. W. Payling 1

2 The Role of Pharmacokinetics in Drug Discovery: Finding Drug Candidates with the Greatest Potential for Success J.H. Lin ... . . . . . . . . . . . . . . . . . . . 33

3 Rapid Permeability Screening in Drug Discovery to Predict Human Intestinal Absorption G.S.l. Mannens, H. Bohets, P. Verboven, K. Steemans, K. Lavrijsen, W. Meuldermans . . . . . . . . . . . . . 49

4 Drug Metabolism Assays and Their Use in Drug Discovery M.K. Bayliss, P.l. Eddershaw ................ 69

5 Prediction of Human Pharmacokinetics Based on Preclinical In Vitro and In Vivo Data T. Lave, O. Luttringer, J. Zuegge, G. Schneider, P. Coassolo, F.-P. Theil .......................... 81

6 In Vitro Screening of Cytochrome P450 Induction Potential O. Pelkonen, J. Hukkanen, P. Honkakoski, J. Hakkola, P. Viitala, H. Raunio .............. .

7 Drug Transport Across the Blood-Brain Barrier

105

G. Fricker . . . . . . . . . . . . . . . . . . . . . . . . . . . 139

8 The Development and Implementation of Bioanalytical Methods Using LC-MS to Support ADME Studies in Early Drug Discovery and Candidate Selection T. V. Olah .................... ..... 155

9 Strategies in Lead Selection and Optimization: Application of a Graphical Model and Automated In Vitro ADME Screening A.K. Mandagere ................ .

10 High-Throughput Screening - Brains Versus Brawn

.... 185

D.A. Smith ...................... ... 203

11 Relation of Molecular Properties with Drug Absorption and Disposition H. van de Waterbeemd .. . . . . . . . . . . . . . . . 213

12 Modelling Human Cytochrome P450-Substrate Interactions D.F. V. Lewis 235

13 Forum Discussion 249

Subject Index . . . . . 299

Previous Volumes Published in This Series 303

List of Editors and Contributors

Editors

A. Baumann Research Pharmacokinetics, Schering AG, MiillerstraBe 178, 13342 Berlin, Germany (e-mail: [email protected])

o. Pelkonen Department of Pharmacology and Toxicology, University of Oulu, PL 5000 (Aapistie 5),90014 Oulu, Finnland (e-mail: [email protected])

A. Reichel Research Pharmacokinetics, Schering AG, MiillerstraBe 178, 13342 Berlin, Germany (e-mail: [email protected])

Contributors

M.K. Bayliss Head Preclinical Drug Discovery, Respiratory, Inflamation and Respiratory Pathogens CEDD, GlaxoSmithKline R&D, Park Road, Ware, Hertfordshire, SGl2 ODP, UK (e-mail: [email protected])

H. Bohets Department of Pharmacokinetics, Janssen Pharmaceutic a, Turnhoutseweg 30, 2340 Beerse, Belgium (e-mail: [email protected])

P. Coassolo F.-Hoffman-La Roche Inc., Drug Discovery Support, PRBN 68/336a, GrenzacherstrBe 124,4070 Basel, Switzerland (e-mail: [email protected])

XII List of Editors and Contributors

A.M. Davis Astra Zeneca Charnwood Discovery, Bakewell Road, Loughborough, LEI 1 5RH, UK (e-mail: [email protected])

J. Dixon Astra Zeneca Charnwood Discovery, Bakewell Road, Loughborough, LEI 1 5RH, UK (e-mail: [email protected])

P.J. Eddershaw DMPK, Pre-Clinical Drug Discovery, GlaxoSmithKline R&D, Park Road, Ware, UK, SGl2 ODP (e-mail: [email protected])

G. Fricker Institut fiir Pharmazeutische Technologie und Biopharmazie, University of Heidelberg, 1m Neuenheimer Feld 366, 69120 Heidelberg, Germany

(e-mail: [email protected] )

J. Hakkola Department of Pharmacology and Toxicology, University of Oulu, 90014 Oulu, Finland (e-mail: [email protected])

P. Honkakoski Department of Pharmacy, University of Kuopio, 70211 Kuopio, Finland (e-mail: [email protected])

J. Hukkanen Department of Pharmacology and Toxicology, University of Oulu, 90014 Oulu, Finland (e-mail: [email protected])

T.Lave F.-Hoffmann-La Roche Inc, Drug Discovery Support, PRBN 68/329, GrenzacherstraBe 124,4070 Basel, Switzerland (e-mail: [email protected])

K. Lavrijsen Department of Pharmacokinetics, Janssen Pharmaceutica, Turnhoutseweg 30, 2340 Beerse, Belgium (e-mail: [email protected])

D.F. V. Lewis School of Biological Sciences, University of Surrey, Guildford, Surrey, GU2 7XH, UK (e-mail: [email protected])

List of Editors and Contributors XIII

J.H. Lin WP26A-2044, Department of Drug Metabolism, Merck Research Laboratories, West Point, PA 19486, USA (e-mail: [email protected])

C.J. Logan Astra Zeneca Charnwood Discovery, Bakewell Road, Loughborough, LEI 1 5RH, UK (e-mail: [email protected])

O. Luttringer F.-Hoffmann-La Roche Inc, Drug Discovery Support, PRBN 68/329, GrenzacherstraBe 124,4070 Basel, Switzerland (e-mail: [email protected])

A.K. Mandagere Compound Management, Parke-Davis Pharmaceutical Research, Pfizer Global Research and Development, 2800 Plymouth Road, Ann Arbor, MI 48105, USA (e-mail: [email protected])

G.S.J. Mannens Janssen Pharmaceutica. N.V., Turnhoutseweg 30, 2340 Beerse, Belgium (e-mail: [email protected])

W. Meuldermans Department of Pharmacokinetics, Janssen Pharmaceutica, Turnhoutseweg 30, 2340 Beerse, Belgium (e-mail: [email protected])

T.V.Olah Drug Metabolism and Pharmacokinetics, DuPont Pharmaceutical Company, Stine-Haskell Research Center, Haskell I, P.O. Box 30, Newark, Delaware 19714, USA (e-mail: [email protected])

D. W. Payling Astra Zeneca Charnwood Discovery, Bakewell Road, Loughborough, LEI 1 5RH, UK (e-mail: [email protected])

H. Raunio Department of Pharmacology and Toxicology, University of Kuopio, 70211 Kuopio, Finland (e-mail: [email protected])

XIV List of Editors and Contributors

G. Schneider F.-Hoffmann-La Roche Inc, Drug Discovery Support, PRBN 68/329, GrenzachestraBe 124,4070 Basel, Switzerland (e-mail: [email protected])

D.A. Smith Drug Metabolism Department, Central Research, Pfizer Limited, Sandwich, Kent, CT13 9NJ, UK (e-mail: [email protected])

K. Steemanns Department of Pharmacokinetics, Janssen Pharmaceutica, Turnhoutseweg 30, 2340 Beerse, Belgium (e-mail: [email protected])

F.-P. Theil F.-Hoffmann-La Roche Inc, Drug Discovery Support, PRBN 68/329, GrenzacherstraBe 124,4070 Basel, Switzerland (e-mail: [email protected])

P. Verboven Department of Pharmacokinetics, Janssen Pharmaceutica, Turnhoutseweg 30, 2340 Beerse, Belgium (e-mail: [email protected])

P. Viitala Department of Pharmacology and Toxicology, University of Oulu, 90014 Oulu, Finland (e-mail: [email protected])

H. van der Waterbeemd Drug Metabolism Department, Central Research, Pfizer Limited, Dandwich, Kent, CT13 9NJ, UK (e-mail: [email protected])

1. Zuegge F.-Hoffmann-La Roche Inc, Drug Discovery Support, PRBN 68/329, GrenzacherstraBe 124,4070 Basel, Switzerland (e-mail: [email protected])

1 Accelerating the Process of Drug Discovery

A.M. Davis, J. Dixon, C.J. Logan, D.W. Payling

1.1 Introduction: Speed with Quality .......................... . 1.2 Chemical Starting Points ................................. 4 1.3 DMPK in Drug Discovery ................................ 9 1.4 Metabolic Optimisation .................................. 17 1.5 Prediction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 26 1.6 Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 30 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 31

1.1 Introduction: Speed with Quality

This introductory chapter is intended to provide a close examination of the role of drug metabolism and pharmacokinetics (DMPK) in accelerating the drug discovery process. In concentrating on this aspect, however, it is impossible to separate the role and importance of physical properties of the compounds under study. Whilst biological screening provides information of the effect of the compound on the biological system, DMPK data give a feedback of the effect of the biological system on the compound. The importance of physical properties in this will be highlighted. To do justice to this we have had to omit many other factors. Some of those which will not be discussed include the very important interaction of the discovery departments with other functions within pharmaceutical research and development (R&D). These include, for example, the input of the safety assessment function in an

2 A. M. Davis et al.

appropriate manner to ensure that early information is available to guide decision-making by discovery management.

Later chapters in this volume cover many of the specialised aspects of the role of DMPK in discovery. These range from relatively constrained in vitro studies on the one hand to the wider aspects of prediction of pharmacokinetics in man on the other. The present chapter, therefore, also seeks to avoid more detailed discussion of these special aspects of DMPK. Instead it is intended to concentrate on the process of drug discovery and the role of DMPK in acceleration of optimisation. This will include discussion of the meaning and process of metabolic optimisation, together with the strategies and tools which have become or are becoming available to assist in its achievement. Our own approach to prediction and scaling of pharmacokinetics to man is also discussed in some detail since it is so central to our process of optimisation.

To illustrate the various points discussed, some projects have been selected from recent work at AstraZeneca. Choice has proved difficult since, in our experience, no project exemplifies only a single point. The particular factors which determine the origins of the acceleration of a project can consequently be a matter for debate. Similarly, we have been constrained to preserve proprietary information on the identity of projects and chemical structures whilst simultaneously trying to give information which is topical about the discovery process. We have attempted to reconcile these to the best of our abilities and to illustrate the factors we believe to be the most important in accelerating the process of drug discovery.

1.1.1 Corporate Requirement

The achievement of speed in the drug discovery process alone is not a sufficient goaL Speed can be obtained in its own right by application of the technologies of high throughput chemical synthesis, biological screening and drug metabolism. The requirement of corporate management, however, is for major products or "megabrands". This ambition is often emphasised and its requirement comes from the costs of R&D, particularly clinical development costs. Estimates of today's costs are in the order of US $500-$600 million per new chemical entity (NCE)

Accelerating the Process of Drug Discovery 3

(Drews 1997; UK Office of Health Economics 1999). This translates into a requirement for an NCE for the general practice market to achieve sales in excess of US $600-$800 million per year at its peak (Drews 1997). However, at present only a disappointing 8% of products reach sales of US $350 million per year (Andersen Consulting 1996).

Which NCEs reach the desired financial target? Examples currently include such products as arnlodipine (Pfizer), an anti-hypertensive calcium antagonist for once-daily dosing (US $3.0 billion). Others include Claritin (Schering Plough) a once-daily dosed histamine HI-antagonist for rhinitis (US $3.0 billion) and Singulair (Merck), a leukotriene-<f4 antagonist for once-daily treatment of asthma (US $1.0 billion). Oncedaily dosing is a common factor in these products, and it is apparent that in any sector of general practice medicine this high degree of pharmacokinetic quality is of key importance. It is this area of drug discovery which will be addressed in the following discussion. Other factors and considerations would apply, for example, in the case of drugs for topical application, or where it is necessary to design compounds which limit or avoid systemic exposure in the patient.

The converse situation is the proportion of candidate drugs (CDs) which fail in development through pharmacokinetic (pk) deficiencies. In recent years the proportion has been as high as 31 % (excluding anti-infectives) (Prentis et al. 1988). More recent figures (McAuslane 1999) suggest the proportion of failures (1994-1997) may remain as high as 24%. It is clear, therefore, that excellent pk properties are a key attainment in successful medicines, and that these DMPK properties are a significant factor in failures in development. In achieving corporate financial goals for products, therefore, DMPK has a prominent and strategic role in the discovery process.

In addressing the acceleration of the process of drug discovery, therefore, it is essential to define the DMPK properties of a CD. At the time of CD nomination these should include the expectation of appropriate (or excellent) pks in man. They should also include the minimisation of the potential for drug-drug interactions caused by cytochrome (CYP) inhibition or induction.

These pk and metabolic properties are most readily achieved if the chemical starting point for lead optimisation is appropriate. We start, therefore, by discussing chemical leads and their properties.


1.2 Chemical Starting Points

It seems a straightforward assumption that the choice of chemical starting point or lead can be the key in the rapid optimisation towards a potential candidate drug. But often there is little choice in the starting point. In the past, leads may have been the natural ligand or substrate, natural products, folklore medicines, other drugs identified by their side effects, rational or de novo design or indeed in a me-too project based on drugs discovered by other companies. In recent years, high throughput screening has become probably the most favoured source of leads. High-throughput screening (HTS) provides a number of advantages over other sources of leads. It can provide a number of chemically distinct starting points at once. With a novel corporate screening library, hits will be novel and patentable, and from the development of a suitable screen for HTS, leads can be identified in only a few months. The potential of high throughput screening is only limited by the size and quality of the screening file - and the drive to "feed" the screening robots has led to the rapid evolution of combinatorial chemistry. The initial focus for combinatorial library design to supply HTS was library size and library diversity (Alper 1994). But the importance of the quality of the HTS-derived chemical starting point eventually became apparent.

1.2.1 "Lead-Like" Properties

In the 1990s it was realised at Pfizer that the physicochemical properties of their compounds entering development were becoming more extreme - becoming larger and more lipophilic (Lipinski et al. 1997). The source of this change was clearly traced to the beginning of high throughput screening - by then heavily relied on in many laboratories as the source of new projects. In trying to develop guidelines for chemical synthesis, Chris Lipinski at Pfizer, in a seminal piece of work, analysed the property distributions of orally active compounds in clinical phase II development. Lipinski found that 90% of compounds with a USAN number (United States Adopted Name - applied when compounds enter phase II clinical development) had log P<5, Mwt<500, number of Hbond donors<5 and a sum of Ns and Os<1O - and that 95% of these compounds did not break more than two of these physical property rules

Accelerating the Process of Drug Discovery

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5

CD target plCso >8

..................

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- which have become known as "the rules of five". In an attempt to control the natural tendency of chemists to make increasingly large and lipophilic molecules, Pfizer introduced alerts at the point of registration on the company compound collection and high throughput solubility screening to detect problem compounds. The message was not lost on combinatorial chemistry. The new paradigm was the synthesis of "druglike" libraries for HTS screening through the application of Lipinski's rules to library design (Sadowski et al. 1998; Ajay et al. 1998, 1999).

Historically, two classes of starting point could be identified. The first consisted of "lead-like", low affinity (>0.1 flM) compounds which have low molecular weight and Clog P, typified by some endogenous molecules, e.g. histamine and g-aminobutyric acid (GABA). These had been converted into drugs through the optimisation of potency and pk profile, by increasing molecular weight and lipophilicity. The second major source of leads was typified by high affinity and molecular weight. It encompassed many peptidic materials and certain potent natural products. This type of lead has provided a number of drugs, e.g. indinavir from the substrate for HIV protease (Dorsey et al. 1994) and trimethoprim from dihydrofolate (Then 1993). Here the issue is usually one of retaining sufficient potency whilst improving the pk profile. This


Early GPCR Library Lead-like Library

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25 "E ;: :::I 20 _ 0 15 :::I

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Fig_ 2_ "Lead-like" library design

is often achieved by reducing molecular weight and increasing lipophilicity.

However, HTS (Fig. 1) had introduced a third class of starting point. This consisted of compounds with low affinity, but with "drug-like" molecular weight (350-500) and lipophilicity (log P-3-5). Our experience was that optimisation of these starting points with "drug-like" properties and "lead-like" affinity was difficult. "Drug-like" leads probably achieve micromolar affinity using many poorly optimised interactions, and we often found that attempted optimisation was hampered by a very flat structure-activity relationship (SAR). The other outcome was that optimisation was successful - but potency increase occurred with increase in molecular weight and lipophilicity. This took our physicochemical properties outside the oral window - which was also, of course, the experience at Pfizer. We have responded to this by changing our focus to one which has become the search for "lead-like" leads of lower molecular weight and lipophilicity as the preferred choice of hits (l-lO flM) from HTS (Teague et al. 1999). In order that these quality starting points can be found, our strategy has been to populate our screening library with more "lead-like" compounds. Our view was that application of "drug-like" filters in library design was not a sufficiently demanding criterion for libraries intended as the source of leads.


In Fig. 2 it can be seen that library properties need to be "left-shifted" relative to the oral drugs profile for lead generation. The development of our combinatorial library design strategy is illustrated in the figure, using a g-protein-coupled receptor (GPCR) library as an example. Whilst our early GPCR library was significantly "right-shifted" in terms of both molecular weight and log P (not shown) from the oral drugs profile, our later library is clearly more "lead-like" in nature.

1.2.2 Development Pressure on Candidate Drugs

While the "drug-like" paradigm and its natural extension to the "leadlike" paradigm seem obvious, they are not without their critics. The most common criticism is that the whole "drug-like" paradigm is flawed, as the physicochemical property profiles of oral drugs is more a reflection of yesterday's drugs and yesterday's chemistry and not of today's. Thus it would be argued that all the easy "druggable" targets have been addressed. Therefore, we are left to work on the more refractory targets. However, our chemical skill is increasing as chemical synthesis continues to provide cleaner, more selective and complex transformations, allowing more complex structures to be synthesised more easily.

We have recently analysed the physicochemical properties of compounds from the R&D Insight database reported to have been in development at some point in the 8 years up to January 2000 (R&D Insight 2000). This analysis, grouped by the development phase, tells a different story (Fig. 3). It shows that progress through the development process

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R' = 0.9798

Fig. 3. Development pressure on candidate drugs

Eight years data from R&D Insight


applies a Darwinian selection pressure, pruning the mean molecular weight towards that of the oral drugs (Physician's Desk Reference 1999). In addition the compounds in phase I clinical development are our industry's most recent successes in CD nomination. They still show a significantly higher mean molecular weight than the oral drugs, which makes one wonder if we really have learnt from Lipinski's message.

1.2.3 Choice of Chemical Leads

The enrichment of the screening library with "lead-like" probes will significantly favour the HTS-based approach of project generation, and favour a rapid lead optimisation (LO) process. Multiple leads of high quality from HTS also offer robustness to the LO process.

Our present process, therefore, aims to take multiple leads forward -to allow for choice during the LO project and at CD nomination (Fig. 4). During LO, unforeseen problems with one series will not slow down the project. When this occurs, a compound series can be halted, or even dropped, allowing others to progress. In order to ensure a flow of new chemical starting points, a rerun of the HTS will also be initiated, assuming the screening collection has grown significantly in the meantime. This· allows the identification of a third and possibly a fourth

o 12 24 28 Hitto Lead 1

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Prenomination 1

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I CD 2 I

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Second Dissimilar Candidate Drug can come from different series 3

and 4 - up to 6 months later?

Fig. 4. Choice between chemical leads


series. The process also increases the probability that the CD back-up compound will come from a different chemical series - decreasing the chance of series-specific toxicity or some other serious development problem which might have the potential to kill the whole project.

Lipinski's initial work was focussed upon absorption properties of development candidates. However, the control of physical properties by appropriate choice of starting point and the maintenance of that control throughout the lead optimisation process benefits all DMPK properties, as we shall continue to highlight.

1.3 DMPK in Drug Discovery

The starting point for DMPK in the discovery process will now be discussed together with the nature of the input at the various stages of optimisation towards a CD.

1.3.1 Pattern of DMPK Discovery Work

The left-hand side of the cyclic, iterative optimisation process in drug discovery (Fig. 5) has been familiar for many years. In some organisations it is known as the "make-test" cycle. The role of DMPK, together with physical organic chemistry in discovery, is depicted on the righthand side, and is shown as a screening or testing process which runs simultaneously with biological testing. The diagram depicts the cyclic process of compounds being synthesised followed by their testing. The resulting information influences further compound design and syntheSIS.

The work of DMPK in the optimisation cycle has both in vitro and in vivo aspects. One of the major aspects of the in vitro work is the use of appropriate liver preparations from appropriate species to obtain an estimate of metabolic stability. However, it is often not appreciated that a key adjunct to this work is metabolite identification. The species and preparations in our own work have concentrated on those which are best understood. These are rat, dog and man. The preparations which have proved most useful are microsomal, hepatocyte and recombinant systems.


Develop information Understanding information

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Fig. 5. Optimisation cycle

The in vivo work is usually concentrated on the rat, with some use of the dog and also the species for in vivo pharmacology [for estimation of pk- pharmacodynamic (pd) relationships]. Together this in vitro and in vivo work forms the basis of a weekly screening cycle. Additional studies are needed on an occasional basis. These are compound or project related and include, for example, in vitro absorption or permeability screening. This is increasingly conducted on a computational basis since it has been found that by choice of appropriate physicochemical parameters it is possible to predict absorption. However, the in vitro screens can be particularly useful when in vivo studies show unexpectedly poor bioavailability. Similar occasional studies or scans are taken to ensure the absence of CYP inhibition and induction. If a CYP inhibition or induction problem is encountered, these activities can become part of the screening cascade for the project. The five most important of these enzymes for drug discovery (IA2, 3A4, 2C9, 2C19 and 2D6) are readily available as single expressed human isoforms. They allow automated screening to obtain the ICso for CYP inhibition by test compounds (Moody et al. 1999).

Efforts are being made to have a similar in vitro estimate of induction. It has proved to be essential for this cycle of work to keep pace with chemical synthesis. Only then can the results influence the design of targets for synthesis in medicinal chemistry. The need for speed implies the need for integration of skills within DMPK. For example, there is


insufficient time within the cycle of work to allow samples from a pk experiment to be handed over to a separate group with bio-analytical skills. The potential for issues of conflict of priorities, of hand-over time and the control of delivery of results to projects are much better avoided. This can be achieved by integration of biochemical and chemical skills within multi-disciplinary teams. This has allowed a major gain in speed for our own organisation.

The ability to carry out metabolite identification within the team also has given a major benefit in speed. The need for specialists in spectroscopy and other chemical and metabolic expertise remains. However, the family or class basis of compound synthesis, and the consequently close relationship of their metabolism pathways, makes identification of many simple key metabolites a manageable objective for an integrated team. Recent advances in software to support mass spectroscopy have also been most helpful. The associated benefits in speed will be discussed in the next section.

Another key element in the success of the integration of DMPK skills is the role and quality of informatic support. Modern analytical and spectroscopic equipment is proving to be increasingly approachable and friendly to non-specialised users. This includes robotic pipetting stations, high-performance liquid chromatography (HPLC), liquid chromatography/mass spectrometry (LC-MC) equipment, etc. Informatic support has proved to be a key element in the achievement of a practical and productive laboratory environment where scientists can use an optimal number of techniques. This has achieved the capture, processing and display of data from modem instruments together with their up-lifting into project or corporate databases, and reporting. Without effective informatic support the scientists would potentially be faced with unmanageable data volumes. In our experience, this uniformity of reporting has assisted medicinal chemists in the assimilation of results and in the development of their understanding of metabolism and its optimisation.

In our experience, the achievement of the integration described above has been a major factor in accelerating the pace of drug discovery. The cycle of provision of physico-chemical and metabolic information to medicinal chemistry (Fig. 6) has proved to be a major influence on synthetic chemical decision making and strategy. The physico-chemical properties of compounds are reflected in their metabolism and pharmacokinetics. The availability of the metabolic data emphasises to the

12

Feedback to Medicinal Chemistry of the Effect of the Biological System on the Compounds

Fig. 6. Creative triangle

A. M. Davis et al.

Physical Chemistry

physical properties

Chemistry

design & synthesis

medicinal chemists in a practical way the key importance of physical properties in drug design.

1.3.2 Starting Point for Discovery DMPK Support

In recent years the starting point for DMPK has moved progressively earlier in the discovery process (Fig. 7). For some years lead optimisation has received full support. During the 1990s, however, the earlier phases of projects based on HTS have also been given attention. Currently our view is that DMPK involvement is essential at all stages where compounds are examined. Where HTS is not available or appropriate, the starting point would coincide with whatever chemical starting point were available. If, for example, only a single chemical starting point were available, DMPK involvement helps to allow an informed project decision to be made. Thus the project could begin despite a poor metabolic starting point. Alternatively it could be decided to search for more amenable leads, or to terminate the project.

For HTS-based projects, the starting point for DMPK is the evaluation of the hits. The objective is to assist the medicinal chemists by identifying the metabolic issues presented by the hits so that an informed choice can be made of potential compounds for the ensuing


Target HTS Hit

Evaluation Hillo Lead

Lead Optimisation

13

4DMPKR&D1989 ~

.. DMPK Research 1996 •

DMPK Research 1998 • .. DMPKResearch 1999 • Fig. 7. DMPK involvement at all stages where compounds are examined

Physicochemical

Properties

Confirmed Hits from HTS

! Metabolic stability

---~~ (Human microsomes, +-Rat microsomes, hepatocytes)

Identify "no hope" chemical series

Hit to Lead Proposal

Fig. 8. Hit evaluation -the starting point for DMPK

CYP 450

Inhibition

hit-to-lead (HtL) campaign. In this way a potential "no hope" series should be possible to avoid, with significant savings in time. The screen covers both physical and metabolic properties as shown in Fig. 8. The work consists of a rapid in vitro assessment of metabolic stability. Rat and human micro somes together with rat hepatocytes have generally proved sufficient. They are supplemented where appropriate with other studies such as CYP inhibition.


Metabolite --+ Identification

Compounds ( Potency filter)

~ Physico-chemical

Properties +

Metabolic Stability Rat microsomes and hepatocytes

(Human microsomes, (hepatocytes) .-Absorption, caco-2

~ Rat PK (IV I PO)

CL,Vdss,T1/2, F% (Clearance mechanism I scaling?) ..

LO Proposal Fig. 9. Generic screening plan for lead identification (hit-to-Iead)

Partition coefficient 10 Screen Solubility lbinding assay)'-.....

In Vitro DMPK

CYP 450

Inhibition

pKa ! ~ Plasma Protein Binding 2<> Screen (Met. Stability, MeU.d)..····)· .. \Cyp inh

rat. dog, human ~functional assay) (Emphasis on : .••• ~ •• lCyp Clint Membrane Distribution : Human data) "

~ ! (Animal Mfe/; PKIPD~ In Vivo DMPK (Rat .... Dog)

(Diseale Model)

~ CD Proposal

Fig. 10. Generic screening plan for lead optimisation


Table 1. Physical and metabolic characteristics of lead compounds

Human microsomes (Clint) In vivo rat (Clp)

Caco-2 permeability Solubility (H20) LogP (D) Molecular weight (MW)

<25 /lUminlmg <55 mUminlkg Moderate >10 /lg/rnl <4 <350

Table 2. Target physical and metabolic properties of candidate drugs

Human therapeutic dose Bioavailability (two species) Human CYP inhibition (ICso) Pharmacokinetics in rat Metabolic routes Metabolites Solubility LogP Molecular weight (MW)

<5 mg/kg >30% >10 iJM Appropriate for safety development Understood No reactive intermediates >10 /lg/ml <4 <450

15

In the HtL campaign, an initial potency filter reduces the number of compounds to be screened to manageable proportions, whether 96-well plate-based or single compounds. The physical and the metabolic screening in liver preparations (Fig. 9) follows the same pattern as in hit evaluation. However, the more interesting compounds are also screened in vivo. This allows the importance of transporter-based and extrahepatic clearance mechanisms to be assessed. The goal of the campaign is to demonstrate controllable SAR and a substantial improvement in affinity for the target protein. The criteria (Table 1) piont to "lead-like" physicochemical properties which leave scope for the subsequent lead optimisation project. The metabolic criteria for the lead are appropriately flexible and are designed to allow the identification of issues such as cul-de-sacs in the chemistry and lack of SAR.

Lead optimisation (Fig. 10) includes the same activities as the HtL campaign. The additional activities are the consequence of the focus on prediction of pharmacokinetics in man. The practical consequence is an emphasis on the requirement for screening in human hepatocytes. Hepatocytes have proved more satisfactory than micro somes in our in vitro-


to-in vivo scaling and prediction of human pk and dosing regimes. The collaboration with our Biosciences Department to establish a pk-pd relationship where possible has proved also to be valuable. Other studies include assessment and optimisation of CYP inhibition (target of ICso> 10 ~) and of induction. Avoidance of these liabilities is regarded as important. The physical and metabolic characteristics of the candidate drug (Table 2) are geared to the pharmacokinetics expected in man, combined with a manageable preclinical safety development package.

1.3.3 Appropriate Level of DMPK Effort

In the drug discovery process it is chemical synthesis which is generally rate limiting. Thus the number of medicinal chemists supported by each DMPK scientist may be taken as a reasonable estimate of the degree of commitment of DMPK resources. An informal and confidential survey of the structure and organisation of DMPK in discovery and in development was conducted by AstraZeneca as part of the new company's merger activities. It was found that the ratio of medicinal chemists to supporting DMPK scientists varied widely. In some companies it was as high as 1:1 whilst in others it was as small as 7:1. In our own experience a ratio of some three medicinal chemists supported per DMPK scientist has proved sufficient for the activities described above in lead optimisation. Approximately one half of this has proved adequate for lead discovery.

Table 3. DMPK in discovery and in development

Colleagues

Operations Goals

Discovery

Medicinal chemistry Physical chemistry Pharmacology NoGLP Prediction Optimisation

GLP, good laboratory practices.

Development

Safety assessment Clinical pharmacology Regulatory GLP compliant Disposition in man Support of safety Assessment Regulatory dossiers


A factor which emerged from the inter-company survey was the move away from the older organisational model of DMPK where the same group supported a project whatever its position in the progression from lead discovery to clinical development. This structure is increasingly being replaced by groups which support discovery with a hand over to another group or department which is then responsible for DMPK support to the pre-clinical and clinical phases of CD development. In our own organisation this separation has been found very helpful. The separate groups have different colleagues, objectives and philosophies (Table 3). Separation has allowed the formulation of wellfocussed, clear and manageable objectives for each group. As time has continued the differences have become more clearly observable. In discovery, the role of DMPK is to influence medicinal chemistry and to become predictive rather than reactive.

1.4 Metabolic Optimisation

This section includes a discussion of the process of metabolic optimisation and the question of how its potential complexities can be simplified. It also addresses the question of what tools are available to assist the process. In the second part, two recent AstraZeneca projects are used as case studies to show how this simplification, combined with the optimal starting point for DMPK support, can accelerate the discovery process and add quality to the CD end point.

1.4.1 Structure-Activity Relationships and Optimisation Strategy

Structure-activity relationships for optimisation of metabolism can be compared with the analogous process for disease target protein affinity. In the latter, the biological response (ICso) of the target protein is related to a combination of the physico-chemical properties [hydrophobic (P), electronic (E) and steric (S)] of a series of compounds. In favourable cases the relationship can be described in a multiple regression such as:

pICso=alogp2+blogP+cE+dS+e


A hypothetical relationship of this kind to describe a simple in vitro system such as clearance by liver microsomes might be as follows:

Log CI (Ox)=alogp2+blogP+cE+dS+e

Although this appears superficially simple, oxidative clearance CI (Ox) represents the sum of the combined clearance due to the five important human cytochromes involved in drug oxidation. Thus:

Log CI (Ox)=Log (ChA2+CbA4+Chc9+Chc19+ChD6)

In such a relationship, however, the degree of contribution of each CYP to the overall clearance measure is concealed. Since this will vary from compound to compound through the family or series, there will be difficulty in using the approach productively for metabolic optimisation. Correlation of clearance in a more complex tissue preparation or an in vivo experiment complicates the relationship further:

LogCI(CYPs+UGTs+extra hepatic+transporters)=alogp2+blogP+cE+dS+e

since only the same physicochemical descriptors are available to relate to the now larger number of independent clearance mechanisms.

The implication of the above is that optimisation of compounds to interact with a target protein is a quite different process from metabolic optimisation. In the former a single expressed human enzyme/receptor can be used for screening to maximise the interaction of a family of compounds. Further screens can be used separately to address issues of selectivity, etc. For metabolism the analogous process of screening against individual transporters and enzymes is impracticable at the moment because there are simply too many to be considered. Other such processes may remain to be discovered. Help from in vivo plasma pharmacokinetics does little to clarify the SAR since it provides a description only of the overall behaviour of a compound. No information is forthcoming as to why and how a compound is cleared. Furthermore, the goal of metabolic optimisation is to minimise the interaction of compounds with mechanisms of clearance, rather than to maximise them. It follows that an approach which is different from the traditional optimisation is required.


1.4.2 Simplification of the Optimisation Process

What can be done to simplify the optimisation process and make it more manageable? Part of the answer may lie in the differences in the degree of understanding currently available for the many clearance mechanisms. Those which are poorly understood, for example, include most transporters. The hepatocyte membrane transporter proteins (Smith and van de Waterbeemd 1999) remove compounds such as lipophilic acids very effectively into the bile. Their SAR is only now beginning to be studied as the individual expressed proteins become available. Knowledge of the SAR of many extrahepatic clearance mechanisms is at a similarly early stage of understanding. For example, the various amine oxidases such as semi carbazide-sensitive amine oxidase and the ketone reductases have caused significant difficulties and delays in discovery projects in our own laboratory. As well as difficulties of optimisation, there is the further factor that at the moment there is little in the literature to support inter-species scaling of these types of clearance. This in tum prevents robustness in predictions of pk in man.

Robust approaches for in vitro-to-in vivo scaling for most extrahepatic clearance mechanisms are still to be developed. It follows that the best prospects for efficient and rapid optimisation are with the best-understood clearance processes. Both for scaling and for optimisation of SAR, these include renal clearance and oxidative (CYP) liver metabolism. Unfortunately, the SAR of renal clearance is only rarely useful since compounds with low lipophilicity and molecular weight are usually cleared by this mechanism. In our experience, leads derived from HTS rarely have these properties. The SAR of the cytochromes, however, is better understood. Experience of inter-species scaling is also available.

Drug design frequently confronts a project with the conundrum (Fig. 11) that the desirable enhancement of potency or affinity for the target protein is tightly correlated with some unwanted property such as insolubility, clearance, CYP inhibition, etc. This is often observed because of the important role hydrophobicity plays in determining receptor affinity - whether that be to the target receptor or to the metabolising enzymes. The figure also attempts to show some of the ways in which optimisation can escape from this close relationship. Examples might, for example, include the discovery of an additional receptor interaction

20

Potency

\

A. M. Davis et al.

New receptor interaction reduce logO

Block metabolic site polar "hole" reduce logO

~ Trade potency for dmpk improvements dose to man focus

Clearance/cyp inhibitionl insolubility

Fig. 11. Overlap of target protein and DMPK properties

or a way to reduce lipophilicity. Equally the blocking of metabolic sites by anyone of many chemical strategies can be effective, as can a change away from the major clearance mechanism.

1.4.3 Tools for Metabolic Optimisation

What tools are available to support a strategy of optimisation based on guiding or steering lead optimisation towards oxidative liver clearance? First, the physical properties of the compounds synthesised need to be in the appropriate regions described earlier in this chapter. They are also well described in Lipinski's rules. Second, there has been major progress in recent years in the development of both pharmacophoric and computational models of the CYPs. These cover the SAR both for metabolic stability and for CYP inhibition. These too are the subjects of later chapters.

Instead it is intended to concentrate here on the process which can be derived from the growing understanding of the SAR of the CYPs. In order to minimise the potential for a compound to be a substrate (either for oxidation or for inhibition) the knowledge of how to achieve CYP interaction has to be converted to the means of how to minimise the


interaction. The two following project examples seek to highlight this approach, although within the constraints of being currently unable to disclose whole structures owing to issues of intellectual property. The examples highlight the role of identification of key metabolites. This ability has proved to be essential in our laboratory as one of the routine aspects of lead optimisation. The structure of primary metabolites can reveal the motifs that increase CYP interaction. Within the current limitations of knowledge of the SAR of CYP interactions this practical information provides highly valuable assistance in lead optimisation. The structural information allows medicinal chemists to devise approaches to preventing the oxidation. This can be a direct steric block, or can be a more indirect remote interaction, or indeed the removal of the vulnerable part of the structure. Indirect changes can include lowering bulk hydrophobicity, or alteration of the electronic or ionic constitution of the compound such that it loses affinity for the metabolising protein but without losing affinity for the pharmacological target protein. Rapid progress, whichever of these approaches is taken, depends on the knowledge of the primary metabolite product, and the mechanisms of its production. We have found the use of recombinant enzymes particularly useful in this regard (McGinnity et al. 2000).

1.4.4 Discovery Project Case Studies

Examples from our recent projects are discussed to highlight some of the factors in their timescales to nominate a CD. Table 4 shows the year of commencement. Only the second project was supported with DMPK from its hit evaluation phase through to CD. The other project, in which DMPK support had started late, has still to produce CDs, although this is expected currently to be within the next 6 months.

1.4.4.1 Project 1 The first project shown in Table 4 was initiated with an HTS and a HtL campaign. No DMPK support was given to either hit evaluation or the HtL campaign. The excellent affinity for the expressed human receptor (pA2 8.0) and selectivity over related receptors was encouraging. However, the DMPK screening at the start of the lead optimisation phase which began in January 1998 soon showed the clearance of the lead


Table 4. Comparison of timescales for two lead optimisation projects

Project

Year commenced HTS (m) Hit-to-lead (m) Lead optimisation (m) Time to CD (m)

1997 6 6*

>36 >48

No DMPK; m. number of months.

2

1998 6 6 21 33

compounds to be greater than hepatic blood flow (Fig. 12, first compound). The clearance mechanism involved oxidation (largely by CYP 3A4) of the lipophilic group, "R". Although DMPK was involved from the start of this phase of the project and had discovered this shortcoming of the lead compounds, the discovery of metabolically more promising compounds took some 9 months of the project. The reason for this delay was the close relationship between the lipophilicity of this part of the structure and both its affinity for the target receptor and for its susceptibility to metabolic clearance. This relationship proved difficult to overcome, as discussed earlier.

The discovery of compound 2 (Fig. 12) broke this connection and provided the start of a new compound series, although compound 2 was similarly unstable to metabolism. The availability of rapid metabolite identification led to the realisation that although the rate of metabolism at the lipophilic group "R" had been reduced, oxidation of the aminopropyl side chain was rapid and this was now the major mechanism of clearance. The new series of compounds proved possible to optimise metabolically as shown in the more recent third compound. Human receptor affinity had been preserved.

A key limitation of the primary amines such as compound 2 was the difficulty of predicting pk and dosing regimes in man for the better compounds. The use of in vitro-to-in vivo scaling is relatively successful where oxidative liver metabolism is the predominant process. The enzyme (semicarbazide-sensitive amine oxidase) which cleared the primary amines in this project is extrahepatic, a tissue protein. It proved more successful for the project to cease synthesis of primary amines and concentrate on those such as compound 3 to overcome this difficulty. The project is now close to its CD criteria and the timescales shown in


January 1997 October 1997 January 1998

r NH N-J ~ RNHY

R4

RNH= bulky.

lipophilic A~NH2

RNHY R4

~ RNHJY R4

RAT Clint CI

pA2hum 8.3

RAT = 46 I.d/min/106 cells Clint = N/A cells

CI

= 23 ~l/min/106

= 84 ml/min/kg

P~hum 7.9

RAT Clint = 5 ~l/min/1 06

cells CI = 12 ml/min/kg

10gP = 3.96 Bioavailability: 79% Half life: 3.6h

Compound rapidly metabolised in vivo

logO = 1.27 at pH 7.4 pKa =9.98 PPB in rat = 97%

Fig. 12. Project 1, 1997-2001

logO = 1.68 at pH 7.4 pKa =8.82 PPB in rat = 94.6%

Table 4 show it to have taken some 48 months from the start of the HtL campaign to achieve CDs of appropriate quality. In this project, metabolite identification proved of great value in identifying opportunities for metabolic optimisation, saving much time. The work poses the question of how can such a finding be generalised. Success in understanding how to avoid the motifs for recognition of substrates by their metabolising enzymes will lead to further enhancement of the discovery process.

1.4.4.2 Project 2 The second project also had its starting point in a screen (HTS). The lead compounds which had emerged from the HtL campaign showed very modest pharmacological and metabolic properties (Fig. 13).

Although the lead optimisation project discovered compounds with a good in vitro-to-in vivo correlation for clearance in rat, it proved difficult to reduce the rate of clearance in a systematic or predictable way. Correlation of intrinsic clearance with log D is common, but was not apparent in this case if all the compounds were considered (Fig. 14). In an attempt to simplify the problem, the rate of intrinsic clearance by the

24

Series A

Compound 2 pA28.1

LogD7•4 3.2 PPB99% F~O%

..

Compound 1 ICso (human)=1IlM

LogD7.4 3.5 Rat Heps Clint 7111/min/106

cells Hu Mic's Clint 27 Ili/min/mg

/

A. M. Davis et al.

Antagonist Lead fromHTS

Series B

Compound 3 -----~.~ pA2 7.4

LogD7.4 4.5 F<6%

Series Discontinued

compiund4 pA2i 8.1

LogD7.4 2.5 F-60%

Optimised Selective Antagonists

Potency, CI(3A4) & lipophilicity inextricably linked in Series A

Nominated CD, 12/00

Fig. 13. Project 2 lead optimisation phase, 1999-2000

Clint in Human Microsomes (ul/min/mg) 333 • • • • • • • • • • • •• • 100 • • • • - • • • -- - • • ••••• • • • • • • • • • • 33 • • ••• • •• • • • • • •• • • • • •• • •• . ... - - • • •• • • -•• •• • -- • •• 10 • • ... _. • • •• •• • • •••• -• _ .. ••• • • • • -• • • • 3 • • • -• • •• • • • • • •

• • • • •• • 2 3 4 5

Measured LogD7.4

Fig. 14. Lack of correlation of microsomal clearance with lipophilicity


3

2.5

2

1.5

0.5

o -2

Lo Human Liver Microsomal CI.

y = 0.5503x + 1.614 R2 = 0.6091

-1.5 -1 -0.5

25

• •

o 0.5

Log CYP3A4 Clint

Fig. 15. Contribution of CYP3A4 to microsomal clearance

1.00

0.50

0.00

-0.50

-1.00

-1.50

-2.00 o

Log CYP3A4 Clint

y = 0.5265x - 1.9049 R2 = 0.7075

•

•

2

•

•

3 4 5 6

Measured LogD7.4

Fig_ 16. Dependency of CYP3A4 activity on lipophilicity


five separate major human recombinant enzymes was measured for a number of the compounds within one of the project's chemical series. Correlation analysis indicated that a significant component of the metabolism was due to CYP3A4 (Fig. 15) for many of the compounds. Rates of metabolism at 3A4 are frequently closely correlated with the lipophilicity of the substrates. This was indeed found to be the case for this sub-set (series A) of the compounds (Fig. 16). The compounds in series A also retained the tight link between receptor affinity, lipophilicity and clearance by CYP3A4 and the relationship proved difficult to break. However, the site of metabolic attack was quickly identified. This allowed series A to be discontinued in favour of a new family of more metabolically stable compounds (series B, Fig. 13) whose pharmacological and metabolic properties proved more amenable to optimisation.

Another metabolic property of the compounds in series B, CYP inhibition (3A4 and 2D6), was also minimised by the achievement of lower lipophilicity (Fig. 13). In this project the team had taken some 27 months from the start of the HtL campaign to achieve high quality CDs. The incorporation of drug-like physical properties, combined with the quick analysis and optimisation of clearance mechanisms, was crucial to this shortening of the overall timescale.

I.S Prediction

Recently there have been many articles published on prediction of aspects of DMPK. As pharmaceutical companies have striven to front load projects, and reduce failure rates in development by trying to avoid problems, prediction of DMPK seems to have been one of the more fruitful areas, although not all aspects have been tractable. For example, inhibition of CYPs has been investigated intensively, but accurate prediction of human in vivo drug-drug interactions is still not possible. This is a particular disappointment as companies are, therefore, obliged to carry out complicated and expensive clinical drug-drug interaction studies, and to expose greater numbers of volunteers to new development compounds.

However, we have found that the calculation of the expected human dosing regimen can be very useful. The calculation brings together both


the pharmacological and the DMPK properties of a compound and this allows the assessment of overall progress that a project may be making. Further, the use of the same calculations as "what if' questions allow a project to see which properties of a compound are most in need of improvement to achieve a high quality candidate drug.

We expect that estimates of the predicted dosing regimen in man will also be of use to management when there are opportunities for choices between compounds to enter into development. The size of the dose, the likely dosing interval and the errors of the estimates (see below) could be used as one of the indicators of the possible success of a project. They can be used as an indicator of the quality of nominations into development both across projects and over time within a company.

1.5.1 Predicting Pharmacokinetics in Man

The origin of the methods we currently use for predicting human pharmacokinetics in man derives from some of the approaches pioneered by Houston and his co-workers. One of the most important aspects of the prediction is the correlation between the in vitro intrinsic clearance (Clint) in an appropriate hepatic system and the in vivo clearance. Implicit in the approach is that the major mechanism of in vivo clearance is hepatic metabolism. This holds true for many pharmaceuticals, and perhaps especially so for compounds that have derived from HTS approaches rather than structure-based projects. This approach for making predictions is particularly appropriate where the chemistry has been steered towards chemical series that rely on metabolism by the CYPs for their major mechanism of clearance.

Allometric scaling can also be used as a prediction tool, and there are many examples of its successful use. We tend to favour the methods based on scaling clearance, as these should be more robust to species differences in metabolism.

It must be stressed that these predictions depend on a significant number of assumptions, and the estimates are approximate. We find it useful not only to calculate the predicted doses in man, but also to calculate the possible errors associated with the estimates. These latter calculations are useful for at least two reasons. First, they are good for maintaining a sense of reality about the calculations, and not allowing


what are in fact insignificant differences to be given too much emphasis during decision making. The error estimates can also be used to understand where the weaknesses in the overall estimates lie, and which group of estimates used in the calculation are contributing most in the overall uncertainty of the prediction. This understanding can then be used to direct further experimental work to areas where it will have the most impact.

The predictions are based on the standard equation for the minimum plasma concentration at steady state assuming a simple one compartment pk model (Rowland and Tozer 1995).

C. = F.Dose mm,ss ~s ( exp( k el r") - 1 )

where F is the oral bioavailability, Dose is the size of the dose given (mg/kg), Vss is the steady-state volume of distribution, kel is the one compartment elimination rate constant (equal to Cl/Vss where CI is the plasma clearance) and 1: is the time interval between each of the doses. For the most successful drugs this is likely to be 24 h.

The equation is re-arranged to allow the calculation of the theoretical predicted dose.

Cminss .Vss (exp(ke1r) -1) Dose (mg/kg) = --'''"-------------'

F

If desired, the approach may be used with the appropriate equations, to base dose estimates on the average steady-state concentration.

The calculation is complex, and at first sight rather daunting. However, much of it is amenable to experimental determination.

The first estimate that is required is what minimum plasma concentrations will be required to achieve the desired pharmacological effect. This, of course, depends on the potency of the compound, but also on an understanding of the interaction at the target protein. Does the protein need to be fully saturated, and if so for what percentage of the dosing interval? These questions can perhaps be best answered by an understanding of the mechanism derived from other well-studied compounds and/or by having a well-described pk-pd model (see below). But to return to the potency, this needs to be based on a measure of the activity in a human system. Ideally it should be a cell-based system that contains


plasma. If not, an isolated human protein may be used, but then allowance for the effect of plasma protein binding is required.

Volume of distribution Vss in man has been shown (Obach et al. 1997) to be well predicted by the Vss in dog after correction for differences in plasma protein binding in the two species. Human absorption can be predicted from absorption in the rat (Chiou and Barve 1998). It seems that the rat is generally a more reliable predictor than the dog. Rat absorption may be estimated by a number of methods. We routinely use an estimate based on the directly measured bioavailability and clearance:

where Q is rat liver blood flow (70 mllminlkg). The next aspect to consider is the estimation of clearance. Here we

use the approach of Houston who has shown that for a set of 35 compounds it is possible to predict from hepatic microsomes or hepatocytes, generally within a factor of two, the in vivo clearance (Houston and Carlile 1997). Although microsomes can often give useful data, we generally use hepatocytes, as these give additional confidence that a wider range of potential clearance processes have been covered. Our approach is to "validate" the scaling process in the DMPK animal models, initially the rat, and then for the better compounds, in the dog. Only when the in vivo clearance can be predicted in at least two species is there sufficient confidence to progress to estimating the human dose. Finally an estimate of the human bioavailability is required. This is obtained in the same way as described earlier, i.e. from the estimates of the human absorption and clearance.

With so many parameters included in the calculation, the likelihood of error is substantial. We prefer to validate the approach by the use of an appropriate animal model. This then requires compounds with sufficient potency at the analogous animal receptor, together with estimates of the in vitro potency at that receptor. This may be considered an unnecessary overhead for a project. However, if suitable compounds and models can be found, there are also other benefits. Clear demonstration of efficacy in an in vivo model is always a very persuasive factor in giving confidence in the utility of the approach and in progressing the


project rapidly into clinical trials. The results from an in vivo model can also be useful in determining the plasma concentrations that need to be achieved relative to the in vitro potency of a compound, i.e. establishing a pk-pd relationship. This gives guidance as to the appropriate plasma levels to be used in the human dose calculation. They may not always be as large a multiple of the measured in vitro potency as theoretical considerations would suggest.

1.6 Conclusions

Recent experience suggests that speed to CD has to be combined with quality. Speed alone is not enough. The lead optimisation process has to be focussed on an end point which includes the expectation of appropriate pharmacokinetics in man. Hence the emphasis on optimising pk in one or more animal species is replaced by achievement of a combination of pk properties which allows confidence in the prediction of the compound's behaviour in man.

The appropriate chemical starting point is essential to allow efficient optimisation on the target protein. In the current environment of projects based on a starting point in HTS, this is a manageable objective. It will depend on the quality of the physico-chemical properties of the chemical library being screened. The properties of the leads which are selected must indeed be "lead-like". Such leads will allow the addition of substituent groups to the molecule to attain the desired "drug-like" properties of a CD. If the lead is already at this state of molecular elaboration it is likely that time will be lost in attempting to remove or exchange elements of structure to try to achieve the parallel goal of optimised metabolic properties. There must be acceptance of the likelihood that one or more of the lead series will prove to be cul-de-sacs and will hence be dropped. This in tum means that alternative or multiple starting points must be available.

The right starting point and involvement for DMPK is essential. This means DMPK screening at all stages where compounds are examined including hit evaluation. There is a major potential for loss of time when optimisation is allowed to continue without information on the pk properties of compounds being synthesised. For this reason DMPK must keep pace with medicinal chemistry. In this way it will be possible to


influence drug design and decisions on synthetic targets. This is the key to having the option available throughout a project to channel or steer chemistry towards the more understandable and manageable mechanisms of metabolic clearance. This will be achievable if an integrated multi-disciplinary scientific environment is created within DMPK, together with the use of standard experimental protocols and productive automated or robotic equipment. The availability of informatic support to optimise the capture, processing, storage and presentation of data are also key elements in speed of DMPK working. Without this the work is likely to be hampered by the sheer volume of data generated.

The discovery of a repertoire of chemical structural motifs which allow an unfavourable mechanism of clearance to be replaced by another, more amenable, metabolism is required. This will enhance the ability of project teams to avoid time-consuming unproductive work. The focus of optimising the pharmacokinetics towards a CD with properties which are consistent with a competitive or "best in class" product is assisted by the appropriate use of scaling to man during the lead optimisation phase. In this chapter we have attempted to show how these various factors can begin to be integrated into a progressively rapid strategy for lead optimisation without the sacrifice of quality.

Acknowledgements. The authors wish to acknowledge the generous assistance and support of colleagues at AstraZeneca. These include Rupert Austin, Patrick Barton, Anne Cooper, Stephen Fowler, Nigel Gensmantel, Andy Gray, Paul Leeson, lain Martin, Harsukh Parmar, Rob Riley, Richard Weaver, Mark Wenlock and many others.

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Dorsey BD, McDaniel SL, Vacca JP, Guare JP, Darke PL, Zugay JA, Emini EA, Schlief WA (1994) L-735,524: The design of a potent and orally bioavailable HIV protease inhibitor J Med Chern 37:3443-3451

Drews J (1997) Cost of novel drug development. Scrip World Pharmaceutical News 2283:8

Houston JB, Carlile DJ (1997) Prediction of hepatic clearance from microsomes, hepatocytes, and liver slices. Drug Metab Rev 29:891-922

Lipinski CA, Lombardo F, Dominy BW, Feeney PJ (1997) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Del Rev 23:2-25

McAuslane N (1999) Accelerating preclinical development. Vision in Business, Conference 25-26 February, Nice, France

McGinnity DF, Parker AJ, Soars M, Riley RJ (2000) Automated definition of the enzymology of drug oxidation by the major human drug metabolising cytochrome P450 s. Drug Metab Dispos 28:1327-1324

Moody GC, Griffin SJ, Mather AN, McGinnity DF, Riley RJ (1999) Fully automated analysis of activities catalyse by the major human liver cytochrome P450 (CYP) enzymes: assessment of human CYP inhibition potential. Xenobiotica 29:53-75

Obach RS, Baxter JG, Liston TE, Silber BM, Jones BC, MacIntyre F, Rance DJ, Wastall P (1997) The prediction of human pharmacokinetic parameters from preclinical and in vitro metabolism data. J Pharmacol Exp Ther 283:46-54

Physician's Desk Reference (1999) Medical Ergonomics Data Production Company, Montvale, NJ, USA

Prentis RA, Lis Y, Walker SR (1988) Pharmaceutical innovation by the seven UK-owned pharmaceutical companies. Br J Clin PharmacoI25:387-396

R&D Insight (2001), Adis International Ltd, Chester, UK Rowland M, Tozer TN (1995) Clinical pharmacokinetics: concepts and appli

cations, 3rd edn. Lippincott, Philadelphia, p 99 Sadowski J, Kubinyi H (1998) A scoring scheme for discriminating between

drugs and nondrugs. J Med Chern 41 :3325-3329 Smith DA, van de Waterbeemd H (1999) Pharmacokinetics and metabolism in

early drug discovery. Current Opinion in Chemical Biology 3:373-378 Teague SJ, Davis AM, Leeson PD, Oprea T (1999) The design of leadlike

combinatorial libraries. Angew Chern Int Ed 38:2743-2748 Then RL (1993) History and future of antimicrobial diaminopyrimidines. J

Chemother (Florence) 5:361-368 UK Office of Health Economics (1999) Scrip World Pharmaceutical News

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2 The Role of Pharmacokinetics in Drug Discovery: Finding Drug Candidates with the Greatest Potential for Success

J.H. Lin

2.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 33 2.2 High-Throughput Screens in Drug Discovery ............ . . . .. 35 2.3 Interspecies Differences in Pharmacokinetics ................. 37 2.4 Prediction of Human Pharmacokinetics . . . . . . . . . . . . . . . . . . . . .. 39 2.5 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 45 References .... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 46

2.1 Introduction

Searching for new drugs is an extremely time-consuming and costly endeavor. Much of the time and cost is expended on clinical studies to obtain efficacy and safety data. Many drug candidates fail during these clinical trials. There are three main reasons for clinical failure, namely, lack of efficacy, serious side effects, and unacceptable pharmacokinetics. In a survey by PMAlFDA (1991), approximately 40% of clinical failures were attributable to poor pharmacokinetics, while lack of efficacy and adverse effects accounted for about 30% and 10%, respectively. Obviously, the ability to predict the efficacy, toxicity, and pharmacokinetics from preclinical and in vitro studies can reduce the high incidence of clinical failures and improve the success rate of drug candidates to reach the market. However, prediction of clinical efficacy

34 J. H. Lin

and toxicity is not easy; in most cases, they can be determined only by clinical experience. In contrast, prediction of human pharmacokinetics is relatively easy. There is an increasing body of evidence to suggest that a reasonable prediction of bioavailability and clearance in humans can be obtained when applying appropriate pharmacokinetic principles (Houston 1994; Iwatsubo et al. 1997; Lave et al. 1999; Lin 1999).

Today, all drug companies include early ADME (absorption, distribution, metabolism, and excretion) evaluation in the process of drug discovery, and employ pharmacokinetic principles to select lead drug candidates for further development. As a result, drug metabolism scientists within the pharmaceutical industry have emerged from their traditional role in drug development to provide valuable support in drug discovery efforts. In the early stage of drug discovery, drug metabolism scientists routinely provide information from pharmacokinetic evaluation for medicinal chemists to optimize and modify the chemical structure of compounds, and for pharmacologists to accurately interpret pharmacodynamic observations. Another important role of industrial drug metabolism scientists is to predict human pharmacokinetics of lead compounds to minimize the probability of unacceptable kinetics in clinical trials.

Recent advances in molecular biology and biotechnology have led to an improved understanding of the specific functions and regulation of enzymes and transporters. With these advances, many in vitro systems are now being used for ADME evaluation (Guengerich 1995; Suzuki and Sugiyama 2000; Tamai and Tsuji 2000). The use of such in vitro model systems will allow for a more accurate prediction of human pharmacokinetics. In parallel to the progress made in molecular biology, the commercial availability of sensitive analytical instrumentation and reliable robots have provided drug metabolism scientists with powerful tools for early ADME evaluation. The purpose of this paper is aimed at discussing the role that drug metabolism scientists play, and the limitations and problems that they face at various stages of the processes of drug discovery and lead candidate selection.

The Role of Pharmacokinetics in Drug Discovery 35

2.2 High-Throughput Screens in Drug Discovery

The progress in chemical synthetic techniques, such as combinatorial and other parallel syntheses, has improved the efficiency of synthesis, generating thousands of compounds per year. To support this level of compound generation, high-throughput screening has become a critical part of drug discovery. However, it should be emphasized that highthroughput screening can only be used to solve the right problem with the right criteria. As pointed out rightly by White (2000), there are at least five criteria (relevance, effectiveness, speed, robustness, and accuracy) that should be met prior to implementation of a discovery screen. Undoubtedly, rapidity is not the most important goal of a discovery screen. The results of the screen should have direct relevance to the in vivo property of drugs, and should be an important decision factor in eliminating a large fraction of synthesized compounds that do not possess appropriate drug-like properties. Although many in vitro methods have been proposed for high-throughput screens, a majority of the methods does not meet the criteria. Often the in vitro methods offer no decision power to eliminate a substantial fraction of compounds. Sometimes the data generated are lacking accuracy and not interpretable. Strictly speaking, most of the in vitro methods used for rapid ADME evaluation, such as P450 (CYP) inhibition, metabolic stability, and Caco-2 cell permeability, do not meet the criteria of high-throughput screening.

High-throughput screens are good for determining the simple intrinsic properties of compounds. The high-throughput approach has been successfully used to screen thousands of compounds for their pharmacological activity by measuring the affinity (Ki or ICso values) to receptors. Similarly, high-throughput screening methods have been routinely used for determination of physicochemical parameters (e.g., lipophilicity and solubility). Unlike the binding and physicochemical parameters that are intrinsic properties of compounds, the processes involved in ADME are extremely complex, and in most cases are not suitable for screening by high-throughput approach. For example, the metabolic stability data generated from human liver micro somes by high-throughput assays provide little information on elimination clearance of compounds unless we have prior knowledge of their elimination pathways in vivo. Likewise, there are many factors, such as degree of ionization, rate

36 J. H. Lin

of dissolution, aqueous solubility, and permeability, that may influence oral absorption of compounds. Caco-2 permeability assay alone provides data on only one factor determining the intestinal absorption of drugs. In addition, the interpretation of Caco-2 permeability is not as straightforward. The permeability of compounds that are good P-glycoprotein (P-gp) substrates is expected to be poor in Caco-2 cells, because of the efflux transport of P-gp. However, the compounds can still be well absorbed from the human intestinal lumen when a normal oral dose (:2:50 mg) is given. The reason for this discrepancy is that Caco-2 permeability is always determined at low drug concentration ranging in the low !-1M, while the drug concentration in the intestinal lumen after a normal oral dose is high enough to saturate the P-gp efflux transporter (Lin et al. 1999). Unless all interrelated factors of each ADME process are fully understood and all factors can be rapidly measured in vitro, high-throughput screening of one of the factors adds little value to the discovery process.

Fortunately, high-throughput screening of receptor binding and physicochemical parameters is able to eliminate the majority of compounds synthesized by chemists. With the advances in high-speed liquid chromatography/tandem mass spectrometry (LCIMSIMS) analysis of biological samples (Brockman et al. 2000), it is now possible to evaluate the absorption and pharmacokinetics of 2-5 compounds for each discovery program in two animal species per week. If the approach of multiple dosing (cassette dosing) is applied, the number of compounds for in vivo kinetic (absorption and pharmacokinetics) evaluation can be easily increased by fivefold (Olah et al. 1997). The speed of in vivo kinetic evaluation using laboratory animals is fast enough to keep pace with the number of compounds that pass the first and second level screens. At Merck, we use the in vivo pharmacokinetic evaluation as a third level of screen to eliminate compounds with poor pharmacokinetic properties and as an important decision-making factor in the selection of lead compounds. We truly believe that only in vivo studies are the most reliable way to determine the pharmacokinetic properties of compounds.

In vitro assays are useful for mechanistic studies, and for confirming and supporting information, such as determining whether compounds are potent inhibitors of cytochrome CYF enzymes. In some cases, however, in vitro screens can be used as an important decision-making


factor to eliminate compounds from further consideration. A good example is in vitro evaluation ofP-gp substrates. The ability of compounds to penetrate the blood-brain barrier (BBB) is essential for drugs acting on the central nervous system (CNS), whereas for peripherally acting drugs, negligible BBB penetration is preferable to avoid CNS side effects. Because P-gp may limit the entry of drugs into the CNS, in vitro methods to determine whether compounds are P-gp substrates become very important for programs with CNS indications. If the in vitro-in vivo correlation of P-gp substrates has been established in animal species, in vitro screening for P-gp substrates should be considered as a primary screen for CNS programs (Kim 2000).

2.3 Interspecies Differences in Pharmacokinetics

One of the most difficult challenges that drug metabolism scientists face in the use of animals for ADME evaluation is interspecies differences in pharmacokinetics (Lin 1995). Although humans share with other animals many similarities in anatomy, physiology, and biochemistry, there are many differences between humans and animals. One of the most obvious differences is size and shape. Similarly, the relative size and blood flow of organs is also quite different among animals. For example, the liver is 4.5% of total body weight in adult rats (0.25 kg) and 2% in adult humans (70 kg), and the hepatic blood flow is 70 ml/minlkg for rats and 20 mUminlkg for humans. Likewise, the relative size and blood flow of the kidney decreases as animal size increases. Collectively, we would expect small animals to clear drugs more efficiently than humans when compared on a weight-normalized basis.

From an evolutionary viewpoint, it is not surprising that similarities exist in drug-metabolizing enzymes across animal species. Not only are they frequently identical enzyme and transporter systems, but also the primary sequences for these enzymes or transporters are similar among species. Examples include cytochrome P450, uridine diphosphate (UDP)-glucuronosyltransferase enzymes, and P-gp transporters. The available data suggest that there are remarkable conservations of amino acid sequences for these enzymes (or transporters) across the species. In spite of the high degree of structural homology, the orthologous isozymes and transporters show subtle differences in a small number of

38 J. H. Lin

amino acid residues, resulting in significant differences in their functional activities. Generally, the species differences in metabolism are quantitative rather than qualitative in nature, i.e., the metabolic pathways for a given substrate are qualitatively similar, while the rates of metabolite formation are quantitatively different. From our own experience and literature data, there appears to be a trend that the enzyme activity for most drugs is in the order of rats>monkeys>dogs>humans (Smith 1991; Stevens et al. 1993; Lin et al. 1996; Soars et al. 2001). Similarly, the transport of drugs is only quantitatively different across species. If a drug is a P-gp substrate in one species, this drug would also be a P-gp substrate in other species even though the rate of transport may be different (Yamazaki et al. 2001). The available data show that metabolic clearance of drugs in humans is much closer to dogs than other laboratory animals. Therefore, the dog is a good animal model for the evaluation of drug metabolism.

Similarly, the dog is also a good animal model for the assessment of oral bioavailability. This can be considered from two aspects, namely oral absorption and first-pass metabolism. Comparative studies reveal that both gastrointestinal anatomy and physiology are very similar between dogs and humans, suggesting similar oral absorption between these two species (Dressman 1986; Ritschel 1987). In addition, as in humans, most drugs are subject to less significant first-pass metabolism during oral absorption in dogs as compared to rats and monkeys. However, it should be noted that there is an obvious species difference in gastric acid secretion between dogs and humans. Dogs are known to have a very low basal gastric acid secretion. The basal acid secretion in dogs is approximately 0.1 /lmollminlkg, which is less than 1 % of the maximal capacity of 18 /lmol/minlkg (Hirschowitz 1968). In contrast to the dog, the human is a good gastric acid secretor. The basal gastric acid secretion is about 30% of maximal capacity of 30 /lmollminlkg (Deb as 1987). Depending on the extent to which solubility of a drug is pH-dependent, conceivably the absorption of the drug would be very different between humans and dogs.

Indinavir, a potent human immunodeficiency virus (HIV) protease inhibitor, exhibits a pH-dependent oral absorption in dogs (Lin et al. 1995). The aqueous solubility of indinavir is pH-dependent, being greater than 100 mg/ml at pH 3.0 and less than 0.03 mg/ml at pH 6.0. When the crystalline free base of indinavir was given orally to fasted


dogs as a suspension of 0.5% methocel (pH 6.5) at 10 mg/kg, the bioavailability was low (16%). However, the bioavailability increased to 72% in the same fasted dogs when the same dose of indinavir was administered as a solution in 0.05 M citric acid (pH 2.5). The pH-dependence of indinavir was further supported by the observation that absorption of indinavir in dogs was increased significantly after feeding. Both the Cmax and AVC were two- to threefold higher in fed dogs than in fasted dogs, when the free base of indinavir was given orally as a 100-mg capsule 1 h after feeding or in the fasted state. The increased absorption of indinavir after feeding is believed to be attributed to the stimulation of gastric and duodenal acid secretion, and hence enhancement of solubility of the drug. In contrast to dogs, the rat, like the human, is a good gastric acid secretor. There were only small differences (16% versus 23%) in the bioavailability of indinavir in rats after the oral administration of methocel suspension and citric acid solution. The minimal differences in bioavailability may reflect the marginal differences in the final pH in the rat stomach. The low bioavailability of indinavir observed in the rat was attributed to extensive first-pass metabolism of the drug in this species. Collectively, these results suggest that if a drug's solubility is pH-dependent, the dog will not be a good model for evaluation of drug absorption. Alternatively, as in the above example of indinavir, one can use the proper formulation to modify the gastric pH of dogs and evaluate drug absorption in this species.

2.4 Prediction of Human Pharmacokinetics

The ultimate goal of pharmacokinetics studies in animals and in vitro metabolism studies conducted at the stage of drug discovery is to predict human pharmacokinetics. Bioavailability, elimination half-life, and clearance are the most important pharmacokinetic parameters that determine the intensity and duration of pharmacological effects in patients. A great deal of effort has been expended on the theory and practice of quantitative prediction of these three parameters in humans using in vivo animal and in vitro metabolic data. While prediction of human metabolic clearance and bioavailability has been successfully demonstrated (Houston 1994; Iwatsubo et al. 1995; Lin 1999), prediction of plasma half-life is not as straightforward.

40 J. H. Lin

The fraction of an oral dose that actually reaches the systemic circulation is defined as the oral bioavailability of a drug. Because the entire blood supply of the upper gastrointestinal tract passes through the gut wall and liver before reaching the systemic circulation, the drug may be metabolized by enzymes in the liver and gut wall during the first passage of absorption. Kinetically, the oral bioavailability (F) of a drug can be described as:

F=Jabd 1-Jg)·(I-fh) (1)

where Jabs is the fraction of dose absorbed from the gastrointestinal lumen, and fg and fh are the fractions of the drug metabolized by intestine and liver enzymes, respectively, during the first passage of drug absorption.

Because the biochemistry and composition of the membrane of intestinal epithelial cells are similar across species, and because the absorptive process (simple diffusion) is basically an interaction between the drug and the biomembrane, the permeability of a drug across the gastrointestinal membrane would be similar among species. With the exception of pH-dependent solubility, the physicochemical properties (solubility, ionization, and lipophilicity) for a given drug should be similar across species. Consequently, the Jabs of a given drug is expected to be similar across species, and the value of the Jabs for humans can be estimated from laboratory animals. On the other hand, the fg and fh can be obtained from in vitro metabolic data (VmaxlKm) using human intestinal and hepatic microsomes. With the Jabs from animal studies and the Jg and fh from in vitro studies, the bioavailability of drugs can be estimated using Eq. 1 (Lin et al. 1999). With the exception of drugs that are given orally at very low dose, such as midazolam (lor 2 mg for adult patients), the fg is normally insignificant as compared to the fh and is negligible for most drugs (Lin et al. 1999). Thus, Eq. 1 can be simplified as Eq. 2:

F=Jabs·(l-fh) (2)

To validate whether the values offg andfh can be accurately predicted from in vitro metabolic data, a study was conducted to compare the in vitro and in vivo fg and fh values in rats and monkeys, using indinavir as


a model compound (Lin 1999). By comparing the steady-state plasma concentration during portal and femoral vein infusion, the hepatic ftrstpass extraction (fh) of indinavir was estimated to be 68% in rats. On the other hand, in situ experiments with intestinal loop preparation showed that intestinal ftrst-pass extraction (fg) was estimated to be less than 8% in rats. Consistent with the in vivo data, in vitrojg andJh were estimated to be 5% and 55%, respectively, using rat intestinal and hepatic microsomal VmaxfKm data. Similarly, the in vitroJh value (58%) of indinavir estimated from monkey hepatic microsomes correlated reasonably well with the in vivo fh value (65%) in monkeys. Collectively, these results suggest that the values ofjg andfh can be predicted reasonably well from in vitro metabolic data.

The bioavailability of indinavir was 23.6% in rats following an oral dose of 10 mg/kg. Using Eq. 1 with the in vivo values ofjg (8%) andJh (68%), thejabs was approximated to be 81 % in the rat. Similarly, thejabs of indinavir was estimated to be 80% for the dog and 55% for the monkey. These results strongly support the notion that the extent (fabs) of a drug absorbed from the gastrointestinal lumen is quantitatively similar across species, including humans. Assuming that the jabs of a drug in humans is similar to that in animals, one can predict the bioavailability of the drug in humans using the in vivo jabs value from animals and the in vitrojg andfh from human intestinal and hepatic VmaxlKm data. Using this in vivo/m vitro approach, we predicted the oral bioavailability of indinavir in humans to be 40%-60%, and this predicted value was one of the decision-making factors that brought indinavir from discovery to development. When clinical data became available, the bioavailability was found to be approximately 60% in patients, as we predicted (Yeh et al. 1999). The above example clearly demonstrates that it is possible to predict the bioavailability of drugs in humans before entering clinical trials when appropriate pharmacokinetic principles are applied.

In addition to bioavailability, the prediction of clearance of drugs is also very important for the selection of drug candidates for further clinical development. High-clearance drugs normally will have short plasma half-lives and are subject to extensive fIrst-pass metabolism. Furthermore, high-clearance drugs are more susceptible to dramatic increase in their AUC as a consequence of potent P450 inhibition (Lin 2000). This is because coadministration of a high-clearance drug with

42 J. H. Lin

an inhibitor will change the plasma AVe by increasing bioavailability through inhibition of first-pass metabolism and decreasing the hepatic clearance. It is, therefore, highly desirable to select drug candidates that have low clearance. In order to accurately predict clearance from in vitro data, it is necessary to know the relative contribution of hepatic metabolism (fm) to overall elimination. At the stage of drug discovery, the information on fm in humans is not available, and can only be indirectly estimated from preclinical animal species by measuring the recoveries of unchanged drug in the bile and urine. If the fm of a given drug is similar among three or four animal species tested, then it is reasonable to assume that humans will have a similar metabolic fraction (fm). Although there still remains some uncertainty in this inductive approach of estimating the fm of a new drug candidate, at Merck we have found that the fm is generally quite similar across species.

If the fm of a new drug candidate is greater than 80%, one can predict the clearance of the drug candidate from in vitro metabolic data with the assumption that the liver is the major site of biotransformation. On the other hand, if a new drug candidate is mainly eliminated from the body by renal and/or biliary excretion, an allometric approach can be used to predict the clearance of the drug candidate. However, it requires at least four or five animal species to define properly the allometric relationship between the animal body weight and the renal or biliary clearance (Lin 1995). Generally, a good allometric relationship is expected for the kinetic parameters reflecting physiological functions, such as biliary and renal excretion, but not biotransformation.

To accurately predict the clearance of new drug candidates that are mainly eliminated by biotransformation, it is important to know the enzyme systems that are involved in the metabolism. Detailed enzyme kinetic studies can then be conducted to determine the Vrnax and Krn with appropriate enzyme sources, and the in vitro metabolic clearance determined from (VrnaxIKrn) can be extrapolated to predict in vivo clearance. An in vitrolin vivo correlation is always made with animal data prior to the prediction of human clearance to validate the process of extrapolation. Using this approach, so far we have successfully predicted metabolic clearance of at least six new drug candidates, including indinavir (Lin 1999). It should be emphasized that these predictions were made before they were selected as drug candidates for development.


Table 1. Comparison of in vitro and in vivo metabolic clearance of indinavir in rats, dogs, and monkeys (Lin 1999)

Species VmaxlKm In vitro intrinsic In vivo intrinsic In vitrolin vivo (Illlmin/mg protein) clearance clearance intrinsic clearance

(ml/minlkg) (mllminlkg) ratio

Rat 69.7 157 250 1.60 Monkey 108 162 241 1.49 Dog 23.4 29 48 1.65

The scaling factors employed for the calculation of intrinsic clearance (mllminlkg) are as follows: microsomal protein yield is 50 mg/g liver, and the liver weight is 45 g/kg for rats, 25 g/kg for dogs, and 30 mg/kg for monkeys.

As shown in Table 1, the in vitro metabolic (intrinsic) clearance values of indinavir calculated from microsomal data were 157, 162, and 29 ml/min/kg for rats, monkeys, and dogs, respectively. For comparison, in vivo metabolic clearance of indinavir was calculated for the rat, monkey, and dog from their blood clearance, assuming that the liver is the major site of biotransformation. In all three species, the in vivo intrinsic clearance values were consistently greater than those obtained from in vitro VmaxlKm data by a factor of approximately 1.55 (Table 1). Clearly, these results suggested that extrapolation of in vitro intrinsic clearance to an in vivo situation required a scaling factor of 1.55. Although the nature of the scaling factor remains obscure, the scaling factor may reflect the underestimation of microsomal protein yield or enzyme activities due to unrecognized artifacts. Using the scaling factor of 1.55, we predicted that hepatic metabolic clearance of indinavir in humans would range from 3.0 to 10.6 mllmin/kg based on the wellstirred model, or 3.2 to 13.5 mllminlkg based on the parallel-tube model (Table 2). Subsequently, when clinical data became available, the observed clearance (8-10 mllminlkg) was in the range of predicted clearance values (Yeh et al. 1999). Collectively, the above example of indinavir illustrates the point that a reasonably accurate prediction can be achieved with careful application of appropriate pharmacokinetic principles.

Unlike the prediction of bioavailability and clearance, the prediction of plasma half-life in humans is not as straightforward, because the half-life is determined by two independent parameters, namely, clear-

44 J. H. Lin

Table 2. Predicted hepatic clearance of indinavir in humans

Predicted hepatic clearance (rnl/minlkg)

In vitro intrinsic In vivolin vitro Well-stirred Parallel-tube Observed hepatic clearance ratio model model clearance (ml/minlkg) (rnl/min/kg)

5.5-36 1.55 3.0-10.6 3.2-13.5 4.2-17

Mean value of in vivolin vitro ratio obtained from Table 1.

ance and volume of distribution (V d). The relationship of half-life and these two independent parameters can be simply expressed as Eq. 3:

Half -life=O. 693 x V dss/ clearance (3)

where Vdss is the steady-state volume of distribution. Although the clearance can be accurately predicted from in vitro metabolic data, prediction of the V d of new drug candidates in humans is very difficult. Unless we can determine protein binding in all tissues, in vitro prediction of V d that is determined by both plasma and tissue protein binding is almost impossible. Furthermore, prediction of human V d from animal species is also difficult because there are considerable interspecies differences in the Vd values. For example, there were more than 20-fold differences in the V d values of propranolol among six animal species (rats, rabbits, cats, dogs, monkeys, and humans) tested, being lowest in the monkey and highest in the rabbit (Fichtl et aI1987).

However, industrial drug metabolism scientists are often being asked whether a drug candidate is suitable for once-a-day administration. In some cases, the pressure on drug metabolism scientists to predict the plasma half-life in humans is so high that we have to make our best "guess." There are several ways to estimate the plasma half-life of new drug candidates for humans. From the literature, the lowest values of V d ever reported for humans is about 0.2 l/kg, which is equivalent to the volume of extracellular water. Therefore, the most conservative way to estimate the half-life of new drug candidates in humans is to use their predicted clearances from in vitro metabolic data with the assumption that their Vd is 0.2 lIkg. Another approach of estimating half-life is to use the range of V d values that are obtained from at least three animal


species. The latter approach is based on the assumption that the V d of a given new drug candidate in humans will lie within the range of the V d of animals. Obviously, the estimation of half-life can only be an approximation, and there still remains high uncertainty in the estimation of the plasma half-life of new drug candidates for the latter approach.

2.5 Conclusion

With the pressure on drug companies to improve the success rate of new drugs, pharmacokinetic evaluation has become an integral part of the drug discovery process. The most valuable role of pharmacokinetics at the stage of drug discovery is to supply kinetic information for the lead optimization, and to provide a rationale for the selection of new drug candidates for further development. Undoubtedly, animal studies are the most reliable way to obtain pharmacokinetic information and to predict the pharmacokinetics of new drug candidates in humans. However, the process of in vivo pharmacokinetic evaluation is relatively slow and sometimes could become a bottleneck for compound selection. For this reason, many in vitro methods have been proposed for rapid screening of pharmacokinetic properties even though accuracy can often be compromised by rapidity. It is, therefore, important to have a good balance between accuracy and rapidity. Inaccuracy at least will lead to misinterpretation, and at worst to erroneous conclusion. It should be re-emphasized that rapidity is not the most important goal of the discovery screen. Furthermore, the in vitro methods are not always validated in terms of in vivo relevance. This may also result in misinterpretation and erroneous conclusion. Until all the interrelated factors that govern the processes of ADME are fully understood and in vitro methods are completely validated, the in vitro methods should not be considered as decision-making screens.

46 J. H. Lin

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Fichtl B, Nieciecki AV, Walter K (1987) Tissue binding versus plasma binding of drugs: General principles and pharmacokinetic consequences. In: Testa B (ed) Advances in drug research. Academic Press, London, pp 117-166

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Yeh KC, Stone JA, Carides AD, Rolan P, Woolf E, Ju WD (1999) Simultaneous investigation of indinavir nonlinear pharmacokinetics and bioavailability in healthy volunteers using stable isotope labeling technique: study design and model independent data analysis. J Pharm Sci 88:568-573

3 Rapid Permeability Screening in Drug Discovery to Predict Human Intestinal Absorption

G.S.J. Mannens, H. Bohets, P. Verboven, K. Steemans, K. Lavrijsen, W. Meuldermans

3.1 Introduction ............................................ 49 3.2 Absorption Flowchart. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 50 3.3 Use of Filter-Immobilized Artificial Membranes (Filter-lAM) .... 51 3.4 Cellular Models for Permeability Screening .................. 53 3.5 In Silico Prediction of Absorption .......................... 63 References .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 66

3.1 Introduction

After oral administration of a drug, several hurdles have to be passed before the drug becomes systemically available. First of all, the drug has to be liberated from the pharmaceutical formulation. Within the gastrointestinal (GI) tract, the drug has to be resistant to enzymes and different pH environments. The dissolved compound has to be absorbed through the intestinal cell layer, which means it has to traverse many barriers formed by cell membranes. These cell membranes are composed of phospholipid bilayers, which are oily barriers that hinder the passage of charged or hydrophilic molecules. After absorption from the intestinal tract, the compound can reach the systemic blood stream via the portal vein through the liver. First pass metabolism in the intestine and in the liver and biliary excretion can limit the systemic availability.

50 G.S.J. Mannens et al.

The systemic availability is a crucial driver in the "drugability" of new discovery compounds. Several models are available for assessing the different processes involved in the oral bioavailability (dissolution and solubility testing, absorption and metabolism screening). This paper focuses on the technologies that can be used for permeability screening of new compounds. The models can be classified into cellular and non-cellular models. The use of the different models will be discussed as a function of the discovery status of new compounds. The use of in silico models will be briefly dealt with.

3.2 Absorption Flowchart

A flowchart for the assessment of absorption properties of new compounds is shown in Fig 1.

Drug discovery activities in phase A concentrate on target identification, in phase B on hit identification. Later phases deal with the optimization of the lead series and finally during the drug evaluation (DE) phase, bridging discovery and full development, the selected compound is prepared for its first administration in man.

With respect to permeability screening, the earliest technology that can be applied is the permeability measurement of new compounds through phospholipid layers (filter-immobilized artificial membranes method) (lAM). Later on, the transport characteristics are determined in

A ----+- B ----+- LO ----+- Late LO----+- DE----+-

Filter-lAM penneability

Caco-2 I MOCK In vivo (rat)

mechanistic studies

In silico predictions ~ "----------=---~

Fig. 1. Absorption flowchart. Phase A, target identification; Phase B, hit identification; LO, lead optimization; lAM, immobilized artificial membrane

Rapid Permeability Screening in Drug Discovery 51

cellular systems (Caco-2 or Madin-Darby canine kidney [MDCK] cells), and this information, together with solubility measurements can be used to make in silico predictions of the absorption that can be expected in man. As the compounds approach the selection for Full Development, more in-depth studies are set up, looking, for example, at the involvement of P-glycoprotein (P-gP). During Drug Evaluation, the absorption properties of a selected compound are assessed in vivo in animal studies. In silico models can be used at any stage of compound development. Needless to say that parallel with this permeability screening, a lot of other absorption, distribution, metabolism, and excretion (ADME) and Tox (toxic) properties of the drug are also determined.

3.3 Use of Filter-Immobilized Artificial Membranes (Filter-lAM)

A modified version of the new membrane permeability in vitro assay, called PAMPA (Parallel Artificial Membrane Permeation Assay) (Kansy et al. 1998), has been recently introduced (Avdeef 2001). The method is called filter-immobilized artificial membrane (filter-lAM) permeability assay. The main objective is the classification of passively transported compounds, via the transcellu1ar route. Measurements of lAM profiles can be used to prioritize molecules for further studies and to reject some molecules altogether. For the evaluation of this technology, the filterlAM permeability of 32 Janssen compounds was compared with that in Caco-2 cells. A commercial instrument PSR4p (Permeability-Solubility-Retention) from pION (Woburn, Mass., USA) was used. This instrument enables high-throughput permeability screening using artificial membranes in a 96-well microtitre plate format. The technology uses a Genesis robotic system (Tecan) and direct UV spectrophotometry. In the filter-lAM assay, a sandwich is formed from a 96-well microtitre plate and a 96-well filter plate. Each composite well is divided into two chambers: donor at the bottom and acceptor at the top, separated by a 125-llm microfilter disc (0.45-1..Lffi pores). The microfilter disc is coated with a 2% (w/v) dodecane solution of dioleoylphosphatidy1choline, under conditions that multilamellar bilayers form inside the filter channels when the system contacts an aqueous buffer solution. Samples are introduced as lO-mM dimethylsulphoxide (DMSO) solutions and


-4r------------------a.---.

• ,....... C\I

• •• • • • 8 -5 • • •• • • CII () ....... •• • • • • • • ••

pH 7.4 -7~--~--~----~--~--~

-9 -8 -7 -6 -5

log Pe (filter-lAM) -4

Fig. 2. Comparison of the penneability in Caco-2 cells and immobilized artificial membranes (filter-lAM)

mixed into an aqueous buffer system at pH 7.4. The final compound concentration was 50 J1M and the final DMSO concentration was 0.5%. Acceptor and donor concentration are measured by direct UV spectrophotometry in the 250-500 nm domain at time 0 and at 15 h (permeation time at ambient temperature). Membrane retention is estimated by measuring both the acceptor and donor well sample amounts, and by comparing those values with the total amount of compound introduced into the system.

The comparison between the permeability in Caco-2 cells versus the filter-lAM permeability is shown in Fig. 2. This comparison was made up for 25 of the 32 compounds, as some compounds gave no reliable value because of precipitation or inappropriate UV properties.

One compound (Caco-2 permeability of7.8xlo-7 cm/s) was characterized before as a P-gP substrate in the Caco-2 experiments. A clear trend was obtained between the Caco-2 and lAM permeabilities. Statistical analysis showed a correlation coefficient (r) between Caco-2 and lAM permeability of 0.31 and a Spearman's rank correlation coefficient of 0.49. Two highly permeable compounds in Caco-2 cells (Caco-2 permeability of lxlO-6 cm/s and 4xlO-5 cm/s) showed almost no permeability in the artificial membranes. In fact, these compounds were retained for 76% and 96% in the artificial membrane at pH 7.4. How-


ever, at pH 4.4, these compounds had a good permeability and much less (24%-30%) membrane retention. Statistical analysis on 23 compounds (omitting the two compounds that showed membrane retention) gave a correlation coefficient (r) of 0.37 and a Spearman's rank correlation coefficient of 0.69. This method can be used as a primary classification for large sets of new compounds that are essentially transported via the transcellular route.

A good correlation has also been described between percentage absorption and permeability measurements in hexadecane membranes, another type of artificial membranes (Wohnsland and Faller 2001).

3.4 Cellular Models for Permeability Screening

3.4.1 Comparison Between Caco-2 and MDCK Cells

Two cellular systems for permeability screening, the human colon adenoma Caco-2 cell line and the dog kidney MDCK cells, were compared. Both cell lines were obtained from ATCC (American Type Culture Collection, Rockville, Md., USA). Caco-2 cells of passage number 80-100 and MDCK cells of passage number 60-80 were used. A comparison between the properties and characteristics of the two cell types is given in Table 1. Both cell types are polarized epithelial cells with tight junctions and an apical and basolateral membrane domain. During growth, Caco-2 cells go through processes of proliferation, confluency and differentiation (De1ie and Rubas 1997). When grown under standard conditions on semipermeable membranes, fully differentiated Caco-2 cells are very similar to normal enterocytes with regards to their morphological characteristics. Primarily, they have functional tight junctions, they develop apical and basolateral domains and brush border cytoskeleton. Like Caco-2 cells, MDCK cells have been shown to differentiate into columnar epithelium and to form tight junctions when cultured on semipermeable membranes (Irvine et al. 1999).

The cells were grown in culture T-flasks (75 cm2 or 175 cm2) in a C02-incubator at 37°C and 5% C02. Caco-2 cells were grown in DMEM culture medium (Dulbecco's Modified Eagle Medium, Life Technologies) with 1% MEM non-essential amino acids, 1% L-glutamine (200 mM, Life Technologies), 100 U/ml penicillin/streptomycin

Tab

le 1

. Com

pari

son

betw

een

inte

stin

al c

ells

, Cac

o-2

and

MD

CK

cel

ls

Cha

ract

eris

tics

Int

esti

nal c

ell

Mor

phol

ogy

Bru

sh b

orde

r T

ight

junc

tion

s

Cel

l typ

es

Pol

ariz

atio

n

Tra

nspo

rter

s

Met

abol

ism

Ent

eroc

ytes

(90

%);

cal

cifo

rm c

ells

(m

ucus

pro

duct

ion)

A

pica

l sur

face

: fa

cing

int

esti

nal

lum

en; b

rush

bor

der

Bas

olat

eral

: fa

cing

blo

odst

ream

A

ctiv

e: b

ile

acid

; am

ino

acid

; di

pept

ide;

P-g

lyco

prot

ein

Pha

se I:

CY

PIA

l, C

YP

3A;

hydr

olas

e P

hase

II:

GS

T a

; su

lpho

tran

sfer

ase;

gl

ucur

onid

ase

Cac

o-2

Bru

sh b

orde

r T

ight

junc

tion

s

Ent

eroc

ytes

(90

%)

Api

cal

surf

ace:

bru

sh b

orde

r

Bas

olat

eral

A

ctiv

e: b

ile

acid

; am

ino

acid

; di

pept

ide;

P-g

lyco

prot

ein

Pha

se I:

CY

PIA

l; h

ydro

lase

P

hase

II:

GS

T a

; su

lpho

tran

sfer

ase;

gl

ucur

onid

ase

MD

CK

B

rush

bor

der

Tig

ht ju

ncti

ons

Dis

tal r

enal

tubu

lar

cell

s

Api

cal

surf

ace:

bru

sh b

orde

r

Bas

olat

eral

A

ctiv

e: N

a, K

, tr

ansp

orte

rs;

P-g

lyco

prot

ein

Pha

se I:

Fra

ge

Pha

se II

: G

ST

1t,

Il

~

Ci) en !:- s:::

III

:J

:J

CD

:J

(f) sa ~


(Life Technologies) and 5% fetal calf serum (FCS; Life Technologies). MDCK cells were grown in MEM culture medium (Modified Eagle Medium, REGA 3, Life Technologies) with 2% sodium bicarbonate (7.5%, Life Technologies), I % I-glutamine (200 mM, Life Technologies) and 5% heat-inactivated FCS (Life Technologies).

At a confluency of 70%-80% (3-4 days) the cells were split at a ratio of 113 to 114 for Caco-2 cells and 115 or more for MDCK cells.

For use in the transport experiments, the cells were seeded in culture inserts (Millicell culture plate inserts: tissue culture treated, Isopore track-etched polycarbonate membrane, O.4-llm pore size). Caco-2 cells were seeded at a density of 63,000 cells/cm2, the MDCK cells at a density of 500,000 cells/cm2. The medium was refreshed at 24 h after seeding. Further, the medium for Caco-2 cells was refreshed every 48-72 h during 20-23 days; for MDCK cells the medium was refreshed daily during 3 days. At that time the cells were ready to be used in the permeability experiments.

To assure the integrity of the monolayer during the course of the experiment, quality control was done by measuring the TEER values (transepithelial electrical resistance) and the permeability of mannitol. For Caco-2 cultures, a TEER value of 1,200-1,500 Q.cm2, for MDCK cells a TEER value of 600-700 Q.cm2 for 6-well inserts and 150-200 Q.cm2 for 24-well inserts was obtained. Caco-2 inserts with a TEER value below 800 Q.cm2 (24-well), and MDCK inserts with a TEER value below 400 Q.cm2 (6-well) or 100 Q.cm2 (24-well) were discarded. Caco-2 permeability of 14C_ or 3H-mannitol should be lower than 0.5xlO-6 cmls. The MDCK cells were discarded at higher values than 0.5xlO-6cmls for mannitol permeability.

In each experiment, three markers (Janssen compounds with known intestinal absorption) were included, one for low (Papp=0.26xlO-6cmls), intermediate (Papp=2.04xlO-6cmls) and high (Papp=7.53xlO-6cmls) permeability. In experiments on the involvement of P-gP, taxol was used as a substrate and verapamil as an inhibitor.

Permeability values were calculated as

Papp= dQldt A.Co

and are expressed as cmls, where, A is the surface area across which transport is measured (cm2), dQldt is the amount of drug transported as


~.---------------------------------------------~

2S +----------------------------------------------n�

~ W +-----------------------------------------~="I j ~ IS+-----------------------------------------i ~

J IO+-------------------------------------~

s+----------------------=~~Inrn

Fig. 3. Ranking of discovery compounds (grey) according to their Caco-2 permeability value in comparison with three marker compounds (black) for low, intermediate and high permeability

a function of time (nmol/s), and Co is the initial donor concentration of the drug (nmol/ml).

3.4.2 Permeability Studies with Caco-2 and MDCK Cells

Caco-2 cells are widely used as absorption model for discovery compounds. In an early discovery phase, classification of different compounds is the major objective. The compounds within a given chemical series are ranked according to a low, intermediate or high permeability. For that purpose, the permeability of the discovery compounds is compared with that of the marker compounds for the different classes of permeability (Fig. 3). This information, together with other ADME/Tox-parameters, is taken into account for the selection of lead molecules.

The major differences between Caco-2 and MDCK cells are the absence or presence of active transporters and drug metabolizing enzymes. With respect to permeability screening, differences can be expected between Caco-2 cells and MDCK cells for passively transported compounds, but especially for actively transported compounds.

The Caco-2 and MDCK cells were compared experimentally for their predictability of the permeability of new compounds and for their use in mechanistic studies. For that purpose the permeability of a series


100.0 ,......,

] \0

I 10.0 o

1.0

0.1

0.1

•

•

y = O.9926xO.9407

R2 = 0.8827

1.0 10.0 100.0 Caco-2 [papp ( 10-6 cmls)]

Fig. 4. Comparison of the permeability of 12 compounds in Caco-2 and MDCK cells

of 12 compounds was measured in both cell types. None of them was a P-gP substrate. MDCK and Caco-2 permeability results are shown in Fig. 4. A very good correlation (r2 value of 0.88) was observed in the permeability values over the tested range of low to high permeable compounds. The Spearman's rank correlation coefficient for MDCK to Caco-2 P app was 0.87. Irvine at al. (1999) also concluded that, at least for passively absorbed compounds, MDCK cells can be used as an alternative for Caco-2 cells. They published a high rank correlation coefficient of 0.93 (r2 value of 0.79) for MDCK to Caco-2 Papp.

When P app values were plotted against human intestinal absorption, an approximately sigmoidal relationship was obtained for Caco-2 cells (Fig. 5). Compounds with effective permeability coefficients of > lOx 10-6 cmls in Caco-2 cells showed complete absorption in humans (>90%). A comparable correlation between the permeability in Caco-2 cells and the absorption in humans has been reported by Pade and Stavchansky (1998). In MDCK cells, the relationship was less strongly correlated (Fig. 5). Permeability values tended to be lower in MDCK cells than in Caco-2 cells.

100

so

~ 60 ~ .c ~ 40

20

0.1

58

Caco-2 cells

CO (l)0 100

til <D 0 80

0

<:'0 ~ 60

7i u. 40

20 0

til

1.0 10.0 100.0 Papp (1O-6cm/s )

0

0

0.1

G.S.J. Mannens et al.

0

0

CD •

MDCK cells

CO •

0

•

1.0 10.0 Papp (lO·6cm/s )

o aD

0

0

'b

0

Fig. 5. MDCK and Caco-2 Papp values versus human absorption. Open symbols, test compounds; closed symbols, marker compounds for low, intermediate and high permeability

3.4.3 Methodological Studies with Caco-2 and MDCK Cells

3.4.3.1 Effect of Agitation The aqueous boundary barrier, the so-called unstirred water layer can be a rate-limiting barrier to drug transport (Hidalgo et al. 1991; Karlsson and Artursson 1991; Palm et al. 1999). Because of this layer, the drug concentration at the cell surface can be considerably different from that in the buffer above the cells. Especially for highly permeable lipophilic compounds, the diffusion from the bulk to the cell membrane is the rate-limiting step, as it is much slower than the permeation into the membrane. The un stirred water layer can be decreased by stirring the layer in contact with the cells. The effect of agitation during the experiment was tested for Caco-2 cells. A negligible effect was observed for the low and high permeable marker, when stirring (at 25 or 75 rpm) as compared to the un stirred incubation.

3.4.3.2 pH-Dependent Permeability The Caco-2 cells can be successfully used to study the pH-dependency of drug transport. It is well known that the permeability of the un-ionized form can be substantially higher than that of the ionized form (ionized acid or protonated base). A pH permeability profile is useful information for ionizable compounds to simulate the compound's be-

100.0

Rapid Permeability Screening in Drug Discovery

---~ "= 'P'"4 '-' ... li' ~

15

• 10

• 5

• •

O+---~---r--~--~~-=~--r---L---+

3 5 7 pH

9 11

59

4.0

3.0

+ 2.0 ~

1.0

0.0

Fig. 6. Permeability (closed symbols) and ionization curve (open symbols) of a basic compound (pKa=9.5) as a function of pH

haviour in the pH-gradient in the GI tract. The pH-dependency of the transepithelial (apical to basal) transport of an ionizable Janssen compound (base with a pKa of 9.5) was examined in the pH-range 5.1-7.4. The permeability value of the compound increased as a function of pH (Fig. 6). This suggests that the intestinal absorption of this compound might be highly influenced by changes in pH along the GI tract. However, this pH curve was shifted about 2 log units compared to the aqueous titration curve, and thus the ionized/un-ionized ratio cannot be the sole explanation for the observed pH effect. An alternative explanation might be that the compound is actively transported, but more likely it is believed that this shift in permeability pH profIle is due to the unstirred water layer. Similar observations are also reported with artificial membranes (Wohnsland and Faller 2001).

3.4.3.3 P-Glycoprotein Involvement Several compounds are transported by more complicated mechanisms than passive transcellular or paracellular diffusion. Both Caco-2 and MDCK cell lines are known to express some of these active transport proteins. An important mechanism in the absorption of many com-


250000

200000

.~ 150000 ell I=: .s

100000 .s

50000

0

0 5 10 15 20 25 30 Time in culture (days)

Fig. 7. P-glycoprotein expression in Caco-2 cells (Western blot analysis) as a function of culturing time on semipenneable membranes

pounds is the efflux by P-gP. Substrates of P-gP tend to be compounds with low oral bioavailability due to limited absorption. Both Caco-2 cells and MDCK cells can be used to identify P-gP substrates.

The P-gP expression in Caco-2 cells was measured by Western blot analysis as a function of culturing time. The time in culture has a major influence on the P-gP expression in Caco-2 cells. The classical3-week culturing period was sufficient to obtain a level of P-gP expression comparable to that in culture flasks (value at time zero) (Fig. 7).

Caco-2 and MDCK cells have been successfully used in mechanistic studies on drug--drug interaction at the level of transport by P-gP. For a given Janssen compound, that is known not to be metabolized in vivo and in vitro in man, a significant increase in plasma AVC (40%) was seen upon co-administration of ketoconazole. As this fmding cannot result from metabolic drug--drug interaction, the hypothesis was investigated that this compound might be a P-gP substrate and that ketoconazole might act as a P-gP inhibitor (Takano et al. 1998). Inhibition of P-gP might then result in elevated plasma levels, either because of increased absorption or decreased renal elimination of the compound (less efflux). Indeed, it could clearly be demonstrated that the compound was a P-gP substrate and that ketoconazole, in a concentration-dependent manner, inhibited the efflux of the compound (Fig. 8). MDCK cells

Rapid Permeability Screen ing in Drug Discovery 61

Fig. 8. Basolateral to apical transport of two P-g!ycoprotein substrates in the presence of verapamil and increasing concentrations of ketoconazo1e. Upper panel, Janssen compound in Caco-2 cells; Iower panel, taxo! in MDCK cells


Table 2. Affinity (Km in !-1M) of P-glycoprotein substrates in Caco-2 and MDCKcells

Compound Caco-2 MDCK

Vinblastine 26.6 10.2 Taxol 65.8 5.3 Digoxin 350.8 81.4

behave similarly, as exemplified with taxol as substrate (Fig. 8). So, drug-drug interaction at the level of absorption or renal elimination can explain the elevated plasma levels in vivo. These results illustrate that Caco-2 cells as well as MDCK cells can be used as an in vitro model for this type of studies.

The functionality of P-gP in both MDCK and Caco-2 cells has been illustrated above. However, the affinity of different P-gP substrates substantially differs between the two cell types. Vinblastine, taxol, and digoxin, three P-gP substrates, had a higher affinity (lower Km) in MDCK cells than in Caco-2 cells (3- to 12-fold) (Table 2). Thus, although MDCK can be used to identify P-gP involvement, it should be kept in mind that the P-gP in MDCK cells has different properties than that in Caco-2 cells.

The effect of different solvents on the P-gP-dependent transport of taxol and vinblastine was determined because most compounds are initially dissolved in a non-aqueous solvent before they are added to the buffer system on the cells. The transport of both compounds was determined in the presence of 0.25% methanol, 0.25% DMSO or 2.5% hydroxypropyl-~-cyc1odextrin. The influence of P-gP on the transport was calculated from the difference between the basolateral to apical minus the apical to basolateral transport. For both MDCK and Caco-2 cells DMSO increased the P-gP-dependent transport of taxol and vinblastine. In the presence of hydroxypropyl-~-cyc1odextrin, the P-gP-dependent transport decreased, most probably due to effects of hydroxypropyl-~-cyc1odextrin on the monolayer. This was evidenced by the increased mannitol permeability in the presence of hydroxypropylp-cyc1odextrin, which is indicative for the disturbance of the monolayer.


3.5 In Silico Prediction of Absorption

Ultimately, the final goal of the different screens is to obtain predictive information, relevant to the in vivo absorption in humans. Depending on the discovery phase of a compound, simple classification (low, intermediate or high) can be sufficient, but before the first-in-man study, one is really interested in the actual prediction of the pharmacokinetics in vivo in man. For this purpose, several in silico methods are in development.

The use of these methods can be illustrated with the iDEATM software, which is being developed by Trega Biosciences (San Diego, Calif.) (Norris et al. 2000). iDENM (in vitro determination for the estimation of absorption) is a physiologically based computer simulation model of the GI tract. The programme takes into account physiological parameters such as pH, transit time, intestinal surface area and blood flow. The model is also based on some drug-related properties, such as dissolution rate, solubility and permeability. iDEA allows to predict the fraction of the dose that enters the portal vein (FDp) based on some limited solubility and permeability data.

Since there are large interlaboratory differences in the absolute values of the cellular permeability, the permeability of nine marker compounds was determined (Table 3) to find out how the permeability

Table 3. Inter-laboratory variability in the permeability (in 10-6 cmls) (mean±SD, n=3) of nine marker compounds in Caco-2 cells

Compound Trega AtoB Efflux

Janssen AtoB Efflux

Naproxen 21.1±2.56 0.98 48.7±5.99 0.90 Atenolol 1.07±0.431 2.09 0.202±0.0825 2.53 Hydrochlorothiazide 0.919±0.602 3.44 0.689±0.248 2.37 GancicIovir 0.470±0.178 1.89 0.111±0.0304 5.89 Amiloride 1.80±0.585 1.46 0.367±0.0205 1.10 Ketoprofen 8.40±1.79 1.39 4.43±0.688 3.68 Etoposide 1.22±0.140 8.02 0.350±0.158 17.94 Metoprolol 28.2±0.834 0.92 32.1±4.56 1.08 Propranolol 28.7±6.22 0.71 40.9±5.51 1.00

A to B, apical to basolateral transport (in 10-6 cmls); efflux, ratio ofthe basolateral-to-apical!apical-to-basolateral transport; FDp, fraction absorbed in the portal vein.


values correlate between different laboratories. The compounds were selected as permeability markers, to represent different classes of the BCS classification (Biopharrnaceutical Classification System).

All compounds were analysed using a high-throughput triple quadrupole liquid chromatography-mass spectrometry (LC-MSIMS) method after a limited sample clean-up procedure. To 100 III of each individual sample, 50 f.!l of methanol and 350 f.!l of acetonitrile were added. The samples were filtered over a 96-well filter plate (3 M). Next, 1 III of the filtrate was injected onto a C-18 BDS-Hypersil column (50 mmx2.1 mm, 3 flill) and the analytes were eluted with 0.01 M ammonium acetate (pH4)-acetonitrile (60/40 v/v) at a flow rate of 0.3 mUmin and a column temperature of 35°C. For some compounds (amiloride, atenolol and hydrochlorothiazide), small modifications of the HPLC-conditions were needed to allow the compounds to elute away from the solvent front. For naproxen, no salts were added to the HPLC-eluent. Both adaptations were needed to minimize the ion suppression during ionization in the LC-MS interface. The MSIMS, operated in the positive or negative ion mode using the TurboIonSprayTM-interface (Electro Spray Ionisation, ESI), was optimized for each individual compound to measure a selective parent~daughter transition. For each compound, a set of calibration samples was prepared in the incubation medium. These calibration samples were taken through the filtration and LC-MSIMS procedure together with the study samples. Final concentrations in the study samples were obtained by backcalculation from these individual calibration curves.

The Janssen permeability values of the nine compounds are randomly distributed around the Trega values (Fig. 9). The effective permeability coefficients of the selected drugs from apical to basolateral direction ranged from 1. 11 X 10-7 to 48.7xlO-6 cm/s. The Papp(ap-bl) and the Papp(bl-ap) did not differ significantly for naproxen, atenolol, hydrochlorothiazide, amiloride, ketoprofen, metoprolol and propranolol, indicating a passive diffusion pathway for their transport. A considerable difference between Papp(ap-bl) and Papp(bl-ap) was observed for ganciclovir and etoposide (efflux ratio of 6 and 18, respectively). A correction curve has to be used before entering Janssen Caco-2 permeability values in the iDEA software. These corrected Caco-2 permeability values, together with solubility data, enable to predict the FDp. The iDEA predicted FDp versus the known FDp of the nine compounds is shown

Rapid Permeability Screening in Drug Discovery

100 o •

80 o 0 ••

---- 60 ~ '-'

"" Q 40 ~

20

0 o •

1.0 E-8 1.0 E-7 1.0 E-6 1.0 E-5

P app (em/sec)

•• m .,

65

1.0 E-4

Fig. 9. Inter-laboratory variability in the permeability of nine marker compounds (see Table 3): Caco-2 Papp values versus human absorption (FDp is fraction absorbed in the portal vein)

100

80

D.. 60 c LL

~ 0 c 40 ~

20

0 0

• Janssen Penneability

--Unity

20 40

• ••

•

60

iOEAlM Predicted FOp

••

80 100

Fig. 10. Known versus iDEA predicted FDp of nine marker compounds (see Table 3) (FDp is fraction absorbed in the portal vein)


in Fig. 10. The fraction absorbed in the portal vein was very well predicted with the iDEA programme for all nine compounds.

The application of the iDEA software will further be illustrated in compound optimization, to predict the impact of efflux on the absorption of a compound, dose-dependent absorption, and for predicting region and rate of absorption.

Acknowledgements. We thank Kevin Holme and Ron Christopher (Trega Biosciences) for their contribution. We acknowledge the bioanalytical support of Philip Timmerman and the excellent technical assistance of Mrs. Ria Poels.

References

Avdeef A (2001) High-throughput solubility measurements. In: Testa B, van de Waterbeemd H, Folkers G, Guy R (eds) Pharmacokinetic optimization in drug research. Verlag Helvetica Chimica Acta, Zurich, pp 305-326

Delie F, Rubas W (1997) A human colonic cell line sharing similarities with enterocytes as a model to examine oral absorption: advantages and limitations of the Caco-2 model. Critic Rev Ther Drug Carrier Syst 14:221-286

Hidalgo J, Hillgren KM, Grass GM, Borchadt RT (1991) Characterization of the un stirred water layer in Caco-2 cell monolayer using a novel diffusion apparatus. Pharm Res 8:222-227

Irvine JD, Takahashi L, Lockhart K, Cheong J, Tolan JW, Selick HE, Grove JR (1999) MDCK (Madin-Darby canine kidney) cells: a tool for membrane permeability screening. J Pharm Sci 88:28-33

Kansy M, Senner F, Gubernator K (1998) Physicochemical high throughput screening: Parallel Artificial Membrane Permeation Assay in the description of passive absorption processes. J Med Chern 41:1007-1010

Karlsson J, Artursson P (1991) A method for the determination of cellular permeability coefficients and aqueous boundary layer thickness in mono layers of intestinal epithelial (Caco-2) cells grown in permeable filter chambers. Int J Pharm 71:55-64

Norris DA, Leesman GD, Sinko PJ, Grass GM (2000) Development of predictive pharmacokinetic simulation models for drug discovery. J Controlled Release 65:55-62

Pade V, Stavchansky (1998) Link between drug absorption solubility and permeability measurements in Caco-2 cells. J Pharm Sci 87:1604-1607

Palm K, Luthman K, Ros J, Grasjo J, Artursson P (1999) Effect of molecular charge on intestinal epithelial drug transport: pH-dependent transport of cationic drugs. J Pharmacol Exp Ther 291:435-443


Takano M, Hasegawa R, Fukuda T, Yumoto R, Nagai J, Murakami T (1998) Interaction with P-g1ycoprotein and transport of erythromycin, midazo1am and ketoconazo1e in Caco-2 cells. Eur J Pharmaco1 358:289-294

Wohns1and F, Faller B (2001) High-throughput permeability pH profile and high-throughput alkane/water log P with artificial membranes. J Med Chern 44:923-930

4 Drug Metabolism Assays and Their Use in Drug Discovery

M.K. Bayliss, P.J. Eddershaw

4.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 69 4.2 Data Analysis and Computational Approaches to DMPK . . . . . . .. 71 4.3 In Vitro Approaches ..................................... 74 4.4 In Vivo Approaches. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 76 4.5 Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 78 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 79

4.1 Introduction

Arguably the biggest challenge currently facing the global pharmaceutical industry is the urgent need for improvements in productivity in the discovery and development of new medicines. Improved productivity requires enhancements in both efficiency and effectiveness. Thus, in the context of drug discovery it involves a shortening of the time taken from lead identification to full development, but more importantly, a marked increase in the quality of drug candidates provided for development. The consequences of the past practice of taking sub-optimal compounds into development are all too apparent in the high attrition rate of drug candidates during this phase and the oft-repeated fact that the majority of the cost of bringing a new medicine to market is due to those failures (Abelson 1993).

The most obvious impact of these pressures is the wide-spread move within the pharmaceutical industry towards "front-loaded" drug discov-

70 M.K. Bayliss, P.J. Eddershaw

Fig. 1. Schematic representation of molecules with optimal balance of druglike properties

ery. This entails the integration of what were previously development activities into the earlier phases of the discovery process. Nowhere is this more apparent than in the area of drug metabolism and pharmacokinetics (DMPK). The lack of appropriate pharmacokinetics is a major contributor to the failure of many drug candidates during drug development. Problems such as low (and variable) absorption, insufficient systemic exposure or the potential for drug-drug interactions with co-administered therapies all reduce the clinic al and commercial viability of molecules (Fig. 1). For this reason, DMPK groups now play a key part in the identification and optimisation of lead molecules for subsequent development.

A major factor in the establishment of DMPK within drug discovery has been the relative ease by which traditional in vivo and in vitro technologies were able to be applied to the particular needs of drug discovery projects. Moreover, as will be described in this article, recent developments in automation and bioana1ysis have added further to our capabilities in this respect. However, there is stiH a need to replace the empirica1 nature of DMPK with a more rational, design-led approach if we are to achieve real improvements in efficiency and effectiveness. This requires a gre ater knowledge of the factors relating chemica1 structure and properties such as absorption and drug disposition, as well as potency, safety and other aspects of developability in order to guide

PPB

Drug Metabolism Assays and Their Use in Drug Discovery 71

Poor systemic exposure Poor bioavailability Drug-drug interactions

Distribution

Volume of

distribution

Target tissue

c.,L" ;PM _'.0 ~ I r-:::"bili~ Eo_ Eo_ ~ I Gut st, induction inhibition

Renal Hepatic

Pgp Solubility

Metabolic Biliary Permeability

Fig. 2. Major absorption, distribution, metabolism and excretion/pharrnacokinetic (ADMEIPK) issues encountered during lead optimisation. PPB, plasma protein binding; FPM, first pass metabolism

projects towards truly optimised candidates as quickly as possible (Fig. 2).

4.2 Data Analysis and Compntational Approaches to DMPK

Truly effective in silico approaches to predict drug absorption, distribution, metabolism and excretion (ADME) have long been sought by DMPK scientists since they offer considerable advantages in time and effort over conventional in vivo and in vitro methods. Until recently, such attempts were confounded by a lack of suitable data on which to build robust models that had applicability beyond a given small series of molecules. This situation is now changing with the routine use of higher throughput methods for studying aspects of absorption and drug disposition and an increasing awareness of informatics within the DMPK area.

In silico methods offer three key features for drug discovery. First, they can improve the design of molecules prior to synthesis, so-called virtual screening. This is particularly useful for combinatorial synthesis of large libraries of molecules where many combinations of monomers


are added to a central scaffold. Assessment of these libraries for properties such as absorption and CNS penetrability can help to identify monomers which consistently produce molecules which are unlikely to meet requirements in these areas. By omitting or replacing these monomers, the overall quality of the library and any subsequent active molecules resulting from it should therefore be improved.

Secondly, predictive models of key ADME properties can be used to prioritise compounds for further testing. A common bottleneck often encountered following primary activity screens is the requirement for subsequent activity and selectivity testing, either in a lower capacity in vitro system or an animal model. Computational models can provide an effective way of selecting the best compounds to be progressed, particularly when the number of compounds under consideration is too large for DMPK characterisation, or limited supplies of compound have to be retained for activity work. Although in vitro tests can also be effective at this stage, the ease with which computational systems can be run makes them an attractive alternative. In our laboratories, for example, this has led to the replacement of a cell-based in vitro system for measuring permeation-,with a computational approach to estimate likely human absorption. The cell-based assay used a monolayer formed from a Madin-Darby canine kidney (MDCK) cell line (Irvine et al. 1999) and required liquid chromatography/mass spectrometry (LC-MS) analysis to determine the amount of test compound able to pass from the apical chamber, across the cell monolayer to the basolateral chamber. The combination of a cell-based assay and LC-MS analysis resulted in a relatively labour-intensive system. Moreover, problems with achieving an adequate mass-balance, typical of such systems, meant that the generation of useful results for many compounds was often not possible in a reasonable time frame. The computational approach consists of two complementary models of human oral absorption: the Lipinski rule of five parameters (Lipinski et al. 1997) and an additional physico-chemical model based on calculated log D and molar refractivity (an estimate of molecular size). If a molecule is classed as "OK", i.e. possessing characteristics indicating permeability, by both of these models, it is predicted as likely to have acceptable oral absorption. Analysis of data generated by both the in vitro and in silico approaches for about 1,000 compounds showed agreement of over 90% in identifying molecules with good permeability/absorption. In addition, the in silico approach


was able to highlight compounds likely to be poorly 'absorbed to a greater extent than was possible with the in vitro system. Therefore, not only does the in silico system give comparable or possibly superior results, it is also much less demanding to operate routinely, allowing resources to be re-deployed on more specialised systems providing higher definition data.

The third and most important feature of in silico approaches is the ability to develop structure - property relationships for aspects of DMPK. As mentioned previously, this is a fundamental requirement of rational drug discovery, not only allowing the rationalisation of DMPK issues such as poor absorption or metabolic instability, but providing an indication of possible options to overcome them.

As DMPK has become established as a routine part of lead optimisation, there is increasing interest in its role in the earlier stages of discovery. Once active molecules (hits) have been identified, the concept of template tractability becomes a key factor in selecting lead series to form the basis of optimisation programmes. This has typically centred on synthetic chemical considerations, together with indications of structure-activity relationships (SAR) for the given target. It is becoming more common, however, to include aspects of drug absorption and disposition when assessing the best leads to progress. Here again, the advent of in silico approaches has proved extremely valuable in allowing a comparison of the drug-like properties of many compounds very quickly.

Whilst the assessment of the tractability of potential leads could be considered a key tenet of rational drug discovery, opinion is currently divided as to the actual value of this practice. It can be argued that the path of lead optimisation is determined largely by the drug -like qualities of the starting template and that the subsequent optimisation process can be streamlined by much earlier attention to factors such as safety and pharmacokinetics. Conversely, our lack of knowledge of the SAR surrounding DMPK factors could mean that a poor quality template can very quickly be transformed into a good one, or vice versa, through small chemical modifications. This is possibly the case for aspects such as metabolic stability but perhaps less so for absorption. It would certainly appear a common experience that difficult lead series present a significant challenge to drug discovery projects and often fail to produce viable development candidates; one would hope that selection of a more


drug-like starting point should minimise the number of iterations required to produce a suitable drug candidate. As our ability to predict ADME in humans on the basis of physico-chemical and basic in vitro data advances, it should become possible to eliminate those apparent leads which in reality prove impossible or impractical to refine, and which currently waste much valuable resource.

4.3 In Vitro Approaches

The use of in vitro systems for studying metabolic stability and membrane permeation is commonplace in drug discovery. These systems are usually suitable for automation and thus present relatively high throughput approaches for identifying compounds for progression. A further advantage of such in vitro systems is the availability of human-derived or human-like materials which can provide additional insight into likely disposition in man.

The primary focus of in vitro systems for drug metabolism is rate. The aim is usually to identify those compounds which appear to be resistant to extensive metabolic attack and thus more likely to have sufficient exposure in vivo. The reverse may apply for topical or inhaled therapies where systemic exposure may lead to unwanted side-effects. For the screening process to be effective, it is necessary to establish a degree of correlation between the in vitro system and in vivo PK properties, usually in an animal. Given the simplistic nature of in vitro systems mentioned earlier, it is unrealistic to expect absolute agreement with in vivo disposition. However, it is often possible to provide a broad categorisation of compounds according to their metabolic stability and to use this as a means of prioritising subsequent in vivo studies. It is important that in vitro screens are regularly validated against in vivo data to ensure that decisions based on in vitro data remain relatively sound, if not totally predictive, particularly as the chemical series evolve. The lack of an apparent correlation with in vivo disposition can sometimes highlight mechanistic factors such as the importance of phase II metabolism, cell permeation or protein binding which may be absent from the initial screen. A specific in vitro system could then be employed to target such factors.


In addition to identifying compounds with low rates of metabolism, those compounds that are extensively metabolised can also be studied further to elucidate metabolic routes. This information is valuable in directing the chemical programme towards more stable molecules. Here again, the relative simplicity of an in vitro system and the ability to increase the concentration of the parent molecule offer advantages for the identification of primary metabolites. The widespread availability of recombinant human P450 preparations means that information on the enzymology of specific routes of metabolism can also be obtained during the drug discovery stage. Reliance on a single isoform for metabolic clearance can have implications for clinical use, particularly for isoforms such as CYP2D6 and 2C9 which display polymorphism in the human population. Moreover, knowledge of the SAR for the major human P450s provides further guidance in minimising extensive metabolism by these enzymes.

With most in vitro systems the requirement for liquid chromatography coupled with mass spectrometry (LC-MS) or LC-tandem mass spectrometry (MSIMS) limits their throughput. Despite this, it is possible to achieve capacities of up to several hundreds of compounds studied within 1-2 weeks (Eddershaw and Dickins 1999). Alternatively, assays for measuring the potential to inhibit P450 metabolism are available which use pro-fluorescent probes and allow simultaneous rapid analysis of compounds using a fluorescence plate-reader (Crespi et al. 1997).

Although much emphasis has been placed on the throughput of in vitro systems, even low throughput systems can provide valuable information to discovery projects. The absence or control of factors such as blood flow, protein binding, pH and co-factor availability means that specific mechanistic issues can be isolated and studied in detail. It is important in supporting effective decision making in drug discovery that a sound understanding of the ADMEIPK issues impacting a given project is obtained wherever possible. A good example of this principle is provided by a recent lead optimisation project within our laboratories. The PK of over 30 compounds was determined in rat, with all but 2 compounds showing poor bioavailability. The clearance of all the molecules was low to moderate, which suggested that the bioavailability was limited by absorption from the gastrointestinal (GI) tract. Examination of the physico-chemical properties of the series identified several molecules likely to be poorly absorbed, but this did not explain the majority


of compounds which were predicted to be well absorbed but showed poor bioavailability. The compounds were examined in an in vitro permeation model using a cell line expressing the P-glycoprotein (Pgp) efflux system. This system showed that all the compounds with poor bioavailability were substrates for Pgp, whereas the two compounds with acceptable bioavailability were not. This very clearly identified the nature of the problem and allowed further chemical effort to focus on minimising the impact of Pgp on bioavailability.

4.4 In Vivo Approaches

Despite the continuing improvement of computational and in vitro methods, the use of in vivo animal models remains the definitive method for studying pharmacokinetics during drug discovery. It is only through such studies that the combined impact of the myriad processes affecting drug absorption and disposition can be observed. However, the use of in vivo models is constrained by both ethical and practical factors. There is a widespread desire, reflecting public concern over animal rights, to reduce the use of animals for research purposes. In addition, animal studies are traditionally costly in terms of staff and facilities, relatively time consuming and often require significant amounts of compound. The impact of these factors on drug discovery projects has been reduced through the development of cassette dosing approaches. Cassette dosing is now an established method within the pharmaceutical industry, since it provides a relatively quick way of ranking compounds according to their pharmacokinetic properties and requires the use of fewer animals (Frick et al. 1998).

The full potential of cassette dosing has awaited the development of powerful analytical techniques such as HPLC coupled with tandem mass spectrometry (HPLCIMSIMS) (Korfmacher et al. 1997). The coadministration of several compounds exacerbates the problems normally associated with the analysis of drugs. In addition to the problems caused by the interference of endogenous materials, cassette dosing increases the demand for selectivity of detection, since the multiple compounds and/or their metabolites may also co-elute and interfere with each other. The need to administer lower doses of the individual components in order to avoid pharmacological overdose and the potential for


drug-drug interactions places an additional burden on the sensitivity of assays. For these reasons, cassette dosing depends greatly on the multiple reaction monitoring (MRM) technique afforded by tandem mass spectrometry.

In addition to the analytical requirements arising from cassette dosing, significant logistical challenges also need to be addressed. Most notably these are the design and formulation of the dose cassettes and the data reduction and processing following analysis.

In our hands, computer-aided approaches to various steps in the process have been crucial to the utility of cassette dosing. Tasks that took hours, such as designing the cassettes, can now be done in one or two minutes. The appropriate design of cassettes is vital, in order to reduce the likelihood of analytical interference in the mass spectrometer from compounds sharing the same molecular weight or likely to give rise to metabolically-derived clashes.

Processing of the data obtained from MS analysis is especially complex when large numbers ·of compounds are involved. Automatic transfer of data to a spreadsheet provides greater processing power than is typically available with commercial MS data systems. Custom built algorithms to derive concentrations of each the components of the cassette can then be applied and the resultant data exported to a pharmacokinetic software package.

As with the use of computational or in vitro methods for ADME, cassette dosing requires validation against discrete in vivo PK studies. Provided a reasonable correlation exists, the need for discrete in vivo studies can be minimised. Very often, a "top and tail" strategy is possible whereby discrete studies are used at the beginning of a lead optimisation programme to characterise the PK properties of early templates and then later to provide definitive data on promising molecules approaching candidate selection. The bulk of the lead optimisation process concerned with ranking compounds for further progression can then be efficiently served by appropriate use of in silico, in vitro and/or cassette dosing methods. Where the use of cassette dosing is not found to be valid, automation of the pre- and post-in life stages of discrete PK studies also reduces the time and effort required to conduct such work (Watt et al. 2000).


Fig. 3. Effect of in silico and in vitro methods on compound failure in development. Retrospective analysis performed on compounds studied at GlaxoWellcome over a 10- to -12-year period

4.5 Conclusions

Rational drug discovery requires an appraisal of ADMEIPK issues alongside other "develop ability" factors from the earliest stages of drug discovery. An integrated approach involving the various computational, in vitro and in vivo methods outlined in this article offers an effective means of producing good quality drug candidates with the balance of properties necessary for clinical efficacy. A retrospective analysis of compounds which had been lost during the drug development phase at GlaxoWellcome over the last 10--12 years shows that three quarters of these molecules would have been flagged as poor using a combination of the computational and in vitro approaches now routinely in use (Fig. 3).

ADMEIPK considerations are now being included in the generation of tractable hits at the early stages of drug discovery. This process, which has benefited from the development of high-quality computational models of ADMEIPK properties, reflects the gradual change from


an empirical science to a conceptual one based on greater understanding of the underlying principles governing drug absorption and disposition. It remains to be seen whether this change is sufficient to bring about the required improvements in productivity necessary for drug discovery organisations to remain viable.

Acknowledgements. The authors would like to acknowledge the contribution of the GlaxoWellcome Combinatorial Lead Optimisation Project (CLOP) team and the Research support Drug Metabolism Group.

References

Abelson PH (1993) Improvements in health care. Science 260:11 Crespi CL, Miller VP, Penman BW (1997) Microtiter plate assays for inhibi

tion of human, drug-metabolizing cytochromes P450. Anal Biochem 248:188-190

Eddershaw PJ, Dickins M (1999) Advances in in vitro drug metabolism screening. Ph arm Sci Tech Today 2:13-19

Frick LW, Adkison KK, Wells-Knecht KJ, Woollard P, Higton DM (1998) Cassette dosing: rapid in vivo assessment of pharmacokinetics. Pharm Sci Tech Today 1:12-18

Irvine JD, Takahashi L, Lockhart K, Cheong J, Tolan JW, Selick HE, Grove JR (1999) MDCK (Madin-Darby canine kidney) cells: a tool for membrane permeability screening. J Pharm Sci 88:28-33

Korfmacher WA, Cox KA, Bryant MS, Veals Ng K, Watkins R, Lin CC (1997) HPLC-APIIMSIMS: a powerful tool for integrating drug metabolism into the drug discovery process. Drug Discov Today 2:532-537

Lipinski CA, Lombardo F, Dominy BW, Feeney PJ (1997) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Ad Drug Del Rev 23:3-25

Watt AP, Morrison D, Evans DC (2000) Approaches to higher-throughput pharmacokinetics (HTPK) in drug discovery. Drug Discov Today 5:17-24

5 Prediction of Human Pharmacokinetics Based on Preclinical In Vitro and In Vivo Data

T. Lave, O. Luttringer, J. Zuegge, G. Schneider, P. Coassolo, F.-P. Theil

5.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 5.2 Methods. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 83 5.3 Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 90 5.4 Discussion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 98 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 101

5.1 Introduction

During the drug discovery process, candidate compounds are screened for their main drug metabolism and pharmacokinetic (DMPK) properties (absorption, distribution, metabolic stability, excretion) to assess their potential to become new drug products. Prentis observed that, of 247 new chemical entities which were withdrawn from drug development before 1985, 198 (80%) failed because of inappropriate pharmacokinetics (Prentis et al. 1988). Kennedy confirmed this conclusion in 1997 by reporting that, apart from a lack of efficacy, poor pharmacokinetic properties were still the main reason for terminating the development of drug candidates (Kennedy 1997). Nowadays, the drop out rate because of pharmacokinetic reasons has probably decreased because DMPK issues are being considered in the discovery process. Therefore, approaches to predict human pharmacokinetic profiles are highly desir-

82 T. Lave et al.

able to help select the best candidates for development and/or to reject those with a low probability of success. This can drastically reduce the time and expense of drug development (Norris et al. 2000). Since drug discovery has to screen a large number of compounds, the methods used must be capable of predicting the human pharmacokinetics from a limited set of input data.

Both physiological and empirical approaches have been developed for predicting human pharmacokinetics (Lin et al. 1982; Mordenti 1985, 1986; Boxenbaum and D'Souza 1990; Campbell 1994; Boxenbaum and Dilea 1995; Lave et al. 1995a,b, 1996a-c, 1997a,b, 1999a,b; Iwatsubo et al. 1996; Izumi et al. 1996; Iwatsubo et al. 1997; Obach et al. 1997; Lin 1998; Richter et al. 1998; Obach 1999; Cross and Bayliss 2000). Due to its simplicity, allometric scaling of in vivo data from different animal species has been the most widely applied approach. However, the prediction of pharmacokinetic parameters (and especially metabolic clearance) by this method frequently produces inaccurate results (Lave and Coassolo 1998; Lave et al. 1999a). With the recent increases in the availability of human liver tissue, in vitro metabolic data can now be used to predict in vivo clearance in humans more accurately. For example, metabolic clearance can be predicted more reliably by combining allometry with in vitro and in vivo data, even when large species differences are observed in the rates of drug metabolism either in phase I or phase II (Lave et al. 1999). However, allometric scaling methods require both animal and human in vitro data, as well as animal in vivo data. It is, therefore, not very practical in the drug discovery context, especially during the early phases of the process.

For this reason we have investigated alternative ways of predicting hepatic metabolic clearance in man, in particular: direct scaling, in vitro-in vivo correlation, and artificial neural networks. In this paper we describe a comparative evaluation with 23 extensively metabolized compounds for predicting hepatic metabolic clearance in humans. These results indicate that in vitro data alone can be used to reliably predict clearance in humans. As this parameter can also be used as the main input for physiologically based pharrnacokinetic (PBPK) models, we then investigated the use of such models to provide rapid estimates of distribution/excretion parameters in man.

In contrast to the more empirical approaches, PBPK models have the potential to integrate information from various pharmacokinetic proc-

Prediction of Human Pharmacokinetics 83

esses, including absorption, metabolism and distribution. They can be used not only to estimate pharmacokinetic parameters, but also to predict the complete concentrations versus time profiles in both plasma and tissues. Furthermore, the input parameters needed for the physiologically based approach can be derived solely from in silico and in vitro data, which are routinely generated in the drug discovery process for new drug candidates.

5.2 Methods

5.2.1 Prediction of In Vivo Clearance in Man

5.2.1.1 Data Collection The in vivo and in vitro pharmacokinetic data for the various compounds (antipyrine, bosentan, caffeine, diazepam, diltiazem, felodipine, ibuprofen, lorazepam, mibefradil, midazolam, mofarotene, naloxone, nicardipine, nilvadipine, nitrendipine, oxazepam, propranolol, remikiren, Ro 24-6173, Ro 48-6791, Ro 48-8684, theophylline, tolcapone) were obtained from literature and in-house data as described elsewhere (Lave et al. 1997a,b). For all compounds selected, liver was assumed to be the main site of metabolism and in vivo pharmacokinetic data obtained after intravenous administration were available in animals and/or humans (except for mofarotene where oral data were used). For all test compounds, the in vitro data were generated in human hepatocytes in primary culture, with the clearance being determined from the disappearance of parent compound in the incubation medium, as described elsewhere (Lave et al. 1997a,b).

5.2.1.2 Allometric Scaling For allometric scaling, the blood clearances of total (bound+unbound) drug in vivo (CLh in vivo) in the various animal species were correlated with their corresp~nding mean body weights (B), using allometric equations of the form:

CL=a·BX (1)

84 T. Lave et al.

For this extrapolation, CLh, in vivo was normalized by the ratio of the metabolic clearances in vitro, for example: CLh, in vivo, rat (CLint, in vitro,human/CLint, in vitro,rat) (Lave et al. 1997a). These normalized values were then extrapolated using allometric scaling (Lave et al. 1997a), leading to Eq. 2:

( J ( )x CLint, in vitro, human Bhuman

CLh,invivo,human = CLh,invivo,animal' CL· .. . . B . mt, In vztro, ammal ammal

The values of the allometric coefficients, a, and exponents, x, in Eq. 1 were estimated by linear least squares regression of the log transformed allometric equations (Eq. 3).

log( clearance )=log(a)+x log(B). (3)

In addition, hepatic metabolic clearance in man was calculated from the rat data by combining in vivo and in vitro data in rat and in vitro data in man (Lave et al.1997a), according to Eq. 4:

CL ()0.86 CL - CL int, in vitro, human Bhuman h, in vivo, human - h, in vivo, rat' CL. .. . B

mt, In vztro, rat rat

Several modifications to this general allometric scaling approach have been proposed, taking into consideration the impact of neoteny on clearance (Boxenbaum 1984; Boxenbaum and Fertig 1984; Boxenbaum and D'Souza 1990; Boxenbaum and Dilea 1995). These modifications add factors like brain weight or potential life span to Eq. 4. Such prediction models are not included in the present paper, since the allometric approaches incorporating in vitro data have been previously shown to be superior to these more empirical approaches (Lave et al. 1999a).

5.2.1.3 Direct Scaling (In Vitro-In Vivo Scaling) The in vitro-in vivo extrapolation followed the strategy proposed by Houston and co-workers (Houston 1994a,b; Carlile et al. 1997; Houston and Carlile 1997). The hepatic blood clearance (CLh in vitro) was derived using the equation for the well-stirred model. To' convert hepatocyte

(2)

(4)


clearances from "number of cells in the incubations" to "per gram of liver", a scaling factor of SFdir=1.2x108 cells per gram ofliver was used (Bayliss et al. 1999). The resulting scaled in vitro hepatocyte clearance (CLint, in vitro) was then used to derive the in vivo human clearance (Eq.5).

LBF.(f~I).CLint, in vitro ·SFdir ·LW CLh, in vivo = tful \ (5)

LBF + \ I ful!" CLint, in vitro' SFdir . LW

Based on Roche in-house values, the average liver weight (LW) and liver blood flow (LBF) for humans are 1,800 g and 20 mllmin/kg, respectively. The parameters fu and fu' in Eq. 5 represent the free fractions of drug in blood and hepatocytes, respectively. The drug binding in the hepatocyte assay was assumed to be identical to the binding in blood (fu=fu') as reliable predictions could be obtained for a number of compounds under this assumption when extrapolating in vitro (microsomal and hepatocyte) data to the in vivo situation (Lave et al. 1996c; Obach 1996, 1999). This assumption simplifies Eq. 5 to obtain Eq. 6:

LBF . CLint in vitro . SF dir . L W CLh in vivo = -------'-, -------

, LBF + CLint. in vitro . SF dir . L W

5.2.1.4 In Vitro-In Vivo Correlation For this approach the well-stirred model was again used to describe the relationship between the in vitro clearances in human hepatocytes (expressed as CLint in vitro) and the corresponding in vivo clearance of the 23 compounds i~ the reference data set, as given by Eq. 7:

LBF· CLint in vitro' SFcorr CLh in vivo =------'-' -----

, LBF + CLint, in vitro' SFcorr

In this model SFcorr is the in vitro-to-in vivo scaling factor. Its value was estimated by a non-linear iterative least squares regression, as described previously (Lave et al. 1997b). The values derived from this curve fitting were used to calculate the in vivo clearance from the corresponding in vitro clearances in human hepatocytes. The compound for which the prediction was performed was always excluded from the

(6)

(7)

86 T. Lave et al.

regression (leave-one-out procedure). The in vitro-to-in vivo scaling factor (SFcorr) in Eq. 7 is not directly comparable to the number of hepatocytes per kg body weight (SF dir) for the direct scaling approach (cf. Eq. 6). This is because SFcorr is a hybrid parameter that takes into account various factors, such as in vivo/in vitro protein binding and membrane permeability.

5.2.1.5 Artificial Neural Network A neural network was constructed with the architecture shown in Fig. 1.

Linear neuron activation was used. This architecture has been shown to be suitable for predicting clearance from hepatocyte data (Schneider et al. 1999). The overall relationship between the five input neurons and the single output neuron is given by Eq. 8.

eL predicted = f. Wh T( f Wi Xi + .9h J + ,9 h=l li=l

The inputs for this network were human CLint, in vitro, and rat CLh, in vivo. The network was trained in a supervised manner to predict human hepatic in vivo clearance (CLh, in vivo, human), as previously described (Schneider et al. 1999).

5.2.1.6 Statistical Analysis of the Various Prediction Models The errors produced by a prediction model on the test data - which provide a useful measure of prediction accuracy and hence the usefulness of the model- can be estimated by cross-validation studies. Usually the leave-one-out procedure is applied. The prediction error sum of

Input layer

Hidden layer

Output

CLpredicted

x

w

A W

Fig. 1. Schematic of a three-layered artificial neural network

(8)


squares (PRESS, Eq. 9) and the squared linear correlation coefficient r2

(Eq. 10) are commonly calculated to estimate prediction accuracy: n n

PRESS = 'Lerror/ = 'L(CLi,observed -CLi,predicted f (Eq. 9)

i=l i=l

r2 = ['L~=l (CLi,predicted -( CLpredicted )XCLi,observed - (CLobserved ))]2

(n -1). S predicted . S observed

The terms

. (CLobserved) and \ CL predicted)

represent the mean of the observed and predicted clearance values, respectively, and Sobserved and Spredicted are the corresponding standard deviations. The total number of observations (training or test data) is given by the variable n.

An alternative - more intuitive but practically useful- measurement of error that is commonly employed for clearance predictions is the fold-error (Eq. 11). A prediction is usually considered successful if the absolute value of the fold-error is less than two (Obach et al. 1997).

fold - error =

- CL predicted

CLobserved

CLobserved

CL predicted

, if CLpredicted < CLobserved

, else

To describe a meaningful average of the fold-error, which weights over- and under-predictions equally, Obach and co-workers (Obach 1999) proposed the formula in Eq. 12.

I.7=111og( CLi,predictedjCLi,observed lin average fold - error = 10

88 T. Lave et al.

5.2.2 PBPK Model

5.2.2.1 Disposition Model The minimal PBPK model proposed by Arundel (1997) was used in this evaluation. The model, which was developed for the rat, was adapted to man by considering human blood flows and tissue volumes (Bernareggi and Rowland 1991).

The human clearance values that were used for this model were those estimated by the physiologically based direct scaling (Eq. 6), as discussed above. Ten compounds for which the Vdss values were known in man were included in the evaluation. Their disappearance rate constants (kr values) were estimated from the predicted human clearance and the actual Vdss, j in man, using Eq. 13:

kT=(kIixVdss)/Vdss, j (13)

where Vdss, j is the volume of distribution for an individual drug "j". In our model, rate constants for the disappearance of drug from

individual tissues (kri) were subdivided into 6 groups: (1) lung; (2) heart, brain, kidney; (3) gut, stomach, spleen, pancreas; (4) liver; (5) muscle, bone, skin, testes; (6) adipose. The six-tissue compartment model used in this evaluation is shown in Fig. 2.

All of the compounds were assumed to be eliminated only by the liver. Each compartment was modelled as a first order differential equation, which was solved using the Runge-Kutta algorithm (fourth order,

Table 1. Derived data on tissue volumes, flow and the standard values of (knxVdss) in man

Man

Tissue group VT Qi QN kTXVdssi (ml) (mllmin)

Lung 1,170 5,240 4.48 21,6974.1 Heart, brain, kidney 2,027 1,950 0.96 40,125.9 Gut, stomach, spleen, pancreas 2,081 1,350 0.65 40,962.5 Liver 1,690 1,650 0.98 24,325.5 Muscle, bone, skin, testes 4,6506 1,300 0.03 2,316.9 Adipose 1,0000 260 0.03


Fig. 2. PBPK model with subdivided tissue eompartments and hepatie elimination

variable step size) with the software package ModelMaker (version 3.0.4, Cherwell Scientific, Oxford).

Based on the observation that, for compartments 1-5, the product of (krixVdss) is constant over a range of drugs (ArundeI1997), the kri of the compounds in the different tissue groups were estimated from their actual Vdss values in man. The predicted values of kri in adipose tissue were calculated using the correlation between log Kpu and Log D (log Kpu=-0.6+0.8xLog D) as proposed by Arundel (1997), where Kpu and Log D represent the unbound blood/tissue partition coefficient and the octanol water partition coefficient, respectively. The tissue volumes, flows and the standard values of (krixVdss) are listed in Table 1. The standard values of (krixVdss) in man were derived from the rat values assuming that the ratio (Vdss/Kp) is constant across species.

90 T. Lave et al.

5.3 Results

5.3.1 Clearance prediction

For the 23 compounds included in this evaluation, their human clearances were predicted by five different approaches: allometric scaling (multiple species, Eqs. 2 and 4), direct scaling (Eq. 5), in vitro-in vivo correlation (Eq. 7) and an artificial neural network (ANN, Eq. 8). The prediction results are presented in Table 2.

The r2 values varied between 0.38 (allometry, Eq. 4) and 0.84 (in vitro-in vivo correlation), and the PRESS ranged from 196 (in vitro-in vivo correlation, Eq. 7) to 1,086 (allometry, Eq. 4). Overall the two allometric approaches resulted in both lower r2 values and higher PRESS values than the other methods.

Results for the individual compounds are illustrated by the scatter plots shown in Fig. 3. These indicate that allometry again gave the highest deviations, with approximately tenfold over-estimates for oxazepam and diazepam (Eq. 2) and more than a sixfold under-estimates for diltiazem clearance (Eq. 4). However, for all five methods the average fold-error was fairly constant, ranging from 1.64 (empirical in

Table 2. Accuracy of human clearance predictions (from Zuegge et al. 2001)

Approach PRESS 2 Average Successful Successful Maximum r (Eq.9) (Eq.lO) Fold-error prediction prediction fold-error

(Eq. 12) (%)a (%)b

Multiple species 833 0.44 2.03 68.2 81.8 10.2 allometric scaling (Eq.2) Rat allometric scaling 1086 0.38 1.99 54.6 72.7 6.4 (Eq.4) Physiologically based 638 0.77 2.01 63.6 77.3 6.2 direct scaling (Eq. 5) Empirical in vitro- 196 0.84 1.64 63.6 95.5 14.6 in vivo correlation (Eq.7) ANN (Eq. 8) 267 0.78 1.81 68.2 81.8 21.8

aWithin twofold of observed CL (Obach et al. 1997). bWithin threefold of observed CL.

Prediction of Human Pharmacokinetics

20 A

15

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i 5

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20 c 15

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20

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15

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~ [ 5

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• • •

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•

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CLh• 1~served 15

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Fig. 3A-E. Scatter plots of the predicted vs. the observed hepatic clearances (ml/minlkg). A Multiple species allometric scaling (Eq. 2). B Rat allometric scaling (Eq. 4). C Physiologically based scaling (Eq. 5). D Empirical in vitro-in vivo correlation (Eq. 7). E ANN (Eq. 8). (Adapted from Zuegge et al. 2001)

92 T. Lave et al.

vitro-in vivo correlation) to 2.03 (multiple species allometric scaling). Interestingly, therefore, even for the two allometric approaches the average fold-error values were close to 2, and 68% and 55% of the total predictions, respectively, were within a factor of two of the actual clearance values in man.

Figure 3 also shows that for all the five of test methods the number of under-predicted results exceeded the number of values, which were over-estimated. These systematic under-estimations might be related to the well-documented losses of cytochrome P450 activities that are known to occur when hepatocytes are cultured in monolayers. Studies are ongoing to determine and take into account these losses for the in vitro-in vivo scaling of clearance.

This tendency to under-estimate human clearance values was particularly marked for the direct scaling approach (Fig. 3C), which is one of the two methods for which human hepatocyte data is the only input variable. Consequently, although the r2 value (0.77) was relatively high, its PRESS (638) was higher than those for the in vitro-in vivo correlation (196) and ANN method (267). Nevertheless, 64% of its predicted clearances were still within a factor of two of the observed values in man, with the worst cases being sixfold under- and over-estimations for the human clearances of bosentan and diazepam, respectively.

Despite its lower PRESS value, r2 for the in vitro-in vivo correlation (the second approach that is based exclusively on human hepatocyte data) was virtually identical to that for direct scaling. In this case, 73% of its predicted values were within a factor of two of the observed clearances in man. However, with this approach the worst individual result was a I5-fold over-estimation of the observed clearance for diazepam (Fig. 3D).

U sing an artificial neural network to combine the data for rat and human hepatocytes overall gave the highest r2 and lowest PRESS values (0.84 and 196, respectively; Table 1). Despite these lower error values, however, the overall accuracy of this method for predicting human clearance was no higher than of the other four approaches. In this case the average fold error was 1.81, 68% of the predictions were within a factor of two of the observed clearances in man, and its worst-case prediction was a 22-fold over-estimation of the actual human in vivo clearance of diazepam.

Tab

le 3

. Pre

dict

ed a

nd o

bser

ved

phan

naco

kine

tic

para

met

ers

in m

an f

or te

n ex

tens

ivel

y m

etab

oliz

ed c

ompo

unds

Com

poun

d t1

/2 (

h)

CL

(m

lJrn

inlk

g)

Vss

(l/k

g)

Pre

dict

ed

Obs

erve

d F

old-

erro

r P

redi

cted

O

bser

ved

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d-er

ror

Pre

dict

ed

Obs

erve

d F

old-

erro

r

48-6

791

2.61

1.

50

1.74

14

.26

25.8

0 -1

.81

2.13

2.

50

-1.1

7 48

-868

4 3.

53

2.10

1.

68

16.8

5 32

.10

-1.9

1 3.

22

4.80

-1

.49

Bos

enta

n 8.

52

4.30

1.

98

0.69

2.

60

-3.7

7 0.

50

0.67

-1

.34

C

affe

ine

17.8

7 4.

20

4.25

0.

42

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0.65

0.

70

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8 M

ibef

radi

l 3.

66

3.40

1.

08

6.12

3.

40

1.80

1.

62

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M

idaz

olam

1.

38

1.70

-1

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8.38

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40

1.55

0.

81

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1.

25

Pro

pran

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93

3.10

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91

8.74

19

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42

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niki

ren

2.04

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30

-1.6

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41

10.4

1 -1

.62

0.75

0.

46

1.63

T

heop

hyll

ine

14.0

8 7.

50

1.88

0.

35

0.85

-2

.43

0.43

0.

45

-1.0

5 T

olca

pone

0.

56

1.30

-2

.32

3.10

1.

69

1.83

0.

14

0.11

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27

Ave

rage

fol

d er

ror

1.71

-1

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-1.1

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o·

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:::J ""'0

:::T

III .... 3 III

(")

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en

co

w

94 T. Lave et al.

5.3.2 Prediction of IV Concentration Versus Time Profiles

As outlined in the methods section (Sect. 5.2), the minimal PBPK model that Arundel originally developed for the rat (Arundel 1997) was adapted to man, by considering human blood flows and tissue volumes (Bemareggi and Rowland 1991). The human clearance values used for the modelling were those predicted by the physiologically based direct scaling (Eq. 6).

For the seven compounds that were evaluated with this approach, the predicted and observed plasma concentration-time profiles for a single intravenous bolus dose are shown in Figs. 4-10. Since the concentrations time profiles were available for only part of the compounds, a table (Table 3) is included where the predicted and observed PK parameters for all ten test compounds are listed.

Both the plasma concentration versus time profiles simulated by the Arundel method and the predicted PK parameters in man showed reasonable agreement with the observed values. For CL, Vdss and t1/2, the average deviation between the observed and predicted values, was less than twofold. For Vdss, the close agreement between the derived and actual values, with a less than twofold deviation for all ten compounds,

100000.00

~ 10000.00 .s ~ o

~ _ 1000.00 fl c: 8 III E .; 100.00 0..

10.00 +----~.---------.------,.----~ 0.00 100.00 200.00

Minutes

300.00 400.00

Fig. 4. Predicted and observed plasma concentration vs. time profiles of midazolam after single i.v. administration in human. The line and points correspond, respectively, to the predicted and observed profiles

Prediction of Human Pharmacokinetics

E g, .s UI c o ;;

~ C

~ C o OJ 1\1 E IQ ii:

100.00

10.00

1.00.j-----~---~~---~----~

0.00 50.00 100.00 Minutes

150.00 200.00

95

Fig. S. Predicted and observed plasma concentration vs. time profiles of tolcapone after single i.v. administration in human. The line and points correspond, respectively, to the predicted and observed profiles

Simulation of Ro 48-6791 in man (1 mgIkg)

100,00 "'.00 Minutes

Fig. 6. Predicted and observed plasma concentration vs. time profiles of Ro 48-6791 after single i.v. administration in human. The line and points correspond, respectively, to the predicted and observed profiles

l .s 1/1 c 0

E C

8 c 0 u .. E 1/1 .. ii:

96 T. Lave et al.

Simulation of Ro 48-8684 in man (1 mglkg) 10000D.00

10000.00

1000.00

-I'"""

l:

10000

10oo+---________ ~------------._------------~----------_, 0.00 10000 200.00 300.00 .... 00

Minutes

Fig. 7. Predicted and observed plasma concentration vs. time proflles of Ro 48-8684 after single Lv. administration in human. The line and points correspond, respectively, to the predicted and observed proflles

Simulation of Bosantan in man (1 mg/kg) 100000.00

10000.00

l .s 10 c 100000 i ~ 0

~ % ! I 8 % I c

100.00 i 0 u .. E 10 .!! II..

10.00

1.00+-------------..;.---------------.----------------.----------------, 000 100.00 200.00 30000 .... 00

Minutes

Fig. 8. Predicted and observed plasma concentration vs. time proflles of bosentan after single Lv. administration in human. The line and points correspond, respectively, to the predicted and observed proflles

] Dl .s III c: 0

~ c: CD CJ c: 0 CJ .. E III .. ii:


Simulation of mibefradil in man (1 mg/kg) 100000.00

1000000

I

I

1000.00

--l"'~

100.00

10.00-t--------,----------r-------.--------, 0.00 100.00 200.00 300.00 40000

Minutes

Fig. 9. Predicted and observed plasma concentration vs. time profiles of mibefradil after single i.v. administration in human. The line and points correspond, respectively, to the predicted and observed profiles

Simulation of remikiren In man (1 mg/kg)

Fig. 10. Predicted and observed plasma concentration vs. time profiles of remikiren after single i.v. administration in human. The line and points correspond, respectively, to the predicted and observed profiles

98 T. Lave et al.

was to be expected since this was one of the input parameters for modelling. There was also good overall agreement for the individual values of t1/2, with 80% of the compounds showing less than a twofold deviation between the predicted and observed values. Of the two remaining values, a 4.3-fold over-estimation of t1/2 for caffeine, and a 2.3-fold under-estimation for tolcapone, the caffeine value probably results from the corresponding fivefold under-estimation of its clearance.

5.4 Discussion

In this paper, we have explored two preclinical approaches for predicting the human pharmacokinetics of potential drug development candidates:

1. A comparison of five methods for predicting metabolic clearance in man, using 23 compounds that are extensively metabolized by liver enzymes.

2. Subsequent use of the derived clearance values for ten of these compounds as the input for PBPK modelling, in order to predict concentration-time profiles in human plasma and tissues. These data can then be used to derive PK parameters that can simulate human pharmacokinetics under any relevant clinical condition.

The comparison of the methods to predict metabolic clearance in humans indicated that all of the five approaches tested provided reasonably accurate assessments of human clearance (less than twofold differences between predicted and observed clearance for approximately two-thirds of the test compounds). However, in terms of the statistical methods used for the evaluation (prediction error sum of squares and squared linear correlation coefficient between predicted and observed values), the two allometric methods were less reliable than the other three approaches. As reported previously (Lave et al. 1997a, 1999a), "our" allometric approach (which uses the relative rates of metabolism in vitro to scale the in vivo clearance in different animal species) usually gives better predictions of human clearance than the corresponding conventional methods (either allometry alone, or with the inclusion of brain


weight/maximum life span as an empirical correction factor). Therefore, we can assume that none of the allometric methods perform as well as the alternative approaches that were investigated in the present study. Irrespective of these considerations, however, allometry requires a relatively large body of input data (with in vitro results for both animals and humans, as well as in vivo preclinical pharmacokinetic values), so that it is not a very practical approach in drug discovery, especially during the early phases of the process. Potentially, therefore, it is very interesting to note that the approaches which only require in vitro data are at least as accurate as or even more accurate than allometry.

In terms of accuracy and prediction errors, the three other methods tested showed comparable performance. Two of these models (the physiologically based direct scaling and empirical in vitro-in vivo correlation) are based solely on in vitro data, while the third - most abstract - model (the artificial neural network) needs rat and human in vitro data as input. Any or all of these models can, therefore, be considered appropriate as the basis for PBPK modelling.

To construct a PBPK model requires information about the various pharmacokinetic processes that are involved in the absorption and disposition of the compounds, together with some physiological constants in the relevant species. Most of the physiological parameters (e.g. tissue volumes and blood flows) can be considered fixed for a given species. In addition, a number of drug-specific input parameters need to be incorporated into the models. Thus, numerical estimates of partition coefficients, clearances, permeabilities, and so on are needed for each compound. These values can be readily obtained from in vitro and/or in silico data.

In constructing PBPK models the liver presents particular challenges, since it is not only a distribution organ but also often the site of elimination. Thus, the in vitro metabolism data needs to be incorporated in an appropriate manner. In this study, we compared both the physiologically based and empirical methods to predict hepatic metabolic clearance in man from preclinical data using a common set of 23 well-studied compounds. These compounds provide a broad range of metabolic stability, and are metabolized by a variety of hepatic enzymes (which included both phase I and phase II reactions), together with a wide range of protein binding.

100 T. Lave et al.

In this study, a physiologically based pharmacokinetic model that Arundel originally developed for the rat (ArundeI1997) was extended to man. It incorporates two blood compartments, with the tissues subdivided into six groups. With this approach, the volume of distribution at steady state can be combined with a standard set of human knxVdss values to estimate the kinetics of the compound in (lumped) tissues and organs (except adipose tissue). This means its tissue-to-blood partition coefficient (Kp) and the rate constant of drug exit from tissues (len) can be predicted without the necessity of taking any tissue samples. Overall, our results showed that this model might be used to reliably predict not only the plasma concentration versus time profiles, but also its human PK parameters. Irrespective of the compounds' pharmacokinetic and physicochemical characteristics, the average error for the predicted pharmacokinetic parameters (CL, Vss, t1l2) was less than twofold. In contrast to the approaches traditionally used to build physiologically based models, the predictions could be obtained from a limited set of prior data (i.e. without tissue sampling and analysis).

In the approach described in this paper, the reliability of the prediction of the in vivo concentration versus time profile in man depends on (1) the quality of the prediction of Vdss, and (2) on the adequate estimation of Kp from the standard set of human (lenxVdss) values. Reasonable estimates of this parameter can be obtained from in vitro data, by using mechanistically based tissue composition models (Poulin and Theil 2000; Poulin et al. 2001) or from in vivo preclinical data by using interspecies scaling. Although large species differences have been reported for volume of distribution of total (unbound and bound) drug, these variations are probably due to species differences in plasma protein binding. With respect to tissue binding, close correlations have been reported between humans and animals. It is, therefore, anticipated that a similar correlation might exist between the volume of distribution of the unbound drug in man and animals (Schuhmann et al. 1987). Thus, the volume of distribution in man could be well predicted by determining the volume of distribution of unbound drug in animals and measuring the unbound fraction in human plasma.

In addition to human plasma concentration-time profiles, physiologically based approaches can also offer the possibility of predicting tissue kinetics. However, in the present study we limited the predictions to the plasma concentrations versus time profiles; for this minimal physiologi-


cally based approach a comparison with experimental tissue data would still be needed to validate the prediction of tissue kinetics.

In summary, our evaluation has demonstrated that models based solely on hepatocyte data can lead to reliable predictions of the human liver clearance. In addition, these data could be successfully incorporated into physiologically based models in order to predict full plasma concentration time profiles in man. Although the number of compounds we have used for assessing the various approaches is probably not sufficient to draw general conclusions, it was nevertheless large enough to show some clear trends. Potentially such models can also be used to predict tissue profiles, without the necessity for tissue sampling. Such approaches are believed to be of considerable value to the drug discovery process.

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104 T. Lave et al.

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Zuegge J, Schneider G, Coassolo P, Lave T (2001) Prediction of hepatic metabolic clearance in man - comparison and assessment of prediction models. Clin Pharmacokinet 40:553-563

6 In Vitro Screening of Cytochrome P450 Induction Potential

O. Pelkonen, J. Hukkanen, P. Honkakoski, J. Hakkola, P. Viitala, H. Raunio

6.1 Introduction ........................................... 105 6.2 Phenomenology of CYP Induction in Humans ............... 107 6.3 Possibilities for In Vitro Screening of Induction

Without Mechanistic Considerations ....................... 111 6.4 Possibilities for In Vitro Screening of Induction

Based on Mechanisms .................................. 115 6.5 Nuclear Receptor-Based In Vitro Models for Detection

of CYP Induction ...................................... 124 6.6 Future Developments ................................... 127 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 128

6.1 Introduction

Induction is defined as the increase in the amount and activity of a drug-metabolising enzyme, which is a long-term (hours and days) consequence of a chemical exposure. Previously, the study of induction of drug metabolism was largely empirical and phenomenological, and prediction beyond the compounds under actual study was practically impossible. During the last decade, however, and particularly as a consequence of the detailed knowledge obtained about regulatory factors governing the expression and induction of cytochrome P450 (CYP) enzymes, induction can be understood on a detailed mechanistic basis and predictability of pharmacological and toxicological consequences

106 o. Pelkonen et al.

has become possible. Mechanistic understanding provides also a basis for the development of new in vitro methods to measure and predict induction.

Classically, the definition of induction involves the de novo synthesis of new enzyme molecules as a result of an increased transcription of the respective gene after an appropriate stimulus. However, in drug-metabolism research the term induction has been used as a generic term, describing an increase in the amount and/or activity of a drug-metabolising enzyme as a result of an exposure to an "inducing chemical", whatever the underlying mechanism. However, in the usual sense of induction, there is a certain lag phase before an increase in enzyme activity can be observed. This lag phase is due to the fact that, whatever the underlying mechanism, it takes time to increase the amount of enzyme molecules, either as a result of increased transcription and translation or as result of the stabilisation of an enzyme by a substrate, which leads to a new steady-state level between synthesis and degradation.

For a clinically used drug, induction is a serious adverse effect leading to unintended effects such as attenuation of the efficacy of the inducing drugs as well as other drugs administered simultaneously (pharmacokinetic effects and drug/drug interactions). This characteristic naturally greatly compromises the usefulness of the inducing drug and may cause medical hazards and economical losses. Induction of CYP enzymes has also a major impact on toxicology and carcinogenicity of chemicals. There is a wide interindividual variability in induction of CYP enzymes (Lin and Lu 2001), resulting in unpredictable clinical responses. In addition to affecting the clearance of drugs, induction may cause altered disposition of endogenous hormones.

Assessment of the induction properties of a chemical would be valuable in early steps of drug development and also for toxicological risk assessment. These goals have been so far hampered by (1) the lack of mechanistic understanding and thus inability to predict induction, (2) the lack of in vitro or cell-based models (especially for the CYP2, CYP3 and CYP4 families) that would accurately reproduce induction detected in vivo, and (3) by the low-throughput nature of existing primary cell culture models. Specifically, inducers of the same CYP gene are often structurally diverse, one inducer may activate several different CYP genes, and sometimes responses are species-specific -all facts empha-

In Vitro Screening of Cytochrome P450 Induction Potential 107

sizing the essential need for well-defined tools for assessment of the induction potential.

This review covers (1) the phenomena and mechanisms of induction of human CYPs and (2) possibilities to measure and screen induction potential in in vitro systems.

6.2 Phenomenology of CYP Induction in Humans

6.2.1 Operational Categories of Induction

Based on mostly animal experiments, but also on human in vivo observations, inducers have been categorised into several classes (Table 1), which can be characterised mainly on the basis of the spectrum of enzymes induced and the potency of induction. Table 1 gives only a qualitative view of the spectrum and mechanisms of induction, and in the following section more background is given on mechanistic details and quantitative aspects of induction in man or human-derived systems. It has to be stressed that in many cases we have to rely on what we know from animal experiments.

Several individual agents that induce CYP enzymes have been identified in man, and the list of drugs whose pharmacokinetics and pharmacodynamics are affected by induction is rather long. For comprehensive updates on such drugs, the reader is referred to relevant reviews

Table 1. Classification of inducers of drug-metabolising enzymesa

Class Prototype inducer Principal enzymes affected

PAR-type 2,3,7,8-Tetrachloro- CYPIA, UDP-

Omeprazole-type Ethanol-type Rifampicin-type Phenobarbital-type

dibenzo-p-dioxin Omeprazole Ethanol Rifampicin Phenobarbital

Glucocorticoid-type Dexamethasone Peroxisome proliferator-type Clofibrate

glucuronosyltransferase CYPIA2 CYP2EI CYP3A4 CYPIA, CYP2A, CYP2B, CYP3A CYP3A CYP4

aThis classification is based mainly on animal studies, and the types of induction are not necessarily as clear-cut in humans.


(Wrighton and Stevens 1992; Goldstein and de Morais 1994; Guengerich 1995; Wilkinson 1996). Only the basic classes of induction as well as the mechanisms involved will be dealt with here.

Many inducers of CYP-mediated drug metabolism in humans are drugs that are used in high daily doses, such as phenobarbital, carbamazepine, phenytoin, and rifampicin. This probably reflects the underlying mechanisms, where intracellular receptors governing the induction process have low affinity to these compounds. For some classes of inducers there is a clear structure-activity relationship (e.g. planar molecules inducing CYPIA members), while other classes, especially CYP3A4 inducers, lack a clear structural relatedness (Smith 2000).

6.2.2 Cigarette Smoking and PAH-Like Inducers

Decreased half-life and/or increased clearance of several drugs have been demonstrated in smokers (Sotaniemi and Pelkonen 1987). The common denominator for these drugs is that they are metabolised by CYP1A forms. Examples include theophylline, caffeine, antipyrine, imipramine, paracetamol (acetaminophen), and phenacetin. The metabolism of these drugs is mediated predominantly by CYPIA2, which represents approximately 10% of the total hepatic P450 content (Shimada et al. 1994). Not only CYP1A-mediated reactions, but glucuronide conjugation of, for example, mexiletine is increased due to cigarette smoking. The inducing effects of cigarette smoking is attributed to the polycyclic aromatic hydrocarbon (PAH) class of compounds (Zevin and Benowitz 1999).

CYP1A1 is mainly an extrahepatic enzyme. It is highly induced in the lung, mammary gland, lymphocytes, and placenta by PAHs and cigarette smoke. CYP1A2 is inducible by smoking, charbroiled food, cruciferous vegetables, omeprazole and even vigorous exercise (Wrighton and Stevens 1992). CYPIB1, a relatively recently found CYP enzyme, is highly inducible by PAHs in several extrahepatic tissues of rodents, but the inducibility in human tissues appears to differ from that of CYPIA1 (Hakkola et al. 1997).


6.2.3 Omeprazole

The CYPIA-inducing capacity of omeprazole in the human liver and primary hepatocytes was first reported in 1990 by Diaz et al. (1990). Shortly afterwards, omeprazole was shown to induce CYPIA also in the human alimentary tract (McDonnell et al. 1992). Both ofthese findings have been confirmed by different methodological approaches, but also negative findings have been reported, especially using the standard therapeutic doses of omeprazole (Pelkonen et al 1998). The overall omeprazole-dependent increases in CYPIA activities in the liver and gut in vivo are rather low (usually less than twofold) and high doses and/or prolonged treatments are needed to produce the inducing effect. The clinical use of omeprazole and related proton pump inhibitors is currently extensive all over the world, but major drug interactions due to induction have not been reported.

6.2.4 Ethanol

Ethanol induces liver drug metabolism in humans as measured by both in vivo and in vitro parameters. It has become amply evident that CYP2E 1 is the mediator of the inducible oxidation of ethanol and it may metabolise up to 10% of the ingested alcohol. CYP2El also metabolises a wide variety of drugs and toxic chemicals, including several procarcinogens, making its inducibility of great practical importance (Lieber 1997). Several commonly used drugs, including chlorzoxazone, paracetamol, halothane and related anaesthetics, are metabolised by CYP2El. Thus, an individual with a history oflong-term heavy ethanol consumption has an accelerated elimination of these agents. The clinical consequence may be a decrease in drug activity or an accumulation of active, often toxic metabolites (Klotz and Ammon 1998).

6.2.5 Phenobarbital and Other Antiepileptic Drugs

Phenobarbital is the archetypical inducer of drug metabolism (Waxman and Azaroff 1992). Phenobarbital is still being used in the therapy of epilepsy, and it has long been known to be a strong and broad-spectrum

110 O. Pelkonen et al.

in vivo inducer of drug metabolism. Also, other antiepileptic drugs, especially phenytoin and carbamazepine, have been shown to induce drug metabolism in humans. Carbamazepine is a broad-spectrum inducer, enhancing the metabolism of numerous drugs, including warfarin, theophylline, oral contraceptives and carbamazepine itself (autoinduction) (Brodie and Dichter 1996).

In rodents, phenobarbital induces CYP forms in several subfamilies, including CYPIA, CYP2A, CYP2B and CYP3A, the members in the CYP2B subfamily reacting most sensitively (Waxman and Azaroff 1992). Several lines of evidence suggest that in humans, the CYP3A forms are the ones most affected by phenobarbital and carbamazepine. Some of the newer antiepileptics, such as felbamate, topiramate, and oxcarbazepine also have inducing properties (Benedetti 2000). The inducing effect of antiepileptic drugs on several CYP forms explains the clinical observations that several of the antiepileptics affect a number of structurally unrelated pharmaceuticals by reducing their bioavailability.

6.2.6 Rifampicin and Glucocorticoids

The inducing effects of rifampicin on drug metabolism in vivo was noticed soon after its introduction to clinical practice (Baciewicz et aL 1987). For example, rifampicin accelerates the elimination of quinidine, 17a-ethinylestradiol, cyclosporine and a number of other drugs. Consistent with the fact that most drugs affected by rifampicin are substrates of CYP3A4, rifampicin has been shown to induce mainly CYP3A enzymes in the liver and also in the gastrointestinal tract (Lin and Lu 2001).

Using the CYP3A4 substrate cyclosporine as a marker, Hebert et al. (1992) reported that rifampicin treatment decreases cyclosporine bioavailability more than would be predicted from by increased hepatic metabolism. This phenomenon was ascribed to an elevation of intestinal CYP3A4-mediated metabolism of cyclosporine. This is clinically important, since combination of cyclosporine with CYP inducers leads to decreased cyclosporine concentrations in blood and the risk of organ rejection, and, upon termination of CYP-inducing drug therapy, cyclosporine concentrations rise to levels which may cause adverse effects.


Induction of drug metabolism has been claimed to be also the primary cause of drug interactions observed with corticosteroids. The analysis of in vivo inducing properties of corticosteroids is complicated by the fact that they are often also substrates and hence inhibitors of the reactions under study. For example, methylprednisolone, prednisolone, and prednisone either increase or decrease cyclosporin A clearance, depending on the experimental set-up (Christians and Sewing 1993).

6.3 Possibilities for In Vitro Screening of Induction Without Mechanistic Considerations

Screening of induction of drug metabolising enzymes should be possible if cells isolated from a tissue would preserve, to a sufficient extent, the capability to be induced by in vitro exposure to inducing chemicals. Although profound changes certainly occur upon isolation and culture, long experience has shown that human isolated cultured hepatocytes can be used for studying induction phenomenon. Table 2 lists some advantages and disadvantages of using human liver cells, slices or permanent cell lines as an in vitro system to measure enzyme induction.

6.3.1 Induction in Primary Human Hepatocytes

6.3.1.1 General Considerations The use of primary human hepatocytes in the evaluation of CYP induction was recently evaluated by an international panel (Li et al. 1997). The following observations were concluded by the panel to be generally true: 1. Human hepatocytes isolated from both biopsy samples and trans

plantable livers are suitable for induction studies. 2. Hormonally defined media can be used for the evaluation of CYP in

duction. 3. Isozyme-selective induction of CYPIA and 3A by known inducers

are observed. 4. Reproducibility of induction could be improved by using hepato

cytes plated as confluent cultures.


Table 2. Advantages and disadvantages of various in vitro intact cell systems in induction screening

Cultured human hepatocytes

Advantages Expression of many CYPenzymes Response to several inducer classes TCDD

PB

Ethanol

Human liver slices

Expression of many CYPenzymes Response to several inducer classes TCDD

PB

Ethanol Rifampicin Rifampicin

Dose-response-time Dose-response-time relationships relationships Inhibitory interactions Inhibitory interactions Good correspondence between Good correspondence between in vitro and in vivo in vitro and in vivo

Disadvantages Procurement difficult and opportunistic

Logistically cumbersome Technically demanding Interindividual variability Stability of activities

Future possibilities Cryopreservation

Long-term cultures Reuse of cultures "Miniaturisation" Sensitive assays in living cells

Immortalisation (without loosing phenotype!)

Procurement difficult and opportunistic

Logistically cumbersome Technically demanding Interindividual variability Stability of activities

Cryopreservation

Long-term cultures Reuse of cultures "Miniaturisation"

Sensitive assays in living cells

Permanent cell lines

Practically limitless availability

"Monoclonality" and stability

Available from practically all tissues and cell types Induction present in few cases (almost all cell lines and CYPIAl; Caco-2 and CYP3A4; A549 and CYP3A5)

Dedifferentiation results in down-regulation of CYP expression Not useful for general screening

Systematic screening of cell lines derived from various tissues and organs Genetic engineering


5. Induction could be observed for hepatocytes treated at 1-3 days after culturing.

6. Treatment duration of 2 days in general leads to near maximal induction.

7. In general, there is a good qualitative correlation between human hepatocyte results in vitro and clinical observations in vivo.

8. When the same inducers are evaluated in independent laboratories, similar data are generally observed. The panel concludes that primary human hepatocytes represent an appropriate model for mechanistic evaluation of CYP induction and as a screening tool for CYP induction potential of xenobiotics (Li et al. 1997).

A comparison of two popular matrices, collagen and Matrigel, used in coating of plates for primary hepatocyte culture, revealed that neither was superior in maintaining basal and inducible CYP3A4 expression. The use of percentage induction relative to a standard inducer appears to give less variable data than calculating fold induction, allowing for rank: ordering of compounds to be tested for induction potential (Silva et al. 1998).

6.3.1.2 PAH and Omeprazole It has been demonstrated that human hepatocytes retain their ability for PAH-type induction in culture. CYPIA2 activity is increased by the prototype PAH inducer 3-methylcholanthrene (Morel et al. 1990). Before its in vivo-inducing properties were discovered, omeprazole was shown to induce CYPIA activity in primary human hepatocytes (Diaz et al. 1990). Among proton pump inhibitors, the capacity for CYPIA induction has a rank: order of omeprazole>lanzoprazol>epantoprazole (Masubuchi et al. 1998). Omeprazole and lanzoprazole also appear to modestly induce CYP3A members, and both agents stimulate CYPIAI in the human colon adenocarcinoma-derived cell line Caco-2 (Curi-Pedrosa et al. 1994; Daujat et al. 1996). The inducing effect is strictly species specific, since the CYP lAl gene is activated in human but not in mouse hepatocytes, possibly due to a repressor mechanism in mouse cells (Kikuchi et al. 1998; Dzeletovic et al. 1997).


6.3.1.3 Ethanol In primary human hepatocytes, ethanol treatment increases the activity of p-nitrophenol hydroxylase (Donato et al. 1995) and elevates the amounts of CYP2E1 and CYP3A apoproteins (Kostrubsky et al. 1995). In HepG2 cells transfected with the coding sequence of CYP2E1 cDNA, ethanol increased CYP2E1 protein but not mRNA levels, indicating that the elevation is due to protein stabilisation (Carroccio et al. 1994). CYP2E1 is not inducible in human hepatocytes by rifampicin, phenobarbital or 3-methycholanthrene (a PAH type inducer) (Runge et al. 2000).

6.3.1.4 Phenobarbital and Other Antiepileptic Drugs Phenytoin elevates the activity of cyclosporin A oxidase in human primary hepatocytes (Pichard et al. 1990). Recent data obtained with human hepatocytes suggest that CYP2B6 is inducible by phenobarbital as well as by rifampicin and dexamethasone (Chang et al. 1997). In addition, members of the CYP2C subfamily (CYP2C8 and CYP2C9) are inducible by these agents (Morel et al. 1990). A recent study showed that CYP2A6 is modestly induced in response to exposure of human primary hepatocytes to phenobarbital (Donato et al. 2000).

6.3.1.5 Rifampicin and Corticosteroids Human primary hepatocytes have proved to be very sensitive to the inducing effect of rifampicin. Treatment of primary hepatocytes with rifampicin produces increases in several CYP3A-mediated catalytic activities, including oxidation of cyclosporine (Pichard et al. 1990) and the oxazaphosphorine cancer drugs cyclophosphamide and ifosfamide (Chang et al. 1997). These effects are caused by rifampicin concentrations that are equal to the 2-30 mM serum concentrations achieved after standard therapeutic doses. Rifampicin increases the amounts of CYP3A4 mRNA and apoprotein, but does not affect the amount of CYP3A5 in primary hepatocytes (Chang et al. 1997). An interesting finding is that the mRNA encoding CYP3A 7, a form present almost exclusively in the fetal liver, is inducible by rifampicin in primary hepatocytes derived from adult liver (Greuet et al. 1996). In addition to its inducing effects on CYP3A, rifampicin elevates also CYP2A (DaletBeluche et al. 1992) and CYP2C (Morel et al. 1990) apoprotein levels, resembling phenobarbital in this respect. Dexamethasone increases the


catalytic activities mediated by CYP3A4 in human primary hepatocytes (Pichard et al. 1990; Donato et al. 1995). Prednisone, but not prednisolone or methylprednisolone, elevates the amounts of CYP3A mRNA, protein, and catalytic activity in human hepatocytes (Pichard et al. 1992).

6.3.2 Induction in Other Cellular Systems

As far as we know, there are no systematic surveys of permanent cell lines as to their performance in induction studies. Attempts to demonstrate induction in permanent (immortalised) cell lines have produced variable results. There are reports showing induction in human-derived HepG2 cells. CYPIAI is inducible by PAH-type inducers, a phenomenon that seems to be almost universal. The effects of other types of inducers seem less consistent. Phenobarbital and other antiepileptics has a clearly decreased effect, if at all, but rifampicin has a relatively pronounced effect on CYP3A4 in HepG2 cells, and CYP3A 7 was also elevated in this cell line (Schuetz et al. 1993).

Other cells have also been used in induction studies. CYP3A5 appears to be induced by rifampicin in human colon carcinoma-derived cell lines (Schuetz et al. 1996). CYP3A5 is inducible by glucocorticoids in human lung cancer-derived A549 cells (Hukkanen et al 2000).

6.4 Possibilities for In Vitro Screening of Induction Based on Mechanisms

Although induction in vivo and, less reliably, in ex vivo (cultured primary hepatocytes) systems, remains the final arbiter for estimating therapeutic and toxicological significance of induction screening, there are several reasons to develop mechanism-based systems for induction screening. First of all, fresh or stored human liver may not be available in sufficient quantities and there are also ethical considerations in this respect. It is also well recognised that cells derived from different individuals are very variable, in terms of genetic background, physiological host factors, environmental influences and so on. Interindividual variation creates difficult problems in the interpretation of find-


ings which have to be performed always on a limited number of individual samples. On the other hand, variability is the fact of life. For the preliminary screening, artificial or manipulated systems are preferable.

6.4.1 Receptor-Mediated Induction of CYP Genes

Recent studies indicate that most drug-metabolising CYP genes within families 1--4 are induced via receptor-dependent mechanisms that utilise a ligand-responsive transcription factor and its heterodimerising partner (Table 3). Upon ligand binding, the heterodimer can bind to its cognate DNA sequences and activate CYP gene transcription through recruitment of nuclear accessory factors called coactivators. CYP gene families 2--4 have a similar mechanism of gene activation through ligand-activated nuclear receptor (NR) and a common heterodimerising partner, retinoid X receptor (RXR) , both of which are structurally related to steroid hormone receptors. The AhR receptor regulating CYPI family belongs to the helix-loop-helix family of transcription factors but may share accessory factors with NRs. Finally, the regulatory mechanisms of CYP2C and CYP2A genes and possible involvement of these or other nuclear receptors are currently unknown, although these genes are also inducible.

6.4.1.1 Aryl Hydrocarbon Receptor Aryl hydrocarbon receptor (AHR) is a basic helix-loop-helix (bHLH) protein belonging to the Per-Arnt-Sim (PAS) family of transcription

Table 3. Receptor-mediated induction of CYP genes

Ligand-responsive receptor Principal CYP gene family affected

Aryl hydrocarbon receptor AhR CYP 1 (heterodimer partner Arnt) Constitutive androstane receptor CYP2B CAR (heterodimer partner RXR) Pregnane X receptor PXR CYP 3A (heterodimer partner RXR)

Peroxisome proliferator-activated CYP4A receptor PPAR? (heterodimer partner RXR)

Some receptor ligands

TCDD, polycyclic aromatic hydrocarbons omeprazole? Barbiturates, phenothiazines, pesticides, PCBs, etc. Glucocorticoids, antibiotics, pesticides, PCBs, etc. Lipid-lowering drugs, plasticisers, fatty acids


AHR ligand

• I _ ----,..._-"'-"=------' CYP1A1, CYP1A2,

CYP1B1

Increased transcription

Fig. 1. Schematic presentation of the mechanism of aryl hydrocarbon receptor (AHR)-mediated CYP1A induction. (Hankinson 1995)

factors. It transcriptionally induces expression of human CYP lAl, CYP1A2 and CYP1Bl (Quattrochi et al. 1994; Tang et al. 1996; Whitlock 1999), as well as several other genes, including some phase II metabolising enzymes (Schmidt and Bradfield 1996), in response to AHR ligands PAHs and 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) (Whitlock 1999). The unliganded AHR is maintained in cytoplasm in a complex containing chaperon proteins, such as a dimer of HSP90 (heat shock protein 90), ARA9 and p23 (Fig. 1) that are required in the correct folding and stabilisation of AHR (Gu et al. 2000). Upon ligand binding, AHR sheds the chaperon proteins and translocates to the nucleus, where it forms a heterodimer with the AHR nuclear translocator (ARNT) (Hoffman et al. 1991). This heterodimer binds to the xenobiotic response elements (XRE) of CYP genes activating transcription (Hankinson 1994). ARNT also belongs to the bHLHlPAS family. A novel PAS protein called AHR repressor inhibits AHR signal transduction by competing with AHR for ARNT and also by binding to XRE. The AHR repressor is induced by AHR, thus forming a negative feedback loop for the regulation of AHR (Mimura et al. 1999; Gu et al. 2000). Inhibitors of protein kinase C and tyrosine kinase block the induction of AHR


target genes (Carrier et al. 1992; Berghard et al. 1993; Gradin et aI. 1994; Kikuchi et al. 1998). While AHR is necessary for CYPIAI induction by omeprazole, it appear not to bind omeprazole as such, but rather omeprazole mediates its effect through more indirect mechanisms possibly requiring chemical conversion or metabolism (Dzeletovic et al. 1997), or may even affect through ligand independent mechanism (Backlund et al. 1997). Three AHR knockout mice have been generated which exhibit decreased liver size, hepatic fibrosis, decreased constitutive expression of CYPIA2, and resistance to TCDD-elicited CYPIAI induction (Fernandez-Salguero et aI. 1995; Schmidt et al. 1996; Mimura et al. 1997; Lahvis and Bradfield 1998). Two strains of ARNT-null mice have also been generated, but these mice die in utero (Kozak et aI. 1997; Maltepe et al. 1997).

6.4.1.2 Constitutively Active Receptor Constitutive androstane receptor (CAR; Fig. 2) is a novel orphan nuclear receptor, which was originally characterised as a constitutive activator of retinoid acid response elements (RAREs). It is called "constitutive" because of its ability to transactivate RAREs and other response elements without being bound to ligand (Baes et al. 1994; Tzameli et al. 2000). CAR is predominantly expressed in liver (Baes et al. 1994), and it mediates the induction of CYP2B6 and, to a lesser extent, CYP3A4 and CYP3A7 (Sueyoshi et al. 1999; Tzameli et al. 2000; Xie et aI. 2000b; Bertilsson et al. 2001). Recent results indicate that CYP2C8 and CYP2C9 might also be regulated by CAR (Pascussi et al. 2000a; Gerbal-Chaloin et al. 2001). CAR is down-regulated by the inflammatory cytokine interleukin-6, which could partly explain the repression of CYPs by inflammatory mediators (Abdel-Razzak et al. 1993; MuntaneRelat et al. 1995). CAR is induced by nanomolar concentrations of glucocorticoids (Pascussi et al. 2000b). Importantly, CAR was recently shown to mediate the widely studied induction of CYP2B genes by phenobarbital, the classic inducer of xenobiotic metabolism (discussed below) (Honkakoski et al. 1998a,b). However, the only activator shown to bind to human CAR is 5~-pregnane-3,20-dione. Phenobarbital has not been shown to bind to CAR (Moore et al. 2000a). Deactivators or inverse agonists, such as androstanol and clotrimazole, also bind to human CAR (Forman et al. 1998; Moore et al. 2000a). CAR acts differently than the more traditional receptors: as mentioned above,


CYP286

t I Increased

CYP2B6 transcription

t CAR ligand • • ~ Decreased


Fig. 2. Schematic presentation of the mechanism of constitutive androstane receptor (CAR)-mediated CYP2B6 induction. (Negishi and Honkakoski 2000)

C3

G $ t ~-+t Phenobarbital 0 ~ • •

G I Increased


Fig. 3. Schematic presentation of the possible role of phenobarbital in the mechanism of CAR-mediated CYP2B6 induction. (Sueyoshi and Negishi 2001)

120

PXR ligand

•

o. Pelkonen et al.

L....!C=='-=..J CYP3A4, CYP3A7 Increased transcription

Fig. 4. Schematic presentation of the mechanism of pregnane X receptor (PXR)-mediated CYP3A induction

CAR is constitutively active without ligand. Upon binding an inverse agonist, CAR is deactivated through the release of the co-activator SRC-1 from the ligand-binding domain (Forman et al. 1998; Moore et at. 2000a). In contrast, agonist binding to CAR results in a further increase in the basal binding of CAR to SRC-1 (Moore et al. 2000a). Therefore, it has been proposed that CAR is deactivated in vivo by endogenous inverse agonist steroids related to androstanol, thus suppressing CYP2B6 transcription (Fig. 3). This suppression is overcome by agonist binding to CAR, which abolishes the inhibitory inverse agonists from CAR leading to the induction of CYP2B6 (Waxman 1999).

As mentioned above, phenobarbital induction of CYP2B6 is mediated by CAR, even though phenobarbital is not a ligand of CAR (Honkakoski and Negishi 1998; Moore et al. 2000a). The exact mechanism of phenobarbital induction is still unclear, but recent results suggest that phenobarbital not only facilitates the translocation of CAR to the nucleus, but also activates CAR in the nucleus (Fig. 4) (Zelko and Negishi 2000). These steps are dependent on phosphorylation, since translocation and activation are inhibited by protein phosphatase (PP)


and CaM kinase (CK) inhibitors, respectively (Zelko and Negishi 2000; Sueyoshi and Negishi 2001). This model is supported by the finding that, in mouse primary hepatocytes, CAR is located in the cytoplasm and is only translocated to the nucleus after inducer treatment (Kawamoto et al. 1999). Thus, the regulation of CAR function would be dependent not only on the repression and derepression of constitutive activity, but also on the nuclear translocation and activation of CAR (Honkakoski and Negishi 2000; Tzameli et al. 2000). Recently, CARnull mice were produced showing no induction of CYP2B by phenobarbital (Wei et al. 2000).

6.4.1.3 Pregnane X Receptor The pregnane X receptor (PXR, also called SXR and PAR) is a recently identified orphan nuclear receptor (Kliewer et al. 1998). It mediates the induction of CYP3A4 (Bertilsson et al. 1998; Blumberg et al. 1998; Lehmann et al. 1998), CYP3A7 (Pascussi et al. 1999; Bertilsson et al. 2001), CYP2C8 and CYP2C9 (Pascussi et al. 2000a; Gerbal-Chaloin et al. 2001), as well as the human carboxylesterases HCE-l and HCE-2 (Zhu et al. 2000). CYP2B6 is probably also induced by PXR (Pascussi et al. 2000a; Xie et al. 2000b). Recent results indicate that the genes involved in the biosynthesis and transport of bile acids are regulated by PXR, since PXR suppresses CYP7 Al and induces organic anion transporter 2, at least in mice (Staudinger et al. 2001). PXR, similarly to its principal target gene CYP3A4, is mainly expressed in liver, small intestine, and colon (Bertilsson et al. 1998; Blumberg et al. 1998; Lehmann et al. 1998). Its ligands include a wide variety of structurally diverse, low-affinity exogenous and endogenous chemicals, e.g. bile acids, such as lithocholic acid (Staudinger et al. 2001; Xie et al. 2001), steroid hormones and steroid metabolites, such as progesterone, oestrogen, corticosterone, 5~-pregnane-3,20-dione, and androstanol (Blumberg et al. 1998; Moore et al. 2000a), and dietary compounds, such as coumestrol (Blumberg et al. 1998) and carotenoids (Pichard-Garcia et al. 2000). Recently, it was shown that hyperforin, a constituent of St. John's wort, a herbal remedy for depression, is the most potent PXR activator reported with ECso of 23 nM (Moore et al. 2000b). Pharmaceuticals activating PXR include rifampicin, phenobarbital, nifedipine, clotrimazole, RU486 (mifepristone), and metyrapone (Harvey et al. 2000; Moore et al. 2000a). Many of the PXR ligands are also shared by

122

GR ligand

•

8 t

O. Pelkonen et al.

CYP2C8-9, CYP3A5

Increased transcription

Fig. 5. Schematic presentation of the mechanism of glucocorticoid receptor (GR)-mediated CYP3A5 induction. (Hukkanen 2001)

constitutively active receptor (CAR) (Moore et al. 2000a) (discussed below). Upon ligand binding, PXR forms a heterodimer with the retinoid X receptor-a (RXRa) and trans activates ER6 (everted repeat with a 6-bp spacer) elements upstream of the CYP genes (Fig. 2) (Waxman 1999). There is also a second binding site for PXR called xenobiotic responsive enhancer module (XREM) in the -8-kb upstream 5' -flanking regions of the CYP3A4 and CYP3A7 genes (Goodwin et al. 1999; Bertilsson et al. 2001). RXRa serves as a common heterodimerisation partner for many nuclear receptors, including CAR (Wan et al. 2000). The binding ofPXRlRXRa to response elements is followed by recruitment of coactivator proteins, e.g. SRC-l (steroid receptor coactivator-l), and transcriptional activation of the respective gene (Savas et al. 1999). Two strains of PXR-null mice were recently produced showing no induction by typical mouse CYP3A inducers (Xie et al. 2000a; Staudinger et al. 2001). The loss of PXR increased the basal CYP3A expression in one ofthe strains (Staudinger et al. 2001), but not in the other (Xie et al. 2000a). Transgenic mice containing human PXR were also produced showing induction by human specific inducers, such as rifampicin (Xie et al. 2000a).


6.4.1.4 Glucocorticoid Receptor Glucocorticoids, of which dexamethasone is the most widely studied, influence several aspects of CYP induction. However, most of these effects are not dependent on glucocorticoid receptor (GR) binding to CYP genes, but rather on complex protein-protein interplay between GR and various other receptors (Honkakoski and Negishi 2000). For example, dexamethasone has been shown to potentiate CYPIAI induction by TCDD (Celander et al. 1997). Nanomolar concentrations of dexamethasone induce PXR, CAR and RXRa expression, leading to potentiation of CYP2B6, CYP2C8 and CYP3A4 inductions (Pascussi et al. 2000a,b). This explains the results on the dexamethasone-elicited induction of CYPs in human hepatocytes (Pichard et al. 1992; Schuetz et al. 1993; Chang et al. 1997). The only human CYP genes induced directly by GR are CYP2C8, CYP2C9, and CYP3A5 (Fig. 5) (Schuetz et al. 1996; Gerbal-Chaloin et al. 2001). There is no consensus glucocorticoid responsive element in the CYP3A5 gene, but instead GR binds to the glucocorticoid responsive element half-sites in the 5'-flanking region of CYP3A5 (Schuetz et al. 1996). However, this report is probably flawed since the studies examining the 5'-flanking region of the CYP3A5 gene (Jounaidi et al. 1994; Schuetz et al. 1996) actually studied the 5'-flanking region of the highly similar CYP3A5 pseudogene (Finta and Zaphiropoulos 2000). The GR response elements of CYP2C8 and CYP2C9 genes are currently uncharacterised.

6.4.1.5 Other Regulatory Mechanisms for Xenobiotic-Metabolising CYPs The regulation of CYP2A6 expression is mostly unknown. CYP2A6 has been shown to be induced by phenobarbital and rifampicin (DaletBeluche et al. 1992; Sotaniemi et al. 1995; Rodriguez-Antona et al. 2000), which points to the influence of CAR and/or PXR. However, there is no direct evidence to confirm this. A related CYP2A gene in mice, CYP2A5, is induced by cyclic adenosine monophosphate (cAMP)-elevating agents and several hepatotoxic compounds (Raunio et al. 1999). At least mRNA stabilisation is involved in the regulation (Aida and Negishi 1991; Tilloy-Ellul et al. 1999). The significance of these observations for CYP2A6 induction is unknown.

CYP2E1 is regulated in a complex manner, since it is regulated transcriptionally, pretranslationally, translationally, and posttranslation-


ally (Song 1995). The most important steps are the stabilisation of mRNA and protein (Ronis et al. 1996). Transcriptional regulation seems to playa minor role in contrast to many other CYPs. However, starvation and chronic ethanol intake are thought to increase transcription as well as CYP2E1 protein stability (Ronis et al. 1996). Hepatocyte nuclear factor 1a has been shown to activate rat hepatic CYP2El gene expression (Liu and Gonzalez 1995). Unlike other cytokines, interleukin-4 induces human CYP2E1 in primary hepatocytes (Abdel-Razzak et al. 1993). Several other cytokines, including interleukin-1 [3, interleukin-6, tumour necrosis factor-a, and interferon-y, down-regulate CYPIA2, CYP2C, CYP2E1, and CYP3A (Abdel-Razzak et al. 1993; Muntane-Relat et al. 1995; Pascussi et al. 2000a). A partial explanation to this down-regulation might be the fact that PXR and CAR expression is down-regulated by the inflammatory cytokine interleukin-6 (Pascussi et al. 2000a).

6.5 Nuclear Receptor-Based In Vitro Models for Detection of CYP Induction

Current in vitro models to detect induction are often difficult, unreliable and inefficient. The development of improved, mechanistically based models is founded on recent advances in understanding of CYP gene regulation and identification of nuclear receptors (NR) as prime regulators of major inducible drug-metabolising CYP enzymes (Waxman 1999; Honkakoski and Negishi 2000). As a general approach (see Fig. 6), reporter genes that are under control of DNA-binding fusion protein of nuclear receptor ligand-binding domains can be expressed in mammalian or yeast cells. Binding of a chemical to the ligand-binding domain results in recruitment and association of CoA and subsequent activation of the reporter gene expression (Glass et al. 1997). Thus, the increased reporter activity is a measure of the induction potential for a particular NR and presumably, for CYP genes regulated by it. Other assays do not use reporter gene activation as the endpoint. Rather, they measure either ligand-dependent physical interactions between an NR and a CoA, or when a suitable radioactive ligand is available, the displacement of the labelled ligand from the NR by the test chemical. These assays, although some of them are in a relatively advanced devel-


Fig. 6. Schematic presentation of a receptor-based scrcening system for detecting inducing chemicals

opment stage, are not yet properly validated by comparison with CYP induction profiles in primary hepatocyte cultures or with in vivo data. The assays can broadly divided into four categories, which are briefly described in the following.

6.5.1 Direct Binding Assays

Direct binding assays rely on the ability of the putative inducer to displace a radioactively labelled NR ligand as described for CAR and PXR (Moore et al. 2000a; Moore and Kliewer 2000). This requires a ligand that has sufficiently high affinity for the receptor and that can be labelled to high specific activity, and very lipophilic ligands may pose technical problems in the assay.

6.5.2 Indirect Binding Assays

Indirect binding assays measure the in vitro association of fluorescently labelled peptide derived from a CoA with the inducer-bound receptor. The receptor can also be linked to another fluorophore to utilise the fluorescence resonance energy transfer (FRET) phenomenon in the assay (Moore et al. 2000a). Other versions use radioactively labelled CoA peptides and affinity- or immunoprecipitation of the ligand-dependent NRICoA peptide complexes (Krey et al. 1997; Forman et al. 1998; Lehmann et al. 1998), but they are less suitable for mid-to-high-through-


put screening. Indirect binding systems have been used to detect activators of CAR, PXR and PPARu.

6.5.3 Transfection Assays

Transient (or stable) mammalian transfection assays measure the ability of inducer-bound NR to interact with CoA molecules that are supplied by the cell line. After binding to its DNA response element, the NR then triggers the expression of the reporter gene, as described for CAR, PXR and PPARu (Fonnan et al. 1997; Sueyoshi et al. 1999; Moore et al. 2000). In principle, various host cell lines, e.g. HepG2 hepatoma, various other human and rodent cell lines, or primary hepatocytes, can be transiently transfected with expression vectors for full-length NRs or fusion proteins (Honkakoski et al. 2001). Cell extracts can be assayed for luciferase and beta-galactosidase in 96-well plate fonnat 24-40 h after transfection. These assays are subject to variation in transfection efficiency, interfering endogenous nuclear receptors competing for same DNA response element, and naturally, the full repertoire of CoA molecules required by the NR may not be expressed in the cells. Creation of fusion proteins between a heterologous DNA-binding protein and the NR ligand-binding domain and developing cell lines that pennanently express these fusion proteins circumvent the fIrst two obstacles. Careful selection of the cell line for the assay may alleviate the third problem.

Generation of stable cell lines offers an unlimited supply for induction screening assays. Basically, mammalian cell lines yielding strong induction responses can be stably transfected with expression vectors for fusion proteins of rodent and human NRs, together with a antibiotic resistance selection cassette. Resulting antibiotic-resistant colonies can be screened by transient transfection and treatment with model inducers.

6.5.4 Yeast Cell Assays

Yeast cell systems carry individual genes for a fusion protein between a heterologous DNA-binding protein and the NR ligand-binding domain (described in Sect. 5.3) and for a fusion protein between a CoA peptide to the yeast GAlA activation domain (Masuyama et al. 2000). These


systems appear to reach quite robust induction responses and they also facilitate the identification of preferred CoA species for a particular NR. In addition, culture and analysis of yeast cells is much simpler and cheaper than for mammalian cells. On the other hand, factors that may be important for NR activation in mammalian systems and thus induction in vivo may be missing from the yeast. In addition, the yeast cell membrane may not be permeable to all test chemicals.

6.5.5 Pros and Cons of the Assays

Because all the systems described above are based on the inducer/receptor interaction, they can not detect inducers that require in vivo transformation to active species nor inducers that act via an alternative mechanism. For instance, dexamethasone may increase the expression of the PXR receptor and thus induce CYP enzymes or synergise with other inducers (Pascussi et al. 2000a). Even though the mechanism-based induction screens may not detect every CYP inducer, they are still expected to be valuable as preliminary screens.

6.6 Future Developments

In the near future, several in vitro systems, based on various inducersensing and reporter constructs, will be available for high-throughput screening of drug molecules. Successful candidates would then be screened by cultured human hepatocytes in very long-term cultures after cryopreservation. However, it would seem useful if we'd have available an immortalised hepatocyte cell line, which had preserved all the functions required for induction by various potential inducers. Whether the development of this kind of cell line were possible and in which time frame, remains to be seen.

Acknowledgements. This review was written to contribute to the goals of the COST Action B 15. The work in the authors' laboratory has been supported by The Academy of Finland Medical Research Council, by the Biomedl project and by the Biomed2 project EUROCYP.


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7 Drug Transport Across the Blood-Brain Barrier

G. Fricker

7.1 Introduction ........................................... 139 7.2 Anatomy of Brain Capillaries. . . . . . . . . . . . . . . . . . . . . . . . . . . .. 141 7.3 Transport Processes at the Cerebral Endothelium ............. 142 7.4 In Vitro Models for Studying the Function Blood-Brain Barrier . 146 7.5 Conclusion and Perspectives ............................. 150 References .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 151

7.1 Introduction

Case numbers of neurodegenerative disorders, such as Alzheimer's disease, Morbus Parkinson, epilepsy, brain tumours or human immunodeficiency virus (HIV)-related encephalopathy, are continuously increasing, thereby causing enormous costs to the public economy. For example, following the World Health Report, more than 12 million people are suffering from Alzheimer's disease worldwide. With more than 4 million patients at an annual expense of over US $50 billion, the disease has currently become the third largest medical problem in the United States (Friden 1996).

Unfortunately, many drug candidates for treatment of these disorders have high efficacy in pharmacological in vitro models, but are oflittle or even no effect in patients. One major problem in the CNS disease management is the restricted access of exogenous compounds to the brain. They have to pass across the endothelial cells of brain capillaries,

140

in vitro

· isolated · microvessels

cultured capillary · · endothelial cells

· · co-cultures with

astrocytes or pericytes

defined conditions long term viability

lack of cellular environment • monolager tightness

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in situ

brain uptake index

brain efflux index

microdialysis

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disadvantages

• organ damages leakage

G. Fricker

in vivo

. effect models

intact organ 3-dimensional

structure

• "black box" • mechanistic

information (?)

Fig. 1. Summary of experimental models to study drug transport across the blood-brain barrier

which form the so-called blood-brain barrier. This barrier maintains the physiological homeostasis required for a proper cerebral function (Paulson et al. 1999). Investigation of the functional properties of the blood-brain barrier is difficult because it is not directly accessible in vivo. Therefore, efforts are ongoing to develop representative cellular in vitro models that mimic its structural and functional characteristics (Greenwood et al. 1995) and may help to understand the regulation of the barrier function. Figure 1 summarises available models of different complexity with their advantages and disadvantages on various biological levels ranging from relatively simple cells culture systems up to brain perfusion techniques. Purpose of this article is to give a short overview on these models and the possibilities they offer for studying

Drug Transport Across the Blood-Brain Barrier 141

drug transport in vitro. Thereby, the focus will be on isolated brain capillary endothelial cell cultures and isolated intact brain capillaries.

7.2 Anatomy of Brain Capillaries

More than 100 years ago, the German pharmacologist Paul Ehrlich described the first documented observation of a barrier preventing the access ofaxenobiotic to the central nervous system. He saw that a peripherally administered inorganic dye, Evans blue, was unable to penetrate the brain tissue (Ehrlich 1885). In contrast, when the dye was injected into the cerebrospinal fluid, brain tissue was stained, indicating that some kind of barrier exists on the level of the cerebral capillaries. Some years later these observations inspired Goldmann to define the term of a "blood-brain barrier" (Goldmann 19l3). In the 1960s, existence of the blood-brain barrier was verified by utilisation of electron microscopy and application of labelled horseradish peroxidase. It was shown that the endothelium is indeed the principal anatomical site of the blood-brain barrier (Reese and Karnovsky 1967; Brightman and Reese 1969). Structurally, the cerebral capillary endothelium (Fig. 2) performs several differences compared to peripheral endothelial capillaries. Peripheral capillaries are fenestrated with gaps up to 50 nm wide. In

Basement membrane

Fig. 2. Schematic cross-section of a brain capillary. The endothelial cells are sealed by tight junctions and covered by a basement membrane including the pericytes. The capillary is covered by astrocytic foot processes. (Adapted from Goldstein and Betz 1986)

142 G. Fricker

contrast, endothelial cells of brain capillaries are closely connected by tight junctions and zonula occludens. As a consequence, extremely high electrical resistances of approximately 1,500-2,000 nxcm2 have been measured at the blood-brain barrier in vivo (Crone and Olesan 1982). In addition to these tightly connected cells, the capillaries are surrounded by a continuous basal membrane, which embeds the pericytes, an intermittent cell layer. The pericytes have been postulated to be involved in defence mechanisms. The outer surface of the basement membrane is covered by astrocytic or glial foot processes (Goldstein and Betz 1986; Bradbury 1993). The function ofthese cells has not yet been completely clarified. But there is mounting evidence that soluble growth factor(s) are secreted by astrocytes playing a role in endothelial cell differentiation (Schlosshauer 1993).

7.3 Transport Processes at the Cerebral Endothelium

Drugs directed to the central nervous system have to cross the blood-brain barrier. Originally, the brain capillaries were considered as a passive anatomical lipoid barrier , preventing uptake of hydrophilic compounds from the brain except for some nutrients. It was thought that the barrier was permeable for uncharged lipophilic substances, thereby determining brain entry of molecules mainly by their molecular weight and lipophilicity. However, remarkable exceptions have been found; for example, cyclosporin A, an uncharged immunosuppressive drug with an extremely high lipophilicity, has very little access to the brain. Thus, the traditional view has considerably been changed within the last 10 years, and the blood-brain barrier is now regarded as a dynamic interface with all the possibilities of physiological transport systems (Fig. 3), including active carrier proteins and receptors undergoing endocytosis and even transcytosis.

Diffusion between paracellular spaces is almost negligible due to the restriction of the pathway through the tight junctions. These junctions effectively close off diffusion through intercellular pores. As a result, most solutes must cross the blood-brain barrier either by diffusing across the lipoid endothelial cell membranes or by carrier-mediated specific transport. However, with regard to passive diffusion, it seems that a correlation between permeation and lipophilicity can only be

Drug Transport Across the 8lood-8rain 8arrier 143

Fig. 3. Potential transport routes across the blood-brain barrier. a, Vascular surface of the cells; b, Cerebral surface of the cells

made for compounds with a molecular weight between 400 and 600 Da (Schinkel et al. 1995, 1996). For higher hydrophilic nutrients, active transport is essential to satisfy the metabolic demand of the central nervous system; for example, the transport of amino acids across the endothelial wall has an important control function for the overall regulation of cerebral metabolism, including protein synthesis and neurotransmitter production. (Pardridge 1998; Smith 2000). Glucose uptake is predominantly mediated by the glucose transporter Glut -1, which is expressed on high levels at the blood-brain barrier (Maher et al. 1994). The vesicular pathway, which includes absorptive endocytosis and specific receptor-mediated endocytosis, mediates protein uptake into the brain. Here, particular attention is given to the transferrin receptor, which may be used for drug delivery by transcytosis across the capillary wall (Huwyler et al. 1996, 1997; Cerletti et al. 2000).

In addition to the various transport mechanisms controlling the permeation of molecules from the blood into the brain, active efflux transport proteins like the multidrug resistance (mdr1)-gene product, p-glycoprotein (p-gp), and proteins belonging to the Mrp (multidrug resistance-assocÎated proteins) family have been found to be expressed in high levels at the blood-brain barrier (Golden and Pardridge 1999; Miller et al. 2000; Zhang et al. 2000). Both types of carriers belong to the large class of the primary active ATP binding cassette (ABC) transport proteins. Although discovered originally in tumour cells, p-gp and Mrp1l2 are present at high levels in a variety of normal epithelial and endothelial tissues including adrenal cortex, renal proximal tubules, enterocytes in the gut, testis, and the endometrium of the pregnant uterus. At the blood-brain barrier, expression of p-gp has been shown to be a critical element in preventing access of many drugs to the central

144 G. Fricker

Fig. 4. lmmunohistochemicallocalisation of p-glycoprotein in freshly isolated brain capillaries form porcine brain. White lines show the detection of p-glycoprotein with highest density at the luminal surface; light grey areas are ceH nuclei stained with propidium iodide. (With permis sion from Nobmann et al. 2001)

nervous system (Schinkel et al. 1994, 1995, 1996). This has impressively been demonstrated for the HIV protease inhibitors indinavir, ritonavir and saquinavir, which are substrates of p-gp in brain capillary endothelial cells (Kim et al. 1998; Gutmann et al. 1998; Drewe et al. 1999; Choo et al. 2000). Other clinically relevant drugs, which are actively transported by p-gp, include the anthelmintic drug ivermectin (Schinkel 1996; Nobmann et al. 2001) and anticancer drugs, such as vinca alkaloids or doxorubicin. Similarly to the HIV protease inhibitors, for these anticancer drugs the presence of p-gp results in a reduced permeation and hence a diminished therapeutic efficacy in the chemotherapy of brain tumours (Tsuji 1998). In light of the potential clinical significance of p-gp, inhibitors such as PSC-833, a cyclosporin A analogue, or verapamil are currently being used in clinical trials.

Although there is convincing functional evidence on the protective action of p-gp, the exact localisation of this export pump on the blood-brain barrier is still under debate. Whereas some authors (Miller et al. 2000; Nobmann et al. 2001) unequivocally show its localisation at the luminal surface of the capillaries, others claim substantial expres sion of the protein also in astrocytic foot processes (Pardridge et al. 1997; Golden et al. 1999). Figure 4 shows an immunostaining of freshly isolated porcine brain capillaries with p-gp localised predominantly on the lumenal surface of the vessel.

The extent of expres sion and the localisation of the other export pumps mentioned, the Mrp proteins, is still under discussion. Mrp 1


Fig. 5. Immunohistochemical localisation of Mrp2 in isolated brain capillaries from rat. A Capillary from Wistar rats (controls). B Capillary from TR- rats lacking Mrp2. White lines indicate the outline of the capillary

146 G. Fricker

seems to be expressed on a very low level in intact capillary tissue, but it is apparently being upregulated in cultured cells (Regina et al. 1998). Results on the presence of other Mrps on the blood-brain barrier are not really affirmative so far: RT-PCR analysis demonstrated the presence of Mrpl, Mrp4, Mrp5 and Mrp6 in bovine brain endothelium. Low levels of Mrp3 were detected in cultured cells, but not in a capillary-enriched fraction (Zhang et al. 2000). Another study, based on kinetic experiments, immunostaining as well as quantitative PCR gives also functional and molecular evidence for the expression of Mrp2 in intact porcine brain endothelial capillaries (Miller et al. 2000). Figure 5 also shows the immunostaining of Mrp2 in isolated brain capillaries from normal Wistar rats (5a) and TR- rats (5b), lacking Mrp2 and displaying consequently no immunostaining.

7.4 In Vitro Models for Studying the Function Blood-Brain Barrier

Cell culture models mimicking the real characteristics of the blood-brain barrier in vitro would have a broad range of application in experimental, pharmaceutical and clinical studies. In contrast to in vivo studies, they offer direct access to brain capillary endothelial cells without interference with other structures of the brain. At present, two in vitro models gain broader attention: isolated, functionally intact cerebral capillaries and isolated endothelial cells, which can be cultured as monolayers on plastic dishes and on permeable filter supports either as primary cultures or passaged cells.

Isolated capillaries have been used for almost three decades to study transport and metabolic function of the blood-brain barrier (Goldstein et al. 1975; Mrsulja et al. 1976; Williams et al. 1980). They can easily be isolated by dissecting little pieces of brain tissue. Brain homogenates may be separated by dextran centrifugation and the resulting pellets being resuspended in appropriate buffers and filtered through a nylon mesh to obtain larger quantities. After passage of the filtrate on a glass bead column, adhering capillaries can be removed from the beads by gentle agitation. For a detailed protocol the reader is referred to Nobmann et al. (2001). However, there is one limitation in using capillaries for the study of transport processes. Tested substrates approach the


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Fig. 6. Extrusion of the p-glycoprotein substrate BODIPY-iverrnectin into the lumen of freshly isolated brain capillaries from porcine brain in the absence and in the presence of unlabelled iverrnectin and the p-glycoprotein blocker PSC-833. The p-gp inhibitor totally abolishes BODIPY-ivennectin excretion. No inhibition of excretion is seen in the presence of 1eucotriene C4, a modulator of Mrp2. (With permission from Nobmann et al. 2001)

capillaries from the abluminal side, which is exactly opposite to the in vivo situation. Therefore, isolated capillaries are especially of interest, when the influence of ABC transporters has to be investigated. For this purpose they offer a brilliant model. The use of fluorescent-labelled drug derivatives combined with confocal laser scanning microscopy shows the active extrusion of both p-gp and Mrp substrates into the lumen of isolated, intact brain capillaries (Miller et al. 2000). In the same model, it was shown, that the excretion of compounds like BODIPY-ivermectin could effectively be suppressed by p-gp blocking agents such as PSC-833 or unlabelled ivermectin, but not by the Mrp2 substrate leucotriene C4 (Nobmann et al. 2001; Fig. 6). This system can easily be used for testing the potency of unlabelled drug candidates to inhibit the p-gp or Mrp-mediated excretion of labelled substrates. An

148

700

600

500

~ 400

~ 300

200

100

Strong substrates

o - .. • 0

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x

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G. Fricker

... CydosporfnA

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..... lvenneclin

..- Ritonavlr

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.... Saquinavir

... MoIp/1in

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o Clozapin

x Yohimbin

Fig. 7. Accumulation of ca1cein in isolated porcine brain capillary endothelial cells after incubation of the cells with increasing concentrations of p-glycoprotein modulators with different affinity to the carrier protein

advantage of using intact capillaries is the susceptibility of cell cultures to induction or inhibition of protein expression. In contrast, isolated capillaries directly reflect the expression of ABC transporters on their luminal side in vivo and their impact on drug concentration in the capillary lumens.

Nevertheless, the interaction of drugs or drug candidates with p-gp can also be studied in capillary endothelial monolayer cultures. Recently, a calcein acetoxymethyl ester (calcein-AM) assay has been developed, utilising the capability of the non-fluorescent compound to diffuse into the cells and being hydrolysed to free, fluorescent calcein. Calcein-AM serves as a substrate of p-gp. In presence of other p-gp substrates or blockers, free calcein accumulates within the cells and an increase in cellular fluorescence can be detected. Figure 7 shows the inhibition of calcein-AM extrusion by a series of compounds, which are claimed to be strong, moderate and weak p-gp modulators (Seelig 1998a,b). This kind of assay can readily be performed in 96-well dishes. Thus, it may also be adapted to high throughput screening. A similar


experimental set-up can be used with other fluorescent p-gp substrates, such as rhodamine 123.

For the investigation of drug permeation across brain capillary endothelial cells in vitro, freshly isolated or passaged brain microvascular cells can be grown as mono layers on plastic dishes for uptake studies or permeable filter supports for permeation studies. The cells should retain the major characteristics of brain endothelial cells in vivo such as morphology, specific enzyme markers of the blood-brain barrier (e.g. y-glutamyl transpeptidase, alkaline phosphatase or the von Willebrand factor-related antigen) and the intercellular tight junctional network. The methods of isolation are similar for capillaries from most mammalian species. As an example, the isolation of porcine brain capillary endothelial cells is shortly described: Cerebral matter of freshly obtained pig brain is mechanically homogenised and digested in 1 % dispase followed by a dextran density gradient centrifugation to obtain capillary fragments. The fragments are further treated by a second digestion in 0.1 % collagenase/dispase solution. The free endothelial cells are collected from the interface of a density gradient centrifugation. Cells are plated on collagen-coated culture flasks and cultivated in an appropriate medium. One day after initial plating, cells are washed with phosphate buffered saline containing Ca2+ and Mg2+ and supplied with fresh culture medium. Primary cultures of capillary cells can be passaged at the third day of culture by gentle trypsinisation to achieve a further purification. This enzymatic treatment selectively releases endothelial cells, leaving behind contaminating cells such as pericytes and smooth muscle cells. Purified endothelial cells are then seeded on rattail collagen (Bornstein 1958) coated cell culture inserts.

Uptake assays are regularly performed in confluent monolayers of endothelial cells approximately 7-10 days after cell isolation. Such studies can be done in single or multi-well plates, up to 96-well dimensions. Thereby, cells should be incubated in serum-free transport solutions, unless binding studies with serum proteins are to be carried out. The substance of interest is added, in case of a water-insoluble compound it may be dissolved in dimethylsulphoxide (DMSO) with a final concentration of less than 1 % in the incubation buffer. At distinct time points the supernatant is removed and after 1-2 washings with ice-cold buffer solution the cell mono layers can be solubilised and subjected to further analysis.

150 G. Fricker

For permeation experiments the cells are grown on permeable polycarbonate or polyester filter supports, placed in 6-well or 12-well filter dishes. The transendothelial electrical resistance or the flux of paracellular markers such as radiolabelled mannitol, sucrose or fluorescence-labelled compounds such as fluorescein isothiocyanate (FITC)-dextrans should be determined in order to assess the monolayer integrity. Achievement of sufficient confluency is one of the major problems of these cell monolayers and their integrity can easily be impeded, e.g. by unequal coating of the filter support or while replacing the cell-culture media. In addition, the contamination of cultures by pericytes, smooth muscle and other cell-types may be considered. Nevertheless there is a considerable correlation between permeation of compounds in the in vitro cell monolayer model and in vivo brain permeation studies at least in a semiquantitative sense of a compound ranking (Pardridge et al. 1990).

During the past few years, many groups have put enormous efforts into improving the quality of brain capillary endothelial monolayers and several models are currently used, ranging from co-culture with astrocytes, use of astrocyte conditioned media, or comparative studies with cell lines (Arthur et al. 1987; Rist et al. 1997; Wiijsman et al. 1998; Huwyler et al. 1999; Sobue et al. 1999). Each of these models has its distinct characteristics and there may be differences in functional and structural properties, such as expression of carrier proteins or enzymatic activity. Therefore, it is certainly of advantage to correlate not only from one in vitro model to the in vivo situation but from a combination of different models applied.

7.5 Conclusion and Perspectives

Our understanding of the complexity of the blood-brain barrier has rapidly increased in the last decade. Apart from the traditional perspective of a lipoid barrier, it was recognised that this barrier is equipped with a large number of receptors and membrane carrier systems, which may be relevant for drug absorption. Beside pharmacokinetic and pharmacodynamic in vivo studies, in vitro techniques such as isolation of functionally intact capillary fragments or diverse cell culture models, allowed a deeper insights on a cellular and a molecular level into the


functional mechanisms of the blood-brain barrier. The further improvement of culture conditions of primary cells, use of conditioned media or co-culture with astrocytes as well as the development of immortal cell lines is continuously ongoing. Thus, in the future, better in vitro models resembling the characteristics of the intact blood-brain barrier will become available. Clearly, we need a deeper understanding of the mechanisms limiting permeation across the blood-brain barrier to improve access of therapeutics to the brain and to better predict potentially toxic drug interactions. Therefore, from applying the appropriate models, we may be able to identify specific probes to distinguish transporter subtypes as well as tools to transiently modify barriers to drug permeation. Finally, a deeper understanding of the molecular mechanisms involved may lead to simple tests that will allow us to better tailor drug dose to patient physiology.

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Schlosshauer B (1993) The blood-brain barrier: morphology, molecules, and neurothelin. Bioessays 15:341-346

Seelig A (1998a) A general pattern for substrate recognition by P-glycoprotein. Eur J Biochem 251:252-61

Seelig A. (1998b) How does P-glycoprotein recognize its substrates? Int J Clin Pharm Ther 36:50-4

Smith QR (2000) Transport of glutamate and other amino acids at the bloodbrain barrier. J Nutr 130: 1 016S-1022S

Sobue K, Yamamoto N, Yoneda K, Hodgson ME, Yamashiro K, Tsuruoka N, Tsuda T, Katsuya H, Miura Y, Asai K, Kato T (1999) Induction of bloodbrain barrier properties in immortalized bovine brain endothelial cells by astrocytic factors. Neurosci Res 35:155-164

Tsuji A (1998) p-Glycoprotein-mediated efflux of anticancer drugs at the blood-brain barrier. Ther Drug Monit 20:588-590

Wiijsman JA, Shivers RR (1998) Immortalized mouse brain endothelial cells are ultrastructurally similar to endothelial cells and respond to astrocyteconditioned medium. In Vitro Cell Develop BioI Anim 34:777-784

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Zhang Y, Han H, Elmquist WF, Miller DW (2000) Expression of various multidrug resistance-associated protein (Mrp) homologues in brain microvessel endothelial cells. Brain Res 87:148-53

8 The Development and Implementation of Bioanalytical Methods Using LC-MS to Support ADME Studies in Early Drug Discovery and Candidate Selection

T.V.Olah

8.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 155 8.2 ADME in the Discovery Process .......................... 157 8.3 Project Team Support and Study Design .................... 158 8.4 Experimental Design ................................... 160 8.5 The Bioanalytical Process ............................... 163 8.6 Compound/Sample Receipt .............................. 164 8.7 Method Development ................................... 166 8.8 Characterization of Compounds by LC-MS .................. 169 8.9 Preparation of Standard, Quality Control, and Test Samples. . . .. 171 8.10 Sample Extraction and/or Concentration . . . . . . . . . . . . . . . . . . .. 172 8.11 Sample Analysis ..... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 174 8.12 Data Processing, Reporting, and Archiving .. . . . . . . . . . . . . . . .. 177 8.13 Data Interpretation ..................................... 180 8.14 Conclusions ........................................... 180 References .... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 181

8.1 Introduction

The dramatic improvement in bioanalytical method development and application within the pharmaceutical industry began in the early 1990s with the commercial availability of liquid chromatography-mass spec-

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trometry (LC-MS) systems. As the power of this type of detection was realized, research groups [drug metabolism, safety assessment, product research and development (PR&D), pharmacology, medicinal chemistry] throughout the industry acquired this technology insatiably. Companies purchased LC-MS systems from a variety of vendors and incorporated this technology into their respective areas of research in a number of innovated ways. Clearly, this technology has become the primary analytical tool within the pharmaceutical industry and its incorporation and efficient use have helped to accelerate the generation of high-quality data in support of research and development. By the end of the last century, essentially every bioanalytical department in a pharmaceutical company possessed a number of mass spectrometers, including single-stage, triple quadrupole, time-of-flight (TOF) and ion trap instruments. In addition, many of these groups also purchased automated liquid handling instruments and incorporated them into a variety of sample preparation procedures that are carried out daily in laboratories around the world.

Improvements in the synthetic capabilities of medicinal chemists, including combinatorial chemistry, have lead to the generation of increasing numbers of potential drug candidates. Advances in the development of high-throughput screening (HTS) bioassays have also increased the prospect of identifying potent and selective compounds from these libraries in selected therapeutic classes. The evaluation of pharmacokinetic (PK) and metabolic properties in animals and human tissues is a critical factor in selecting drug candidates and it is now required much earlier in drug development. As a consequence, bioanalytical chemists are expected to rapidly develop sensitive and specific quantitative analytical procedures in order to keep pace with drug discovery. One approach that has been undertaken in pharmaceutical bioanalyticallaboratories is to develop LC-MS-based analytical methods. These methods are designed to quantify multiple analytes in a variety of biological matrices from several types of experiments designed to evaluate specific absorption, distribution, metabolism and excretion (ADME) properties of new chemical entities.

Ideally, the selection of potential drug candidates is most effectively made when compounds are evaluated in well-designed experiments and samples are analyzed using well-controlled analytical methods. The rapid development and implementation of semi-automated LC-MS-

The Development and Implementation of Bioanalytical Methods 157

based multiple component bioanalytical methods has accelerated the speed at which this analysis can be performed and has significantly increased the number of compounds that can be evaluated. To help accelerate the evaluation of larger numbers of compounds synthesized by medicinal chemistry, our laboratory has developed strategies to streamline the development of bioanalytical methods using multiple component bioanalysis with LC-MS-based detection.

Additionally, the transition from manual to automated techniques in the area of sample preparation was necessary to increase throughput and to efficiently transfer information throughout the development process. The incorporation of bench-sized robotic pipetting stations into routine laboratory operations has become commonplace in today's bioanalytical laboratory.

Additionally, automated screening procedures that evaluate selective metabolic properties of compounds have also been established in order to obtain viable information that improves the selection process at early stages of discovery.

Clearly, mass spectrometry and automation have played major roles in changing the routine operations of the bioanalyticallaboratory.

As a further consequence of the increased volume of data generated by LC-MS technology, serious consideration has been given as to how this information is generated, captured, processed, distributed, utilized, and stored. Efforts to coordinate the flow of information generated in laboratories include the incorporation of laboratory information management systems (LIMS).

Our current strategies for developing and implementing experimental and analytical processes involving multiple component analysis to accelerate drug discovery and product development are described within the context of this manuscript.

8.2 ADME in the Discovery Process

Advances in automated HTS to assess potency and selectivity of new chemical entities has lead pharmaceutical companies into novel therapeutic classes and also has introduced the concept of "industrialization" into the drug discovery process. Although some researchers may argue that potency and selectivity of compounds drive projects, others contend

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that acceptable pharmacokinetics is the ultimate destination. Compounds, regardless of their potency and selectivity, are worthless as drugs unless they can be easily administered, safely circulated to their target, and ultimately removed from the body in a timely manner.

Drug metabolism's main role in drug discovery is to evaluate a drug candidate's ADME characteristics and to work with medicinal chemists to modify the architecture of a compound until it can function safely as a drug. However, factors such as disease prognosis, patient population, current treatment regime, etc. should also be considered when evaluating a candidate's potential as a drug within a particular therapeutic class. Therefore, a comparison of a compound's ADME characteristics should be made in conjunction with the severity of the disease and illness that afflicts the patient population. As a consequence, different standards of acceptability and of risk may be applied to a drug candidate depending upon the consequences of an individual taking, or not taking, the medication.

The main challenge facing the researcher in the drug metabolism department is that there is no single study that can be performed in early drug discovery that will determine, with certainty, if a compound will be safe and effective in humans. Therefore, it is very important that highquality data be collected from a variety of well-designed, well-controlled experiments assessing the ADME properties of potential drug candidates in order for project teams to select the best compound.

As part of the discovery process, it is important that drug metabolism researchers have a good understanding of the needs of the project team and have prepared a strategy to evaluate the ADME characteristics of the class of compounds under consideration.

8.3 Project Team Support and Study Design

Prior to undertaking responsibility for providing bioanalytical support for the identification and the subsequent development of a compound in a particular therapeutic class of compounds, analysts and key members of the project team must mutually agree upon what role the bioanalyst will play in the selection process. It is important that the bioanalytical chemist is fully aware of the key ADME issues in the program and of the analytical requirements in sample analysis in order to incorporate their


expertise in designing methods to effectively support the project team. High-quality analytical data are needed to identify problematic compounds and to help direct the project team's efforts to design better compounds. Therefore, each analyst must understand the needs of the project team in order to provide sufficient bioanalytical support.

Obtaining answers to the following questions will help define a suitable bioanalytical strategy:

- What disease or ailment will this compound be used to treat? - What is the desired dosing regime, i.e., once a day, oral? - What biological assays or screens are being used to determine po-

tency and selectivity? What effect does potency have on the required PK characteristics

and profile? Is anything known in regards to the physical properties of the compounds to be evaluated, i.e., solubility, log P, chemical stability?

- Is the team identifying a new lead or a back up and, when applicable, what is known about the ADME of the lead compound?

- In in vivo screening procedures, for example, what is the primary PK parameter (half-life, Cmax, AVe, etc.) that is most important in the selection process?

- Is absorption, first-pass metabolism or direct elimination the key PK obstacle to overcome in lead identification and development?

- Has the compound undergone any in vitro analysis prior to its selection in a proposed in vivo experiment? What is the size of the synthetic effort undertaken by medicinal

chemistry?

This information will be used to establish an ADME screening approach and to design effective bioanalytical methods that are used to support the analysis of samples from these screening experiments. For example, this information can be used to determine if an in vivo study should be designed as an IV or PO administration, single or co-administration, which animal species will be used, what are the bleeding requirements: time points and amount, etc. Additionally, it may be decided that an in vitro screen (protein binding, metabolic liability, P450 inhibition) will be used to select compounds prior to further in vivo evaluation.

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In all cases, bioanalytical support will be required and the analysts must work closely with the project team in order to develop an analytical strategy that produces data that are sufficient to select compounds. Considering that the analytical data will drive the program, it is essential to determine the level of quality of the data that will be required to successfully select compounds. The quality of the bioanalytical method need not be to the same high standard for all samples analyzed in support of a discovery project. However, it must be scientifically acceptable in order to select the best candidate.

8.4 Experimental Design

8.4.1 In Vivo Pharmacokinetic Screening

If resources are available, most discovery chemists would like to assess the ADME characteristics of a compound following its administration to an animal model. There are a variety of experiments that are used as in vivo PK screening procedures that are performed by discovery groups. These studies are typically designed as either the discrete administration of single compounds to individual animals (Korfmacher et al. 2001) and/or the co-administration of multiple compounds to individual animals (Olah et al. 1999). Additionally, the design of these studies can vary significantly in parameters such as: dose, route of administration, formulation, etc. Since in vivo studies are usually designed to address PK issues at specific stages of a program in development, it is rare that a standardized in vivo screening strategy is applied across programs.

Additionally, screening strategies vary in different companies and/or in different groups within the same company. In some cases, in vivo studies (discrete or co-administered dosing) are used as the primary ADME screen when a compound shows some level of pharmacological response in a bioassay. Compounds are typically selected for further evaluation as drug candidates, based upon their PK profiles as compared to a lead compound or within a series of compounds. (Olah 1999).

The analytical requirements for in vivo screening experiments can also vary dramatically between programs depending on the number of compounds, amount available for testing, animal model, resources, etc. However, due to the high cost of performing in vivo experiments and to


accelerate the candidate selection process, a greater emphasis has now been placed on the quality of data generated initially in these types of screens. In some cases, data generated in the in vivo screening phase has have added as part of submissions to regulatory agencies. This inevitability has put pressure on the analyst to consistently provide high-quality data on every sample analyzed. Essentially, there is no longer time for the "quick-and-dirty" analysis; rather, analysts are striving to generate accurate and precise data right from the start.

8.4.2 In Vitro Screening Procedures

Ideally, the validation of an in vitro screen that predicts an in vivo result would be the most efficient drug discovery strategy to pursue. However, this has proved to be a difficult task with limited success (Rodrigues 1997). In order to successfully develop such models, both in vitro and in vivo screening procedures should be carried out concurrently. In most cases, however, limitations on resources, prohibitive infrastructure, or differences in the philosophies of an organization preclude the execution of additional studies, such as metabolic inhibition, metabolic stability, protein binding, or Caco-2 transport, concurrent with an in vivo screen.

The justification of performing additional tests early in the discovery process has been an ongoing debate. On one side of the issue, a number of scientists believe that the generation of such data is not necessary on all compounds at such an early stage of discovery. It is considered a waste of resources and an unnecessary contribution to the already massive amount of data generated in discovery.

However, other scientists believe that the concurrent generation of supporting data provides useful information on a compound's ADME properties and might shed light on the PK characteristics of a selected set of compounds. Additionally, the ability to generate and to capture reproducible data on more compounds from a series of well-controlled experiments at the initial phase of discovery would produce a database that could allow the comparison of ADME characteristics of compounds within, and perhaps across, programs. Although some companies have adapted this approach in order to aid in the selection and the design of novel chemical structures, it remains to be seen if this approach will enable scientists to design or to identify successful drugs faster.

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Is it realistic to expect that every compound synthesized in a discovery effort be subjected to the experiments described above? Perhaps it is not realistic, at this time. However, it is imperative that a series of standardized, well-controlled experiments be in place to ensure that accurate and reproducible data are generated over a wide range of programs, when requested.

The early in vitro screening approach has gained acceptance in some sectors of the industry. Efficient strategies are being developed that conduct in vitro procedures concurrently with in vivo experiments. This approach is aimed at obtaining a better understanding of the specific PK characteristics (i.e., metabolic inhibition and stability, protein binding, compound permeability) of these compounds within a series or structural class. In cases when a good correlation has been established, data generated from in vitro experiments will determine if an in vivo procedure is to be performed.

Regardless of the source or timing in generating samples, the current trend in bioanalytical support of drug discovery is to continue to improve the sensitivity, selectivity, accuracy, and precision of every analytical method developed, regardless of its classification as a screen or development study. There will always be a need for more-sensitive and better-quality methods. This is driven by pressure from several sources. Medicinal chemists are synthesizing greater numbers of diverse compounds, usually in small quantities (1-2 mg). If there is sufficient potency and/or selectivity in a screen, there is an almost immediate request to obtain some information on the compound's ADME properties, ideally in an in vivo study. Additionally, because there are tremendous demands on the synthetic scale-up of a compound, the quality of data generated from the ADME assessment must be sufficiently high to warrant the dedication of resources in synthetic chemistry groups to this endeavor. As a consequence, bioanalytical chemists must rapidly develop accurate, precise, selective, and sensitive analytical methods to keep pace with, and to help direct, medicinal chemistry's effort.


8.5 The Bioanalytical Process

8.5.1 Workflow Diagram, Steps in the Process, Core Technologies

In order to determine in what direction one would like to proceed, it is very important to know where one currently is. Therefore, a thorough evaluation of the current "workflow" of our bioanalyticallaboratory was a good place for us to start in order to improve the efficiency of our operation. Figure 1 is a schematic of the functions that any analytical laboratory essentially undertakes on a routine daily basis.

Consequently, we have used this diagram to help us detennine where improvements might be made in our existing bioanalytical process. As a result of this evaluation, the incorporation of key core technologies or specific instruments at slow steps in the process has helped us to improve our overall efficiency to develop and to implement bioanalytical methods.

Quantitative Bioanalytical Work Flow Process

CD Method

Preparation of Analytical Standards

&QCs

Sample Preparation

o Sample ReceiptITracking

Information Management

Antomation

Standard Operating Procednres

CD Sample Analysis

® Reporting

\ Notebooks and Ancillary

Data

o Data Processing

Fig. 1. Quantitative bioanalytical workflow process. This schematic identifies the steps in the routine functions of a bioanalytical laboratory and the core technologies that are utilized during the process

164 T.v. Olah

As shown in Fig. 1, the main steps in the process are as follows:

1. Sample receipt/tracking 2. Method development 3. Preparation of standards and quality controls 4. Sample preparation: extraction and/or concentration 5. Sample analysis 6. Data processing and interpretation 7. Data reporting and archiving

The following sections describe in varying detail each step in the bioanalytical process and outline efforts that we have taken to establish a more efficient and productive group that is well suited to support the discovery and the development of potential drugs. Examples will be provided where the use of automation in conjunction with multiple component analysis by mass spectrometry has been used to accelerate the discovery process.

8.6 Compound/Sample Receipt

As the number of compounds slated for analysis increases, an efficient strategy must be in place to coordinate the transfer of compounds and information pertaining to their physicochemical properties between the medicinal chemistry and drug metabolism departments.

There are several factors relating to compound availability that play an important role in establishing efficient procedures to identify new leads. Drug metabolism, in conjunction with medicinal chemistry, must establish a process that efficiently tracks the request and the delivery of compounds between research groups on a daily basis. Typically, early discovery compounds are delivered to bioanalytical groups from a variety of sources. Compounds are usually delivered pre-weighed in vials with minimal information (such as salt factor, molecular formula, purity, and chemical structure) available. In some cases, access to this information may also be possible through corporate databases. However, because limited information is available on the physical properties of each compound at such an early stage of development, the capacity to de-


velop accurate and precise analytical methods can sometimes be hampered.

For example, two useful physical parameters that are not always available on discovery compounds are solubility and log P estimation. The whole screening process grinds to a halt if an analyst must first determine a suitable solvent in which to dissolve a compound prior to analysis. Ideally, this process would function most efficiently if a single stock solution of compound was generated and was used in subsequent experimental and analytical procedures within the department. However, solvents such as dimethylsulfoxide (DMSO) may not be suitable for certain types of experiments (Chauret et al. 1998).

Additionally, log P estimates would also be useful piece of information to have at an early stage of development. This information could be used to determine appropriate chromatographic conditions or sample extraction procedures for a series of compounds.

Frequently, compounds with drastically different physical properties are grouped within discovery experiments either as part of the design of the experiment (i.e., co-administration) or in a pool for analysis. Under these circumstances, the ability to extract every compound from the matrix under one set of conditions can be affected. It may also force the analyst to compromise chromatographic conditions to avoid long analysis times, which periodically leads to less-than-ideal peak shape and overall assay performance. Sometimes this compromise can affect the quality of data generated.

On occasion, isobaric compounds have been dosed together in co-administration studies or pooled together for analysis. This oversight slows down the efficiency of the process (due to longer assay development including chromatographic run times). Frequently, additional experiments must be performed and analytical methods developed to specifically distinguish these compounds.

Early intervention by an analyst to determine which compounds should be included in a specific study will avoid analytical issues later on. Therefore, it is essential that analysts actively participate in the selection of compounds for multiple component experiments, such as co-administration or pooling studies. If compounds having similar physical characteristics are grouped together for analysis, more efficient and cleaner separation procedures can be developed that improve the quality of and the speed at which data are generated.

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The use of combinatorial libraries in screening experiments offers unique challenges to the analytical chemist in several ways: all of the experiments designed, methods developed, and results obtained are complicated by the fact that mixtures of compounds were used throughout the process. Concerns over the total amount of compound dosed to an animal, drug interactions, specificity of detection, and comparison between results obtained from single compound and co-administration studies must be addressed (and better understood) if this approach is to be developed to its full potential (White and ManitpisitkuI2001). However, a rapid evaluation of larger numbers of compounds in a structural series can be made using this approach. The researcher, prior to incorporating this approach in a discovery project, must assume the associated risks (Christ 2001).

Although most libraries are synthesized as individual compounds, the overall number of compounds created has significantly increased. Because of this, it has become necessary to design alternative approaches to generate meaningful data for the selection of potential drug candidates.

One immediate goal is to improve existing bioanalytical methods in terms of sensitivity and the number of compounds that can be selectively measured within a specified timeframe. As the numbers of compounds requested for screening increase, faster analytical methods are needed. As a result, more attention is being made to chromatographic resolution and mass spectral detection in multiple component analysis to avoid crosstalk between similar compounds and their metabolites.

8.7 Method Development

8.7.1 LC-MS Characterization: Instrumentation

Bioanalytical chemists are fully dependent on the quality of instruments that are installed in their laboratories. One of the innovative analytical group's perennial objectives is to evaluate new instrumentation and to consult in their development with the manufacturers of hardware and software. Through the years, several research groups have developed productive working relationships with instrument manufacturers. It is essential that pharmaceutical research and development groups continue


to provide a test site for new products during their development. A collaborative relationship ensures that future analytical needs are met in the areas of mass spectrometry and automation by the active participation of bioanalysts in the development and evaluation of future technology in these areas.

Currently, triple quadrupole mass spectrometers are the optimal instrument for performing quantitative bioanalytical methods, especially multiple component analysis. The power of LC-MS-based detection techniques lie in their ability to resolve mixtures of components and to specifically detect compounds based upon their chromatographic retention time and unique precursor-product ion pairs when using selected reaction monitoring (SRM). However, because quadrupole mass spectrometers are scanning instruments, there are limitations on the number of compounds that can be accurately and precisely detected during analysis. There are numerous published examples describing how mixtures of compounds are quantified with analysis using a triple quadrupole mass spectrometer (Olah et al. 1997; Beaudry et al. 1998; Scott et al. 1999). However, there are limitations in this type of analysis that preclude its incorporation into routine analytical processes. In particular, the analysis of mixtures of compounds, either by pooling or in co-administration studies, are prohibited by the time and energy expended in the development of more complex multiple component analytical methods (n25 components) and in the interpretation of data from these types of studies.

Currently, bioanalytical groups are finding new ways to incorporate a variety of mass spectrometers [LC-MS, electro spray ionization (ESI)TOF, matrix assisted laser desorption/ionization (MALDI)-TOF, ESIquadrupole (Q)-TOF] into their research, as the needs arise and the technology is further developed to support different types of analysis.

Detection of mixtures of compounds in biological matrices by singlestage instruments (LC-MS) can be difficult due to the limited specificity afforded by these systems. It is possible to quantitate multiple compounds on a single quadrupole system, but only after sufficient sample clean-up procedures and/or good chromatographic conditions are established to resolve interference from biological matrices and/or isobaric compounds. However, single-stage instruments have found a niche within the bioanalyticallaboratory and their use has proved beneficial in several areas of research. Consequently, these instruments have been

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shown to work well for "clean" samples and when high sensitivity is not required. Bioanalytical methods have been developed and validated on the single-stage instruments. Additionally, samples from in vitro experiments such as Caco-2 transport and metabolic stability in microsomes or recombinant enzymes are well suited for this type of instrumentation due to minimal background interference from the biological matrix.

Bioanalytical methods have also been established using ion trap mass spectrometers. However, these instruments require lengthy dwell times that limit the number of compounds that can be accurately and specifically detected in any single analytical occasion. These instruments were essentially designed for the structural elucidation of compounds and related substances in a variety of experiments. Although analytical methods have been developed and validated using ion traps, these instruments are limited in this capacity and are best suited for structural characterization.

Due to their mode of operation, TOF mass spectrometers can specifically detect both proteins and large "drug" compounds and mixtures of compounds simultaneously. Because these instruments capture full scan spectra, they have the potential to be utilized in a number of areas of drug metabolism. For example, mass spectra initially acquired in the analysis of biological samples following the administration of a drug candidate can be reexamined for the presence of potential metabolites at a later time when additional information becomes available. Because TOF is not a scanning instrument, it has the potential to be used in multiple component analysis for the quantitation of even larger numbers of compounds. Additionally, accurate mass measurements allow for the specific detection of compounds and related substances in complex biological matrices. This technology, however, currently lacks the sensitivity needed to support low-level detection in biological fluids. Analysts are working closely with instrument manufacturers to help realize the potential of TOF-MS and to find ways of incorporating its use into the bioanalyticallaboratory.

As a researcher, it is important that the analytical requirements of an experiment be matched with the type of mass spectrometer purchased. For example, when high sensitivity is required, a research-grade triple quadrupole may be needed. If the mass spectrometer is going to be used for higher throughput methods that have been established for reproducibility, then a mid-performing instrument may suffice. Samples with high


concentration and minimum number of analytes may be analyzed on a single quadrupole instrument. Ion traps and TOF instruments are best suited for qualitative and structural characterization.

8.8 Characterization of Compounds by LC-MS

The main challenge that analysts face in early ADME characterization is the development of a method that has sufficient sensitivity and selectivity to detect a component in the biological matrix of the sample. This challenge is further complicated when the analysis of mixtures of compounds is performed. Tandem mass spectrometers are excellent instruments for distinguishing compounds when coupled with good chromatographic methods. An experienced analyst is quite capable of quickly establishing instrument parameters to detect small molecules in standard stock solutions in a short period of time. Typically, a full scan spectrum is acquired to confirm that expected molecular ions are present. (Consequently, degradation products or impurities might also be detected at this stage, if this is requested of the analyst.) Secondly, product ion spectra are generated and appropriate fragment ions are selected to establish the precursor-product ion pairs that will be used in selected reaction monitoring. Mass spectrometer operating parameters are adjusted to obtain adequate sensitivity. Unit mass resolution is maintained for selectivity. The compounds are then combined and chromatographic conditions are established to obtain good peak shape and retention. Chromatographic resolution is maintained to avoid potential interference from endogenous substances, metabolites, or isobaric compounds within the mixture to be analyzed.

Similar physical properties of related compounds in a mixture make it possible to establish extraction procedures and chromatographic conditions to measure each component in the mix sufficiently. However, closely related compounds may also have a limited range of molecular ions and, therefore, resolving them requires good analytical practices: unit resolution and/or chromatographic separation. Also, as the number of compounds within a mixture increases, there is a greater chance of "crosstalk" affecting the detection of a particular compound in a mixture of similar compounds due to the possibility of common ions from isobars, isotopes, or metabolites (Matuszewski et a1.l998). Additionally,

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as the number of compounds increases, dwell times for the detection of each ion pair may need to be adjusted in order to maintain precision and accuracy across the series.

On the other hand, mixtures of dissimilar compounds typically have different chromatographic and mass spectral properties. Although this might make the specific detection of each individual component easier, it may be more difficult to analyze these compounds simultaneously as mixtures under general chromatographic and instrument conditions that are desired to maximize throughput in a screening procedure. Because of the differences in the physical make-up of these compounds, properties such as ionization and/or collision induced fragmentation potential and hydrophilicity, instrument conditions may need to be compromised in order to adequately detect all of the compounds within a single analysis. This may affect the integrity of the bioanalytical method and the quality of the data generated. Consequently, it is a persistent challenge to resolve these mixtures either chromatographically or mass spectrometrically in order to produce an acceptable multiple-component analytical method.

It is apparent that mass spectral data are becoming an increasingly important piece of information that can be used by a variety of research groups within a pharmaceutical company. This information, if generated under controlled standardized conditions and made accessible to research teams throughout a company, can manifest itself in many ways. For example, characteristic and definitive product ion spectra obtained on newly synthesized compounds could later be used to assess a compound's chemical stability, degradation products, metabolites, etc. (Kerns et al. 1997). It might also be more efficient to characterize new compounds by mass spectrometry as part of their registration process rather than during subsequent evaluation. For example, product ion spectra could be obtained on each compound registered as part of the proprietary filing process. Analytical groups could then use standardized spectra to confirm the compound's identity and integrity and also to assist in the establishment of instrument conditions for detection. If the chemical stability in a series of compounds was established, the analyst could eliminate the need to repeat this type of analysis in their laboratory. Subsequently, the overall assay development process would be accelerated.


8.9 Preparation of Standard, Quality Control, and Test Samples

The contribution of an analytical group to a discovery program is measured by their ability to rapidly generate high-quality data. The integrity of any analytical method is based upon the values obtained for standard and quality control samples that have been prepared, processed, and analyzed under identical conditions as test samples.

The preparation of standards and samples for analysis is one area where automation has already made a significant impact on both the efficiency of the process and on the quality of the data produced.

Semi-automated procedures have been developed, validated, and implemented in the preparation of standards, quality control, and test samples using automatic pipetting stations, such as those available from a variety of vendors. Although the use of these instruments does not necessarily increase the speed at which samples are prepared, it does, however, standardize the dilution scheme and minimize the inter-analyst variability. The use of automated pipetting procedures also minimizes an analyst's exposure to untested new chemical entities, especially performance-based level of exposure control (PBLEC) class IV compounds, and to biological fluids, particularly from primates.

These systems have been used to perform the following routine functions:

1. Pipet aliquots of test biological fluids from sample acquisition tubes into a 96-well plates

2. Dilute and combine stock solutions of the test substances 3. Prepare standard curves and quality control samples 4. Add internal standard(s) to all samples 5. Perform solid phased extraction, liquid/liquid and protein precipita

tion procedures 6. Reconstitute extracted samples for LC-MS analysis

These routine procedures can be performed prior to, or concurrent with, LC-MS system set-up. Additionally, placing identical robotic pipetting systems in multiple laboratories has facilitated the transfer of such methods to other groups involved in drug development. This practice

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ensures that common bioanalytical procedures are performed by wellcontrolled validated systems.

Quality control samples gauge the accuracy and reproducibility of results obtained from bioanalytical methods. It is important to prepare and analyze these types of samples in a manner that is identical to that applied to test samples. Calculating the precision and the accuracy of a particular method is important, especially when considering that the results derived from experimental data contain the variance of each of the steps involved in generating that result. For example, if analytical data at the limit of quantification (LOQ) for a specific method has a coefficient of variation (%CV) of (+/-) 25%, then when that data are used to calculate the "half-life" of a compound, the range of the "halflife" determination from that data set is at least (+/-) 25 %. Of course, additional "variance" will be added to the final determination from other sources throughout the experimental procedure.

Nonetheless, the demand for more accurate and precise data on screening studies is becoming an increasing challenge for bioanalytical chemists.

Incorporating other types of "control" samples into each experiment will also ensure that the data generated will be of a measurable quality. The addition of "biological internal standards" to co-administration studies (Olah et al. 1999), "benchmark" compounds to metabolic stability assays, "positive and negative" controls to P450 inhibition studies (Crespi et al. 1997) are examples were results can be "calibrated" during the lifetime of a screen. Data on these selected compounds can also be used to assess assay performance and to detect trends in the results obtained.

8.10 Sample Extraction and/or Concentration

One of the most important steps in the bioanalytical process is the extraction and/or concentration of analytes from biological fluids. Currently, three types of off-line concentration procedures are typically used in the majority of the bioanalytical methods developed in our laboratory: solid phase extraction (SPE), protein precipitation, and liquid-liquid extraction. As many analytical chemists can attest, there is a good correlation between sample preparation and the speed at which


samples can be analyzed: the "cleaner" the samples, the less "chromatography" is typically required for acceptable results. Performing multiple component analysis on mixtures of compounds can be more difficult than developing selective isolation procedures on only one or two analytes. Generalized conditions are applied to ensure that every compound will be present in the extract, although sometimes at less than optimal recoveries and/or without adequate removal of interfering endogenous materials. However, most methods employed are sufficient when used in conjunction with selected reaction monitoring (SRM) on triple quadrupole mass spectrometers. Nonetheless, there is a concerted effort to enhance the extraction and/or concentration efficiency of each analyte in order to improve the overall quality of the bioanalytical method.

Parallel processing procedures have also been applied to specific steps in order to accelerate the process. Specific equipment, such as 4-, 8- and 12-channel manual pipettes or robotic pipetting systems (RPS), designed to either manually or automatically perform liquid transfer steps, have also been incorporated successfully into the bioanalytical process (Watt et al. 2000). Systems have been assembled to perform all of the necessary steps in an extraction procedure: pipetting of sample and appropriate buffers, conditioning, elution, transfer, evaporation, reconstitution, mixing, and injection. These systems work best for methods that are applied to large batches of samples, typically supporting clinical studies (Plumb et al. 2001). However, it can be very expensive to compile in terms of time and method development. Additionally, it is also extremely challenging to maintain throughput in support of drug discovery using a "complete" system since new bioanalytical methods are developed daily in multiple programs.

The strategy that we employ uses a variety of instruments in a semi-automated manner. Common 96-well plate formats are used in each procedure. There are several advantages to incorporating a standard design format into the daily procedures that are performed in the laboratory. These advantages include a compact footprint design that limits the physical requirements of different instruments that are used in the process (pipetting stations, centrifuge rotors, solvent evaporators, autosamplers), labeling tubes are no longer required, and batch processing can be applied, on occasion, to accelerate specific steps in the process. Some disadvantages of this format, however, include compromising optimal separation procedures, i.e., lower centrifugation speed to

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concentrate protein precipitate, the expense per cartridge, and the potential waste of not using every well in each 96-well solid phase extraction (SPE) cartridge, etc. However, the gains that are realized in the number of samples analyzed support the standardization of a basic scaffold on which to build the analytical process.

Consequently, four- and eight-channel RPS have become common fixture in the analytical laboratory and their scope will undoubtedly increase as analysts gain more experience in their operation and their capabilities. Additionally, 96-channel pipettors that can simultaneously dispense solvent to each well and can transfer complete batches of samples to separate cartridges during the course of typical sample preparation routines have also been used to accelerate sample preparation significantly. Complete batch processing ensures the rapid throughput of samples during the extraction or concentration procedure. For example, SPE or acetonitrile precipitation of 96 samples can be completed in approximately 20 min using a 96-channel pipettor. Additionally, other instruments designed for specific steps in these processes (i.e., concentration of samples in a 96-well plate format) are available to complete a system. Although the overall process is not fully automated, it is flexible and efficient and can be used to support the different preparation procedures on a variety of samples from multiple programs.

Alternatively, "on-line" sample processing methods have been successfully incorporated in a number of laboratories using conventional column switching (Needhan et al. 1998; Jemal et al. 1999), disposable columns (McLoughlin et al. 1997) and turbulent flow chromatography (Wu et al. 2000). These methods have the advantage of performing both sample preparation and analysis within a single system. As this technology advances, it will undoubtedly find wider applications in the bioanalytical chemist's laboratory.

8.11 Sample Analysis

As the efficiency of one step in the bioanalytical process is improved, the capability to analyze more samples successfully over a period of time also increases. Improvements in the ability to prepare samples for analysis has presented another challenge to the analyst: to accelerate the speed at which samples can be introduced into the LC-MS system yet


still maintain the quality of data generated. Although it is possible to inject samples directly into an LC-MS system, it is important that some degree of chromatographic resolution be maintained in order to obtain acceptable results. Some of the challenges of injecting biological extracts onto the LC-MS system using rapid chromatography will be discussed in the following sections.

8.11.1 Ionization Suppression

When LC-MS was first introduced, many touted its superior detection capabilities as an excuse to minimize chromatography in order to increase throughput. Our experiences through the years have increased our understanding of the limitations of LC-MS systems in bioanalysis. An example of this would be the observation of analytical performance degrading as "dirty" samples are injected onto the system. The result is that throughput is ultimately decreased. Most often this is the consequence of a column going over pressure and shutting down the liquid chromatograph or that analyte detection is reduced due to ionization suppression or a blocked orifice that precludes ions from entering the instrument. The installation of divert valves and post column "splits" prior to introduction of samples into the mass spectrometer can significantly improve the performance of these instruments and increase the time-period that they are operational. Assay performance on systems that have these features has been noticeably improved. As we better understand and make improvements in the area of sample clean up and sample analysis, our goal is to eliminate downtime and to increase throughput during the course of an analytical run.

King (King et al. 2000) and Bonfiglio (Bonfiglio et al. 1999) published their research on the ionization suppression effects in the extracts from different biological fluids. In these articles, they described a set-up for the direct examination of the effect on the signal response of analytes by endogenous materials that remain in the extracts of samples following processing. This approach has helped in the development of analytical methods in several programs by quickly providing information based upon the evaluation of extraction procedures and HPLC conditions on matrix effects and signal response. Determination of the potential effect, or lack thereof, by ionization suppression has also allowed for

176 T.V.Olah

the acceleration of sample throughput by minimizing chromatographic run-times in cases where no effect is observed. This observation has significant implications in improving the drug discovery process by accelerating the analysis of samples in PK screening studies.

8.11.2 New Sample Introduction Approaches to Increasing Throughput

It follows that reductions in the time required to prepare samples would also lead to the development of new techniques to effectively reduce the time that it takes to analyze these samples. One of the current limitations on throughput is the speed at which samples can be injected onto LC-MS systems and still produce acceptable results. HPLC conditions have been standardized for the analysis of mixtures of compounds within a therapeutic class as a means of minimizing method development times. These conditions can then be adjusted, as necessary, to improve the performance of the analytical method for newly synthesized compounds as the program progresses.

Examples of rapid HPLC-gradient conditions using higher flow rates with smaller diameter columns have been published (Zweigenbaum et al. 1999; Romanyshyn et al. 2001). Run times for multiple component analyses have been reduced significantly and can be applied to a wide variety of compounds (Miller-Stein et al. 2000). Alternatively, monolithic columns have recently been shown to produce good chromatographic resolution in significantly shorter run times (Wu et al. 2001).

Parallel separations with staggered injections (Bayliss et al. 2000; Van Pelt et al. 2001; Wu 2001) or multiple ion source interfaces (Yang et al. 2001; Hiller et al. 2000) have also been reported, although the results have been less accurate and precise when compared with traditional analytical methods.

Since improvements will continue to be made in instrument performance, sample preparation, and HPLC separations, the expected result will be that more samples will be analyzed for longer periods of time.


8.12 Data Processing, Reporting, and Archiving

One of the biggest challenges that we are currently facing in drug discovery is the vast amount of data that are generated during the course of a project. Although instrument manufacturers have made improvements in data processing software, limitations become apparent as the number of components analyzed during a single run increases. For example, a co-administration study involving 12 compounds and 96 samples (standards and quality controls included) generates over 1,100 data points. The length of time that is required at each step of the data evaluation process adds up quickly as the number of compounds and/or samples increase. These steps include the generation of peak areas, the construction of standard curves, the subsequent evaluation of quality control and subject samples, data review and assay acceptance, and eventually, data interpretation, reporting, and archiving [sometimes all under good laboratory practices (GLP) guidelines]. We have attempted to streamline some of these processes through the establishment of standardized protocols, analytical formats, and information transfer between systems.

The following sections describe some of the specific approaches that we are undertaking to improve the throughput and to manage data effectively.

8.12.1 Standardization

Although the concept of standardization and quality control is not always well defined nor readily embraced by members of a department or company, it is an inexpensive way to increase the efficiency of a laboratory. Standardization in a bioanalytical laboratory can be applied at essentially any step in the bioanalytical process. It can mean a set of experimental conditions, the type of tubes that test samples arrive to the laboratory, the placement of standards and quality control samples in a sample queue, the format of data in a report. Once sets of conditions are established, analysts can begin to streamline the process and to apply some type of automation or routine to the process. Consistency can be measured and tracked through the incorporation of some type of quality

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assurance and links to other steps in the bioanalytical process can be integrated more easily.

However, to some individuals standardization implies a rigid set of inflexible conditions that must be followed in order to generate a set of limited data. These same individuals contend that conditions "standardized" for a particular set of compounds are not appropriate for use in the evaluation of a second set of compounds.

Although most scientists agree that the majority of data generated by competent research groups is usually very good, it can also be generated under a variety of experimental conditions and therefore, cannot always be cross-correlated. For example, although data are generated and reported by several groups assessing the potential for new compounds to "inhibit" P450 metabolism, the experimental conditions that were used to measure this observation can vary signIficantly and may generate conflicting results. Describing the experimental conditions used to generate "inhibition" data is necessary when different conditions are used. Understanding the basis of the effect of these differences on the results would be better.

As we more fully comprehend the nature of the biological systems that we are studying, data generated under standardized set of experimental conditions could be beneficial for several reasons. As data are generated under identical conditions within different laboratories, the results can be cross-correlated and compared across larger sets of compounds and, perhaps, uncover trends in the data sets. Controversial data will be eliminated, especially between different laboratories performing the same experiments (i.e., inhibition). Additionally, ifthese experimental conditions are carried out on automated equipment, common methods can be developed and transferred between laboratories much more easily.

8.12.2 Incorporation of LIMS into the Bioanalytical Process

Since the quantity of data generated in the bioanalytical laboratory in support of drug discovery has increased significantly, some standardized means of organizing this information is needed to maintain productivity, to remain compliant with GLP standards, and to accelerate the overall process required for data acquisition, processing, and reporting. One


possible solution is the incorporation of a laboratory information management system (LIMS).

This system is the framework in which data generated in the laboratory can be processed and distributed. Typically, a well-designed LIMS designates a unique file-name for every standard, quality control, and sample that will be generated in a study. The program will create a database that organizes this information in terms of the design of the study, the number and types of samples to be analyzed, the status of the samples, and the acceptance criteria of the analytical procedure that will be used. Most LIMS have the ability to communicate with LC-MS systems by creating a sequence list of samples that can be downloaded to the mass spectrometer's operating system. After samples have been analyzed and data acquired as peak areas by the instrument's proprietary software, this information can be uploaded and the data processed within the LIMS. Laboratory acceptance criteria, based upon our standard operating procedures that have been programmed into the software, will compare acquired data against defined criteria. Analytical runs can be accepted or rejected and outliers identified by the LIMS. Any changes to the data are automatically noted and flagged for auditing purposes. Following review of the data by the designated analyst, pharmacokinetic parameters can be calculated quickly and reports can be assembled and distributed electronically to members of the project team. Ultimately, this information can then be directly uploaded into the corporate discovery database.

Although there is tremendous flexibility built into this type of system, concerns have been expressed over the standardization of data management and the limitations of commercially available packages, especially in regard to the PK calculations.

However, as a department becomes more involved in the development and implementation of higher throughput screens to support drug discovery, they must address their capacity to coordinate and manage information. This includes the generation, processing, analysis, and reporting of large amounts of data from both in vivo PK studies and in vitro experiments such as P450 inhibition, metabolic stability, protein binding, etc.

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8.13 Data Interpretation

As noted earlier, screening procedures have the potential to generate large amounts of information on numerous compounds in a short period of time. In order to utilize this information efficiently, tools are needed that will help search this information in order to identify compounds with effective pharmaceutical properties based upon unique features of the molecule. Most pharmaceutical ADME research groups have only recently begun to venture into this arena. Collaborations with biostatisticians and molecular modeling groups are essential to develop systematic routines that are capable of selecting compounds based upon defined criteria. Although there are a few examples of in-house or commercially developed software packages that describe potential applications, it is still rather limited in its use. However, this is also a new tactic for ADME characterizations. There are many obstacles that must be overcome in order for this approach to be realized. Nonetheless, the best chance for these tools to be successful is when the data to be evaluated comes from the results obtained on samples derived from well-designed experiments using well-controlled bioanalytical methods that have been validated in terms of accuracy and precision.

8.14 Conclusions

Bioanalytical chemistry is a fundamental part of drug discovery and plays a critical role at every stage of pharmaceutical research and development. In the drug metabolism department, the bioanalytical group plays a vital role in the generation of accurate and precise data from a variety of experiments designed to evaluate their adsorption, distribution, metabolism, and excretion characteristics of potential drug candidates. Because speed, accuracy, and precision is a requirement for every bioanalytical method developed and implemented in the early stages of drug discovery, strategies have been designed that incorporate key technology, automation, and instrumentation at several steps in this bioanalytical process.

Acknowledgements. The author gratefully acknowledges Ms. Ellen Sulzbach for her assistance in preparing this manuscript for publication.


References

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Beaudry F, Le Blanc JC, Coutu M, Brown NK (1998) In vivo pharmacokinetic screening in cassette dosing experiments; the use of on-line Pprospekt liquid chromatography/atmospheric pressure chemical ionization tandem mass spectrometry technology in drug discovery. Rapid Commun Mass Spectrom 12:1216-1222

Bonfiglio R, King RC, Olah TV, Merkle K (1999) The effects of sample preparation methods on the variability of the electro spray ionization response for model drug compounds. Rapid Commun Mass Spectrom 13:1175-1185

Chauret N, Gauthier A, Nicoll-Griffith DA (1998) Effect of common organic solvents on in vitro cytochrome P450-mediated metabolic activities in human liver microsomes. Drug Metab Dispos 26: 1-4

Christ DD (2001) Cassette dosing pharmacokinetics: valuable tool or flawed science? Drug Metab Dispos 29:935

Crespi CL, Miller VP, Penman BW (1997) Microtiter plate assays for inhibition of human, drug-metabolizing cytochromes P450. Anal Biochem 248:188-190

Hiller DL, Brockman AH, Goulet L, Ahmed, S, Cole RO (2000) Application of a non-indexed dual sprayer pneumatically assisted electrospray source to the high throughput quantitation of target compounds in biological fluids. Rapid Commun Mass Spectrom 14:2034-2038

Jemal M, Huang M, Jiang X, Mao Y, Powell ML (1999) Direct injection versus liquid-liquid extraction for plasma sample analysis by high performance liquid chromatography with tandem mass spectrometry. Rapid Commun Mass Spectrom 13:2125-2132

Kerns EH, Rourick RA, Volk KJ, Lee MS (1997) Buspirone metabolite structure profile using a standard liquid chromatographic-mass spectrometric protocol. J Chromatogr B Biomed Sci AppI698:133-145

King RC, Bonfiglio R, Fernandez-Metzler C, Miller-Stein C, Olah TV (2000) Mechanistic investigation of ionization suppression in electrospray ionization. J Am Soc Mass Spectrom 11:942-950

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Matuszewski BK, Chavez-Eng CM, Constanzer ML (1998) Development of high-performance liquid chromatography-tandem mass spectrometric methods for the determination of a new oxytocin receptor antagonist (L-368,899) extracted from human plasma and urine: a case of lack of specificity due to the presence of metabolites. J Chromatogr B Biomed Sci Appl 716:195-208

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Plumb RS, Dear GL, Mallet DN, Higton DM, Pleasance S, Biddlecombe RA (2001) Quantitative analysis of pharmaceuticals in biological fluids using high-performance liquid chromatography coupled to mass spectrometry. Xenobiotica 31 :599-617

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9 Strategies in Lead Selection and Optimization: Application of a Graphical Model and Automated In Vitro ADME Screening

A.K. Mandagere

9.1 Introduction .......................................... . 9.2 Rationale for In Vitro ADME Screening ................... . 9.3 Higher Throughput In Vitro ADME Screening .............. . 9.4 Graphical Model for Estimating Species-Specific

Oral Bioavailability .................................... . 9.5 Strategies in Lead Selection and Optimization .............. . 9.6 Conclusion .......................................... . References ................................................. .

9.1 Introduction

185 187 189

192 196 199 200

The drug discovery process in the pharmaceutical industry has undergone a dramatic change in the last decade due to advances in molecular biology, high-throughput pharmacological screens and combinatorial synthesis. In addition, the high cost, the long development time and the high failure rate in bringing drugs to market have also been the driving forces behind this change. The convergence of computational power, information management, robotics and automation technologies have aided in the dramatic acceleration in the pace of early drug discovery.

The current high-throughput and ultra-high-throughput screening systems can rapidly screen an entire library containing a million or more

186 A.K. Mandagere

Fig. 1. Causes of 1ead compound failure in drug discovery and development

compounds and identify "hits" - compounds with potency and selectivity - for an increasing number of therapeutic targets. However, this dramatic increase in the rate of hit identi:fication has only managed to shift the bottleneck of the drug discovery process to the lead selection and optimization stage.

Traditionally, investigational new drug (IND) candidates are selected from lead compounds that exhibit in vivo efficacy, sufficient duration of action and lack of toxicity. These evaluations are conducted manually in live animals and are inherently slow, labour intensive, expensive and capacity limited. Therefore, as a practical necessity, only a small number of compounds are evaluated by the in vivo studies, selected primarily based upon their in vitro potency. Historically, the lead candidates selected by this process exhibit very high failure rate (90%) due a multitude of causes, which include poor pharmacokinetics (PK), insufficient efficacy and toxicity (Fig. 1) (Kennedy 1997; Prentis et al. 1988). Poor PK could be attributed to low oral bioavailability (%F) or rapid systemic clearance. Low %F could be attributed to poor absorption and or first pass metabolism. Further, poor correlation between animal and human oral bioavailability, attributed primarily to the differences in bio-transformation between species, has contributed to the problem (Sietsema 1989). Similarly, short duration of action could be attributed to frrst pass metabolism or rapid elimination. Typically, lack of efficacy could be explained tlrrough poor oral absorption and or high first pass metabolism and or failure of drug to distribute to the target tissue or site

Strategies in Lead Selection and Optimization 187

of action. Finally, toxicity could be attributed to CYP450 biotransformation, inhibition or induction. Thus, the ability to optimize lead candidates for human absorption, distribution, metabolism, excretion (ADME) and toxicity has become a strategic necessity. In an attempt to overcome these limitations, in vivo cassette dosing has been employed to increase the throughput of PK studies (Berman et al. 1997; Bryant et aI. 1997; Olah et al. 1997). However, even cassette dosing has its limitations in terms of speed, overall throughput capacity, potential drug interactions that could lead to erroneous PK assessment, larger compound requirement and the limited number of species that can be tested.

9.2 Rationale for In Vitro ADME Screening

Although, increasing the throughput of the traditional in vivo studies in lead selection and optimization has proven to be impractical, in vitro ADME screening has emerged as a viable alternative. In vitro ADME screening is rapid, has a large capacity and is amenable to automation. Recently, higher throughput in vitro ADME screening systems along with a graphical model for estimating human and animal PK parameters have been applied as rapid tools for screening, selecting and optimizing lead compounds (Mandagere 2000). These screens include Caco-2 cell permeability, metabolic stability in human and animal liver enzymes, enzyme inhibition, drug-drug interaction and cytotoxicity, along with physical property measurements, such as aqueous solubility, log P and pKa. The in vitro ADME screens have large screening capacity and speed to evaluate several thousand potential lead candidates per year (Fig. 2). Human and animal oral bioavailability parameters can be estimated from the in vitro data with PK graphical model by integrating the rates of Caco-2 permeability, metabolic stability in human and rodent isolated liver enzymes (Mandagere et al. 2002). Further, in vitro intrinsic clearance in human liver micro somes has been used in estimating in vivo hepatic extraction ratios (Iwatsubo et al. 1997; Lave et al. 1997; Ho et al. 1999; Obach et al. 2000). During the lead selection phase, the objective of the in vitro screen is to rapidly answer the question of whether a given compound will have a satisfactory or unsatisfactory oral bioavailability in humans and in animals. Further, the in vitro ADME data aid in categorizing compounds into high, medium and low groups,

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Therapeutic Teams t't ............... . In vitro

Pharmacological Screen

Electronic request for in vitro

ADME screening

Compound Management

.-~:..................... Report ........ ...... ·····r·. In Vivo Evaluation

A.K. Mandagere

Visualizer

Fig. 2. Process flow in high-throughput in vitro ADME screen

based on potency, estimated systemic exposure (oral bioavailability and clearance) and physical properties for in vivo evaluation. In the lead optimization phase, in vitro ADME screen provides specific information to medicinal chemist for structure modification to optimize solubility, permeability and metabolism properties that could result in improved systemic exposure (%F) and half-life. Finally, this approach provides a rational and efficient means of utilizing the limited in vivo resources for evaluating potential lead compounds that have a higher probability of success in development.


9.3 Higher Throughput In Vitro ADME Screening

The in vitro ADME screens consists of physical property assessment, cell-based assay to determine absorption potential, metabolic stability in liver enzyme preparations to assess first pass metabolism, potential toxicity through enzyme inhibition and cell viability determinations. These screens incorporate many of high-throughput screening concepts, such as the use of compounds solvated in dimethylsulphoxide (DMSO) in 96-well microtitre plates, electronic barcode tracking, use of robotics and automation for parallel batch processing, high-speed sample analysis, automated data acquisition, analysis, visualization and reporting (Fig. 2). Finally, archival storage of the data in centralized database and Web-based data retrieval (Mandagere 2000).

In standardized in vitro ADME screens, compounds requested for screening are electronically submitted to a centralized compound management group via a Web interface. The compound request is processed by an automated system that dispenses appropriate volumes of 10 mM DMSO solvated compounds into barcode-labelled 96-well plates. Copies of these master plates are created for each screen and distributed to the appropriate group. The compound information, such as compound identification number, its location in the 96-well plate, structure, molecular weight, purity are stored in the a centralized database which can be retrieved by scanning the plate barcode. The barcode data is used in creating samples and tracking samples during liquid chromatography/mass spectrometry (LC-MS) data acquisition, analysis and result calculation. Automated scripts are used in calculating parameters such as, solubility (Cs), permeability (Papp or %D and, metabolic stability (t1/2 or %R) and in storing in the centralized database along with the pharmacological screening data.

Data Management and Interpretation. The data can be retrieved from the central database for analysis and visualization with a Web browser. The in vitro ADME and pharmacology screening data can be visualized in a two-dimensional (2D) or three-dimensional (3D) format using "Spotfrre" (Cambridge, Mass.) or a similar software application with links to the structure database. Large data sets can be divided into smaller groups based on structural class and therapeutic area. These applications allow the simultaneous visualization of six variables such

190 A.K. Mandagere

as potency, solubility, log P, permeability, metabolic stability and structure. Further, the system can generate standardized reports with summarized results, graphs and recommendations, which can be delivered to the therapeutic teams bye-mail and published to the central database.

9.3.1 Physicochemical Properties

Physicochemical parameters such as solubility, log P and pKa are essential for interpreting absorption data (Lipinski et al. 1997). Currently, computational methods are used to estimate log P or pKa of drug candidates. The most common method of determining high-throughput aqueous solubility is by turbidimetric method using a light scattering nephelometric detector. An automated high-throughput turbidimetric solubility procedure in microtitre plates has been described recently by Bevan and Lloyd (2000). This system consists of an automated multiprobe liquid handling workstation and a laser nephelometric microtitre plate reader. The method involves performing a tenfold serial dilution of the 10 mM drug solutions in DMSO and dispensing a small volume (5 ~) of the diluted drug standards into buffer solutions. Solubility is determined from the concentration at which a precipitate is detected by nephelometric microtitre plate reader. While this method is not meant to replace a careful determination of solubility for a solid dosage form, it generally gives "kinetic" solubility, which does not differ by more than a factor of two from the classical thermodynamic solubility measurements.

9.3.2 Caco-2 Permeability Studies and Automated Cell Culture System

Caco-2 and Madin-Darby canine kidney (MDCK)4 cell systems have been well characterized and show good correlation with in vivo drug absorption in animals and in humans (Artursson et al. 1996; Rubas et al. 1995; Delie and Rubas 1997; Irvine et.al. 1999). Caco-2 and MDCK4 cells are grown as a monolayer on a semi-permeable, transwell membrane and reach confluency after 21 and 3 days, respectively. During the growth phase, the cells require media change at 24- to 48-h intervals.


Currently, automated systems for seeding, changing growth media and to conduct transport studies in 24- or 96-well transwell membrane inserts in a microtitre plate format are available from CRS Robotics, Brandel, Zymark and Tecan. These systems consist of one or more high capacity incubators integrated with a robotic arm to a liquid handling system. The system can be programmed to produce a steady supply of Caco-2 or MDCK4 cell monolayers under sterile conditions.

Drug permeability studies have been automated with liquid handling workstations (Mandagere et al. 1996; Garberg et al. 1999). These systems can prepare 10- to 50-11M dosing solutions by diluting the 10-mM DMSO drug standards in buffer solution and dispensing them into the apical (donor) well of the cell monolayer and incubated at 37°C under 95% oxygen and 5% C02. The system removes samples from the apical (Co) and the basolateral (receiver) wells at various time intervals. The drug concentrations in the samples are determined by high speed LCMS. This analytical technique involves injecting the sample on a short HPLC column to trap the compounds of interest and eluting the buffer salts to waste with water, then the compounds of interest are eluted into MS source with acetonitrile. The drug permeability is expressed as apparent permeability (Papp) or as percent flux. Percent flux or transport (%1) is being used in the high-throughput permeability screening as it requires only two samples, initial donor sample and the receiver well sample at 60 or 120 min. Excellent correlation has been observed between P app and %Tvalues.

P-glycoprotein (Pgp) is a membrane bound protein and it is present in intestine, brain and other tissue. It is an energy dependent efflux pump capable of transporting many structurally unrelated compounds out of cells. Intestinal Pgp has been shown to efflux compounds into the lumen and thus lower the oral bioavailability of some drug molecules. The Pgp substrate potential of compounds are determined from their bi-directional permeability (apical to basolateral and basolateral to apical) in Caco-2 cell monolayers, at multiple concentrations.

192 A.K. Mandagere

9.3.3 Metabolic Stability in Liver S9 or Microsomes

Metabolic stability impacts both oral bioavailability and duration of action (half-life). Thus, it is essential to establish the first pass metabolism potential of test compounds in human and animal liver preparations. Further, metabolic profiling to identify labile functional groups can aid in structure modification. Drug metabolism studies have been automated with liquid handling workstations (Ro et al. 1999; Mandagere et al. 1999; Mandagere 2000). Typically, 5-10 J.1M oftest compound is incubated at 37°C with pooled human or rodent liver microsomes or S9 preparations containing nicotinamide adenine dinucleotide phosphate, reduced (NADPR) regenerating system. Metabolic stability is measured by the loss of parent compound over time in the liver enzyme incubation mixture. The samples collected at various time intervals are de-proteinated with acetonitrile (ACN) and analysed by high-speed LC-MS flow injection analysis. The drug metabolic stability is expressed as the percentage remaining at 30 min (%R), or as a rate of disappearance or half-life (tl/2)'

9.3.4 CYP450 Inhibition and Drug-Drug Interaction

High-throughput screens are available in the identification of drug candidates with cytochrome P450 (CYP450) inhibitory potential. The inhibition of human CYP3A4 and CYP2D6 are of primary interest and can be assessed with microsomal preparations and fluorescent substrates. CYP450 inhibition potential is assessed from the reduction in the amount of fluorescent metabolite formed from the substrate in presence of the test drug. Data are reported as IC50 values or percentage inhibition when using only one or two concentrations of test compound.

9.4 Graphical Model for Estimating Species-Specific Oral Bioavailability

Recently, we have described a graphical method that integrates and simplifies the in vitro data for estimating species-specific oral bioavailability in humans and other species (Mandagere 2000; Mandagere et al.


2002). The objective in applying the graphical model to the in vitro screens is to rapidly answer whether a given compound will have a satisfactory or unsatisfactory oral bioavailability in humans and in animals. Absolute oral bioavailability (%F) is an estimate of systemic exposure of an orally administered drug. It is determined from the amount of drug that enters into the systemic circulation following oral administration, relative to an intravenous dose (Eq. 1).

%F = Ave oral X iv dose

Aue x oral dose iv

X 100 ---- (Eq 1.)

The premise of the graphical model is based on the fact that %F of drugs is primarily dependent upon the extent of its absorption and the rate of systemic clearance. When in vivo hepatic clearance is much greater than renal, biliary and gastrointestinal (GI) clearances, then the rate of hepatic clearance (metabolism) is equal to systemic clearance. Under these conditions, one can approximate %F with in vitro or in situ rates of absorption and hepatic metabolism (Fig. 3). The graphical model categorizes species-specific oral bioavailability into low, medium and high regions by integrating the in vitro Caco-2 absorption potential (Papp or %T) and metabolic stability (% remaining) data. In our method, we have used Caco-2 permeability (Papp) values as a measure of absorption for all species, since excellent correlation has been demonstrated between Caco-2 permeability and extent of absorption in humans and other species (Artursson and Karlsson 1991; Rubas et al. 1995; Chiou and Barve 1998).

Species-specific oral bioavailability (%F) estimates can be derived by generating a map from an x,y plot of the in vitro rates, Papp versus rate of metabolic disappearance, that is expressed as the percentage of drug remaining after 30 min (%R) or as t1/2 (Fig. 4). Spotfire software (Cambridge, Mass.) was used in generating the oral bioavailability estimation map. The predictive capacity of this model was examined with in vitro and in vivo data for 21 drugs representing 10 different therapeutic areas with a wide range of oral bioavailabilities in humans, rats, dogs and guinea-pigs (Fig. 5). The criterion used to judge this model's success in predicting %F was based on the accuracy of assignment of drugs into their appropriate %F region. Predictions were judged to be either false positive and false negatives when the estimated %F

194 A.K. Mandagere

Fig. 3. Integration of in vitro permeability (Papp) and metabolic stability data in estimating human oral bioavailability (%F). (Mandagere et al. 2002)

differed from the observed value by a factor of two. Verapamil, mannitol, metoprolol and carbamazepine were used as reference compounds in defining the boundaries of the low, medium and high regions %F estimation map. Since Papp and metabolic stability data could vary widely depending on ceH types (3 and 21 day Caco-2 or MDCK cells) and the level of CYP450 enzyme activity.


P,..,D"'p ... d AC1I"e MeclMtilt'il

f Metabolic Stability

(% Remaining or 1,/2)

pcp£m.J .ad Adk'e rrauport

Fig. 4. Graphical oral bioavailability estimation map. (Mandagere et aI. 2002)

c ;:; J:>

~ E .. .. c.. .... ~ "" .. u

~l,.,t! ... --..

" • .

.; .. . i · 0

"" 'f f;o;l

--" !!: e::

n . •

Metabolic Stability (%R)

I • NK IfNJa -lOS (G P)

I • Xo AllAJoe II (R)

3- 'KI Ja 1M (GP)

4-nl AnologA (R)

3 - 'KJ -IU - 104 (R)

6 - M""nllOl (lI)

7 - NKIINIO- 211 (R)

8 - V .... "....II(lI)

9 - Propranolol (II)

10 - _

II-NK.lINKl - 111(D)

12 - Mdoprolol (H)

13 - £1 A_aloe 101 (R)

I. - NKIfNK1 -111 (GP)

15 - 3S440(R)

16 - T1moIoI (H)

17 · NKIfNKl - I04 (D)

18· NKlfNlO - I04 (Gp)

" - Cor1>omoupl •• (H)

2t • E l ...... Joe 102 (R)

11 - :W1908 (R)

I I -3<i8177 (R)

lJ-w"" ..... (lI) 14 - Indomt'lbada. (8)

Data points sized by oral bioavailabilily i.n bum.ns, rats, dogs aod guinea pigs.

o - Low "/oF (0--20%) • -Medium %F (20--50%) • -High %F (50--100'-.)

Fig. S. Bioavailability estimation of 21 drugs in humans, rats, dogs and guinea-pigs using the graphical model. D, dog; GP, guinea-pig; H, human; R, rat. (Mandagere et al. 2002)

196 A.K. Mandagere

9.4.1 Prediction in Humans, Rats, Dogs and Guinea-Pigs

Figure 5 shows that a majority of the test compounds were placed in the correct regions of the bioavailability map with no false positives or false negative, as defined earlier. Two compounds along the medium and high %F boundary lines show a small degree of overlap. More importantly, all low %F compounds were correctly placed in their region. This model correctly predicted the low oral bioavailability of polar compounds and positively charged quaternary amine compounds, despite high stability to phase I metabolism. Apparently, the oral bioavailability estimates of these compounds were accurate due to the counter balancing effect of their poor Caco-2 permeability. Similarly, this model correctly predicted the low %F of highly permeable and rapidly metabolized drugs.

There are some precautions to be noted for the use of this graphical approach during the early selection phase. This model is best applied for compounds that are passively diffused. Further, bioavailability estimates could easily be under- or overestimated when compounds that fall into the two hyper-variable regions in the %F map (Fig. 4). In this case, even a small variance in the Papp or %R determinations could result in erroneous %F estimations. Typically, small polar compounds or larger compounds with carboxylic acid or charged functional groups are found in the lower right hyper variable region and ester pro-drugs and highly lipophilic compounds are present in this upper left hyper variable region.

9.5 Strategies in Lead Selection and Optimization

The objective in using the in vitro ADME screen is to provide guidance in the selection of potential lead candidates for in vivo evaluations (Fig. 6). Compound selection is based on potency, physical properties and estimated systemic exposure (oral bioavailability and clearance). Moreover, this process could provide information in a timely manner to medicinal chemists for structure modification to optimize solubility, permeability and metabolism properties for compounds.

Optimizing permeability and metabolic stability properties concurrently with solubility and potency (binding affinity ICso and Ki) through structure modification can be quite daunting given that both permeabil-

Strategies in Lead Selection and Optimization

Structural Analogs

Secondary Pharmacological Screening Potency and Selectivity

I" Vitro ADME and To •. Screening Permeability, Metab. Stab., CYP -450 Inhibition

Pre-Leads " Pre-c:linical Proof -of..contept "

Secondary In Vitro ADME In Vivo Pharmacology and PK Screening

Lead Declaration

Pre-Clinical Development

Pharmacology PK TOX

Directed Synthesis

197

Fig. 6. Overview of lead selection and optimization process in drug discovery

198 A.K. Mandagere

ity and metabolism often have opposing requirements. In general, lipophilic compounds tend to be more permeable; however, they also tend to make good substrates for CYP450 enzymes, resulting in rapid metabolism and consequently have low %F (Smith 1994). In contrast, polar or ionizable (anionic and cationic) compounds tend to be metabolized by phase II or cytosolic enzymes or excreted unchanged in vivo. Consequently, these compounds typically exhibit high stability in microsomal or S9 incubations. On the other hand, these compounds will exhibit low permeability in the Caco-2 model due to their polarity or ionization at neutral pH.

Structure modifications to solve a metabolic stability problem may not necessarily lead to a compound with an overall improvement in PK properties. As solving metabolic stability problems at one site could result in the increase in the rate of metabolism at another site, a phenomenon known as "metabolic switching". Further, reduction in metabolic (hepatic) clearance may lead to increased renal or biliary clearance of parent drug or inhibition of one or more drug-metabolizing enzymes. Thus, any apparent improvement in in vitro metabolism properties may not result in improved in vivo performance, since the compound may exhibit saturable metabolism, non-linear PK or drug-drug interactions. Therefore, it is advisable that in vitro metabolic stability data be integrated with inhibition screening. It is essential that optimization of ADME properties in animals without regard for in vitro human data needs to be avoided. Thus, the design of drugs with optimal potency and PK properties will be very challenging given the opposing requirements for absorption and metabolism and the general lack of sufficient in vitro and in vivo data sets for a large diversity of compounds in guiding this process.

One rational approach to this dilemma would be to thoroughly explore the structural requirements for in vitro pharmacological activity through combinatorial synthesis and identify the key structural features that are essential for activity for each chemical series (Fig. 6). The next step is to attempt to improve the permeability and metabolism properties through structure modifications to the regions of the molecule that have little or no impact on the activity. Thus, one can maintain a desired level of potency while introducing structural features that will improve the metabolism and permeability characteristics. Further, it would be prudent to evaluate a small number of compounds with good potency and


moderate oral bioavailability in in vivo pharmacological models in the early stages of the optimization process to serve as a pre-clinical "proofof-concept". This test need only to produce a desired physiological or biochemical effect that is in line with pharmacological activity, so that a great deal of optimization effort is not wasted on a series of compounds that have no in vivo activity.

In the optimization phase the in vitro ADME screens should include models for determining active transport and Pgp efflux, in cell cultures, gut segments or in gut loop. Further, stability in hepatocytes, blood and plasma; metabolite and metabolic pathway identification should also be included. Further, in vivo pharmacology screens for activity may also be necessary for some compounds that are extensively metabolized as they might exert activity through potential active metabolites. This method could also help organize structure-absorptionlmetabolismlphysical property data in order to enhance solubility, absorption and metabolism properties that could result in developing in silico models for predicting solubility, metabolism, systemic exposure and half-life. Finally, this approach provides a rational and efficient means of utilizing the limited in vivo resources for evaluating potential lead compounds that have a higher probability of success in development.

9.6 Conclusion

Application of automation and high-throughput concepts to in vitro ADME screens along with predictive models for estimating systemic exposure and clearance in humans have emerged as a rapid means of selecting and optimizing potential lead compounds. These screens incorporate many of the high-throughput screening concepts, such as the use of robotics, standardized assays, electronic barcode tracking, highspeed sample analysis, automated data acquisition, analysis, visualization and reporting, thereby bringing PK into the realm of high-throughput screening. The graphical model aids in estimating oral bioavailability (%F) in human and other species by integrating the in vitro Caco-2 permeability (Papp) and metabolic stability (% remaining) and categorizes compounds into low, moderate and high %F. These screens provide rapid estimates of systemic exposure in humans and animals for potential lead candidates. Lead compound selection is based

200 A.K. Mandagere

on multiple factors such as potency, physicochemical properties, toxicity and estimated systemic exposure (%F) in humans. In the lead optimization phase, the in vitro ADME screens enables one to provide timely information to medicinal chemists for structure modification to improve solubility, permeability and metabolism properties. This process helps organize structure-absorption and structure-metabolism data in the synthesis of compounds with enhanced ADME properties. Further, large data sets on structural analogues will be essential in developing in silico models for predicting ADME and physicochemical properties. In conclusion, in vitro ADME screening strategy is a rational, efficient and rapid means selecting and optimizing lead candidates, which could result in successfully bringing more drugs to market.

References

Artursson P, Karlsson J (1991) Correlation between oral drug absorption in humans and apparent drug permeability coefficients in human intestinal epithelial (Caco-2) cells. Biochem Biophys Res Commun 175:880-885

Artursson P, Katrin P, Luthman K (1996) Caco-2 monolayers in experimental and theoretical predictions of drug transport. Adv Drug Deliv Rev 22:67-84

Berman J, Halm K, Adkison K, Shaffer J (1997) Simultaneous pharmacokinetic screening of a mixture of compounds in the dog using API LC/MS/MS analysis for increased throughput. J Med Chern 40:827:829

Bevan CD, Lloyd RS (2000) A high-throughput screening method for the de-termination of aqueous drug solubility using laser nephelometry in microtiter plates. Anal Chern. 72: 1781-1787

Bryant MS, Korfmacher WA, Wang SY, Nardo C, Nomeir AA, Lin CC (1997) Pharmacokinetics screening for the selection of new drug discovery candidates is greatly enhanced through the use of liquid chromatography atmospheric pressure ionization tandem mass spectrometry. J Chromatogr 777:61-66

Chiou WL, Barve A (1998) Linear Correlation of the fraction of oral dose absorbed of 64 drugs between humans and rats. Pharm Res 15:1792-1795

Delie F, Rubas W (1997) A human colonic cell line sharing similarities with enterocytes as a model to examine oral absorption: advantages and limitations of the Caco-2 model. Crit Rev Ther Drug Carrier Syst 14(3):221-286

Garberg P, Eriksson P, Schipper N, Sjostrom B (1999) Automated absorption assessment using Caco-2 cells cultured on both sides of polycarbonate membranes. Pharm Res 16:441-445


Ho JC, Brown PW, Emary WB, Thompson TN, Toren PC, Yerino PP (1999) Higher throughput metabolic stability screen. Annual AAPS conference, New Orleans

Irvine J, Takahashi L, Lockhart K, Cheong J, Tolan J, Selick H, Grove R (1999) MDCK (Madin-Darby Canine Kidney) cells: a tool for membrane permeability screening. J Pharm Sci 88:28 -33

Iwatsubo T, Hirota N, Ooie T, Suzuki H, Shimada N, Chiba K, Ishizaki T, Green C, Tyson C, Sugiyama Y (1997) Prediction of in vivo drug metabolism in the human liver from in vitro metabolism data. Pharmacol Ther 73:147-171

Kennedy T (1997). Managing the drug discovery/development interface. Drug Discov Today 2:436-444

Lave T, Dupin S, Schmitt Valles B, Ubeaud G, Chou RC, Jaeck D, Co as solo P (1997) The use of human hepatocytes to select compounds based on their expected hepatic extraction ratios in humans. Pharm Res 14: 152-155

Lipinski CA, Lombardo F, Dominy BW, Feeney PJ (1997) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 23:3-25

Mandagere AK (2000) Automation of higher throughput in vitro ADME screening system for lead candidate selection in drug discovery and development. Millennial World Congress of the Pharmaceutical Sciences, San Francisco, CA

Mandagere A, Thompson T, Hwang K (2002), A graphical method for estimating oral bioavailability of drugs in humans and other species from their Caco-2 permeability and in vitro liver enzyme metabolic stability rates. J Med Chern 45:304--311

Mandagere AK, Correll MA, Mooney JP, Poole JC, Hwang KK, Cheng LK (1996) Application of automation to Caco-2 drug diffusion studies. Pharm Res 13:S237

Mandagere AK, Yerino PP, Miller T, Connor LA, Danison T, Geary JL, Thompson TN (1999) Validation of automated in vitro drug inhibition studies using the Hamilton Microlab 2200 mph work station: inhibition of Terfenadine oxidation by troleandomycin and fluconazole as a case study. PharmRes

Obach R.S, Baxter J.G, Liston T.E, Silber M R, Jones B.C. MacIntyre F, Rance DJ, Wastall P (1997) The prediction of human pharmacokinetic parameters from preclinical and in vitro metabolism data. J Pharmacol Exp Ther 283:46-58

Olah TV, McLoughlin DA, Gilbert JD (1997) The simultaneous determination of mixtures of drug candidates by liquid chromatography/atmospheric pressure chemical ionization mass spectrometry as an in vivo drug screening procedure. Rapid Commun Mass Spectrom 11:17-23

202 A.K. Mandagere

Prentis RA, Lis Y, Walker SR (1988) Pharmaceutical innovation by the seven UK-owned pharmaceutical companies (1964-1985). Brit J Clin Pharmacol 25:387-396

Rubas W, Villagran J, Cromwell M, McLeod A, Wassenberg J, Mrsny R (1995) Correlation of solute flux across Caco-2 monolayers and colonic tissue in vitro. STP Pharrn Sci 5(1):93-97

Sietsema WK (1989) The absolute oral bioavailability of selected drugs. lnt J Clin Pharmacol Ther Toxicol 27: 179-211

Smith AD (1994) Design of drugs through a consideration of drug metabolism and pharmacokinetics. Eur J Drug Metab Pharrnacokinet 3:193-199

10 High-Throughput Screening -Brains Versus Brawn

D.A. Smith

10.1 Introduction ........................................... 203 10.2 Where Did the Drugs Come from Pre-1990? . . . . . . . . . . . . . . . .. 204 10.3 The HTS Revolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 205 10.4 Drug-Like Definitions .................................. 205 10.5 What Is the Place for Drug Metabolism in Library Design? ..... 206 10.6 Should Drug Metabolism Be Prominent in Lead Selection? ..... 206 10.7 What Should We Screen in Lead Development? .............. 209 10.8 The Future. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 211 References .................................................. 212

10.1 Introduction

Several authors have highlighted an apparent decline in the productivity of the pharmaceutical industry as measured by new drug introductions. Horrobin (2000) has stated that total worldwide new chemical entities (NCE) launched every year have fallen from 80-100 per year in the 1960s to 50--60 per year in the 1980s to 30--40 per year in the late 1990s. From this analysis is missing the link between NCE production and successful marketed drugs. Only the top 160 drugs make enough revenue to repay the cost of development. Against this background, the industry is optimistic that technology will transform the downturn in productivity and move to its highest level of innovation as it exploits the fruits of mapping the human genome. Within this new paradigm, drug metabolism departments are reshaping. Screening is now a common

204 DA Smith

term within industrial departments and used with full understanding in terms of the commitment to producing data on many compounds within a rapid timescale. However, in the race to adapt, meet customer needs and be ahead of the pack, has the science moved too fast and lost its traditional roots?

10.2 Where Did the Drugs Come from Pre-1990?

Whilst not exclusive to pre-I990, in fact covering many drugs up to 2000, many NCEs were discovered from natural products, in particular the aminergic endogenous neurotransmitters and autacoids. Molecules like adrenaline, 5-hydroxyhistarnines, histamine, etc. actually are ideal lead material being small (low molecular weight), having nanomolar potency and being water-soluble. Thus early [3-adrenoceptor antagonists and H2 receptor antagonists contained pharmacophores closely related to their natural lead. These years could be described as ligand before target discovery. From patents and other literature, the competitive nature of medicinal chemistry creates surprising diversity as leads are developed in different companies. Small water-soluble lead material lies behind the discovery of drugs which have peptides as the natural agonist. For instance, serendipitous findings such as simple benzyl-substituted imidazoles (SieglI993) possess angiotensin II (All) antagonist activity, albeit weak (40 /Jm) led to the discovery oflosartan, the first A2 antagonist to be marketed (Fig. 1).

Existing small molecule drugs such as fibrates with hypolipidemic action could be modified so that the propionic acid is modified by a

N{ Jl N COOH-:;>N

H,C~~ 7 \

CI

"'" N", NH

10

Fig. 1. Structures of the benzyl-imidazole lead (left), losartan (centre) and its active metabolite

High-Throughput Screening - Brains Versus Brawn 205

potential isostere thiazolidine-2,4 dione (Hulin et al. 1996) to generate a new class of antidiabetic drug, the glitazones.

10.3 The HTS Revolution

High-throughput screening became a necessary source of lead matter, as targets became identified by mechanism other than an existing ligand. To fill rapidly gaps in compound files, and to increase the chance of identifying ligands, the promise of combinatorial chemistry was seized. Molecules entering files showed little relationship to the traditional ligands before. Ligands would always have polar interacting groups in the correct position for activity, but because a single methyl group adds potency (0.7 kcal/mol) by displacing water, the trend became to unearth large and lipophilic lead material.

The starting point for many chemistry programmes thus moved from low molecular weight, often water-soluble leads to high molecular weight lipophilic leads. Programmes based on these materials classically failed to demonstrate acceptable oral activity despite achieving high potency

10.4 Drug-Like Definitions

Development candidates often look similar to leads and the HTS revolution was providing compounds with a large number of H-bond donors and acceptors, lipophilic substituents and a high molecular weight. Lipinski et al. (1997) analysed marketed drugs and concluded that oral bioavailability coincided in most drugs with the drug conforming to certain boundary rules (the rules of five):

Poor permeability produced by: - More than 5 H-bond donors - More than 10 H-bond acceptors - Molecular weight over 500

Poor dissolution produced by: - Log P over 5

206 DA Smith

Judged against these rules, which define drug-like properties, the products of HTS normally failed. The dependence on HTS is high and the only solution is to rebuild the library file with drug-like molecules.

10.5 What Is the Place for Drug Metabolism in Library Design?

Drug-like properties are actually largely concerned with the disposition properties of a compound. The rules of five outline properties that, if exceeded, generally confer poor absorption/bioavailability. With combinatorial techniques using a single template with multiple derivatives, it is possible for drug metabolism departments to screen for problem properties in any series. Since lead material will invariably be subject to further chemistry, it is important not to screen for properties that can be attenuated by chemical modification. Thus, selected examples or a single plate can be screened. The decision point is that all examples show the undesirable property and the template is flawed. Most of the undesirable properties centre around P450 inhibition, particularly that caused by metabolic activation. Metabolism by polymorphically expressed enzymes (CYP2D6, CYP2C19) could also be considered in a similar vein.

10.6 Should Drug Metabolism Be Prominent in Lead Selection?

Assuming the desired end product is an oral drug, the question needs to be asked: What properties are needed in a lead molecule? The properties required of an oral drug are:

- Dissolution - Adequate transfer across the membranes of the gastrointestinal tract - Adequate intrinsic potency - Low clearance/metabolic stability - Minimal consequences of any dose- or time-dependent pharmacoki-

netics

High-Throughput Screening - Brains Versus Brawn

H,C

CH,

CH,

H'C~CH' 50 R2

N ..,; R1/ 'N CH

90

,

H,CB R2

R1/N,~ CH,

207

Fig. 2. Human microsomal stabilities expressed as half-life (minutes) during development of a lead in an internal Pfizer project. R groupings remained unchanged

The number of these properties that need to be incorporated into the lead molecule is a matter of debate. Probably, a potency less than I 11M and a selectivity against related targets of greater than 10 are desirable. But should the lead also show the properties listed above? An alternative view, and one held by the author, is that many of these are subject to chemical manipulation. Figure 2 shows an internal Pfizer discovery project around a core template and indicates how human microsomal stabilities are widely altered by even simple changes in substitution.

An alternative proposal is that lead material should ideally confrrm to the properties outlined below and not include metabolism or pharmacokinetic criteria:

- Potency (low 11M) - Selectivity (> lOx) - Molecular weight less than 400 - Solubility or scope for ionised/polar function

The molecular weight is particularly important as invariably molecular weight and probably lipophilicity will increase during lead development. The example of losartan illustrates how a small lead is vital in moving to a potent drug, allowing the scope for introduction of substantial further functionality. The small size of the lead also allows development in a different direction, fostering chemical diversity. Eprosartan (Fig. 3) is structurally different from other angiotensin II antagonists,

208 D.A. Smith

Fig. 3. Structures of the benzyl-imidazole lead compound, from which losartan and the structurally different eprosartan were discovered

SGF <0.001 PB <0.001

< > t:(:! SGF 0.005 PB 0.846

OI\N-<~yCH3 "---I N~ SGF 0.004 PB 0.18

HN N\\ 1\~5~H3 "---I N

SGF >10 PB 0.025

o

RO

Fig. 4. (2 S)-2-benzoylphenyl)arnino )-3{ 4[2-(5-methyl-2-phenyloxazol-4-yl)ethoxy]phenyl}propanoic acid and more soluble derivatives with increased basicity. Solubilities (mg/ml) are shown for simulated gastric fluid (SGF) and pH 7.4 phosphate buffer (PR)


and yet it utilised the same lead molecule as losartan (Weinstock et al. 1991).

Solubility is often a major stumbling block. Poorly soluble compounds cannot undergo dissolution the first step in oral absorption. Highly soluble lead material is a huge advantage because increasing lipophilicity will proportionally lower solubility. However incorporating polar or ionised functionality into the lead as part of the strategy is a common tactic. Figure 4 illustrates the incorporation of increasingly basic moieties into a series of N-(2-benzoylphenyl)-I-tyrosine peroxisome proliferator-activated receptor (PPAR)y agonists. Thus 4-pyridyl, aminooxazole and piperazine analogues showed substantially greater solubility than the original lead (Collins et al. 1998).

10.7 What Should We Screen in Lead Development?

The purpose of drug metabolism during lead development is to ensure that the oral drug properties, outlined above, are maintained or produced as the early lead is advanced in potency by medicinal chemistry. Figure 5 illustrates an example of ligand development rather than lead to drug development. The neuropeptide Y (NPY) Y-1 indole lead would probably have good oral drug properties based on inspection of the

o 00 II c:cG NY)

~CI

Fig. 5. Example of lead development for a NPY Y -1 antagonist, where despite a 2,OOO-fold increase in potency, oral drug properties have been attenuated

210

Solubility Permeability PgP affinity

Metabolic Stability Protein binding

ILIVER I

Hepatic uptake Metabolic Stability PgP affinity

DA Smith

Fig. 6. Schematic illustration of screening priorities for oral drug properties. GIT, gastrointestinal tract; PgP, P-glycoprotein

structure, certainly molecular weight (369) complies with this. The resultant most-potent compound of the series is 2,OOO-fold more potent (around 1 nM), but is unlikely to have good oral drug properties (molecular weight 591); indeed, its serum levels following oral administration were inadequate to evaluate the compound by this route (Hipskind et aI. 1997).

Some of the design features which attenuate oral drug performance can be simply descriptive, as in the rules of five, or the result of actual experiment. The place of in vitro screen in measuring oral drug properties is of growing importance. Figure 6 indicates how a battery of screens addresses many of the factors in oral drug properties.

Modern technology allows drug metabolism screens to provide data at the level of the primary in vitro pharmacology screen. Screening can be conducted in parallel to the in vitro pharmacology screen and thus drug metabolism data can be provided to chemistry regardless of the pharmacological activity or selectivity of a compound. The data-rich


environment versus one in which drug metabolism data are provided only on those compound which pass-pre-determined potency and selectivity criteria gives two future scenarios for the purpose and role of drug metabolism. The two approaches are contrasted below:

Data-rich

May aid the development of structureactivity relationships (SARs)

Allows full concentration on data production, creates technical excellence Slick seamless automated process

Medicinal chemists contented, they get all the data, they own the SAR

Selective data

Is SAR of inactives valid? These compounds no longer fit the receptor and therefore fall outside the basic structural requirements of the project Limits dialogue with the medicinal chemist Dialogue leads to insight, ideas. Drug discovery has a Brownian motion component Drug metabolism is a store of knowledge and wisdom. That is the real value, data are trivial

The path that departments follow will vary. The data-rich path will lead towards large efforts in building the capacity and invariably keeping protocols as simple as possible (screening). The selected pathway still demands technological capacity but the filters allow choices to be made and more detailed protocols. Instead of screening at a single concentration actual experiments can be run. For instance, a Caco-2 experiment at a single concentration and single direction (A~B) could show low or high flux due to the effects of P-glycoprotein (PgP) efflux. A full protocol looking at (A~B and B~A) flux over a range of concentrations would identify this problem and also give some estimation if it is likely to be a problem in a drug candidate during clinical development.

10.8 The Future

No matter how selective as to the use of technology the drug metabolism department is, the relentless drive towards faster discovery screening will place huge demands on data generation. Advances in artificial intelligence-aided drug design approaches will only increase these de-

212 D.A. Smith

mands. At some point the cost of screening will become a limiting factor: drug metabolism will become an expensive part of drug discovery, in itself a more and more costly business. To offset this, computational methods based on physico chemistry and structure, which predict key disposition processes, are needed. Whilst many systems exist, few or none are sufficiently robust to actually replace a screen, even for such simple processes as plasma-protein binding. Even now it seems we do not have in place a system that predicts permeability of a drug across a membrane. Concerted effort needs to be made in this direction.

References

Collins JL, Blanchard SG, Boswell GE, Charifson PF, Cobb JE, Henke BR, Hull-Ryde EA, Kazmierski WM, Kale DH, Leesnitzer LM, Lehmann I, Lenhard IM, Orb and-Miller L, Gray-Nunez Y, Parks DI, Plunkett KD, Tong W-Q (1998) N-(2-benzoylphenyl)-I-tyrosine PPARy agonists. 2. Structureactivity relationship and optimization of the phenyl alkyl ether moiety. I Med Chern 41 :5037-5054

Hipskind PA, Lobb KL, Nixon IA, Britton TC, Bruns RF, Catlow I, DieckmanMcGinty DK, Gackenheimer SL, Giter BD, Iyengar S, Schober, DA, Simmons RMA, Swanson S, Zarrinmayeh H, Zimmerman, DM, Gehlet DR (1997) Potent and selective 1 ,2,3-trisubstituted indole NPY Y-l antagonists. I Med Chern 40:3712-3714

Horrobin DF (2000) Innovation in the pharmaceutical industry. I Royal Soc Med 93:341-345

Hulin B, McCarthy PA, Gibbs EM (1996) The glitazone family of antidiabetic agents. Curr Pharm Des 2:85-102

Lipinski CA, Lombardo F, Dominy BW, Feeney PI (1997) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 23:3-25

Siegl PKS (1993) Discovery of losartan, the first specific non-peptide angiotensin II receptor antagonist. I Hypertens II:S 19-522

Weinstock I, Keenan RM, Samanen I, Hempel I, Finkelstein IA., Franz RG, Gaitanopoulos DE, Girard GR, Gleason IG, Hill DT, Morgan TM, Peishoff CE, Aiyar N, Brooks DP, Frederickson TA, Ohlstein EH, Ruffolol RR, Stack, EF, Sulpizio AC, Weidley EF, Edwards RM (1991) 1-(Carboxybenzyl)imidazole-5-acrylic acids: potent and selective angiotensin II receptor antagonists. I Med Chern 34: 1514-1517

11 Relation of Molecular Properties with Drug Absorption and Disposition

H. van de Waterbeemd

11.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 213 11.2 Drug Likeness and Properties Relevant to ADME ............. 214 11.3 Towards High-Throughput Estimation of Experimental

Molecular Properties. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 215 11.4 In Silico Molecular Properties ............................ 216 11.5 Prediction of ADME Properties ........................... 217 References .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 230

11.1 Introduction

The change of classical drug discovery to high-throughput screening (HTS) of large new combinatorial libraries and existing compound collections have led to an increased pressure of more rapid ADME (absorption, distribution, metabolism, elimination) evaluation (Smith and van de Waterbeemd 1999; Bertrand et al. 2000; Watt et al. 2000). Furthermore, various analyses of historical attrition data have revealed that pharmacokinetic (PK) problems, including poor absorption, low bioavailability and issues around drug-drug interactions, are important causes of attrition and should be addressed early in the discovery process. In recent years pharmaceutical companies have, therefore, increasingly put an effort in developing high-throughput screens for physicochemical and biopharmaceutical properties, as well as various ADME properties. A deeper understanding of relationships between important

214 H. van de Waterbeemd

Table 1. Overview of approaches to ADME

Experimental ADME property In silico

Caco-2 Formulation Prediction of polymorphism Turbidometric nephelo- Solubility QSAR and neural networks metric, pH-metric Lipophilicity (log Pilog D), Permeability CLOGP liposomes, lAM, artificial membranes, biosensors Caco-2, MDCK Absorption QSAR models, simulations AnimalPK Bioavailability QSARmodels AnimalPK Volume Log D model

of distribution HT assays Metabolism Databases, protein models,

pharmacophore models AnimalPK Clearance Allometric Dose prediction PBPK modelling

ADME parameters and molecular structure and molecular properties has been used to develop in silico models allowing early estimates of a number of drug disposition properties (Testa et a1. 2000, 2001). In Table 1 an overview is presented of a number of key experimental and in silico approaches to ADME. In this chapter in particular, the estimation of oral absorption will be discussed as well as recent developments in property measurement and calculation.

11.2 Drug Likeness and Properties Relevant to ADME

In several studies, the property profiles of typical drug databases such as the World Drug Index (WDI) have been compared to data sets of chemical compounds such as the Available Chemicals Dictionary (ACD) (Lipinski et a1. 1997; Sadowski and Kubinyi 1998; Walters et a1. 1999; Blake 2000; Oprea 2000; Anzali et a1. 2001). Using e.g. neural networks, a compound can be classified as drug-like with ca 80% confidence. One of the key factors for a drug-like compound is its potential to be orally absorbed. Assuming that most drugs in a drug database are intended for oral use, a detailed study of the WDI led to the

Molecular Properties and Drug Absorption and Disposition 215

formulation of the rule of five as a predictor for poor oral absorption (Lipinski et al. 1997). This rule states that compounds with a molecular weight (MW»500, calculated octanol/water partition coefficient (CLOGP)<5, number of H-bond donors>5, and number of H-bond acceptors> 10, are likely to be poorly absorbed, when two or more of these conditions are met.

11.3 Towards High-Throughput Estimation of Experimental Molecular Properties

A number of key molecular properties can now be assessed through automated high-throughput methods and commercially available equipment. Solubility can be measured using turbidimetric, nephelometric or pH-metric (potentiometric) methods. Ionisation constants can conveniently be assessed by a pH-metric approach, or by UV, and even in high-throughput delivering pKa values for 300 compounds per day. Lipophilicity expressed as octanol/water distribution coefficients (log D) can be measured by a shake plate method as a logical development of the traditional shake flask. This property is key and relates to many endpoints in drug research including pharmacokinetics and metabolism (see Fig. 1).

Several options are available to measure permeability, e.g. phospholipid veshicles, liposome binding, immobilised artificial membranes (lAM), immobilised liposome chromatography (ILC), impregnated or artificial membranes (including PAMPA or filter lAM and hexadecanecoated polycarbonate filters), and surface plasmon resonance (SPR)

absorption I permeability

target affinity / " / protein binding

". I D ___ PK - volume of transporter _____ og ~ distribution

affinity /~' clearance ~ ~~~-~~.

metabolism toxicity

Fig. 1. Would there be life on earth without log D? Schematic diagram of how log D relates to many endpoints in drug research


biosensors. Each of these permeability scales offers an alternative to octanol/water partitioning. In a recent study it was demonstrated that lAM and ILC did not outperform log Doct with regard to the prediction of drug transport (Osterberg et al. 2001).

11.4 In Silico Molecular Properties

Calculation of many different descriptors is possible using a range of commercially available software packages, such as Sybyl, Cerius2, Tsar, Molconn-Z, Hybot, etc. A survey of calculated properties can be found in Livingstone (2000) and a 667 pages handbook of molecular descriptors has been published (Todeschini and Consonni 2000).

Due to its key importance, a continued interest is seen to develop good log P estimation programs. Most log P approaches are limited due to a lack of parameterisation of certain fragments. For the widely used CLOGP program, a new version avoiding missing fragments has become available. Log P programs are referring to the octanol/water system. Only one exception based on Rekker's fragmental constant approach has been reported and consists of a method calculating log P for aliphatic hydrocarbon/water partitioning. These latter values may offer a better predictor for uptake in the brain. A number of rather comprehensive reviews on lipophilicity estimation have been published (Mannhold and van de Waterbeemd 2001). Considerable interest is focussed on the calculation of hydrogen-bonding capability, for use in quantitative structure-activity relationship (QSAR) studies, design of combinatorial libraries, and for correlation with absorption and permeability data. Several new descriptor sets are based on quantification of three-dimensional (3D) molecular surface properties and these have been explored for the prediction of, for example, Caco-2 permeability and oral absorption.


11.5 Prediction of ADME Properties

11.5.1 Bioavailability

The quest in most drug discovery projects is to find a candidate with high bioavailability. It would, therefore, be desirable to be able to make reliable estimates about the bioavailability of a new compound, preferably just based on its molecular structure (Chan and Stewart 1996). However, many factors contribute to systemic bioavailability. The key first step is absorption from the gastrointestinal tract.

To predict bioavailability, first-pass metabolism has to be overlayed to gut wall absorption (discussed below). The general equation describing bioavailability (F) is

F=A-E-M (1)

where A is the extent of absorption through passive diffusion via para- and transcellular pathway and active uptake mechanisms, E is efflux by P-glycoprotein (P-gp) and other efflux pumps, and M is gut wall (mainly CYP3A4) and liver (CYPs and phase II enzymes) metabolism.

Several approaches have been followed to attempt to predict bioavailability from molecular structure. Below, three approaches are briefly mentioned.

A QSAR model has been developed using two physicochemical terms and 15 terms to flag the presence of a particular metabolisable functional group (Yoshida and Topliss 2000). A new term, called Alog D and defined as log D6.5-10g D7.4, was introduced. This model based on a training set of 220 compounds therefore only requires measurement of log D values at pH 6.5 and 7.4 and a structural evaluation of molecular fragments potentially involved in metabolism.

In another study, 85 substructures have been defined and used for bioavailability prediction (Andrews et al. 2000). A large scatter is observed when plotting calculated versus actual bioavailability, indicating further room for improvement.

A graphical approach has been suggested which is based on the measurement of permeability (Caco-2) and metabolic stability (see Chap. 9, this volume) (Mandagere et al. 2002).


In the frrst case, experimental log D values are being used and structural alerts for metabolism. In the second approach, only structural features are being considered. FinaHy, in a third approach experimental data related to permeability and metabolism are used for the prediction. It will be interesting to see in the coming years how each of these strategies will further developed.

11.5.2 Oral (Gastrointestinal) Absorption

11.5.2.1 Absorption Screens Complete absorption is a major concern in a drug discovery project. In recent years a whole battery of different "absorption" screens have become available. Some of these are, in fact, permeability screens (mentioned above), while they do not take into account the potential effect of transporters and metabolising enzymes (Pagliara et al. 1999; van de Waterbeemd et al. 2001) (see Fig. 2). Closest to the in vivo biological situation come Ussing chamber models using rat, rabbit Of human intestinal tissues. Very popular for mimicking epithelial and endothelial barriers are ceH cultures (Wunderli-AHenspach 2001).

11.5.2.2 Cell Monolayers Widely used for absorption estimation are ceH lines like Caco-2 or Madin-Darby canine kidney (MDCK), which can be grown on a filter to confIuent monolayers. Such ceHlayers are believed to mimic the essen-

Fig. 2. Transport across a membrane: difference between uptake (entrance of the membrane), permeability (crossing the membrane), absorption (permeability plus interactions with transporters and enzymes)

Moleoular Properties and Drug Absorption and Disposition 219

tial features of membrane barriers with respect to passive permeation, carrier-mediated transport and possible metabolisation of drug compounds upon passage through cells. Caco-2 cells are immortalised from the human colon, but involve a carcinomatous cell line. MDCK cells are faster to grow, but are of dog origin. Nevertheless, MDCK and Caco-2 results are comparable. Caco-2 data have been correlated to octanol/water log D values and can be predicted from other descriptors (van de Waterbeemd et al. 1996; Norinder and Osterberg 1997; Camenisch et al. 1998). There is a sigmoidal relationship between observed human absorption and values from Caco-2 experiments. This makes the evaluation of Caco-2 results and the extrapolation to man in the steep part of this curve rather uncertain. Many companies, therefore, have chosen to classify their compounds based on Caco-2 results in low-medium-high probability of oral absorption. By looking at Caco-2 or MDCK flux in two direction insight is gained in the involvement of transporters, particularly P-gp, and the potential liability for non-linear pharmacokinetics.

11.5.2.3 Human Perfusion Studies U sing effective permeability data in humans, relationships with polar surface area (PSA) and several other descriptors using the MOLCAD module within SYBYL, based on a single minimum energy conformation have been studied (Winiwarter et al. 1998). For the 13 passively transported compounds in their data set, a linear correlation coefficient with PSA was obtained of r2=0.76. A plot of these data shows that the trend is possibly sigmoidal, with some scatter, however. Even more scatter is observed when all compounds in the study (n=22) are plotted against PSA. Clearly, PSA alone is insufficient to account for effective permeability or absorption. In this case, the cut-off for poor absorption seems to be at lower PSA values around 100 A2. This may be due to a scaling difference between methods used by others who observed a cut-off of ca. 120 A2. Some compounds in these studies like glucose, I-dopa and amoxicillin are believed to have active uptake mechanisms and thus are better absorbed than predicted by PSA. The following equations have been obtained:

log PefF-0.01 PSA+0.19 log Ds.s-0.24 HBD-2.88 n=13; r2=0.93; q2=0.90 (2)


log PefF-O.Ol PSA+0.16 CLOGP-0.24 HBD-3.07 n=13; r2=0.88; q2=0.85

log PefP-O.01 PSA-O.28 HBD-2.55 n=13; r2=0.85; q2=0.82

(3)

(4)

In these equations P eff is the in vivo permeability measured with a single-pass perfusion technique. Log DS.5 is the octanollwater distribution coefficient measured at pH 5.5, by the authors believed to be the most relevant value for absorption and reflecting the pH in the un stirred mucus layer adjacent to the intestinal wall. HBD is the number of hydrogens connected to Nand 0 atoms, i.e. the total potential H-donating capacity. Since these models are based on only 13 compounds, the three-parameter Eqs. 2 and 3 have limited statistical significance. No definitive conclusions can be drawn on the role of a lipophilicity descriptor. The best result was obtained by combining two H-bond descriptors (PSA and HBD). However, the partial correlation between PSA and HBD is 0.82. This sheds serious doubts on Eq. 4, despite the fact that it was derived using partial least squares (PLS), which is known to produce robust models when used correctly. A larger data set is required to fully explore this approach.

The continuous variable PSA is correlated with simple count of H-bonds. However, PSA is probably a better reflection of H-bonding capacity, since it takes conformational behaviour into account.

11.5.2.4 Computational Approaches A number of papers appeared on computational or in silico predictions of oral absorption and the development of quantitative structure-absorption relationships (Stenberg et al. 2000; van de Waterbeemd 2000b, 2001). These approaches can be split into different groups. The simplest method involves a single calculated descriptor such as polar surface area (Fig. 3). More complex models include several to many calculated properties and need appropriate multivariate statistics to build the models. Finally, a mixed approach using both calculated and measured properties has been suggested as discussed above (Winiwarter et al. 1998). Debate is ongoing about which are the most useful descriptors for oral absorption prediction. Some descriptors are indeed quite far from properties a medicinal chemist can handle easily in the lab, and therefore


Beta·blockers

100 C o 0 0

propriRblol 0 • 0

~ 80 ... 0 rJ) .c ta 0101 ctI

"iii 60 atenolol

I: • :.;:; rJ)

ace ulolol .e 40 nadolol .!: • I: ctI E 20 :::I :S ~ <c

40 60 80 100 120 140

PSA (polar surface area) in A 2

Fig. 3. Prediction of oral absorption from polar surface area for a series of beta-blockers

minimalistic modelling, involving the simplest possible approaches leading to acceptable predictions, have been explored (Oprea and Gottfries 2000; Osterberg and Norinder 2001). Lipophilicity, H-bonding and molecular size are mostly considered as the key descriptors, though sometimes molecular weight (MW) is dropped and is seen as a redundant descriptor (Egan et al. 2000).

Another question is what is the best multivariate method to describe relationships between absorption and molecular properties (Norinder et al. 1999). As the relationship between absorption and e.g. lipophilicity or polar surface area appears to be sigmoidal, i.e. non-linear, methods such as multiple linear regression or partial least squares seem not to take this into consideration. Therefore, a pattern-recognition model (Egan et al. 2000) or neural networks may be more suitable (Gohlke et al. 2001).

The absorption process involves several processes in parallel, including permeability, active transport, efflux and metabolism. P-gp has been recognised as an important efflux transporter hindering the uptake of drugs from the gastrointestinal tract and even eliminating those already


in circulation or given intravenously (Beaumont et al. 2000; van de Waterbeemd 2000a). Prediction of oral absorption will become more reliable if these various factors can be taken into account. A number of papers have been published to address P-gp structure-activity relationships (Schmid et al. 1999; Osterberg and Norinder 2000; Seelig and Landwojtowicz 2000). P-gp-associated ATPase activity could be modelled with the size of the molecular surface, polaris ability and hydrogen bonding having the largest impact. For each of these high values of these properties translates to high ATPase activity. There seem to be two trends. Better P-gp binding is observed with increasing lipophilicity in a group of compounds with comparable hydrogen bonding capacity. Secondly, higher levels of hydrogen bonding give better P-gp interaction.

Currently it seems that we can predict the fraction absorbed correctly for ca 70%-80% of the compounds. The rest is affected by dissolutiOn/solubility, efflux and metabolism. The first factors can be modelled quite reasonably (see Sect. 11.5.2.5), while much more needs to be learned about factoring the latter two factors.

11.5.2.5 Simulation of Absorption Software has become available to perform simulations of the absorption process, e.g. GastroPlus (Simulations Plus) or the iDEA approach (Lion Bioscience).

Both are based on physiological models of the gastro-intestinal tract split in several sections. The effect of compound properties such as log P, solubility, pKa, as well as formulation properties such as particle size can be simulated to make estimates on the fraction absorbed and its effect on plasma concentration-time curves.

11.5.3 Crossing the Blood-Brain Barrier

The blood-brain is a more selective membrane barrier compared to other membranes such as the gastrointestinal tract. A key difference is the much tighter junctions. Several analyses have therefore pointed out that for the Blood-Brain Barrier (BBB) a lower cut-off in MW should be considered than for the intestinal membrane, i.e. ca. 450 vs 500 (van de Waterbeemd et al. 1998). However, recent studies on the role of P-pg in limiting BBB uptake using mdrla (-/-) mice have demonstrated in-


Table 2. Increased brain penetration in mdr1a (-1-) mice

Drug Brain level ratio MW LogD CLOGP mdrla(-I-) vs mdr1a(+I+)

Amprenavir 27 506 Asimadoline 11 415 3.87 Azasetron 7 350 1.59 Carebastin 8 500 Cyclosporin A 17 1203 2.92 14.36 Dexamethasone 3 392 2.01 1.79 Digoxin 35 781 1.26 1.62 Doxorubicin 3 544 0.10 -1.38 Ebastine 7 470 7.06 Grepafloxacin 3 359 -0.86 Indinavir 11 614 2.92 3.68 Ivermectin 87 >500 4.22 Loperamide 14 477 2.71 4.66 Morphine 2 285 0.76 0.57 Nelfinavir 36 568 5.84 Ondansetron 4 293 2.72 Paclitaxel 12 854 4.95 Quinidine 29 324 1.90 2.79 Saquinavir 7 671 4.38 Tacrolimus 33 804 5.36 Verapamil 10 455 2.15 5.17 Vinblastine 22 811 5.23

Compilation taken from Ayrton and Morgan (2001). Physicochemical data from the MedChem database and the World Drug Index.

creased brain levels even for compounds with relative high MW, such as cyclosporin A and paclitaxel (Table 2). Therefore, it seems that MW alone is an insufficient predictor of brain uptake per se but most likely a reasonable indicator for high or low levels.

Even so it is believed that hydrogen bonding is an important factor for BBB uptake and again a lower limit is observed for most centrally acting agents, i.e.a polar surface area (PSA) of ca 70-100 A2 for eNS compounds vs 120 A2 for oral absorption in the gut wall (van de Waterbeemd et al. 2001). It has been argued that PSA is not a thermody-


namic descriptor and that solvation free energies would be more appropriate. A fast algorithm based on the Born/surface area (GB/SA) continuum solvation model has been proposed as a high-throughput filter for CNS activity (Keserti and Molnar 2001). Ninety-six percent of all known CNS compounds have Gsolv higher than -50 kl/mol and this seems a suitable criterion to select compounds capable of CNS penetration.

A well-known rule of thumb for good CNS penetration is a log D around 2.

Simple models to predict BBB partitioning include a mono-descriptor relationship with polar surface area as mentioned above, or multi-descriptor approaches. For example, a three descriptor model has been derived for 61 compounds using the calculated octanollwater partition coefficient (log P), polar surface area and the number of hydrogen-bond acceptors in an aqueous medium (Feher et al. 2000). However, with an r2=0.73, the true predictive value of such model may be questioned.

Actually, much of the data produced were gleaned from studies of the partitioning of drugs into the whole brain from blood or plasma. These studies have shown a need for compounds with low hydrogen-bond potential and relatively high lipophilicity. Whole brain partitioning actually represents partitioning into the lipid of the brain, and not actually access to drug receptors. For instance, desipramine partitions into brain and is distributed unevenly. The distribution corresponds to lipid content of the brain regions and not to specific desipramine binding sites. For receptors such as 7-transmembrane glycoprotein's (TMs) extracellular fluid (ECF), concentrations determine activity. Cerebrospinal fluid (CSF) concentrations can be taken as a reasonable guide of ECF concentrations. The apparent dramatic differences in brain distribution described for the total brain decrease to a small ratio when free (unbound) concentration of drug in plasma is compared to CSF concentration. Whole brainlblood partitioning reflects nothing but an inert partitioning process of drug into lipid material. Free unbound drug partitioning actually reflects the drug reaching the receptor and pharmacological effect. Unless active transport systems are invoked, the maximum CSFto-plasma partition coefficient is 1. This should be contrasted to the 10-or lOO-fold affinity of total brain compared to blood or plasma. The minimum partitioning based on a limited data set appears to be 0.1. Figure 4 compares lipophilicity (log D) to a series of diverse com-


0.01 iii iii iii 4 ~ ~ ~ 0 1 234

logO

Fig. 4. CSF concentration/free (unbound) plasma concentration ratios compared to log D for neutral and basic drugs ritropirronium (1), atenolol (2), sulpiride (3), morphine (4), cimetidine (5), metoprolol (6), atropine (7), tacrine (8), digoxin (9), propranolol (10), carbamazepine (11), ondansetron (12), diazepam (13), imipramine (14), digitonin (15), chlorpromazine (Hi), and acidic drugs, salicylic acid (a), ketoprofen (b), oxyphenbutazone (c) and indomethacin (d)

pounds that illustrate the limited range of partitioning. It should be noted that log D is not a perfect descriptor and some of the measures which incorporate size and hydrogen bonding may be better. Clearly though, the CNS is more permeable than imagined, allowing drugs like sulpiride (compound 3) to be used for CNS applications (van de Waterbeemd et al. 2001; van de Waterbeemd and Smith 2001).

11.5.4 Percutaneous Uptake

The human skin can be seen as a multilayer or multimembrane barrier. Very similar to the transport through other membranes, lipophilicity, hydrogen bonding and molecular size have been identified as key molecular properties (Cronin et al. 1999; Pugh et al. 2000). The ideal lipophilicity range in terms of octanol/water log P is 2.5-6.


6

5

4 • o

3 '5' "'C 2 ~ Cl .2

0

·1

·2 ·4 ·2 0 2 4 6

log D at pH 7.4

Fig. 5. Effect of log D on unbound volume of distribution for different types of compounds

11.5.5 Distribution

Early studies of the relationship between volume of distribution, extent of protein binding, and apparent partition coefficient go back ca. 20 years (Ritschel and Hammer 1980). The increase in free volume of distribution (Vdu) with increasing lipophilicity (see Fig. 5) largely reflects association with plasma proteins (albumin) for acidic compounds and association with tissues and lipoproteins for neutral and lipid compounds. Basic compounds bind mainly to albumin and partially to aI-acid glycoprotein. With increasing lipophilicity, probably unspecific binding to red blood cells, leukocytes and platelets also increases. The high association for acidic compounds with albumin is due to both ion-pair and hydrophobic interactions. Similar reasons apply to the affinity of basic compounds for tissues. Here the ion-pair and hydrophobic interactions are due to the head-and-tail groups of phospholipids present in cell membranes. Figure 5 shows the results when unbound volume of distribution (volume of distribution at steady state corrected for plasma protein binding) is plotted against log D values (van de Waterbeemd et al. 2001). The earlier observed trend in which basic and


100 .- "\ lIE .. .~ 'JO

lIE

~ 0

~ 80 Cl • • ::J lIE lIE lIE c: 0 o lIE

'S • 0 0

c: 60 o· ~ 0

:c 0

• D:J lIE. c: • D

S • 0 • 40 0 • lIE 0 • ... • ollE o 0

Q.. • I'll • • lIE

E o lIE " " 20 DO 1iJ0 U) 0 0 " I'll

'" lIE ,.

0:: ,. ,. ,.0 0

0 ,. 0

0 lIE ,. ,.

·4 ·2 0 2 4 6

log D at pH 7.4

Fig. 6. Effect of log D on plasma protein binding for different categories of compounds

acidic compounds have higher values than neutral compounds remains clearly visible.

In the free drug concept, we are interested in unbound drug clearance and volume of distribution (Smith et al. 2001). Plasma protein binding permits estimates of these properties although it is important to stress it is free drug clearance that determines free drug concentrations at steady state. Figure 6 shows that within a class of different compounds (acid, base, etc) an approximately sigmoidal relationship exists between protein binding and log D (van de Waterbeemd et al. 2001). The curve for basic and neutral compounds practically overlap and are at higher log D values than the curve for acids. Acidic compounds have high plasma protein binding for log D values above -1, while for basic compounds high protein binding can be expected for log D>2. The four "acids" on the right-hand side of the neutrals and base curve are the barbiturates phenobarbital, hexobarbital and pentobarbital, and the derivative primidone. We believe these compounds have a very delocalised negative charge and practically behave as neutral compounds.


11.5.6 Metabolism

There are different aspects to metabolism, namely the extent and rate, the enzymes involved and the products formed. This may give rise to different concerns. For the extent and rate, the effect is the PK property clearance. Involvement of particular enzymes may lead to issues related to the polymorphic nature of some of the metabolising enzymes and drug-drug interactions. Finally, the metabolites may be active or even toxic. (Semi-) automated 96-well microplate assays are now routinely used to assess properties such as metabolic stability, P450 inhibition using microsomal preparations, hepatocytes and cDNA-expressed drugmetabolising enzymes (Pal amanda et al. 1998; Lin and Rodrigues 2001).

Different computational/in silico methods are being used to study various aspects of drug metabolism:

- Databases - Molecular modelling (pharmacophore and protein models) - QSAR (regression, neural networks, decision trees) - Expert systems

Molecular modelling approaches are being used to generate protein models of various important cytochrome P450s (CYPs), such as CYP2D6 (Smith et al. 1997; de Groot et al. 1999). Other methods such as nuclear magnetic resonance (NMR) and quantum chemical calculations may then be used to fine-tune the initial models. NMR methods based on the paramagnetic relaxation effects of the haem iron have been used to provide experimental data on substrate binding to bacterial and human CYPs (Fowler et al. 2000). Electronic models using quantum chemical descriptions of substrate reactivity have been developed to study reaction rates on CYP3A4 and CYP2E1 (Higgins et al. 2001).

More insight in the role of molecular properties in metabolic reaction has been gained through quantitative structure-activity relationship (QSAR) studies (Hansch and Leo 1995).


Bela·blockers

2.8 esmolol

• 2.4

a a

2.0

a ::l 1.6 ...J U CI 1.2 a a

..Q a a

0.8 a

0.4 a a

0.0 ·2.5 ·1.5 ·0.5 0.5 1.5 2.5 3.5 4.5

log D at pH7.4

Fig. 7. Unbound clearance versus octanol/water log D at pH 7.4

11.5.7 Elimination and Clearance

The liver and kidney play a key role in the clearance and excretion of many drugs. Uptake into the hepatocytes can be a physicochemistrydriven process via passive diffusion through the sinusoidal membrane or involve one or several hepatic uptake transporters (Ayrton and Morgan 2001). Drugs prone to active uptake tend to be ionised, large and contain a number of hydrogen-bonding groups. Larger molecules (MW>400) also have an increased chance to undergo biliary excretion. The degree of metabolic biotransformation of drugs is highly dependent upon their physicochemical properties and structure-metabolism relationships as discussed above (Smith et al. 1996). As an example, we have plotted the unbound clearance (log Clu) of a series of well-known beta-blockers against their log D values (Fig. 7). Apart from esmolol, which is a prodrug, all compounds follow the trend that increasing lipophilicity leads to higher Clu.

A practical example of a structure-disposition relationship is the prediction of drug elimination via either hepatic metabolism or renal excretion. Using VolSurf 3D-derived descriptors, a principal component


plot showed a good separation along the first principal component axis (Cruciani et al. 2001). The question is whether these sophisticated descriptors are really needed. Often a simple rule of thumb does the same job. In this case it has been observed that when log D7.4<0, renal clearance is dominating, and when log D7.4>0, metabolism will be increasingly metabolic/hepatic (Smith et al. 1996).

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12 Modelling Human Cytochrome P450-Substrate Interactions

D. F. V. Lewis

12.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 235 12.2 Methods. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 236 12.3 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 237 12.4 Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 246 References .................................................. 247

12.1 Introduction

The cytochromes P450 (CYP) are extremely important and ubiquitous enzymes, being present in virtually all species studied to date and across the five biological kingdoms where they are associated with the phase I oxidative metabolism of a large number of structurally diverse chemicals, both exogenous and endogenous (Ortiz de Montellano 1995; loannides 1996; Lewis 1996,2001; Rendic and DiCarlo 1997). Of the 1,200 or more P450s sequenced thus far, it has been established that a relatively small number (-10) of the human hepatic enzymes metabolize over 90% of the known drugs in current clinical use, with the CYP2C, CYP2D and CYP3A subfamilies constituting the major catalysts of drug metabolism in man (Rendic and DiCarlo 1997).

In order to understand the varying substrate selectivities of these enzymes, we have constructed homology models of CYP1A2 (Lewis et al. 1999a), CYP2A6 (Lewis et al. 1999b), CYP2B6 (Lewis et al. 1999c), CYP2C9 and CYP2C19 (Lewis et al. 1998a), CYP2D6 (Lewis et al.

236 D.F.V. Lewis

Table 1. Human P450s involved in drug metabolism and their selective substrates: a summary (Lewis 2000a, 2001)

CYP Substrate Reaction Site of metabolism - Inhibitor haem iron distancea

lA2 Caffeine N3-demethylation 4.882 A Furafylline 2A6 CouJllarin 7-hydroxylation 3.040 A Pilocarpine 2B6 Mephenytoin N-demethylation 4.150A Orphenadrine 2C8 Taxol 6a-hydroxylation 2.530 A Sulphinpyrazone 2C9 Tolbutamide 4-methyl hydroxylation 2.720 A Sulphaphenazole 2C19 Omeprazole 5-methyl hydroxylation 4.694 A Fluconazole 2D6 Metoprolol O-demethylation 3.350 A Quinidine 2El 4-Nitrophenol 2-hydroxylation 4.591 A Pyridine 3A4 Nifedipine N-oxidation 5.004 A Ketoconazole 4All Lauric acid ro-hydroxylation 3.892 A lO-Imidazolyl

decanoic acid

aMeasured from the modelled enzyme substrate interactions with the average value (3.85 A) being within the range encountered for P450 crystal structures containing bound substrates.

1997), CYP2El (Lewis et al. 2000), CYP3A4 (Lewis et al. 1996), and CYP4Ali (Lewis and Lake 1999). Much of this work has been reviewed previously (Lewis 1998, 1999, 2000a,b; Lewis et al. 1999d), and the structural determinants of P450 substrate selectivity, binding affinity and rates of P450-mediated metabolism have been explored in some detail (Lewis et al. 1998b; Lewis and Pratt 1998; Lewis and Hlavica 2000; Lewis 2000c) via the techniques of quantitative structure-activity relationship (QSAR) analysis and by molecular modelling of the relevant enzyme-substrate interactions.

12.2 Methods

Using the Sybyl molecular modelling package (Tripos Associates, St. Louis, Mo., USA) individual human P450s were constructed based on sequence homology with CYP102 (Lewis 1996, 1998) a unique bacterial P450 of known crystal structure (reviewed in Lewis 1996, 1998) which bears certain important similarities with mammalian microsomal P450s including the same redox partner [nicotinamide adenine dinu-

Modelling Human Cytochrome P450-Substrate Interactions 237

cleotide phosphate, reduced (NADPH)-dependent flavin adenine dinucleotide (FAD)- and flavin mononucleotide (FMN)-containing reductase] and a 20% homology. Selective substrates of individual human P450 enzymes were docked interactively within the putative active site in each case, and the relevant details of these particular chemicals and the relevant enzymes are shown in Table 1. A recent model of CYP2C8, the details of which have not been published previously, has also been included in this study for completeness. All molecular modelling procedures were carried out on a Silicon Graphics Indig02 IMPACT 10000 graphics work-station operating under UNIX, whereas the crystallographic coordinates of the CYP102 haemoprotein domain were obtained from the Protein Databank (filename: lfag.pdb).

12.3 Results and Discussion

Figure 1 shows the active site of CYP1A2 containing the substrate caffeine orientated for NTdemethylation via interactions with a number of amino acid residues, some of which have been shown by site-directed mutagenesis to have an effect on CYP1A2-mediated activity. In particular, the substrate is held in position via three hydrogen bonds with threonine residues and there are also significant n:-stacking interactions with two aromatic amino acid residues (namely, phenylalanine and tyrosine) the former of which has been subject of site-specific mutation experiments on expressed human CYPIA2 (reviewed in Lewis et al. 1999a).

Figure 2 presents details of the interaction between coumarin and CYP2A6 where specific metabolism at the 7-position is attained by hydrogen-bonded interactions with active site amino acid residues. In this case, two hydrogen bonds anchor the substrate in place, together with a single n:-stacking interaction to a phenylalanine residue. Some of the contacts shown in Fig. 2 have been the subject of site-directed mutagenesis in the CYP2A subfamily, including the aforementioned phenylalanine residue (reviewed in Lewis et al. 1999b).

The interaction between CYP2B6 and S-mephenytoin shown in Fig. 3 demonstrates how a combination of active site contacts are able to orientate the substrate for N-demethylation. In this case, hydrogen bonding and n:-stacking interactions are also in evidence, such that their

238 D.F.V. Lewis

~ \

-s z e 2

Fig. 1. A view of the CYPIA2 active site is shown with caffeine fitted for N3-demethylation. Hydrogen bonds are shown as dashed lines and amino acid residues are labelled according to the alignment with CYP102

disposition in the active site region serves to position the substrate relative to the haem iron for N-demethylation to occur. Various favourable contacts with complementary amino acid residues have been shown via site-directed mutagenesis to be important for substrate binding in the CYP2B subfamily (reviewed in Lewis et al. 1999c).

In Fig. 4, a selective substrate of CYP2C8 is presented within the putative active site of the enzyme which catalyses its metabolism. The


Fig. 2. A view of the CYP2A6 active site is shown with coumarin fitted for 7-hydroxylation. Hydrogen bonds are shown as dashed lines and amino acid residues are labelled according to the alignment with CYP102

substrate, taxol, is orientated relative to the haem iron by specific hydrogen bond contacts with complementary amino acid residues, together with at least one 1t-stacking interaction and several hydrophobic contacts. These cooperatively assist in positioning the substrate for 6a-hydroxylation, which is the experimentally observed pathway for taxol metabolism mediated via CYP2C8.

240 D.F.V. Lewis

Fig. 3. A view of the CYP2B6 active site is shown with mephenytoin fitted for N-demethylation. Hydrogen bonds are shown as dashed lines and amino acid residues are labelled according to the alignment with CYPI02

The active site of CYP2C9 containing the bound substrate, tolbutamide, is shown in Fig. 5, where a combination of rc-stacking and hydrogen bonding orientates the substrate for oxygenation at the known position. The serine residue involved in hydrogen bonding has been mutated in an orthologous protein and shown to be important for substrate selectivity (reviewed in Lewis et al. 1998a). Consequently, the


Fig. 4. A view of the CYP2C8 active site is shown with taxol fitted for 6a-hydroxylation. Hydrogen bonds are shown as dashed lines and amino acid residues are labelled according to the alignment with CYP102

modelled interaction is consistent with the available experimental findings.

Figure 6 shows omeprazole docked into the CYP2C19 active site, where several complementary interactions fix the substrate's position for oxidative metabolism, including a histidine which has been shown by mutagenesis experiments to be of relevance to omeprazole binding (reviewed in Lewis et al. 1998a). The way in which this substrate is shown to fit the CYP2C19 active site is supported, therefore, by experimental observations for omeprazole binding to CYP2C19.

Metoprolol is shown within CYP2D6 in Fig. 7. In this case, an ionic interaction with an aspartate residue is important for substrate binding, although hydrogen bonding and 1t7stacking interactions are also in evidence. Both the active site aspartate and other amino acid residues have

242 D.F.V.Lewis

Fig. 5. A view of the CYP2C9 active site is shown with tolbutamide fitted for 4-methyl oxidation. Hydrogen bonds are shown as dashed lines and amino acid residues are labelled according to the alignment with CYPI02

Fig. 6. A view of the CYP2Cl9 active site is shown with omeprazole fitted for 5-methyl oxidation. Hydrogen bonds are shown as dashed lines and amino acid residues are labelled according to the alignment with CYP102


~7~~9 -/'Z

Fig. 7. A view of the CYP2D6 active site is shown with metoprolol fitted for O-demethylation. Hydrogen bonds are shown as dashed lines and amino acid residues are labelled according to the alignment with CYP102

been shown by site-directed mutagenesis to be important for substrate binding to CYP2D6 (reviewed in Lewis et al. 1997). Therefore, the orientation of metoprolol is consistent with known experimental data on its binding to, and metabolism by, the CYP2D6 enzyme.

Figure 8 presents details of the docked interaction between 4-nitrophenol and the CYP2El active site. A combination of 1t-stacking and hydrogen-bonded interactions cooperatively assists in bringing the 2-position of the substrate directly above the haem iron for oxygenation to occur at this site. Although little evidence from site-directed mutagenesis exists for this enzyme, it has been reported that the threonine residue,

244 D.F.V. Lewis

Fig. 8. A view of the CYP2El active site is shown with 4-nitrophenol fitted for 2-hydroxylation. Hydrogen bonds are shown as dashed lines and amino acid residues are labelled according to the alignment with CYP102

hydrogen bonded to the substrate in Fig. 8, is important for substrate binding and catalytic rate (reviewed in Lewis et al. 2000).

The interaction between nifedipine and CYP3A4 is shown in Fig. 9, where hydrogen bond and 1t-stacking interactions are able to orientate the substrate such that the N-H group is above the haem iron for oxidation to take place at this position. The residue involved in hydrogen bonding to the substrate has been shown via site-specific mutagenesis to represent a likely contact for CYP3A4 substrates (reviewed in Lewis 2001). Furthermore, the CYP3A4 model is consistent with substrate binding and metabolism for a significant number of known CYP3A4-specific compounds (Lewis et al. 1996).


Fig. 9. The CYP3A4 active site with nifedipine fitted for N-oxidation Hydrogen bonds are shown as dashed lines and amino acid residues are labelled according to the alignment with CYP102

Figure 10 shows how lauric acid is able to fit into the active site of CYP4Ali such that end-of-chain hydroxylation is possible via complementary interactions with active site amino acid residues, which include ion-pairing to a basic side chain together with a number of favourable hydrophobic contacts with aliphatic amino acid residues lining the haem pocket. The results of active site modelling of CYP4A subfamily enzymes are in close agreement with experimental findings (reviewed in Lewis and Lake 1999).

Consequently, inspection of Figs. 1-10 gives an indication that it is the number and disposition of complementary amino acid residues within the putative active sites of these enzymes which are responsible

246 D.F.V. Lewis

Fig. 10. The CYP4All active site with lauric acid fitted for ill-hydroxylation Hydrogen bonds are shown as dashed lines and amino acid residues are labelled according to the alignment with CYPl02

for both substrate selectivity and orientation relative to the catalytic centre, i.e. the haem iron. It is, therefore, an example of active site "steering" of the interacting substrate by the enzyme which is giving rise to selective human P450 metabolism in these (and other) examples of substrates, with other mammalian P450s also showing similar functionalities.

12.4 Conclusions

Homology models of human hepatic microsomal cytochromes P450 involved in drug metabolism are able to explain both substrate selectivity towards these enzymes and also the known routes of oxidative phase I metabolism in the majority of examples studied to date. Although these models have been based on the bacterial CYP102 template, it is likely that the advent of a new crystal structure for the mammalian P450 enzyme CYP2C5 will significantly facilitate an advance in this area for the future evaluation of P450-mediated metabolic pathways in mammalian species, including man.


Acknowledgements. The financial support of GlaxoWelicome Research & Development Limited, Merck, Sharp & Dohme Limited, the European Union (EUROCYP project) and the University of Surrey Foundation Fund is gratefully acknowledged.

References

Ioannides C (1996) Cytochromes P450: Metabolic and Toxicological Aspects, CRC Press, Boca Raton, Florida

Lewis DFV (1996) Cytochromes P450: Structure, function and mechanism. Taylor and Francis, London

Lewis DFV (1998) The CYP2 family: models, mutants and interactions. Xenobiotica 28:617-661

Lewis DFV (1999) Molecular modelling of human cytochromes P450 involved in xenobiotic metabolism and rationalization of substrate specificity. Exp Toxicol PathoI51:369-374

Lewis DFV (2000a) Modelling human cytochromes P450 for evaluating drug metabolism: an update. Drug Metab Drug Interact 16:307-324

Lewis DFV (2000b) On the recognition of mammalian microsomal cytochrome P450 substrates and their characteristics. Biochem Pharmacol 60:293-306

Lewis DFV (2000c) Structural characteristics of human P450 s involved in drug metabolism: QSARs and lipophilicity profiles. Toxicology 144:197-203

Lewis DFV (2001) A guide to cytochrome p450 structure and function. Taylor and Francis, London

Lewis DFV, Hlavica P (2000) Interactions between redox partners in various cytochrome P450 systems: functional and structural aspects. Biochem Biophys Acta 1460:353-374

Lewis DFV, Lake BG (1999) Molecular modelling of CYP4A subfamily members based on sequence homology with CYP102. Xenobiotica 29:763-781

Lewis DFV, Pratt JM (1998) The cytochrome P450 catalytic cycle and mechanism of oxygenation. Drug Metab Rev 30:739-786

Lewis DFV, Eddershaw PJ, Goldfarb PS, Tarbit MH (1996) Molecular modelling of CYP3A4 from an alignment with CYP102: identification of key interactions between putative active site residues and CYP3A-specific chemicals. Xenobiotica 26: 1067-1086

Lewis DFV, Eddershaw PJ, Goldfarb PS, Tarbit MH (1997) Molecular modelling of cytochrome P4502D6 (CYP2D6) based on an alignment with

248 D.F.V. Lewis

CYP102: structural studies on specific CYP2D6 substrate metabolism. Xenobiotica 27 :319-340

Lewis DFV, Eddershaw PI, Dickins M, Tarbit MH, Goldfarb PS (1998b) Structural determinants of P450 substrate specificity, binding affinity and catalytic rate. Chem BioI Interact 115:175-199

Lewis DFV, Dickins M, Eddershaw PI, Tarbit MH, Goldfarb PS (1999d) Cytochrome P450 substrate specificities, substrate templates and enzyme active site geometries. Drug Metab Drug Interact 15: 1-49

Lewis DFV, Dickins M, Lake BG, Eddershaw PI, Tarbit MH, Goldfarb PS (1999b) Molecular modelling of the human cytochrome P450 isoform CYP2A6 and investigations of CYP2A substrate selectivity. Toxicology 133:1-33

Lewis DFV, Lake BG, Dickins M, Eddershaw PI, Tarbit MH, Goldfarb PS (1999c) Molecular modelling of CYP2B6, the human CYP2B isoform, by homology with the substrate-bound CYP102 crystal structure: evaluation of CYP2B6 substrate characteristics, the cytochrome bs binding site and comparisons with CYP2Bl and CYP2B4. Xenobiotica 29:361-393

Lewis DFV, Dickins M, Weaver RI, Eddershaw PI, Goldfarb PS, Tarbit MH (1998a) Molecular modelling of human CYP2C enzymes CYP2C9 and CYP2CI9: rationalization of substrate specificity and site-directed mutagenesis experiments in the CYP2C subfamily. Xenobiotica 28:235-268

Lewis DFV, Bird MG, Dickins M, Lake BG, Eddershaw PI, Tarbit MH, Goldfarb PS (2000) Molecular modelling of human CYP2El by homology with the CYP102 haemoprotein domain: investigation of the interactions of substrates and inhibitors within the putative active site of the human CYP2El isoform. Xenobiotica 30: 1-25

Lewis DFV, Lake BG, George SG, Dickins M, Eddershaw PI, Tarbit MH, Beresford AP, Goldfarb PS, Guengerich FP (l999a) Molecular modelling of CYPI family enzymes CYPlAl, CYPlA2, CYPIA6 and CYPIBI based on sequence homology with CYP102. Toxicology 139:53-79

Ortiz de Montellano PR (1995) Cytochrome P450. Plenum, New York Rendic S, DiCarlo FI (1997) Human cytochrome P450 enzymes: a status re

port summarizing their reactions, substrates, inducers and inhibitors. Drug Metab Rev 29:413-580

13 Forum Discussion: ADME-8ased Compound Optimization and Selection Paradigm

Participants: Andreas Baumann, Martin Bayliss, Thorsten Blume, Ulf Boemer, Gerardine Burton, Gabriele Cruziani, Karsten Denner, John Dixon, Gerd Fricker, Nikolaus Heinrich, Thierry Lave, Jiunn Lin, Arun Mandagere, Geert Mannens, Timothy Olah, Olavi Pelkonen, Joe Post, Iris Pribilla, Andreas Reichel, Andrea Rotgeri, Herbert Schneider, Gerd Siemeister, Dennis Smith, Thomas Steger-Hartmann, Babu Subramanyam, Han van de Waterbeemd, Ron Vergona, Bjorn Wail mark, Christian Wienhold

Discussion to the talk of

13.1 Geert Mannens ........................................ 250 13.2 Martin Bayliss ......................................... 251 13.3 Thierry Lave .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 253 13.4 Olavi Pelkonen ........................................ 255 13.5 Gerd Fricker .......................................... 256 13.6 Timothy Olah ......................................... 259 13.7 Arun Mandagere ....................................... 260 13.8 Dennis Smith ......................................... 263 13.9 Han van de Waterbeemd ................................. 266 13.10 Gabriele Cruziani ...................................... 269

13.11 General Discussion ..................................... 272

250 Forum Discussion

13.1 Geert Mannens

I. Pribilla. I have a few questions relating to your filtration or artificial membrane model. Could one explanation for your poor correlation with the Caco-2 data be that you use a different solvent concentration?

G. Mannens. The final solvent concentration in the phospholipid method was 0.25% and this should be comparable to all our Caco-2 work. I agree that the correlation is not perfect, but you certainly see a good trend. It all has to do with the risks you want to take at very early discovery phases. It is just a tool to discriminate between the promising and the very bad compounds at that stage and perhaps it is too dangerous to really try to predict already the final in vivo value from those observations.

I. Pribilla. Can you comment on the reproducibility? And what is the price per well or per plate?

G. Mannens. For that method we do not have enough in-house experience. We are currently at the stage of implementing the method inhouse. The price per plate I don't know.

I. Pribilla. What limits, in your eyes, the throughput of this method?

G. Mannens. It is a method with a very acceptable throughput if you can run 300 samples a day, and you could do more with more equipment. This is the throughput with one TecanlGenesis: one set-up, one person, and limiting yourself to UV detection, of course. If you require LCIMS determination it takes much more time.

B. Subramanyam. With simplistic systems like PAMPA, are you concerned about not having pharmacokinetics input? Working for Schering AG, for example, with steroids, some of these compounds may not do well in your initial simplistic screen. Are you worried about eliminating compounds?

Forum Discussion 251

G. Mannens. Well, I think it should be a joint effort of several departments. Permeability is only one parameter of your compound. For instance, another important parameter is the characterization of the solubility of the compound. It should all be taken together before you can go further.

13.2 Martin Bayliss

G. Burton. At what stage does PK at GSK actually enter the project, at project inception or do you actually wait until concept validation?

M. Bayliss. We are part of the project team and we have worked over the last 5 to 6 years to achieve that position. Once a concept is validated, we would start to become involved on a case-by-case basis helping whenever appropriate. There is no fixed entry point for DMPK, it is case-bycase. If it is appropriate we would become involved at the earliest possible stage. If it is not, then we stay in touch contributing conceptually until it is appropriate for us to become involved either using the in silico models or other methods.

J. Lin. Are you using the in silico methods as a first line and sequentially then go to in vitro experiments, or are you doing in silico and in vitro in parallel? You were giving one example where you screened 900 compounds for permeability and you found that this is OK for 500 compounds, and for 400 compounds it is a problem. And then you went back to in silico to confirm that, so it seems to me that you do not necessarily use a sequential process to eliminate compounds, i.e. using in silico to eliminate most of the compounds and then go to the second line. Could you please comment on that as you said that you use in silico data to select compounds for in vitro and in vivo testing.

M. Bayliss. The models are used on a case-by-case basis. The example I was trying to share with you around the in vitro and in silico permeability was to actually show you that we have re-deployed the resources from the in vitro models because the in silico model is as good. We had to run this system to start with because we did not have in silico tools. We then used the in silico tools to see if we could actually be "as


predictive", if that is the right word, as the in vitro data. We feel we can, so we stepped back from the in vitro screen and we just use the in silico tool as the fIrst line for that particular parameter around permeability. Again I take you back to the cascade I showed you which identifies the issues. I think it has to be project-based and issue-driven in terms of how these tools come together. They may well be sequential in some projects, i.e. it may be that in some projects we move from in silico to in vitro and then to in vivo. In vitro may be an inappropriate tool in some projects and then we move from in silico to in vivo. It may be that at very early stages we just apply some in silico data in terms of identifying appropriate templates. So you have to look at the issues and then employ the appropriate tools. This is my personal perspective as to how we may develop a strategy to move forward.

T. Steger-Hartmann. You mentioned all these in silico tools but you did not mention brand names. Are they in-house models?

M. Bayliss. Yes, they are. However, for the metabolism modelling we have worked in partnership with an external company which has developed some of the P450 modelling tools and we have also had a collaboration with David Lewis at the University of Surrey.

T. Steger-Hartmann. Do you make a similar observation as Merck did, i.e. shifting the attrition rate towards toxicology?

M. Bayliss. Yes. Maybe I can comment on having safety reflected in our cascade. We do have an early toxicology screen both in vivo and in vitro which we now employ prior to moving through one of the key milestones before compounds enter preclinical development. So we are trying to address the issue early, but as you may agree, it is not an easy target to address.

J. Dixon. Martin, do you routinely do cassette dosing in all projects?

M. Bayliss. If we are rich in molecules, yes, we would use it as a screen if possible. We have used both rat and dog.


J. Dixon. And you mentioned 20 compounds. Is that a favourite number or a limit of your tolerance?

M. Bayliss. Well, some of my colleagues have actually evaluated a cassette of 100, but I would not advocate that. Five to lO is the cassette size we have found appropriate and with a dose level of 0.1 mglkg per compound giving a total dose of 1 mglkg one endeavours to avoid any pharmacological effects. In our hands, we had trouble operating po cassette dosing. Our experience is that we tend to operate intravenous cassette dosing and then move to discrete screening if that is appropriate.

13.3 Thierry Lave

J. Lin. I am very interested in the last part of your talk on the physiologically based PK modelling. The concept of tissue-lumping is very unique. When do you do this model to describe V dss and kTI ? Have you tried to look at other species? The reason for this is that for instance propranolol is a most interesting example where the V dss is so much different between the species. We are talking about a 20-fold difference between monkey, rat, rabbit and human. I wonder if you used different species whether you would come up with pretty much the same result. If it works, this is probably the best way to predict the half-life. In the examples you gave, the V dss observed in the rat came out very nicely, even the one compound which was least predictable was still very nice as far as I could see. My question is, have you tried other species as well?

T. Lave. For this evaluation I used the V dss observed in rats and humans for validation purposes, so there was no prediction step for V dss itself. But, of course, there can be huge species differences between the V dss in rats and man, e.g. for beta-blockers. In this case we have to take into account the potential species differences in protein binding to scale V dss across species. Then you should be able to get some useful results.

J. Lin. Because, for the three compounds you showed us, it could just have happened that the V dss is quite similar between rat and humans


and because of that you have a good prediction. However, if for other compounds it happens that there are large differences in the V dss, and this is not unusual, then the appropriateness remains to be seen. But I think the approach is very intriguing and very useful.

T. Lave. Again, an approach is to take into account species' differences in the protein binding which has been shown to be the main reason for species differences in V dss. For lipophilic compounds this represents probably a reasonable approach. For hydrophilic compounds the volume of distribution is restricted to extracellular space and is not that sensitive to differences in protein binding. In this case, the volume of distribution should be similar across species, independent of the free fraction in plasma.

C. Wienhold. You have shown us that for predicting the clearance, the most accurate predictions were based on the in vitro data alone, and you said that adding in vivo data did not give a significant improvement. What are your practical consequences?

T. Lave. It is indeed interesting that the approaches based solely on in vitro data routinely generated in drug discovery and early development represent the most accurate and cost-effective approach for predicting clearance. There does not seem to be any benefit in using in vivo data for this type of predictions. However, the in vivo data is still needed to clarify the absorption and disposition characteristics of the compounds and to confirm, for selected compounds, the validity of the in vitro-in vivo predictions.

G. Cruziani. When you have used the neural network approach, have you used somehow the structure of the compounds or any descriptors?

T. Lave. No, what we have reported so far was solely based on experimental data in vitro and in vivo. But the incorporation of such input parameters is certainly of great interest for early drug discovery.


13.4 Olavi Pelkonen

H. Schneider. The two compounds you mentioned, troglitazone and rosiglitazone, how do they compare in terms of CYP inhibition?

O. Pelkonen. That is a difficult question. I should know that but I don't. But I think this can be easily taken from references. I have been focussing only on induction. My primary interest with respect to these two compounds was in induction only, but I am sure they must have some affinity to CYP P450 enzymes.

G. Fricker. My question is, since many CYP inducers and inhibitors are also MDR modulators, is there a way to distinguish between these mechanisms, because in vivo it is often difficult to decide whether the effect is based on the CYP P450s or on P-glycoprotein?

O. Pelkonen. Principally, on the basis of in vivo studies it is difficult to differentiate between P-glycoprotein and CYP P450, but this can be very easily studied using in vitro systems. For instance, for P-glycoprotein you can use the Caco-2 model and for the P450s you can use hepatocytes or liver microsomes.

J. Lin. You have said that it may not be necessary to look at enzyme induction at the early stages of drug discovery. I think this is probably because it is still very difficult to interpret the data. How about ligandbinding assays or the PXR assay as a means of a high-throughput screen? What is your opinion on that? Or is it too early to apply this kind of screen?

O. Pelkonen. I think that we need to know quite a lot more about the details of these induction responses. I would think it is not enough just to build them up as screening systems, because in the intact organisms there are a number of other factors and mechanisms that regulate induction, e.g. co-activators which you do not necessarily have in these artificial systems you are building in the host cells in which you put these different constructs. I think there needs to be a lot more basic research in this area before we can really recommend that sort of screen for wider use. But maybe I am too pessimistic about that.


T. Lave. I have one question regarding the stability of the CYP P450 enzymes in the hepatocytes. You showed the differences in CYP P450 stability between fresh and cryopreserved hepatocytes during culture. Do you know how much activity we lose already during the isolation procedure?

O. Pelkonen. It is difficult to study and there is no good answer to that. There is a difference between what you can measure in the homogenate or in the microsomes from the fresh liver and isolated hepatocytes from that liver. But that difference is not very large. So the isolation procedure itself does not seem to affect the P450 levels too much. At least as far as I know there is no systematic survey of these immediate changes.

13.5 Gerd Fricker

U. Boerner. Can you expand on the read-out of your P-glycoprotein assay?

G. Fricker. The calcein-AM assay is done in a plate reader and for the isolated capillary set-up we use confocal microscopy.

J. Lin. In one of your slides you showed data on mdrl knockout mice. The difference between wt and KO mice is about SO-fold for ivermectin, 50-fold for digoxin and 20-fold for cyclosporin. And yet for the liver which also expresses Pgp in the canalicular membrane, the differences are often less than threefold. How do you interpret this data? Are you saying that the expression of this transporter at the blood-brain barrier is much greater compared to the liver, or are there also other underlying mechanisms making that difference for brain so high?

G. Fricker. There are probably several reasons for that. First, the expression of Pgp is higher at the blood-brain barrier compared to the bile canalicular membrane, and second, the results you are referring to were obtained from PK studies where the brain was removed after about 12 h, so there might also have been some trapping of compounds in the brain. In total amounts of drug, it is still less than in other organs, it is just


compared to the control where the compounds are more or less absent from the brain.

J. Lin. That means it is possible that there are other reasons why the factor is so large for brain. And this is not only due to the Pgp expression which many papers describe as somewhat higher in brain compared to other organs, e.g. about twofold. But this difference is not enough to fully account for the 80- or 50-fold differences in brain accumulation mentioned earlier. Additional issues such as high protein binding may be involved with some compounds. But I still cannot understand the 50-fold increase in the brain for digoxin, which is not highly proteinbound, and in the liver there is not much of a difference at all. So I believe that there are some additional mechanisms for that.

o. Pelkonen. There is some recent information on the polymorphism of the gene encoding Pgp. Do you know whether there is some difference in brain penetration in polymorphic people?

G. Fricker. In humans, I do not know, but I am aware of one animal study. For a long time there was a rumour that Collie dogs were extremely sensitive to ivermectin, showing CNS side effects. It has been thought that Collie dogs might have no Pgp or a mutant Pgp. About 3 weeks ago at the ABC transport meeting in Gosau I saw a poster by a French group reporting that these dogs indeed have a mutation in the Pgp which seems to make them very sensitive to this drug. But in humans I am not aware of polymorphism studies.

U. Boerner. In your studies, you are using porcine and bovine brain. What about the scalability to the human situation?

G. Fricker. This is actually a very difficult question. What is known with respect to Pgp is if you have patients treated with cyclosporin or saquinavir who have very low drug levels in the brain as measured in the CSF, when you co-administer other strong Pgp substrates, e.g. ritonavir which almost totally blocks Pgp, then you can find the other drug inside the brain. But whether you can transfer data based on the presented in vitro systems and animal studies directly to the human situation is still uncertain. We have done some very preliminary studies with capillaries


from human brain applying the same technique as described. On applying our fluorescent probes we saw similar results compared to porcine brain capillaries, but this is too early to be interpreted in this respect

A. Baumann. I have a question on the accessibility of your biological material, i.e. bovine and porcine brain. Is there any trend towards the use of cell lines ?

G. Fricker. Yes, there are 2-3 so-called human cell lines available, but one of them is a tumour cell line, and actually, tumour cell lines do not really form a tight monolayer and they do not really reflect the situation in healthy tissue. There are also some findings reported at this week's CVB meeting in Cambridge that one of them is not really a human cell line. So far, there is no really good cell culture model for the human blood-brain barrier.

A. Reichel. Currently, there are reports on two immortalized human brain capillary cell lines. One of them, called BB 19, is tumorigenic and there is an overgrowth resulting in multiple cell layers. There is also a digression from the BBB-specific phenotype which may have an impact on its function. As has been the case for hepatocytes also, cross contamination of cell lines originating from different species can occur. Indeed, some labs using the other human immortalized brain endothelial cell model you mentioned, the SV-HCEC cell line, ended up with animal models due to contamination with other cell lines they were using in the lab. Generally, however, brain endothelial cell lines are rather leaky thus making it impossible to use them in the screening for CNS penetration. For this purpose, primary brain endothelial cells, in particular of bovine and porcine origin, are the models of choice. Recently, however, very promising results have been reported using the MDCK cell line in industrial settings.

G. Fricker. In my opinion, the porcine endothelial cells are the best in vitro BBB model because they showed the lowest variability in ABC transporter expression. The expression is much more variable in bovine endothelial cells and I know many researchers who switched from bovine to porcine models, also, of course, as a consequence ofBSE. But


even with pig brain the situation is starting to become difficult now, as the animal material is not easily available anymore.

G. Siemeister. What is the influence of vascular permeability increasing proteins like VEGF on the permeability of the BBB, and would you expect some drug-drug interactions of antiangiogenesis drugs in terms of penetration of other drugs into the brain?

G. Fricker. We have not studied this, but what we have seen is, if you keep the cells in a monolayer system in a static set-up you have a basic expression of Pgp and MRP2 in the cells. However, when you shake the cells for about 1 week so that you have sheer stress, there is a higher expression of the proteins, so I am pretty sure this would also influence the expression of other cell proteins.

A. Reichel. It is well known that brain endothelial cells express VEGF receptors of the Fltl family. Furthermore, VEGF is considered to be a so-called BBB-permeabilizing factor. Thus, exposing the BBB to VEGF will lead to an increase in the permeability. This has been demonstrated in vitro by monitoring the transendothelial electrical resistance which drops after treatment of the brain endothelial monolayer with VEGF in a reversible manner. It may thus well be that drugs interfering with the VEGF receptor cascade at the BBB elucidate changes in the regulation of the BBB and hence affect the eNS penetration of other compounds.

G. Fricker. The question is by what mechanism this opening occurs. If it is via loosening the tight junctions, this would result in unspecific movement of substances between blood and brain in both directions. This may be acceptable for the treatment of brain tumours but, in general, may not be tolerable.

13.6 Timothy Olah

B. Subramanyam. I would like to know if you use a generic internal standard or if you use multiple internal standards?


T. Olah. We have typically gone now to multiple internal standards depending on the class of compounds we are working with. In many cases we are working early in discovery and are looking at several different structures. And since we have no shortage of bad compounds, because compounds that have died in development become internal standards in our hands, we apply multiple internal standards in our analysis.

B. Subramanyam. I thought that you were using solid phase extraction for discovery. What is your opinion about using the typical acetonitrile precipitation method? Do you have some worries about that?

T. Olah. No, we actually use all types of sample preparation methods. It really depends on the class of compounds. We have set up automated methods to do liquid-liquid extraction, acetonitrile precipitation and solid phase extraction, whichever works. Again, it really depends on the quality of data that is needed to support the studies, and that can vary. People were very happy when we were generating CVs of 20%. Now the question is, I cannot interpret this data; I need better accuracy and precision. And then I need to work that much harder in developing these methods.

13.7 Arun Mandagere

D. Smith. You showed the attrition graph with 30% candidate failure due to PK. Do you think that we are actually lowering attrition on pharmacokinetics realistically? Do you know any changes in the figures over the past few years?

A. Mandagere. I don't think we have enough data at this point. Not enough projects have gone through the in vitro ADME screening. At least three of the projects I have been involved with in HMR have made it through to the clinical stage. The amount of time it took to reach that is shortened and the cycle time I can say is reduced, but we do not know yet whether the compounds will make it through. We do know, however, the compounds don't have PK issues and formulation issues as they were addressed at the earlier stage. But overall, attrition to a large degree


is mechanism-dependent. So in some projects there is not a strong correlation between PK and the in vivo activity. Like terfenadine, for example, which had 10% oral bioavailability or less but worked wonderfully and had a long duration of action, that is, of course, before all the toxicity issues came up. So it has to do a lot with the mechanism of the drug.

D. Smith. Yes, it was just a specific question as my impression is that we are not changing the attrition despite all the work.

B. Subramanyam. I would like to ask you about protein binding. You combine Caco-2 data with microsomal data. If you add plasma-proteinbinding data, does your correlation get better? Did you do any retrospective analysis?

A. Mandagere. We did protein binding for one project, but it did not seem to improve the %F estimates. What we really needed to look at was not the percent of protein binding but the dissociation rate. We had seen some compounds which were highly protein-bound, e.g. greater than 99%, but the dissociation rates were very different. Compounds with a high percentage of protein binding but rapid dissociation rates had better oral bioavailability. So we were more interested in measuring dissociation rates rather than a straight protein binding. Typically, protein binding/dissociation is measured at a much later stage. At the early stage we are looking at %F in very broad categories: low, medium and high bioavailability. You may have some shifts in the metabolism or permeability values but it is not really going to have a significant impact on the %F estimates. We also include reference compounds of known oral bioavailability in each screen to minimize the variability of the in vitro screens.

M. Bayliss. In your looking-ahead slide you suggested more effective use of in vitro screening. Would you care to speculate what that might be?

A. Mandagere. We are applying "Lipinski's rule of five" as a means of flagging problem compounds at the synthesis stage. Chemists are beginning to apply the in silico prediction tools for log P, pKa' solubility and


bioavailability at the synthesis stage. As their confidence increases in these in silico methods, we can expect that the quality of the compounds entering the in vitro ADME screens will improve and, hopefully, that should decrease the quantity going through these in vitro screens. For the present, we are looking at solubility as a pre-screening tool. Almost 40% of the compounds that came through the screens had a solubility of less than 3 J.lg/ml, again we were running into questions like: are these really viable candidates, are they really worth investing our time and should we eliminate these 40% so that we would have more time to support other projects? Furthermore, the debate is in using brackets for potency and selectivity. If they are more than 10 J.1M we may not want to screen them, even if they had a better oral bioavailability. At present, it is difficult to take the structural features of successful drugs with good PK properties and apply them to compounds of interest that are structurally very different. That is why we tend to look within a structural series to learn from their cousins and brothers to build molecules with improved PK properties.

A. Baumann. In one figure you differentiated between microsomes and S9 mix data. Did you focus on phase I and phase II substrates?

A. Mandagere. The 21 compounds I showed you included data on micro somes and S9 mix. It was primarily geared towards phase I only. Microsomes are easier to analyse by MS because they are cleaner compared to the S9 mix. We did not see that much of a difference in %F estimates between the two. The compounds that were not metabolized by phase I ended up on the lower right hand corner where they appear to be very stable. We knew however, based on the structure, whether phase II was a likely route, e.g. a hydroxyl or carboxylic acid group make the compounds likely to be conjugated and excreted. So we did not feel that we needed to do a separate study or to include a broader enzyme representation at this early stage.


13.8 Dennis Smith

I. Pribilla. You are facing the same problem that hig-throughput screening has faced over the last years and that is the cost factor. There is another way out and this is miniaturization.

D. Smith. Yes, I think nano-technology is another way forward. There is a certain limit, in terms of necessity, on miniaturization as we probably need an order of magnitude for a biological HTS depending on what you want to see. If you want to measure compound disappearance than you may rely on LCIMS, which is fine, but it is not as sensitive as a fluorescence reader on an HTS.

I. Pribilla. You mentioned that for lead selection you go by molecular weight because you think that your Caco-2 and metabolic stability data can be misleading. Don't you measure them at all?

D. Smith. No, I don't think it is misleading. It is just that for the actual lead the molecular weight is important as it will go up in lead development, and this may create problems on absorption.

I. Pribilla. But you still characterize your leads in terms of Caco-2 permeability and metabolic stability?

D. Smith. Yes, but this is for a different reason. What we would be doing there is to actually have the data to see where we would have to go. It would not be why we selected the lead. So, given something that was of a molecular weight of 400 and soluble, regardless of that data, we would still say that this is a great lead. But you now have to fix the metabolism and build up a little on the lipophilicity to get it more permeable and that gives you the scope to do it. 1'd much rather have that then something that is of 500 molecular weight. We have started off with good leads of 500 molecular weight which are actually drugs, trying to do something to get more selectivity and we have ended in tears as we went up to molecular weights of 600 and lost all the good properties of that drug.


J. Lin. You mentioned combinatorial synthesis. We have a lot of pressure coming from this. How many programs at Pfizer really involve combinatorial synthesis?

D. Smith. Probably half of the programs are amenable to some manipulation by high-speed chemistry. So the actual building block can be done. Now, how many of those compounds tum out with successful activity is still open. Sometimes it is just a blind alley, so it does not actually mean that we get involved in a lot of work. But thinking about the future and expanding the library, those are all built now by highspeed chemistry. So, if you get a lead from such a series it is very amenable to high-speed chemistry because that is what has put it in there in the first place. So the monomer, the building block in the middle can be explored extensively. I would expect as the leads start to emerge over the next few years that we would see more and more of the high-speed chemistry because they are actually set up. At the moment, lets say amlodipine emerges as a lead, you cannot do a lot with that with high-speed chemistry. You can probably just substitute the amine and that's it. But the new ones are built for it, and this is very much part of the way forward.

T. Steger-Hartmann. To stay in your metaphor, the nice thing with brawn is if you fill a spreadsheet your task is accomplished. However, getting brain together is much more complicated. How are your structures organized so that you get your brains together?

D. Smith. I guess the task is only done when we've marketed a drug. In fact, it is usually done 4 years after marketing a drug, i.e. when we know it is successful. This is one of the mindsets that a lot of the industry is facing which worries me, the goal mentality when people say: You've got to get seven candidates. So your part of it is to fill seven spreadsheets and you start filling seven spreadsheets. In fact, we are all busy doing nothing a lot of the time. I think you almost have to say, I am going to bring the spreadsheet to the chemist, I don't mail it, I am going to go and see the chemist. Pfizer uses screen meetings, so every bit of data goes to a fortnightly screen meeting and they meet face to face with the chemist. They are not some service, they are part of the team and they are vital and every bit of data is looked at carefully. The drug metabolism person


knows what nanomolar affinity is. He does not sit quietly and he can say things like: Why are we doing that? That is too weak. Go and build some potency in before we'll look at that one. That sort of dialog is the answer. You just have to devote time to it because it is just so important. People should be in the corridors talking because that is where things happen. If we go into this sterile world of the future which is spreadsheets, e-mails etc. and no dialog, I think we are lost, and I don't think it'll be fun either. Although it sounds trivial, it is actually vitally important, because that is why we get people in and why people work hard. They say drug discovery is fun, it is great to be part of it. If the new world is not, the young people will all go and become accountants.

T. Olah. Do you think we are still at the stage where we don't quite understand how to use this technology right now? There is a huge amount of technology which we have brought in over the last couple of years and we are going through training but we are still low on the learning curve.

D. Smith. Yes, it is an interesting time, because the technology has outstripped the thinking about it in some ways. It is like a new model of a car that comes along and it's got a bigger more powerful and faster engine. I bought last years model and it's got only 200 horse power and the new one comes out with 250 and I think why didn't I wait. But if you really think about it, why on earth do you want it? The car already goes at 120 mph, why would I want to go at 140, I can't drive that fast. And I think this is the same thing for us. We really did invest heavily in technology with the belief that the technology by itself would solve our problems. But we've come now to question how specifically it will solve them. We have built up capacity, we have concentrated hard, but haven't really figured out the best way of applying it. So I think you are totally right. There are things which it is great for, because it saves people. But at the moment we are not using it to save people, instead my experience is that it is demanding more people, e.g. in IT to handle all the data. We should be saying, I don't need any extra staffing this year because I've got all this new technology - I haven't heard that yet. And I think that is the thing I was saying about the miniaturization. It is a bit like being at the roulette wheel and saying if I keep playing and keep doubling my stake, I am going to win. The only worry I have is, when does it end? I


think it is right but I think we want to be much more guarded as we go forward. It's got to be because it is about cost and we've worked right through the sums to say this genuinely does bring the cost down and it'll allow us to progress.

13.9 Han van de Waterbeemd

J. Lin. For the oral absorption as you point out, in addition to the PK properties, intestinal metabolism and P-glycoprotein are very important. Our own experience is that intestinal metabolism and P-glycoprotein efflux only apply to very low-dose compounds. If we give a compound at more than 50-mg dose, then intestinal metabolism and P-glycoprotein have become insignificant. Let me give you an example with midazolam. Some scientists have found that intestinal metabolism is very important for midazolam, but we give it at a dose of 1-2 mg, i.e. very low. On the other hand, for the P-glycoprotein, we do see significant Pgp efflux for digoxin which again is given at a very low dose, 0.5 mg or so. Our own experience is that above a dose of 50 mg, P-glycoprotein efflux and intestinal metabolism are saturated and become very insignificant.

H. van de Waterbeemd. That is, of course, a very nice guideline which I easily accept. The problem is, if we have very potent compounds which are dosed lower, what do we do then? We know we may have a problem, but the question is how do we deal with that.

T. Steger-Hartmann. I'd like to know more about in silico predictions of metabolic fate. What do you personally think is the most promising approach? And, do you routinely apply such tools?

H. van de Waterbeemd. It depends, of course, what you want to know exactly. Sometimes you want to know which metabolites are being formed to perhaps make an estimate about toxicity, that is one thing. Sometimes you want to know which enzymes are involved, sometimes you want to know which site in the molecule is metabolized. They are all different questions.


T. Steger-Hartmann. For metabolites, what do you think is the best approach, database, QSAR or what else?

H. van de Waterbeemd. For the moment it is databases.

T. Steger-Hartmann. Do you routinely apply them?

H. van de Waterbeemd. No.

I. Pribilla. You gave clear warnings that the log D calculations can be quite misleading, because of false pKa assumptions, for example. Does this mean that you don't use these log D calculations in library design anymore?

H. van de Waterbeemd. We have not used them so far, certainly not in library design. We sometimes use them to get a feeling what the log D of a compound may be, but we know that we have to be very careful. For some classes of compounds you are easily off by two units and in terms of absorption this is unacceptable. Here you need to be very accurate, therefore we prefer measurements. We also do not calculate solubility for the same reasons. It does not make sense, certainly not for poorly soluble compounds.

D. Smith. I guess once you have actually measured a few, you can then use calculated values again based on the fragmental constants in library design. We have not done it, but you could probably work out all those values for the fragments which they are putting on a template beforehand and sort of zip them through after measuring just a few.

H. van de Waterbeemd. If the series is close enough and the error is systematic for the whole series, then of course that works, but you are never sure that is exactly the case.

D. Smith. No, but it would work better than just calculated values.

N. Heinrich. If you want to model physico-chemical or pharmacokinetic properties, do you have any experience with 2D and 3D descriptors, BCUT metrics, Ghose-Crippen vectors etc.?


H. van de Waterbeemd. There are different programs with different descriptors often for the same thing. So there are various ways of defining for instance the size of the compound, hydrogen bonding etc. There are many topological descriptors which are, in fact, related to either lipophilicity or hydrogen bonding or size.

N. Heinrich. Are you doing routine calculations and predictions based on this sort of descriptors?

H. van de Waterbeemd. No, not at the moment.

I. Pribilla. You mentioned three different ways for measuring solubility, which ones do you usually use routinely?

H. van de Waterbeemd. We have a nephelometric method in Sandwich and Chris Lipinski in Groton has a turbidimetric method, so we have them all available.

B. Subramanyam. At what stage do you do solubility? Is it prior to optimization, i.e. at the level of hits or are these finished pharmaceuticals?

H. van de Waterbeemd. It is at the stage where we work up to a candidate at the moment. But if we had higher throughput methods available or higher than we have at the moment, then of course, we could use it earlier. I think a good place would be when you start a new library, lets say when you synthesize the first hundred compounds and have a look at their solubility to have an idea how the series looks.

D. Smith. I think the further you leave it the more of a problem it becomes. So if you get near the finished pharmaceutical, that is the usual route to failure, as it often then is an insoluble candidate and you've got all these silly formulations then.

N. Heinrich. What is your experience comparing kinetic and thermodynamic solubilities? We have found that there is sometimes a tremendous difference. For instance, making a decision based on turbidimetric solubility measurements might be misleading.


H. van de Waterbeemd. Again, it depends at which stage of the process you are looking at the compound. The turbidimetric approach is used very early when you don't need very accurate data, and thermodynamic measurements make more sense for candidates and pure compounds. Also, purifying a compound makes a lot of a difference; solubility always goes down for pure compounds. But at early stages you just want to have an idea where you are.

B. Subramanyam. In your absorption database you are looking at marketed drugs. Do you think it is a biased database?

H. van de Waterbeemd. Certainly. The data is from different sources and measured under different conditions.

B. Subramanyam. I agree, but I wonder whether drugs that failed may have similar descriptors. Wouldn't that lead to wrong predictions?

H. van de Waterbeemd. Yes, that is a problem. We have only a limited data set and the data are not all of the same quality, some data are perhaps even very poor and should be taken out. That is probably one of the reasons why the models we have are not more predictive than about 80%. The rest is due to the poor quality of data, imperfect descriptors etc. But that is the status of where we are at the moment.

T. Blume. What is your preferred method of measuring log D?

H. van de Waterbeemd. There are several good methods, so it does not really matter. It depends on the kind of throughput you want. We have the shake-flask method in Sandwich and our colleagues in Groton have a method based on HPLC, which has also been published. The results are comparable but not always identical.

13.10 Gabriele Cruziani

N. Heinrich. I would like to know how flexibility or the conformational problem is taken into account by VolSurf?


G. Cruziani. For flexibility there are two methods we can use, since we are working with a force field that is able to produce this kind of molecular interaction field. First, what we obtain if we change the confrrmation, i.e. if we allow for flexibility, then there are two situations. If the flexibility is low then the descriptors will not change very much. If the flexibility is very high the descriptors will be very different, but this has been the case for only less than 5% of the compounds. Second, the GRID force field allows automatically for the flexibility of lone pairs and external light atoms. So you don't just have a singe point but sort of a distribution, and this is because the lone pairs and the hydrogens are allowed to move freely so you can simulate the partial flexibility. You can simulate tautomeric movements and so on. So you can deal with the majority of problems related to flexibility. If flexibility means that you really change the stage of the molecule, e.g. via internal hydrogen bonding, then this is another compound really which you need to model. In this case you can model both cases and you see from the statistics if this is out or not.

N. Heinrich. How do you take into account the ionization state of molecules at physiological pH?

G. Cruziani. For the protonation stage, I normally work with the compound at pH 7. If you have doubts it is better not to use any charge. With GRID there are two aspects, one is the protonation and the other one is deprotonation, i.e. solvation and de solvation effects that always playa role, in particular if you have a compound for which a fraction is charged. Normally, I don't care about the protonation status at the first step. I make the model and then I look if I have some trend I can explain like this. If not, I have to pay a little bit more attention to the protonation status.

H. van de Waterbeemd. You have a whole list of different descriptors. Which of the VolSurf descriptors are most important?

G. Cruziani. For example, volume terms are always very important for permeation. In a lot of cases I use the capacity factor, which is very similar to the polar surface area. This is important for solubility, for example, or flux. Hydrophobic interactions are very important for pro-


tein binding. So the descriptors contain a lot of information for predicting pharmacokinetics, e.g. the balance between hydrophily and hydrophoby. Sometimes the integy moment, i.e. the amphiphilic moment is very important, e.g. for blood-brain barrier permeation.

N. Heinrich. How do your parameters compare with e.g. daylight fingerprints, Ghose-Crippen parameters, etc.?

G. Cruziani. Some companies have tried to reproduce the VolSurf model with the same compounds but different descriptors. For example, with the different types of fingerprints or descriptors that are generated with the Jorgensen method, which is based on Monte-Carlo simulations, you have a complete mix-up. These descriptors are very good for certain purposes, but in my point of view they are not good for pharmacokinetics. This is why I say that we need new descriptors because we have produced descriptors for certain purposes. They are different and they are based on a certain logic etc. and it is very difficult to find descriptors which we are able to use in a different field. VolSurf descriptors are not fantastic, but on average, they work very well for pharmacodynamics and pharmacokinetics. So they really help you to optimize different aspects of your problems, which you cannot do when you have to change between different sets of descriptors as the mathematics will not cope with that.

N. Heinrich. What is the effort with respect to computer power, for instance looking at a library of, say, 50,000 compounds?

G. Cruziani. Everything you have seen works on my laptop. Typically it runs on a Silicon Graphics machine which is the standard in industry and now under Linux because this will probably be the standard in the future. For a typical drug-like compound we need about 10 sec per probe, so if you want to use the water and hydrophobic probe, i.e. the minimum in VolSurf, you need 20 sec. This means that you can handle about 3,000-4,000 compounds per day. But in a Pentium 500 laptop you will be 10 times faster. And the new method I showed you will be even 100 times faster, that means we can work with a million compounds a day. This was actually demanded by one company which needs to apply the model to virtual libraries. In any case, I think we are in the range


needed by industry. We have been able to work with a thousand compounds and a million descriptors. You can also reverse this, you can work with a million compounds and thousand descriptors, and this is very efficient, all still on a normal personal computer.

13.11 General Discussion

M. Bayliss. The forum discussion will give us an opportunity to examine some of the points that have been raised during the day in a little more detail, and perhaps to revisit any of the presentations to explore any further points. Dennis Smith has cut to the nub of the issue in terms of whether we are actually making a difference. We have seen today a number of presentations around the use of ADME tools in high-throughput screening, and we can come back to perhaps the use of in silico tools, the timing of various studies, are we able to predict to man, should we be using induction or inhibition screens at earlier stages, and what sort of data should we be producing in terms of fitness for purpose? I guess, underlying or underpinning all these questions is the question, Are we actually reducing attrition? Does anybody have any information to suggest that the approach they are undertaking in the hit-to-lead and lead optimization phase of drug discovery has actually reduced the attrition rate? We did see some data showing that attrition has begun to shift from pharmacokinetics to toxicity and a number of companies have been involved in moving safety assessment and toxicity screens back into the earlier lead optimization phases.

J. Post. You mentioned with respect to preclinical toxicity and with respect to a bad template, the two assays that appear to be helpful in the early stages are one in vitro and one in vivo assay. I am curious as to which ones you find most useful with respect to the toxicity issue.

M. Bayliss. Certainly prior to nominating a molecule we have begun to do some toxicity screening with an in vivo screen and in vitro genotoxicity.

J. Post. So the in vitro assay is actually a genotoxicity screen and not cell-based, e.g. thymidine uptake.


M. Bayliss. We have in the toolbox a cell-based toxicity assay that we can also use as part of preselection.

J. Post. The other question I have is that in your presentation it seems like you are willing to take the leap of faith with the in silico approach to having confidence there. And you mentioned that you had an in-house algorithm that you use to get a confidence level with which you select the compounds that would be nice candidates for PK. I was wondering if you could elaborate more on the validation studies for that, and if you use preclinical candidates, maybe compounds that do not have that polished drug profile like aqueous solubility and nice log Ps in your validation studies along with standard drugs, to take that leap.

M. Bayliss. The validation of in silico has involved as many molecules as we could put through the systems, and we were certainly confident that the two in silico absorption screens were complementary to each other. The validation was such that we were comfortable with the particular process and would accept that we may take forward some false positives. In terms of the overall risk and in terms of the resources, we felt that the risk was something that we were prepared to accept at this stage. Because assays involving cell culture are a significant drain on resources when run at higher throughput, the early use of in silico tools is more cost effective.

A. Baumann. I would like to come back to the attrition rates and if they have really changed compared to the reports by Pre ntis and others. Are these 40% really pure PK reasons? When we looked at that internally at Schering we could not find such a high figure. Maybe this also reflects a mixture, e.g. deficiencies in PK with those in toxicity and pharmacology. Maybe nowadays we look at the attrition process with more accuracy. What is the situation in other companies? Are the PK reasons real PKreasons?

D. Smith. I think you are right that this is not a simple factor or primary reason. It is not that poor oral bioavailability itself is going to kill a drug, it is the fact that you are going to give a very high dose and maybe it is due to cost of goods that you do not advance the compound. You could say cost of goods, but it will probably go down as being a PK reason as


the primary reason responsible for high dose rather than, say, the actual intrinsic potency of the compound. But the figure in Pfizer terms, with about 200 compounds going in, is not far off that primary reason with 40% failure due to PK and it does not seem to be shifting despite all that we do.

M. Bayliss. Are we just responding to an increase in the combinatorial chemistry and the fact that there are now more molecules being produced? So we are focussing on reducing attrition, but there is more than one thing changing at anyone time and we are actually broadening the funnel in terms of number of molecules available, so we have to try and manage that as well as attrition.

D. Smith. Yes. I have a theory. It is because pharmacologists cannot screen orally in the rat anymore. It may sound silly, but if you think about it, what is probably one of the hardest tests you can ever give a compound? It is a fairly low oral dose in a rat, because it has this incredible metabolizing system, it has all the transporters that you can name and they all work much better than in humans. So if you get something that works at 1 mg/kg in a rat, you probably have got a pretty good compound. And so we used to be able to screen all these compounds before, going back 10 years or so. Now you have something, e.g. the HIV proteases, you cannot screen in rat and you have to take all the systems we use nowadays and the molecules are much bigger and more complex. I think it is that change. Things have become much more complex now. The targets have become more sophisticated. Now we have to use huge computers trying to understand all the data because we have taken it all apart, because the molecules are far more complex and the targets are more complex.

A. Mandagere. What we are seeing more and more is, as Chris Lipinski has pointed out, molecular weight is going up, log P is going up and we are choosing the best and the worst from within a group of analogs. Regardless of what we do, we are stuck with them, we cannot improve. It is like squeezing a balloon; we can modify one or two factors but we cannot modify all of them and people are not willing to give up on their compounds.


D. Smith. And there are lots of compounds going on the market which do not have good pharmacokinetics. Certainly AIDS compounds were all pretty hopeless, they would fail in any other indication.

A. Reichel. It seems that for new classes of drugs people are willing to compromise a lot more on PK properties than on existing classes. As to the attrition, maybe the total attrition due to PK failures does not seem to go down, but it could be that there is a shift in the sources of attrition. For instance, we may now be better at identifying certain critical properties early in drug discovery such as insufficient oral absorption or polymorphic metabolism, but still less successful at other aspects, e.g. drug-drug interactions or inadequate half-life.

M. Bayliss. Perhaps we can now address the induction issue raised by Olavi Pelkonen. Should induction screening form part of our early screening toolbox?

O. Pelkonen. I have pointed out the review article by Dennis Smith, a pretty thoughtful review article, about the clinical significance of induction and whatever the conclusions we could draw about the suggestions you made. Although I have described the current development in induction research and development of screening systems, in the end I put forward the question whether we really need these high-throughput induction screen systems early in the drug discovery process and development.

M. Bayliss. A corollary to that is, could you use inhibition screening instead as a number of inducers are also inhibitors? Hence, rather than running two screens, an induction and an inhibition screen, could one just use a single screen?

D. Smith. That sounds a bit too simplistic. The known clinical inducers, e.g. phenytoin, carbamazepine, rifampicin - are they strong inhibitors? Potent inhibitors are not necessarily inducers, e.g. quinidine is not. I think this is risky. You'd better screen with hepatocytes or any other receptor systems.


J. Lin. How many compounds on the market are inhibitors and also inducers? I would say if inhibition is irreversible, if it is a mechanismbased inhibitor, then the chances are that it could also be an inducer because the body has feed-back mechanisms to deal with that. We had some examples for that; and thus for mechanism-based inhibitors, I would give some weight to, but I don't think there is a simple connection between, reversible inhibitors to predict inducers.

A. Baumann. I think the point is also, if there was an early highthroughput assay as we have for inhibition, we might be doing an early induction screening. It is easy to automate inhibition studies, but we cannot do this with induction studies at present. If we had it, would we use it, e.g. at the phase of lead selection or lead optimization? I don't think so. Our intention is more to include induction relatively late in the process, e.g. around candidate selection, i.e. shortly before or thereafter.

M. Bayliss. I suspect if we had easy access to something like human hepatocytes we would run induction screens at an early stage using a similar higher throughput system to those that we have heard about in terms of Caco-2 or microsomal screens. But it is the availability of tissue which is the difficulty. One alternative could be to run a PXR ligand assay and that is clearly a higher throughput system providing some read-out for 3A4.

D. Smith. Does anyone run a PXR ligand assay?

J. Lin. I thought Pfizer is already doing the PXR binding study, probably just to use it as a warning that a compound which has a high affinity to PXR potentially could be an inducer. It's that what you get out of it, isn't it?

D. Smith. Yes. What were your results, Martin, when you ran it?

M. Bayliss. The traditional inducing agents do respond.

D. Smith. Did you find lots of compounds coming up all over the place or was it actually interpretable?


M. Bayliss. It was run against a carefully defined compound set, some of which were ligands for PXR and some others were not.

A. Baumann. Maybe we can make a comment based on an assay of the mRNA levels.

A. Rotgeri. We have checked the induction of certain CYP P450 isoenzymes in human cryopreserved hepatocytes which we brought to attachment for a certain time period so that we were able to measure mRNA in response to known inducers, e.g. rifampicin. We are still at a very early stage of validation, but we have seen a reproducible response for CYP 3A4, and I would like to discuss the applicability of mRNA data in comparison to metabolic data. Is there any correlation of how well mRNA data from hepatocytes predict induction?

o. Pelkonen. We have done some work on that but not very much, because there is paucity of material if you use a human system. But for example with respect to 3A4, usually the increase in mRNA levels is a little bit higher than the protein or functional level, but nevertheless, they are in line with each other. The fold increase may be different if you use as an endpoint mRNA, protein or function, but basically they are correlated to each other.

M. Bayliss. Is that for all P450s?

o. Pelkonen. At least for 3A4, but there are also other examples. We have been doing a lot of work on 2A6 and 2A5 in the mouse. With some compounds and with respect to 2A5, you can have increases of up to lOO-fold in mRNA, but only five- to sixfold in the protein amount and the activity. So sometimes a high increase in the mRNA level does not carry to the protein and activity, and we do not know the reasons for that.

M. Bayliss. I think that is an interesting situation. If we were to see a huge increase in mRNA, which was not translated to protein, would we continue or would we terminate the compound?

K. Denner. Is induction really a critical issue for drug development? I mean, for inhibition it is really clear that you may have adverse events in


humans, but if you see induction maybe you can overcome this problem simply by titration of the compound, if it is auto-induction. Only in the case of a co-administration where the metabolism of the co-administered drug is critical, you may run into problems. So, is induction really a critical issue?

J. Lin. I think this is more a marketing reason than a clinical reason. I mean all the companies are in competition, and in the same class of compounds a non-inducer may win. Thus, a lot of the screens we are currently applying do not come from scientific reasons but more from marketing reasons.

A. Rotgeri. But couldn't we run into problems, like we heard already, if compounds inhibit and induce in parallel? Couldn't we eliminate compounds from development because of strong CYP inhibition, but when they also induce so that the net result is levelled? Wouldn't we eliminate the wrong compounds?

O. Pelkonen. A very good example is St. John's wort, Hypericum. That is a pretty potent inducer but also a pretty potent inhibitor. And at least some of the components of St. John's wort are pretty good inhibitors, some of them even in the scale of quinidine. But I do not know what the clinical implications of this are.

D. Smith. So, you get drug-drug interactions both ways. Let me come back to the comment from Andrea Rotgeri. It would be very unlikely that you get something that would be equally balanced to inhibit all the P450s that is has just induced. So maybe it is metabolized by 3A4 and induces 3A4, but it may also induce 2C9 as well. So you will still end up with an interaction problem, and you may find this an unmanageable problem producing just another failure. Let me ask a question related to this. We keep seeing activation in our inhibition screens. I wonder whether anyone knows what to do with it. So when you see a fourfold activation of 3A4, what do you actually do with this data - are you suggesting a clinical study or do you ditch the compound?

O. Pelkonen. We have interesting results with a new antiParkinson's drug. It is partially, to a small extent, metabolized by 3A4 by N-de-


methylation. When we were identifying this enzyme, we used the putative 3A4 inhibitors ketoconazole, itraconazole, gestodene and troleandomycin. Ketoconazole was a very potent inhibitor of entacapone N-demethylation, itraconazole less so, but then gestodene and troleandomycin increased the reaction by about 10- to 1S-fold. So for putative 3A4 inhibitors different effects were seen, but we don't know whether activation also occurs in vivo.

D. Smith. There is no clinical data on this sort of interactions?

o. Pelkonen. No, there is not.

D. Smith. It may be interesting to just check in the patient database to see whether they were co-prescribed.

M. Bayliss. The discussion as I understand has thus far suggested running two screens, i.e. inhibition and induction in parallel, whatever they may look like. And as to attrition, it may be that as yet no impact has been made, and this may be because too many things are changing at anyone time. Another point of interest was "predicting" to man, where I think we would all like to be. How good at predicting are we?

A. Baumann. It was very interesting to hear from Thierry Lave that we need in vitro rather than in vivo data to predict human bioavailability. Is your proposal to focus on the in vitro studies rather than on the high-capacity in vivo studies?

T. Lave. I was not really focussing on oral bioavailability, which is a much more complex parameter than hepatic metabolic clearance, which I was trying to predict. Here it was clear that when we compared all the methods which are available to do these predictions, the simplest and, in fact, most physiological approaches were the most predictive. What is interesting here is that the same model which is used for screening and for further characterization can be used at different stages to get decent semi-quantitative or more quantitative predictions, without the need - in this specific case - for in vivo studies. However, it is necessary in order to increase the degree of confidence in our predictions to perform some in vitro-in vivo correlation within an animal species, for clearance or


other PK processes. And only if! get a good prediction in animals would I feel confident also that prediction in man should be reasonable.

A. Reichel. At what point should we start predicting to man? One possibility is during lead optimization to improve on the right endpoint during the optimization cycles. An alternative, which is less demanding in terms of resources, is to predict human PK characteristics only later on when we want to select a development candidate out of a few compounds which were optimized based on simpler endpoints and parameters.

A. Baumann. The question is also about weighing one-dimensional data, e.g. in vitro data, against multi-dimensional data, e.g. in vivo data. As we have seen, we have to make a puzzle from the one-dimensional in vitro data to bring it together to a multi-dimensional picture, i.e. to that which you might get from one in vivo experiment.

M. Bayliss. The next step to that would be physiologically based modelling, which is a concept one step on from predicting clearance and in vitro-in vivo correlations.

G. Cruziani. I think that we can fix two or three biological models but still we have to check what is going on in the chemical space. It could be that we obtain results by chance. In my point of view, we have to check in any case the variation on the chemical space because this will also produce variation in the biological space we are working in. It could be that by chance we are more or less closing the chemical space and this will not produce enough variation in the biological space. So it could be that biological answers are similar, i.e. the content of information is the same, but we have to prove this allowing for diversity in the chemical space.

J. Lin. I think the beauty of the physiologically based PK model, that was developed in the early 1970s is that it predicts the tissue concentration as well as the plasma concentration. The difficulty, however, is that the kT value, which is the tissue to plasma partition coefficient, is tedious and very difficult to get accurately. Thierry Lave presented a new concept of lumping compartments, an approach which is very


unique and beautiful. The question is, the assumption of kTI as a constant value, is that assumption based only on rat data or on several other species? If this is based only on rat, I still wonder whether it works only by chance. If this could be shown also for other animal species, this approach could be very useful and valuable.

D. Smith. Thierry, does your model use total tissue partition?

T. Lave. We refer to total tissue concentrations.

D. Smith. Do you think that this is a relevant measure for drug activity as against the concept of unbound drug?

J. Lin. Normally, for the kT value, there is a partition between tissue and plasma in terms of total drug. It is thus a measurement of total drug and this is fit into the physiological models to simulate and get tissue concentrations, e.g. in liver and brain, as well as in plasma. Traditionally, this is very difficult and time consuming. You may need one or two persons working 2-3 months to do these measurements in a couple of species. So I think the proposal by Thierry Lave, if the assumption that kT is a constant value is supported by data from several animal species, is going to be a very good and valuable approach.

D. Smith. But does, for example, total concentration in brain mean anything at all for a drug which is actually interacting with a receptor within the aqueous space in the brain?

J. Lin. I am not sure whether I would agree with that. Surely, the receptor occupancy is believed to be the most accurate representation, but we should not forget that non-specific binding is always in there. So once the drug is eliminated, there will be redistribution and again followed by a new equilibrium. So the total concentration in the brain does have some meaning. I don't agree that this has no meaning at all. However, I would in most cases put more trust in CSF levels because, in general, this gives better correlations between occupancy and CSF concentration. Also very importantly, we found for CNS compounds that the half-life in the CNS is not necessarily represented by plasma halflife. I found a lot of compounds with the CNS half-life being much


longer than in plasma, with the pharmacological response corresponding to the CNS half-life rather than plasma half-life.

T. Lave. What I proposed is a very basic PBPK approach where you can simulate total concentration in plasma and tissue. You can very well adapt the model to predict unbound concentrations. As you can make predictions based on a very limited amount of input data, this makes this new model potentially very useful for drug discovery. The question of what the relevant concentration is either for metabolism or for the activity goes somewhat beyond our evaluation. But the tools we discussed certainly contribute to a better understanding of these issues.

A. Reichel. Let me come back to the concept of one-dimensional and multi-dimensional data. I think what many of us are generating at the moment is an ever-growing amount of data which are often quite difficult to interpret. An example is the growing number of assays for P-glycoprotein efflux. I think the future may lead us to combine all this data into a holistic model, putting weighing factors to the various in vitro data and combining them with physiological parameters to model the outcome in the intact organism. So the only way to make sense out of these, often isolated, one-dimensional data points is probably by integrating them into a larger picture. There may be several ways of doing that. One very visual example is the graphic model by Arun Mandagere, where by combination of Caco-2 and microsomal data, a complex read-out, i.e. oral bioavailability, is obtained. A step further, also in time, is PBPK modelling, where you try to integrate these data points into a more physiological concept and then basically recreate the complexity of the body, as much as possible, in an in silico model of the organism, thereby predicting the overall PK in man from the vast amount of in vitro data.

B. Subramanyam. That raises the point, are we simply increasing apparent input? We are doing multiple assays in vitro to create complex data. So we are generating a lot of data, but synthesizing the data into a commodity that we can make decisions on is a complex process. Is simply increasing the throughput of in vitro assays really improving our work?


T. Lave. If you have an approach which allows you to integrate all types of information, e.g. descriptors for absorption, distribution, elimination etc., this would be a very useful approach to identify the key parameters for determining the PK profile of your compounds, and this is a very important feedback to the screening in order, for example, to focus on the assays which give the critical information. So it helps to focus really on the critical issues, parameters and assays and thereby helps also to save resources, hence increase the throughput and fasten the process.

A. Reichel. I think streamlining the process is a very important issue for us. I have a feeling that, at the moment, we are massively trying to expand the amount of data we are generating with all sorts of new assays being performed before interpretation becomes clear. As a consequence, we may produce much more data than we actually need to improve our compounds. Has anybody ever tried a more reductionist approach, i.e. collecting only a minimum on data, i.e. the absolutely necessary information, rather than all other sorts of data which may be nice to have but does not, retrospectively, make a difference.

B. Subramanyam. I think this is difficult to answer. What we are doing is to choose at the front end based on the resources available. We try to prioritize assays in terms of 'nice to have' and 'need to have'.

G. Cruziani. I have some experience with industrial partners I am working with. In one company they were looking at all the parameters and assays they were routinely doing on all the compounds. They were screening like lO,OOO compounds, and at a certain point they stopped and asked themselves, which of the data were necessary and which were redundant. They found that 60% of all the data was not useful at all because the information was present in just 40% of the assays they were doing. This is an experience in terms of biological data. I had a similar experience with a company where they were looking at chemical purity, which you want to check before you start working with a compound as impurities can cause trouble in your assays. They were using NMR and MS techniques. The measurements are obviously different but looking back at over 1,000 compounds, there was a very nice correlation between the two and there was a lot of redundancy. Thus, data mining is


very important for streamlining our processes and for saving on our resources.

B. Wallmark. I think the challenge was put up earlier by Dennis Smith, i.e. why don't we give the compounds per os to a rat and measure the effects and let all the forces of the rat act on the molecule and see if it is still active? On that line, in all the presentations, I saw that pharmacokinetics measurements were actually made before the pharmacodynamic measurements. So in your experience, is pharmacokinetic and metabolism data really used as exclusion criteria for going into in vivo pharmacology? Where I work now, at Schering, we certainly go ahead also testing in vivo pharmacodynamics without prior PK exclusion criteria. I would like to know more about the practice in other organizations.

D. Smith. There are many targets now, where you don't have an animal dynamic model. That excludes a lot. I know of a competitor that actually screened for oral absorption to see if it is worth putting the compounds into a dynamic model, which is the converse of what you were saying, i.e. we get the kinetics and then see whether it is worth doing the dynamics, which seems very labour intensive in a way, although it depends how labour intensive the dynamic model is. We tend to get as much data as possible from the biologists before we actually go ourselves into an in vivo model. So it is like both biology and drug metabolism in vitro first and then we go to biology and drug metabolism in vivo, this is the way We would work.

B. Wallmark. If you had pharmacologists that develop rapid read-outs based on a once or twice oral administration, would you still go along that path?

D. Smith. I think that is something we should challenge biology harder on because I still think there is a lot of mileage in that sort of screening, particularly if you go in vivo and get the rapid read-outs, then it is very valuable.

T. Lave. It is also important during the screening process to learn about the characteristics and the weaknesses of the compounds. So if you just put the compound into an in vivo pharmacological model and look


whether or not there is an effect, then in case of no effect, it is especially important to look with specific DMPK assays at the key characteristics of the compound and really identify the weaknesses of the compound so that you know what you have to improve.

D. Smith. I know we have turned the whole thing round, but if you look at the productivity of the pharmaceutical industry, that type of screening was used when it was most productive. I know it is history and we have moved on, talking about increasing complexity of the targets etc., but undoubtedly it did discover a huge number of important drugs and we shouldn't lose sight of that. That way of working was actually very valuable at the time.

B. Wallmark. I think the challenge for pharmacology is to walk away from the complex disease models that require weeks of dosing to get a non-quantitative read-out, e.g. the discovery of biomarkers that could be translated into the early clinical work.

B. Subramanyam. I think early-stage pharmacological screens will also be a drain on chemistry resources, as you would take away some resources from the optimization. I am not sure how the logistics will work because it requires making more material as typically, pharmacodynamic models are often long-term models where you apply multiple doses.

A. Baumann. How about measuring the systemic exposure in pharmacological experiments as a kind of alternative to full PK studies, at least for the route of administration which is used in pharmacology to get some in vivo information. This does not, of course, give data on clearance etc. which requires i.v. administration, but for us the question is whether such data are adequate to run a pharmacological experiment. So are you generally running PK studies before pharmacological animal experiments?

T. Olah. In some cases we do, it really depends on what the animal model is. Some of the models are actually quite difficult. The question that inevitably comes back when you perform an animal model is, Do you see exposure or not? If you do you perform the pharmacokinetic


study at that time. We typically perform the PK study beforehand. That is because the animal models are often very difficult so we want to be sure that we really have some exposure before we actually perform it. But if you have a simple animal model than we probably wouldn't.

H. Schneider. An additional point is, of course, that it is worthwhile to do the kinetics before the pharmacodynamic experiment because it is easy and generally requires only a single administration. lithe pharmacological model, however, involves chronic administration, do you then test for things like induction which may reduce the exposure during the study and hence may confuse the interpretation of the effects seen?

T. Olah. In most of the chronic studies we do test for this periodically throughout the course by monitoring changes in exposure.

G. Burton. I think it is also important, certainly in some of your studies where we look at blood-brain barrier permeability. It would certainly be useful to know if the compounds are actually getting in the CNS before going into a really complex model.

T. Olah. Yes, we are also doing a lot more tissue penetration studies looking at brain and CSF exposure. We are being asked to develop better and more accurate methods very early on really to answer these questions. For instance, for highly protein-bound compounds it is important to ensure that they get into the brain. The question is always, Is it worth putting this compound into expensive animal models?

H. Schneider. As for brain penetration, would you be satisfied with in vitro data on CNS penetration, or would you prefer a one-time-point brain/plasma ratio?

T. Olah. That is a hard question. I wouldn't be comfortable with an in vitro method at this time. I'd like to know, would the compound survive everything else before it actually gets into the brain? So we actually do more and more assays looking at brain levels in vivo.

R. Vergona. The power of this in vivo data becomes even more useful if you can link it to a relatively simple pharmacodynamic endpoint, espe-


cially in chronic animal models. And that allows some future link to the clinical situation where you can understand what the plasma levels mean in terms of effects. So that is where PK data can help us better understand up-front what is the appropriate dosing schedule to use.

T. Olah. A lot of times I think plasma levels are generated just because it is easy to get those numbers. Whether it correlates to what is going on at the target I don't know. If you feel comfortable with the information it is fine, but we also run into cases where we got great plasma levels and absolutely no efficacy.

A. Reichel. I think to link PK studies after single dosing with chronic pharmacological studies is not simple. Often it is not easy to extrapolate the findings due to unknown dose- or time-dependencies, e.g. there might well be complicating issues such as non-linear pharmacokinetics. One way to tackle this may be to actually accompany chronic animal studies either by sequential blood sampling in the animal throughout the study or by incorporating a satellite group in the study. This may provide you with information on enzyme induction and the data may also be used for modelling and simulations of plasma profiles of further studies so to save on experiments and compound material which we are generally short of that early on.

T. Olah. That raises an interesting point because a lot of us are using in vivo PK studies to select compounds. What always concerns me is that, typically, a compound is given at a very low dose in PK experiments but at much higher doses in pharmacological experiments, where we may achieve a much better exposure of the compound. So does the PK experiment provide enough information to select a compound for further use?

A. Reichel. This is a very difficult question. If saturable processes are occurring in the animal model it is a challenge to foresee if this will also pose a problem in the clinic. One other issue which is critical for in vivo experiments is the question of which formulation is best to be used. Are you screening for different types of formulations routinely or only in particular cases where there is evidence that the formulation affects the outcome?


T. Olah. Absolutely. We have just had discussions about some of the interesting formulations that people come up with. You are right, does a compound simply look bad because it had been dosed in a poor formulation, and could the compound not look better if it is formulated in something else? These are issues that are ongoing and that is why people get nervous if they have to select compounds based on one narrow set of experiments.

B. Subramanyam. Tim, do you have a formulation group that participates in early studies?

T. Olah. At Merck we had an animal group that did, but not to the level that was needed. Typically, the scientist that carries out the experiments did a lot of it and they are using just the basic things, of course.

B. Subramanyam. I think I have seen Pfizer using 100% PEG and I sometimes have to use it myself, but these polyalcohols can disrupt membranes so it is not an ideal formulation and may well affect the outcome of the study.

A. Mandagere. In Ann Arbor the formulation group does get involved in the early stage advising on formulations. They have a set of five to six standard mixtures they can recommend to the pharmacologists and the pharmacokineticists so that they can discuss the best options for their experiments. This works on an ad hoc basis.

G. Fricker. I have a comment with respect to the proposal of data reduction. Geert Mannens mentioned the biopharmaceutical classification system BCS, which actually comes from a regulatory aspect. Here, data are reduced to Caco-2 permeability and solubility. Can something like that be applied to early drug discovery?

G. Mannens. The BCS is just a visual tool, so you get a colour code for high and low permeability and solubility compounds.

G. Fricker. Is the system sensitive enough to give good predictions?


G. Mannens. I think there is still a lot of work to do for good solubility measurements.

B. Subramanyam. From my experience the compounds we produce in discovery are anything but pharmaceuticals, they are simply new chemical entities. If we use a classification system based on permeability and solubility, it may not be appropriate for these chemicals. Sometimes the salt forms are not appropriate and they are not finished chemicals at all. I would be a little concerned about applying the BCS classification system to early discovery compounds and rejecting them. What do others think, are these pharmaceuticals or are these chemicals at this point of discovery?

D. Smith. You are totally correct. While you improve solubility with salt forms the usual thing is they get it crystalline and the solubility disappears.

A. Reichel. A step further on from classifying compounds with regard to solubility and permeability is the role of transport systems. It has long been thought that most of the transfer processes across biological barriers, e.g. intestinal absorption, biliary excretion and CNS penetration, are mediated just by passive diffusion. More recently, we have started to become aware of the fact that a growing number of drngs are actually interacting with transport mechanisms. For instance, there is a lot of activity going on now to study the interaction of drugs with the P-glycoprotein efflux pump. My question is, what is the experience of the other companies in terms of how much drug-transport interactions really determine pharmacokinetics?

D. Smith. We reckon that 20%-30% of our discovery projects have a clearance mechanism based on transporters. And that is not just Pgp, there is also influx into hepatocytes via all these anionic and cationic carriers and subsequently excretion into bile. The trouble is, we are really struggling to get any fix on how you make any calculation for man. It seems in rat you get blood flow clearances and yet the compounds are as stable as a rock against P450s. Where are you going from there? There is no well-established screen to deal with that.


A. Reichel. Wouldn't a pragmatic way out of that problem be to move away from microsomes where you don't have transport systems acting to hepatocytes, so that you have a more complex system to study hepatic clearance including metabolism as well as transport?

D. Smith. Yes, you can screen in hepatocytes where you do a sort of time-stop-flow set-up watching the disappearance of the compound. But I don't think these are easy screens to run and there needs to be something beyond that. Our experience is that monitoring uptake in hepatocytes or disappearance from hepatocytes is not that easy to do, because what you ideally want is a polarized system which takes it up on one side and puts it to the other, so that you can measure disappearance and appearance. What you have got is something disappearing into something which is also being excreted back out again. So you have got to do the initial rate and this is not very easy. You can use animal systems, e.g. perfused liver or so, but then this only tells you how good a rat is at doing it, but it does not tell you how good man is. Again, it may probably reflect the trend of the increasing molecular weight and the complexity of the targets. We seem to be finding more and more that these carrier systems are playing a major role once the compounds have molecular weights greater than 400-450.

A. Reichel. To summarize this part of the discussion, physiologically based PK modelling is a growing area of interest which will help to better understand our in vitro data and may ultimately allow us to predict the PK in man. Integrating data into a PBPK model may also help us to better understand and predict the impact transport processes have on the pharmacokinetic behaviour of compounds. At present there is also a trend for a growing number of assays producing more and more data with the interpretation often being uncertain. Streamlining the screening process based on data mining and retrospective analysis may thus be prudent to help focus our resources to the critical path. Furthermore, the interplay between in vivo PK and PD testing is a very important factor. How it works often depends on resources but experience tells that the better the interactions are between the groups the faster the discovery project will progress.

I want to touch upon another important aspect and that is, how relevant are the PK characteristics which were determined for one or a


few leads for the whole cluster they represent? Lets take CYP inhibition. It certainly is of interest to find out whether the template behind the lead is prone to drug-drug interactions. So it would be of particular value to fmd out whether a possible CYP inhibition relates only to the particular lead molecule(s) tested, or to the whole family of structural derivatives around it in which case one may want to abandon it as a no-hope series.

T. Olah. I think it is also based on how that particular inhibition was actually assessed, e.g. if it was done in a recombinant enzyme system you'd better do some additional tests, for instance a co-administration study or some other assay. If you want to use an assay to kick out compounds you have got to demonstrate that this assay really does hold true in vivo.

A. Reichel. Does this mean you would go and confirm the data derived from the fluorescence CYP inhibition assay by Crespi, which uses recombinant enzymes, in an additional assay, lets say, using liver microsomes?

T. Olah. I would defmitely do that. I definitively wouldn't kill a compound based on an IC50 in the Crespi assay. I think we need additional proof, which can be done by running microsomes with testosterone, for instance, which is a very simply assay, or actually doing a co-administration study with a marker substrate like midazolam in an animal.

A. Reichel. Would you do that already at the stage oflead selection?

T. Olah. Yes. I don't think I would want to throw something out that early on. Again, it really is about building the model and the criteria on which then to base your decision on.

B. Subramanyam. We do both the Crespi as well as microsomal assays. Microsomes can pick up mechanism-based inhibitors, which is another class of inhibitors that we don't want to miss.

H. Schneider. I would like to add that CYP inhibition may surely become dangerous in the clinic but it much depends on the dose of the compound applied to patients. So when we fmd an inhibition constant


of, lets say, 4 f1M for a compound which is dosed in a f.1g range, e.g. a steroid, this may not pose a problem at all.

T. Olah. I agree. I also think that you have to look at what the drug is going to be developed for. I believe that if it is going to be something that will be administered to the general population, you don't want to tolerate it. However, if you are developing a cancer drug or an AIDS drug then I think you take in that additional baggage if that is all you've got.

T. Steger-Hartmann. From our discussions, I have the feeling that we are trying to streamline the set of tests we are doing. But if I listen to what you just said I wonder whether the best set is not always related to the indication you have. This point has not been addressed yet. So I wonder whether we should discuss all these questions in the light of specific indications.

T. Olah. I agree. John Dixon said for an oral drug once-a-day is the way to go if you can, because that tells you that you have good pharmacokinetics. But I think in reality you have got to look at what we are developing these drugs for and who is going to take them and what type of potency you have with these new molecules and then base the decision on whether or not to go ahead. So I agree, I don't think one size does fit all.

c. Wienhold. I have a question regarding data management. As has been said before, we have to deal with more and more data. Key to making the best use out of this data is an appropriate information management system. We have seen several approaches on how to deal with that. Is anybody aware of commercial software which does not require an extensive in-house optimization, or are most of you just running in-house built systems to deal with that?

A. Mandagere. The one example I can give is Spotfrre software, which can be linked to your structural database and also links to the different table of your Oracle database. So you can access pharmacological, physico-chemical and pharmacokinetic data.


C. Wienhold. I was not so much interested in visualization software but in the basic system below, e.g. the integrated database. Is that an inhouse built system?

A. Mandagere. Yes it is, it is based on Oracle tables and was fairly straightforward to set up. Once the tables are created, it is a matter of creating links to each of them and pulling them all together. We then use the Web surfer to access all the information. You can choose whatever type of data you need for each of the simulations.

G. Cruziani. I think there is a lot of software available but all lack parts which are important for the user, e.g. Spotfire is good for visualization but it does not create any descriptors and this is what you need when you want to do correlations. On the other hand, there is a lot of software that calculates descriptors but it is not able to produce visualization to work in a database. I think this is a good challenge for software developers that produce a lot of software for computational chemistry. They have just started now to create software geared more at pharmacokinetics. There is one other point of interest which has become very important to me now. I would like to ask your opinion about the prediction of the site of CYP-mediated metabolism in a molecule.

O. Pelkonen. David Lewis has done a lot of work on in silico predictions of the metabolism of several substances. First of all he has built up protein models based on bacterial cytochrome P450 enzymes and he then went on to the various mammalian P450 models. He then projected the compounds onto the active site of the different P450s and made predictions of which enzyme would act at which site of the molecule.

G. Cruziani. I think this is really challenging. First of all, we do not really know the structure of the enzymes, we rely on homology modelling. Second, we do not really know the reactivity of a molecule in the presence of the enzyme environment or in the presence of the haem group. And we do not really know whether it is a radical reaction or whether the reaction depends on the density of electrons or the carboionic stability. Martin Bayliss mentioned that they have an in-house in silico model to do this kind of job, but I don't know whether it is just looking at the structure of the ligand and recognizes some features or


fragments and then, using a knowledge-based system, assigns a putative reaction site. But I think this is too crude now. It seems to be a very hot topic but very difficult to achieve.

D. Smith. I think to a certain extent it depends on the isoenzyme. The predictive models for 2D6 are quite well advanced. For example, Marcel de Groot at Pfizer Sandwich has published quite a bit on his predictive model which uses a combination of the actual chemistry of the molecule and the ability to produce a radical and then combines it all with a template model and the protein homology. For 2D6 where the SAR is fairly well known it is pretty predictive of the site of metabolism, I think it is something like 95% in a test set where it will predict the N-demethylation that 2D6 does. It is the 3A4 where you really come to a stop, because we already know there is more than one binding site and several ways the substrate can bind in. I think that is where the huge challenge is and, of course, you look at how many pharmaceuticals, maybe 70%, are metabolized by CYP 3A4. So some of it is solvable and the rest will take some time. But I think 2D6 and possibly 2C9 have a homology model now which looks good and is predicting most of the sites of metabolism with high predictivity. But the prediction does take some time, it is not just that you press a button and it will tell you. For us it takes longer to do a prediction than to run it on human hepatocytes and do a mass spec. and an HPLC-NMR and find out what the real metabolites are.

A. Baumann. I have a question conceruing automation and analytics. If you speed up metabolic stability, how do you speed up analytics, e.g. LCIMS? Can you speed up analytics in the same way? Do you really need to automate the metabolic screen if you cannot follow with the analytics?

T. Olah. I think the method development for an individual compound is what takes a bit of time. Again, it depends on what you are trying to get out of a particular screen, e.g. percentage of parent remaining or in vitro half-life or so. We are working on ways to improve throughput but again this is based on LCIMS detection, and this is always going to be a limiting step. We worked with pooling samples and doing the analysis that way, and also developing faster analysis times. It really depends on


how fast we have to go. It is also important to know when you need the information, how much information you need and what amount of time you have to gather the information. This determines the level of accuracy and precision of your analysis.

B. Subramanyam. We have some experience trying to use LCIMS as opposed to HPLC-UV methods for metabolism. It really depends on what output you want, is it precision and accuracy or trend? If it is trend, you can simply use a single quadrupole instrument and look for parent disappearance. However, if you want to quantify exactly how much is there, then you will need to add an internal standard to quantify exactly how much parent there is. So it really depends on what you want to get out of the experiment. We have heard that very often the bottleneck is LCIMS. From our own experience, doing cassette and single dosing, you are not eliminating the bottleneck but you are shifting it from time of analysis to method development as you sometimes encounter ion suppression, ionic enhancement, etc. We are very cautious to balance quality with quantity.

T. Olah. The question for people that are developing methods is, Is there an assessment of the reproducibility of the biological assay, e.g. how reproducible is your metabolic stability assay? Are you taking precautions to determine the reproducibility of the assays? When we started automating the methods for metabolic stability and inhibition, we obviously saw some variance, but what are acceptable ranges for a screening assay?

B. Subramanyam. If the parent is less than 10%, we do not go into quantifying that further. For greater than 10% we try to see how much parent is left in our metabolic assays.

T. Olah. So it is really very broad and from that you can develop accurate models?

B. Subramanyam. Actually, we use it more in a qualitative or semiquantitative sense rather than doing very sophisticated extrapolations as we are in very early discovery. We just want to see whether something is highly metabolized and to suggest clues to the chemistry team which


template they should go ahead with optimizing, rather then spending time on optimizing a template that has fundamental flaws in terms of metabolism. So in that respect less than 10% we don't quantify.

G. Fricker. I have a question concerning the Caco-2 assay. As far as I understood, almost everybody is using this system to generate permeability data. I am interested to know how the different companies qualify the Caco-2 systems in term of comparability of the data produced by other companies? I have participated in a study initiated by the FDA which involved five different laboratories. It took us about a year to standardize the procedures on how to perform the Caco-2 assay. Everybody did it somewhat differently, e.g. one lab used serum in the medium, another used Krebs-Henseleit buffer, the next lab used Hank's balanced salt solution and so on, and the results were quite different for the same set of compounds. Some people use Caco-2 cells from passage 20 to 30, others use passages beyond 80 to 100, where the cells behave very differently. Is there anything ongoing to standardize this system?

J. Post. We use a standard set of compounds where there is literature data on human absorption. We related that data to the permeability we get in our Caco-2 assay. So this is the percentage of human absorption not of human bioavailability, because Caco-2 does not take metabolism into consideration. You are quite right to say that each lab has to develop its own standards and its own quantitative analysis system to know what their permeability values mean. So rather than confirming to another lab we ourselves evaluated several known standards for poorly, medium and highly absorbed and permeable compounds. That is how we use our data to predict the usefulness of the Caco-2 model to the clinical setting.

T. Olah. Could you expand that discussion to P450 inhibition as there are a number of different ways to do inhibition, e.g. using recombinant systems, microsomes, hepatocytes etc. Is there a specific need to standardize a particular method to assess inhibition, in which case you can then make a linker study if you are going to change these conditions to some degree? That is the concern I have for screens that vary between different labs or departments. How useful is that data for putting it all together in a database? I think we need to come to a certain level of acceptance for standardization between labs. Going back and looking at


historical data is probably the worst way to go, because you have no idea how the experiments were done and how valid that information is. Are any of the other companies concerned about this?

D. Smith. Pfizer now operates at six sites and we are now trying to standardize everything in terms of these assays. It was realized, just as you say, that an inhibition of 100 f1M at one site could be 1 f1M or 1,000 f1M at another site, because you used a different protein concentration and you were not measuring binding or so. So we are actually paying a lot of attention to that.

T. Olah. How difficult and how successful has this actually been? Have you come up with a method that is acceptable and reproducible at six different sites?

D. Smith. Clearly all sites are not borne equal and have different resources. It has been very successful once you have shown the need. Until then, there is a bit of resistance, obviously. If you get down to the science level people find it easier to agree to a standardized method. For example, we may have totally standardized the enzyme inhibition by Christmas, doing exactly the same assays at all sites.

A. Reichel. So you believe that you can really succeed in standardizing the assays run at different sites so that the data become indeed comparable? An alternative and more pragmatic way would be to include the same references at all the sites to which you then refer the results obtained for new compounds. For example, people have found with the Caco-2 assay that even when you run it in a fully standardized mode, the cells will still be exposed to different selection pressures, again leading to different results as time goes on.

D. Smith. I think you are totally correct. But I think I would rather start off at the same point. What you are describing is two different trains or actually a train and a plane going in two different directions and trying to compare them. At least I'd like to compare two trains going along the same sort of track. And I think this is where a standard comes in, because how many reference compounds do you need to run? This was one of the problems with the P450 inhibition. How many substrates do


you need to judge 3A4 inhibition, three, four, five, which must be standardized so that everyone is talking the same language.

A. Reichel. At this point, I like to make a brief summary of the final part of the discussion. It has become clear that analytics plays an integral part in facilitating the work of DMPK groups. This even more so as there has been an increasing number of compounds and assays to deal with, many of which depend on compound-dependent analytics. Balancing throughput against accuracy and precision and the search for faster analytical tools will be a key challenge for the future. It has also turned out that data management has become very critical for our work. Appropriate IT support is essential for successful data mining as is the standardization of assays to make sure of a sufficient level of data homogeneity.

In the name of the organizing committee, I would like to conclude the forum discussion by thanking all speakers for their stimulating contributions and all participants for their lively discussion.

Subject Index

absorption 29,49,63, 159, 186, 273

accuracy 162, 170, 172 active transport 199,289 ADME 157, 158, 160, 162, 169,

187,214,265,272 affinity 62, 224, 226, 236 AHR receptor 116 allometric scaling 42, 82, 83, 98 aminergic 204 artificial intelligence-aided drug

design 211 artificial neural networks 82, 86 automation 189, 191,260,294

barcode tracking 189 BCS classification 64, 288 biliary clearance 198 bioanalytical 155,157,158,163,

164,166,172,174, 178, 180 bioavailability 40, 50, 205, 217,

261 blood-brain barrier 139,222,258 brain capillaries 141

Caco-2 51,54,190,219,263 - P-gP expression 60 Caco-2 absorption potential 193 Caco-2 cell permeability 187,269 carbamazepine 194 CAR receptor 118

cassette dosing 76,187,252 cellular models 50, 290 clearance 82,83, 186,289 co-administration studies 165,172 combinatorial chemistry 4,35,71,

156,185,198,205,263 component analysis 173 CYP induction 107,275,277 CYP102 236,246 CYP1A1 108 CYP1A2 108, 235, 237 CYPIB1 108 CYP2A6 235, 237 CYP2B6 235, 237 CYP2C19 235,241,242 CYP2C8 238 CYP2C9 235, 240, 242 CYP2D 235 CYP2D6 192,228,235,241,243,

294 CYP2E1 236, 243, 244 CYP3A 235 CYP3A4 192, 236, 244, 245 CYP450 187 - inhibition 192,275,291,297 CYP4A11 236,245, 246 cytosolic enzymes 198

data processing 164, 176,283 direct scaling 82 distribution 81,82,226

300

diversity 204,208 drug discovery 35, 204 - high-throughput screening 35,

51,205 drug likeness 15,206,214 drug-metabolizing enzymes 37,

236 drug-drug interaction 60, 192,

198,278

efflux pump 142, 191 efflux ratio 64, 289 empirical approaches 82 estimating species-specific oral

bioavailability 192 - graphical model 38, 192 ethanol 109

filter-immobilized artificial membranes 50,51

first pass metabolism 40,42, 186, 210

glitazones 205, 255 gradient 176

hepatic extraction ratios 40, 42, 187

hepatocytes 83, 111,256,290 high speed LC-MS 191, 192 high-throughput permeability

screening 72, 191 high-throughput screening 35,51,

156,213 HPLC 191,295 human pharmacokinetics - prediction 27, 39, 196, 253

in silico models 50,72,251,266, 270

in vitro ADME screening 74, 187, 189

Subject Index

in vitro metabolic clearance 15, 42,83

in vitro/in vivo correlation 42, 85, 280

in vivo pharmacokinetic evaluation 14,36,76,160,264,284,287

Indinavir 38 inhibition screening 198,296 inserts 55 interlaboratory differences 63, 296 internal standards 171, 172 ionization 167 ionization suppression 175

laboratory information management systems (LIMS) 157, 178,292

lead likeness 4,5,207,263 lead selection 8,196,206263,291 library 7,206 LIMS 178, 292 liquid chromatography-mass spec

trometry (LC-MS) 155, 156, 157,166,167,169,174,189,294

liver rnicrosomes 187, 192 liver S9 192, 262 log P 15, 190,261,269,274 losartan 204

mannitol 194 mass spectrometry 164, 167, 170 MDCK 51,72,190,218 mechanistic studies 15,27,36,60,

74,82,100,289 membrane retention 52 metabolic pathway 199 metabolic stability 187,189,190,

192, 193, 196 metabolic switching 198 metabolism 158, 159, 164 metoprolol 194 molecular weight 5, 15, 19,205,

207,215,263

Subject Index

multidrug resistance-associated proteins 143

multiple component analysis 157, 164,167

nephelometric 190, 268 nicotinamide adenine dinucleotide

phosphate, reduced (NADPH) 192

non-linear PK 198,287

omeprazole 109 oral absorption 63,218 oral bioavailability 38, 186, 193

PEPK modelling 88, 99, 253, 282 P-glycoprotein 37,51,59,143,

191,199,211,255,256,266 P-gP expression 60, 143,257 P450 105,206,235 percent flux 191 permeability 50, 189, 190, 191 pH-dependency 38,58 pharmacokinetics 81, 156, 158 - interspecies differences 37 pharmacophores 204 phase II metabolism 54, 74, 117,

198,262 phenobarbital 109 physicochemistry 72,212 potency 5,10,15,190,204,207 pre-clinical "proof-of-concept"

199 precision 162,170,172 prediction 82,86, 196 - in dogs 43, 196

301

- in guinea-pigs 196 - in humans 26,39, 196,253,280 - in rats 196 prediction of clearance 41,83,

229,279 product ion spectra 169, 170 PXR receptor 121,255,276

quality control 164,171,172,177

rifampicin 110, 277 robotic pipetting systems (RPS)

173,174 robotics 51, 189

saturable metabolism 198, 266 selected reaction monitoring (SRM)

167,173 solubility 15, 187, 189, 190,209,

262,268 solvents 62, 250 Spotfire 190,193,292 standardization 170, 174, 177 standards 158,164,171,173,177 structure modification 196, 207 systemic exposure 3, 188,285

taxol 55,62 transport 60,142,191,289 turbidimetric solubility 190,268

unstirred water layer 58

verapamil 55,61, 144194 vinblastine 62 visualization 189,293

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