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Current Chemical Biology, 2008, 2, 215-228 215

1872-3136/08 $55.00+.00 2008 Bentham Science Publishers Ltd.

Advancements in PredictiveIn Silico Models for ADME

Kamaldeep K. Chohan, Stuart W. Paine* and Nigel J. Waters

Department of Physical & Metabolic Sciences, AstraZeneca R&D Charnwood, Bakewell Road, Loughborough,

Leicestershire LE11 5RH, United KingdomCurrent address: Metabolism & Pharmacokinetics Group, Novartis Institutes for Biomedical Research, Horsham, West

Sussex, RH12 5AB, United Kingdom

Abstract: This comprehensive review describes contemporary computational (in silico) quantitative structure-activity re-lationship (QSAR) approaches that have been used to elucidate the molecular features that influence the Absorption,Distribution, Metabolism and Elimination (ADME) of drugs. Recent studies have applied 2D and 3D QSAR,pharmacophore approaches and nonlinear techniques (for example: recursive partitioning, neural networks and supportvector machines) to model ADME processes. Furthermore, this review highlights some of the challenges andopportunities for future research; the need to develop global models and to extend the QSAR for the protein transportersthat influence ADME.

Keywords: In silico, QSAR, ADME, drug metabolism, pharmacokinetics, statistical methods, protein transporters.

INTRODUCTION

The huge cost of pharmaceutical drug development (thecurrent cost of discovering a new therapy is thought to ap-proach US $1.3 to 1.6 billion [1]) and the high attrition ofcompounds entering clinical development, is rightly focusingattention upon every aspect of the efficiency of our industry.While reasons for attrition are varied, including portfoliodecisions and lack of clinical efficacy of the biologicalmechanism, many reasons for compound failure are entirelycontrolled by the chemical structure. Therefore there is stillmuch that can be done in the discovery phase, to improve thechances of success of a candidate drug later in development,by the judicious choice of chemical target. Unfavourableabsorption, distribution, metabolism, excretion (ADME)properties of new drug candidates cause many of these fail-

ures and this has generated intense efforts to identify poten-tial ADME liabilities early in the drug discovery process [2].

Quantitative structure-activity relationships (QSAR) at-tempt to find relationships between the molecular propertiesof molecules and the biological responses they elicit whenapplied to a biological system. The advancements in com-puter hardware and software now allow the molecular prop-erties of molecules to be easily estimated without the need tosynthesise the molecules in question. Thus, the use of predic-tive computational (in silico) QSAR models allows the bio-logical properties of virtual structures to be predicted, and amore informed choice of target to be selected for synthesis.

Considerable progress has been made in computationalQSAR models for predicting ADME properties in recent

years [3,4]. However, the accurate prediction of many keyADME parameters remains a challenge due to the complex-ity of the underlying physiological mechanisms and the lackof high quality data sets for modeling. Despite these

*Address correspondence to this author at the Department of Physical &Metabolic Sciences, AstraZeneca R&D Charnwood, Bakewell Road,Loughborough, Leicestershire LE11 5RH, United Kingdom;Tel: +44 (0)1509 64 4279; Fax: +44 (0)1509 64 5576;E-mail: [email protected]

challenges there has been no respite in the pace of research

on in silico methods for the prediction of ADME properties.QSAR studies are usually carried out using supervised

methods those in which the model is trained using themeasured values of the property to be modelled. Supervisedmethods used in QSAR modeling range from simple statisti-cal regression methods such as multiple linear regression(MLR), principal components regression (PCR), or partialleast squares (PLS), through to more flexible non-linearmethods such as artificial and Bayesian neural networks(ANN and BNN) as well as recursive partitioning methodssuch as classification and regression trees (CART). For areview of QSAR by statistical methods see Winkler [5].Pharmacophore modeling based upon three-dimensionalchemical features associated with activity provide an intui-tive method for modeling chemical-protein interactions.However, pharmacophore modelling is generally limited bythe assumption of a unique mode for substrate binding.

In the absence of measured data, QSAR model predic-tions can be extremely useful and provide a means of identi-fying molecules that may be problematic. QSAR models aremore than a literature curiosity, and successful ones offer thepotential to be used as a virtual screen to filter design targetsbefore synthesis, thus improving the efficiency of pharma-ceutical R&D.

Recent reviews have highlighted the use of in silicoADME models for early decisions in drug discovery [3,4]. Anumber of reviews [6,7] have concentrated on the variouscomputational approaches that have been used in order to

generate ADME models while others have focused on thecurrent challenges, strengths and weaknesses of differentmodeling approaches [8,9]. The purpose of this review is topresent a comprehensive account of contemporary in silicomodeling for drug absorption, distribution, metabolism andelimination. Furthermore, this review includes some veryrecent advances in the use of in silico models for predictingthe steady-state volume of distribution of a drug. The scopeof this review should provide an up-to-date compendium ofin silico ADME models for the pharmaceutical scientist.

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216 Current Chemical Biology, 2008, Vol. 2, No. 3 Chohan et al.

ABSORPTION

The preferred route of administration for pharmaceuticaldrugs is orally, hence, there is great interest in the predictionof intestinal absorption and intestinal permeability; the latteris often represented by measurements using in vitro systemssuch as Caco-2 monolayers. Drug absorption is a complex

process that is dependent on numerous biochemical, physio-logical and physicochemical factors. For instance, drugsmust dissolve in the gastrointestinal tract to be available tocross the intestinal membrane and dissolution is affected bythe aqueous solubility, ionisability and lipophilicity. Absorp-tion from the gastrointestinal tract may be passive or active.Passive transport is controlled by physicochemical propertieswhereas active transport involves specific binding of a mole-cule to a binding site on a transport protein. Despite the com-plex mechanisms involved in absorption, attempts have beenmade to explain and predict drug absorption from a varietyof physicochemical parameters.

The so-called rule of 5 has proved very popular as arapid screen for compounds that are likely to be poorly ab-sorbed [10,11]. This rule states that if a compound satisfiesany two of the following rules, it is likely to exhibit poorintestinal absorption: (1) molecular weight > 500, (2) num-ber of hydrogen bond donors > 5, (3) number of hydrogenbond acceptors > 10 and (4) clogP > 5.0, (i.e. all numbersbeing factors of 5).

In order to obtain a rapid estimation of intestinal perme-ability many cell culture models have been investigated aspotential in vitro models for drug absorption. Among them,the Caco-2 monolayer is the most advanced in vitro modeldue to this cell line serving as a model for both paracellularand transcellular pathways. Caco-2 cells differentiate to formtight junctions between cells to serve as a model for paracel-lular movement of compounds across the monolayer. In ad-dition, Caco-2 cells express transporter proteins, efflux pro-

teins and phase II conjugation enzymes to model a variety oftranscellular pathways as well as metabolic transformation oftest substances. In many respects, the Caco-2 cell monolayermimics the human intestinal epithelium. One of the func-tional differences between normal cells and Caco-2 cells isthe lack of expression of the cytochrome P450 enzymes andin particular, CYP3A4, which is normally expressed at highlevels in the intestine. However, Caco-2 cells may be in-duced to express higher levels of CYP3A4 by treatment withvitamin D3. Metabolic enzymes within the Caco-2 cell linemay confound the apparent estimate of permeability. Goodcorrelations between the extent of oral drug absorption inhumans and rates of transport across the Caco-2 cell mono-layers were obtained by Artursson and Karlsson [12].

A useful QSAR for structure permeability correlations,used to evaluate Caco-2 permeability and hence oral absorp-tion has been summarised by Van de Waterbeemd et al. [13]as follows: Oral absorption = function (logD7.4, molecularsize, H-bonding capacity). Where logD7.4 is the octa-nol/water partition coefficient at pH 7.4, molecular size is ameasure related to mass, volumes and surfaces of the mole-cule in question and H-bonding capacity relates to the num-ber and strength of the hydrogen bonds that can be donatedand /or accepted by the model compound.

Ren et al. [14] obtained a significant correlation for 51compounds between Caco-2 cell permeability and the maxi-mum hydrogen bond-forming capacity corrected for intra-molecular H-bonding, distribution coefficient (logDoctanol),molecular weight and an indicator variable for the charge,with a correlation coefficient of 0.797. When these com-pounds were divided into three subgroups, namely neutral,

cationic and anionic compounds, much better correlations (r= 0.968, 0.915 and 0.931, respectively) were obtained usingdifferent combinations of various physicochemical parame-ters.

A more sophisticated quantitative structure permeabilityrelationship (QSPR) model of Caco-2 permeability using anovel genetic algorithm-based partial least squares (GA-PLS) method was developed by Yamashita et al. [15]. Theapparent permeability coefficients in Caco-2 cell monolayersfor 73 structurally diverse compounds were taken from theliterature. The data for compounds that possess high molecu-lar weight (>1000) or that might be transported via activetransport processes were excluded from the analysis. Mol-conn-Z descriptors of these compounds were calculated, and

the optimal subset of the descriptors was explored by GA-PLS analysis. A fitness function considering both goodnessof fit to the training data and predictability of the testing datawas adopted throughout the genetic algorithm driven optimi-zation procedure. The final PLS model consisting of 24 de-scriptors gave a correlation coefficient (r) of 0.886 for theentire dataset and a predictive correlation coefficient (rpred) of0.825 that was evaluated by a leave-some-out cross valida-tion procedure.

The possibility of directly modelling human intestinalabsorption (HIA, usually expressed as a percentage doseabsorbed) as opposed to a more simplistic surrogate such asCaco-2 is extremely attractive as it removes the assumptionsin the relationship between Caco-2 permeability measure-

ments and human intestinal absorption. It is therefore notsurprising that the majority of contemporary absorptionmodels are based on measured intestinal absorption. Palmand Clark [11,16,17] used a theoretical method, based on thedetermination of dynamic surface properties - polar molecu-lar surface area (PSA), to predict the human intestinal ab-sorption. An excellent sigmoidal relationship was establishedbetween the absorbed fraction after oral administration tohumans and PSA for 20 drugs (r2=0.94). Drugs that are com-pletely absorbed (>90%) had a PSA 60 , whereas drugsthat are

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Advancements in Predictive In Silico Models for ADME Current Chemical Biology, 2008, Vol. 2, No. 3 217

A non-linear PLS absorption model that can be achievedwith minimal computational efforts has been described byOprea and Gottfries [19]. The QSAR model correlates hu-man intestinal absorption data and apparent Caco-2 cell per-meability data, to parameters calculated from molecularstructure. Two properties were found to be relevant for ab-sorption predictions, namely H-bonding capacity and hydro-phobic transferability. A one PLS component model wasselected and the use of both %HIA and Caco-2 permeabilitydata was found to stabilise and improve the model.

A quantitative structure HIA relationship was developedusing artificial neural network (ANN) modelling by Aga-tonovic-Kustrin et al. [20]. A set of 86 drug compounds andtheir experimentally derived intestinal absorption valuesused in this study was gathered from the literature and a totalof 57 global molecular descriptors, including constitutional,topological, chemical, geometrical and quantum chemicaldescriptors, were calculated for each compound. A set of 76compounds was selected for training and testing the ANNsand 10 compounds were used as an external prediction set. Asupervised network with radial basis transfer function was

used to correlate calculated molecular descriptors with ex-perimentally derived measures of HIA. A genetic algorithmwas then used to select important molecular descriptors. In-testinal absorption values (%HIA) were used as the ANNsoutput and calculated molecular descriptors as inputs. Thebest genetic neural network (GNN) model with 15 input de-scriptors was chosen, and the significance of the selecteddescriptors for intestinal absorption examined. The trainingset root mean square error (RMSE) of the logged %HIA was0.59 and the testing set RMSE was 0.90. Based on theRMSEs of the training and test sets it is clear that a link be-tween structure and HIA does exist. The strength of the linkwas measured by the quality of the external prediction set.With an RMSE of 0.425 and a good visual plot, the external

prediction set ensured the quality of the model. This modeldoes not require experimental parameters and could poten-tially provide useful prediction of gastrointestinal absorptionof new drugs. However, the data set employed here washighly biased towards well-absorbed compounds and con-tained compounds that are subjected to various active trans-port mechanisms.

Zhao et al. [21] assembled a HIA dataset of 241 drugsand in combination with solvation descriptors modelled theHIA data of 169 drugs that were considered to have reliabledata. The model contained five main descriptors: solute ex-cess molar refractivity, dipolarity/polarisability, summationhydrogen bond acidity and basicity, and the McGowan char-acteristic volume. They were calculated by the ABSOLV

program and are commonly known as the Abraham descrip-tors. They developed several training sets based on differentnumbers of chemicals. One of these models used 38 drugs,had an r2 of 0.83 and an RMSE of 14%. This model wasvalidated by testing a data set of 131 compounds with goodstatistical results, RMSE also being 14%. Another modelwas built using a set of 169 drugs but had less desirable sta-tistical parameters, r2 being only 0.74. The results showedthat Abraham descriptors can successfully predict HIA if thehuman absorption data are carefully classified based onsolubility and the administered dose to humans.

In a follow-up paper Zhao et al. [22] developed a QSARabsorption model that could predict the following threeclasses of compounds: Class I, high solubility and high per-meability; class III, high solubility and low permeability;class IV, low solubility and low permeability. The absorptionmodels over-predicted the absorption of class II, low solubil-ity and high permeability compounds because dissolution is

the rate-limited step of absorption.

A number of publications in 2002 presented a series ofHIA models. A novel approach for the prediction of HIAbased upon a combination of hydrogen bond donors andstructural similarity was proposed by Raevsky et al. [23].The approach made a new assumption that structurally simi-lar drugs are absorbed by similar mechanisms or combina-tions of mechanisms. By using similarity measures, theycould identify similar drugs. By considering the known frac-tion absorbed value as well as H-bond character they couldcorrectly estimate the effect of structural changes on the ab-sorption of a drug of interest.

Deretey et al. [24] developed a model based upon oneand two descriptor nonlinear models and despite the simplic-ity of the model, the quality of fits and predictions was com-parable to more complex approaches as outlined above. In anattempt to build a quantitative structure permeability modelthat included compounds that are transported by differentmechanisms through intestinal membranes, Klopman et al.[25] developed a model based on a modified contributiongroup method in which the basic parameters are structuraldescriptors identified by the CASE program, together withthe number of hydrogen bond donors. The HIA model in-cluded 37 structural descriptors derived from the chemicalstructures of a data set containing 417 drugs. The model wasable to predict the percentage of drug absorbed with an r2 of0.79 and a standard deviation of 12.32% of the compoundsfrom the training set. The standard deviation of the large

external test set (50 drugs) was 12.34%.Zmuidinavicius et al. [26] developed an algorithm

builder (AB) for identifying compounds with poor intestinalmembrane permeability. The analysed data set included over1000 drug-like compounds with experimental HIA values. Asequence of recursive partitioning analyses based on multi-ple physicochemical and structural descriptors led to thederivation of the rule based algorithm. The obtained rulesreveal a simple physicochemical model of intestinal perme-ability; they also account for the specific effects caused byquarternary nitrogens and biphosphonate groups. Compari-son of the observed and predicted values revealed a very lowpercentage of disagreement (15% false-positives and 3%false negatives). The unusual absorption of compounds thatdeviated from the predicted values was explained in terms of

active transport, efflux, chemical stability, chelating abilityand solubility. Most of these effects could be accounted forby new, substructure-specific rules that can be added into theexisting filter.

An approach by Perez et al. [27] allows the identificationand quantification of the structural fragments that are re-sponsible for the HIA value. The HIA of drugs was studiedusing a topological sub-structural molecular approach design(TOPS-MODE). The drugs were divided into 3 classes ac-cording to reported cut-off values for HIA. Poor absorp-tion was defined as HIA 30%, high absorption as HIA

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80%, whereas moderate absorption was defined betweenthese two values. Two linear discriminant analyses werecarried out on a training set of 82 compounds. The percent-ages of correct classification, for both models were 89.0%.The predictive power of the models was validated by 3 tests:a leave-one-out cross validation procedure (88.9% and87.9%), an external prediction set of 127 drugs (92.9% and

80.31%) and a test set of 109 oral drugs with bioavailabilityvalues reported (93.58% and 91.84%). Finally positive andnegative sub-structural contributions to the HIA were identi-fied and their possibilities in the lead generation and optimi-sation were evaluated.

The majority of the models described above try to quanti-tatively predict the absorption of molecules. However,Deconinck et al. [28] have predicted absorption classes,ranging from low to high absorption, using the classificationand regression trees (CART) methodology. Models werebuilt using the published absorption values for 141 drug-likemolecules as the response variable and over 1400 moleculardescriptors as potential explanatory variables. Both the roleof 2 and 3-dimensional descriptors and their relative impor-

tance were evaluated. For the data set used, CART modelsshowed high descriptive and predictive abilities. The predic-tive abilities were evaluated based on both cross-validationand an external data set. Application of the variable rankingmethod to the models showed high importance for logP andpolar surface area (PSA).

A model for prediction of HIA of neutral molecules wasdeveloped based upon surface charges of the molecule calcu-lated by density functional theory (DFT) [29]. The surfacecharges are composed into moments, which are correlatedto a partition coefficient representing transfer of the mole-cule between water and the epithelial membrane. The modelwas built and tested using a data set of 241 drugs. It achievedan RMS deviation of 13% on a training set of 38 compounds

as well as on a test set of 107 drugs for which the experimen-tal data were classified as high quality. However, the resultswere low for drugs where absorption is solubility or formula-tion dependent. The potential advantages of this approach arethe calculation from molecular structure without the need fora fragment database and the production of colour codedproperty maps of the molecular surface to aid drug design. Apossible disadvantage is that the speed of calculation is moresuited to comparing a limited set of molecules than for high-throughput screening.

Recently, Polley et al. [30] developed predictive HIAmodels that use Bayesian regularized neural networks. Thebest models were produced using physicochemical descrip-tors or Abrahams descriptors and were capable of explain-ing ~85-90% of the variance in the training and test sets.

These models compared favourably with the neural networkmodel of Agatonovic-Kustrin et al. [20] that were derivedfrom similar descriptors but a smaller dataset.

Subramanian and Kitchen [31] have cast doubt on theability to generate HIA and Caco-2 Papp QSAR models thatcan predict the absorption of both passively and activelytransported compounds. Datasets were collected from theliterature and compounds were divided into passively andactively transported sets. The training set was comprised of22 and 30 structurally diverse passively absorbed moleculesfor Caco-2 Papp and HIA respectively. Two test sets were

used; the first contained passively absorbed molecules andthe second actively transported molecules. The quantitativelogPapp and HIA results were used to categorise the mole-cules based on the extent of intestinal permeability and ab-sorption using the following threshold values. Thus, com-pounds with computed logPapp < -6 and < -5 are classified aspoorly and moderately permeable, respectively. All the re-

maining molecules are considered to possess good intestinalpermeabilities. Similarly, a%HIA of < 30 suggests poor in-testinal absorption, while molecules exhibiting HIA > 70%possess good intestinal absorption. Compounds with mediumHIA range between 30 and 70%. There was an excellentcorrect classification for the training sets (> 90%), however,the test sets containing passively absorbed compounds gaveapproximately a 70% success rate. Predictions for the testsets containing actively transported compounds gave a mod-erate success rate of approximately 40%. These models werecompared with models generated using literature data setscontaining a mix of passively and actively transported mole-cules. The models using literature data sets produced weakerpredictions suggesting that inclusion of actively transportedcompounds into the data sets is not conducive to predictivemodels.

DISTRIBUTION

Drug distribution represents a key paradigm in pharma-cokinetics, which determines half-life, dosing regimen andfree drug concentrations in tissues likely to drive a pharma-codynamic or toxic effect. Until recently, the majority ofADME modelling approaches to distribution involved pre-diction of blood-brain barrier permeation or plasma proteinbinding and these studies have been reviewed by Lombardoet al. [32]. In addition, there have been specific and exten-sive reviews on the modelling of these properties; plasmaprotein binding [33,34] and blood-brain barrier permeability[35]. Stephen Currys group at Imperial College London

present an in-depth crystallographic analysis of the structureof human serum albumin with various drugs bound includingdiazepam, ibuprofen, indomethacin, phenylbutazone andwarfarin providing insight into the binding specificity, ca-pacity and pharmacophore of this plasma protein [36].Greater understanding of the structural determinants of se-rum albumin binding is paramount, as plasma protein bind-ing has a fundamental impact on the pharmacokinetics(clearance and volume of distribution) of pharmacologicalagents.

More recently, the focus has turned to the computationalprediction of the proportionality constant, steady-state vol-ume of distribution (Vss). Vss represents the volume inwhich a drug would appear to be distributed during steadystate if the drug existed throughout that volume at the sameconcentration as that in the measured fluid (blood orplasma). It is a function of binding to plasma and tissuecomponents and as such is commonly expressed via the Gil-lette equation.

t

p

tpssfu

fuVVV +=

Where Vp is the volume of plasma, Vt is the volume oftissue, fup is the fraction unbound in plasma and fut is thefraction unbound in tissue. Early work by Lombardo et al.

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[37] involved prediction of Vss using a combination of insilico and in vitro measured parameters (logD7.4 and freefraction in blood plasma). Turner et al. [38] using a smalltraining set of 50 compounds built computational models forplasma protein binding, Vss, and clearance (total and renal)in human using an artificial neural network methodology.The error in prediction was encouraging with RMSE of 2,

0.6, 0.7 and 0.2 for clearance, renal clearance, Vss andplasma protein binding respectively. However, the smalltraining set restricted the chemical space represented in themodel and therefore provides limited utility of these modelsfor compounds in drug discovery.

Two independent labs recently published in silico model-ling approaches for Vss. Gleeson et al. at AstraZeneca util-ised Bayesian neural networks (BNN), classification andregression trees (CART) and partial least squares (PLS)techniques to model human (n = 199) and rat (n = 2086) Vssdatasets [39]. The use of three distinctly different statisticalmethodologies allowed a more complete exploration of themultidimensional molecular hyperplane that controls Vss,explicitly accounting for both linear and non-linear depend-

encies, and as such consensus predictions using all threetechniques proved the most accurate. The results in predic-tion of an independent test set showed the human model hadan r2 of 0.60 and an RMSE in prediction of 0.48. The corre-sponding rat model had an r2 of 0.53 and an RMSE in predic-tion of 0.37, indicating both models could be very useful inthe early stages of the drug discovery process.

Vss is often assumed to lie within certain ranges basedsolely on charge type, for example the Vss of acidic com-pounds is expected to be between 0.1 and 0.3 L/kg. As partof the in silico model validation, the RMSE in predictionwas calculated for this in cerebro prediction and compared tothat of the computational model. This comparative analysisshowed that the rather tight distributions expected for the

different ionisation states are unrealistic, and the model didsignificantly better than a scientists intuitive guess (in cere-bro prediction) simply on the basis of charge type.

Interestingly, the rat model predicted the human test setwith no bias (low mean error), suggesting that the small pro-tein binding differences between rat and human that maycontribute to systematic bias in predicted Vss are not identi-fied by the model. From the PCA scores analysis the major-ity of human compounds had rat near-neighbours, but theconverse was not true. The PCA scores and loadings plotsidentified this difference was simply a reflection of the lowerlogD, logP, molecular weight and calculated molar refractiv-ity (CMR) of the literature derived compounds.

Similarly, Lombardo et al. at Pfizer developed a compu-

tational approach that could predict the Vss of new com-pounds in humans, with an accuracy of within 2-fold of theactual value, using a larger dataset of 384 drugs [40]. A hy-brid mixture discriminant analysis-random forest (MDA-RF)model was employed with 31 computed descriptors coveringlipophilicity, ionisation, molecular volume, and various mo-lecular fragments. Model validation was performed on anindependent test set of 23 proprietary compounds where thegeometric mean fold-error (GMFE) was 1.78-fold. In addi-tion, a leave-class out strategy wherein subsets of drugsbased on therapeutic class were removed from the trainingset of 384, the model recast, and the Vss values for each sub-

set predicted was utilised with GMFE values ranging from1.46 to 2.94-fold, depending on the subset. Further validationwas undertaken comparing computational predictions of aset of 74 compounds with predictions based on animalpharmacokinetic data. The computational prediction of Vsscompared favourably with an accuracy equivalent to predic-tions requiring substantially greater effort.

These studies have highlighted the potential of in silicoADME to provide key information at stages in drug discov-ery prior to chemical synthesis, allowing predictions to bemade on virtual compounds, and preceding detailed allomet-ric or mechanistic assessment of Vss. Such models of Vssaid the medicinal chemist in modulating half-life via tangi-ble, easily-interpretable physicochemical descriptors. In ad-dition, with an estimate of clearance (in vitro orin silico) onecan predict pharmacokinetic dwell time, in the form of half-life or mean residence time.

METABOLISM

Many drug leads are found to be unsuitable for clinicaluse at later stages of development due to problems with me-

tabolism [41]. Drug metabolism involves the elimination of adrug by an enzyme and the rate of this elimination influencesthe half-life of the drug. Hepatic metabolism is the primaryelimination mechanism for the majority of drugs. Metaboliz-ing enzymes that exhibit genetic polymorphism and in-volvement in undesirable drug-drug interactions may causerejection of a compound as a useful therapeutic agent. Drugmetabolism is normally divided into two phases: phase I (orfunctional reactions) and phase II (or conjugative reactions).Cytochrome P450 (CYP), a heme monooxygenase, andUDP-glucuronosyltransferase (UGT) are quantitatively themost important phase I and phase II enzymes respectively,involved in drug and chemical metabolism [41,42].

Recent reviews have highlighted the use of QSAR for

predicting substrate binding, inhibition and induction of CYP[43, 44] as well as modelling metabolism by UGT [45]. Herewe present contemporary QSAR modeling for phase I and IImetabolism. This will be limited to CYP and UGT QSARmodels as these are responsible for the elimination of morethan 90% of drugs cleared by the liver.

QSAR Modelling for Cytochrome P450 (CYP) Metabo-lism

QSAR modelling for CYP catalysed biotransformationshas progressed in parallel with the increasing availability ofCYP isoform substrate and inhibitor selectivities, and thedevelopment of such models has also been aided by access tohomology models of human CYPs. Contemporary QSARmodelling for the major CYP isoforms (CYP3A4, 2D6, 1A2,

2C9 and 2C19) is discussed.CYP3A4

CYP 3A4 is the most abundant human hepatic CYP iso-form responsible for the metabolism of almost 50% ofknown drugs and has a very important role in first pass gutmetabolism (see Absorption section). QSAR models havebeen reported for both substrates and inhibitors. A 3D QSARapproach to assess substrate binding to CYP3A4 was carriedout by Ekins et al. [46]. Observed versus predicted correla-tions for the apparent Km were reasonable (r = 0.67) and allpredictions were within a log unit of the residual. However,

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this type of analysis assumes binding modes are identicaland in the case of CYP3A4 the large binding pocket, con-formational freedom and binding stoichiometry (3A4 canbind 2 molecules simultaneously) somewhat limit the predic-tive power of a 3D QSAR model. A combined 3D pharma-cophore and 3D/4D QSAR PLS strategy has been applied toCYP3A4 inhibition [47]. The PLS analysis was based on

multiple conformers and alignments, hence 4D, and usedmolecular surface weighted holistic invariant molecular de-scriptors (MS-WHIM) for size and shape. It was possible toobtain good observed versus predicted correlations on threepharmacophore models (r = 0.91, 0.77, 0.92) suitably crossvalidated (both internal and external test sets) which werecomparable with the 3D/4D QSAR PLS technique.

Wanchana et al. [48] employed a 2D QSAR approachusing topological indices and a genetic algorithm (GA)-PLSmethod to predict CYP3A4 inhibition. The dataset consistedof 44 structurally diverse drugs which were split into training(n = 35) and test (n = 9) sets. The model gave reasonablepredictions with an rpred of 0.75 and 0.74 for training and testsets, respectively. The predictability is comparable to that

obtained from the 3D QSAR approach and eliminates con-formational ambiguities. Furthermore, Molnar et al. [49]developed neural network models to classify CYP3A4 in-hibitors using 2D structural data [Unity fingerprints gener-ated using Sybyl (Tripos International, 1699 South HanleyRd., St. Louis, Missouri, 63144, USA)]. Binary assignmentof the dataset to non-inhibitor (n = 145) and inhibitor (n =145) classes was carried out. The model correctly classified97% of the inhibitors and 95% of the non-inhibitors. Furthermodel validation was achieved by assessing prediction accu-racy on 9 Lilly in-house discovery compounds, where 8 werecorrectly classified. The ANN approach recognised at least89% of 3A4 inhibitors illustrating the potential of this modelin a virtual screening paradigm.

CYP2D6CYP2D6 is a polymorphic P450 isoform and its meta-

bolic activity in humans has a tetramodal distribution: poor(5-10%), intermediate (11%), extensive (78-83%) and ultra-rapid (

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Moon et al. [63] have used MLR and back propagationneural networks (BPN) to model CYP1A2 inhibition data offlavonoids. Models trained on 14 compounds had an r2 rang-ing from 0.87 to 0.98. Based on r2 the neural network modelwas the best. Test set statistics based on 5 compounds alsosuggests that the neural networks perform better than theMLR model. The models imply that the Hammett constant,the highest occupied molecular orbital energy (HOMO), thenon-overlap steric volume, the partial charge of C3 carbonatom, and the HOMO coefficients of C3, C3 and C4 car-bon atoms of flavonoids play an important role in CYP1A2inhibition. Iori et al. [64] also looked at flavonoids. The in-

hibitor potency of the first series of flavonoids was related tothe stability of the complex it is able to form (r2 = 0.86 andr2cv = 0.82). Also, strong correlations were found betweenCYP1A2 inhibition and (i) interaction between ligand andheme (r2 = 0.95 and r2cv = 0.91), (ii) hydrogen bonding termsbetween ligand and CYP1A2 and (iii) the energy cost oftransferring ligands from water into the binding site (r2 =0.77 and r2cv = 0.70). A similar analysis was carried out withthe second and third series of flavonoids. Briefly, for thesecond series a strong correlation was obtained between sta-bility of complex and CYP1A2 inhibition. The hydrogenbond acceptor term was also important for this class of fla-vonoids. The HOMO energy was important for the third se-ries of flavonoids (r2 = 0.93 and r2cv = 0.88).

Two very recent publications have attempted to model

CYP1A2 inhibition using a structurally diverse set of com-pounds. A 3D-QSAR approach was adopted by Korhonen etal. [66]. The fifty-two compounds tested for inhibition con-sisted of naphthalene and lactose derivatives together withcompounds that were planar, small-volume, neutral orweakly basic. The latter are generally features that are asso-ciated with CYP1A2 substrates and inhibitors. A GRID/-Golpe model (r2 = 0.90, r2cv = 0.79 and standard deviation =0.52) trained on 42 compounds was obtained consisting of 3PLS components. The CoMFA model was also a 3 compo-nent model with r2 = 0.87, r2cv = 0.69 and standard deviation= 0.67. The CoMFA model proved predictive for an externaltest set (n = 11) with the residual error varying from 0 to0.67 log units. CoMFA contours suggested that partial nega-tive charge close to a nitrogen atom and positions 6 and 8 ofthe quinoline ring are associated with an increase inCYP1A2 inhibition. The GOLPE coefficient map suggeststhat hydrogen bonding is an important interaction betweenquinoline type inhibitors and CYP1A2.

Chohan and Paine et al. [65] have used four statisticalapproaches; PLS, MLR, CART and BNN have been usedtogether with consensus modelling to build global CYP1A2inhibition QSAR models. Models have been built on a rela-tively large and structurally diverse training set (n = 109).All models have been validated using a set of oral drugs (n =

249) that have been measured at a different laboratory to thetraining set but still within the same company. In fit, themodels have a good r2 and RMSE (r2 ranging from 0.72 to0.84). The models suggest that lipophilicity, aromaticity,charge, and the HOMO/LUMO energies are important fea-tures for CYP1A2 inhibition. Test set statistics suggest thatthe models show promise as a rapid computational or virtual

screen for CYP1A2 inhibition potential.

CYP2C9

The polymorphic CYP2C9 enzyme plays a significantrole in metabolism, metabolising approximately one-fifth ofall drugs [67]. The majority of CYP2C9 substrates are acidiccompounds such as the nonsteroidal anti-inflammatory drugs(NSAIDs) and hypoglycemics [68]. In addition they alsotend to contain an aromatic group that has the capacity toform - interactions with the enzyme-binding site [69].Other interactions, such as hydrogen bonding are also impor-tant. Inhibitors of CYP2C9 include compounds such as sul-faphenazole [70] and benzbromarone [71,72].

A stepwise multiple linear regression for a small set of

weakly acidic CYP2C9 substrates (binding data) and phys-icochemical properties has been carried out by Lewis et al.[62]. Their QSAR analysis indicates the importance of lipo-philicity and acidic character to the overall binding affinityof substrates to CYP2C9. In addition, the number of hydro-gen bond donors also contributes to the binding of substratesto the enzyme [73].

Here we discuss some of the recent QSAR modellingwork for CYP2C9 inhibition. Afzelius et al. [74] have calcu-lated alignment-independent descriptors within ALMOND.The dataset consisted of compounds that were competitiveCYP2C9 inhibitors and compounds that had no affinity forthe enzyme (non-inhibitors). The PLS discriminant analysisinvolved 21 inhibitors and 21 non-inhibitors. A 2 component

model was obtained with an r

2

= 0.74 and q

2

= 0.64. Themodel was able to correctly predict 74% of compounds thatmade up a test set and 13% of compounds were predicted asborderline cases (test set = 14 inhibitors and 25 non-inhibitors). The authors also generated a quantitative modelusing PLS and the GRid INdependent Descriptors (GRIND)and experimental Ki values for 21 competitive inhibitors.The model (r2 = 0.77 and q2 = 0.60) was able to predict 11out of 12 external test set compounds within 0.5 log units ofthe measured data. Other alignment-independent methodshave also yielded predictive models for structurally diversecompetitive CYP2C9 inhibitors [75]. Furthermore a 3-DQSAR (CoMSIA modelling) approach has been used tobuild quantitative models for a potent class of CYP2C9 in-hibitors - benzbromarone analogs [76]. A general structure

for these is shown in (Fig. 2).Other methods for addressing CYP2C9 have involved

using support vector machines by Yap and Chen [77]. Themodels produced show high prediction accuracies. For inhi-bition the classification accuracy was 88.9% for inhibitorsand 96.3% for non-inhibitors. The classification accuracy forsubstrates and non-substrates was 85.7% and 98.8% respec-tively. The model descriptors were consistent with the find-ings that the substrates of CYP2C9 are primarily polar com-pounds that contain an aromatic group. For the inhibitorsthere may be fewer hydrogen bonds but increased electro-

Fig. (1). Basic skeleton structure for flavone.

O

O

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static interactions at the active site compared to the sub-strates.

CYP2C19

The polymorphic CYP2C19 enzyme is involved in themetabolism of (S)-mephenytoin, omeprazole, diazepam andimipramine. An example where CYP2C19 polymorphismplays an important role is with the proton pump inhibitoromeprazole (racemate). Significant variability in systemicexposure of omeprazole can be observed between patientswith different genotypes. Esomeprazole (S enantiomer)

shows a 3-fold decrease in intrinsic clearance and is a poorersubstrate for CYP2C19. Removing the efficiency ofCYP2C19 R-enantiomer based metabolism also removes thepotential for interactions with CYP2C19 substrates and ge-netic polymorphism variability. Omeprazole produces clini-cally significant interactions whereas esomeprazole does noteven though it is still primarily cleared by 2C19. UnlikeCYP2C9 it has little preference for anions, despite sharinghigh sequence homology. By using multiple linear regres-sion, Lewis et al. [62] have discussed the correlations andimportant molecular features for CYP2C19 activity. Theyfound an overlap between features that are important for 2C9substrates and inhibitors. Features included the relative mo-lecular mass, number of hydrogen bond donors and accep-tors for inhibitors. For substrates the logP becomes more

important than the number of hydrogen bond acceptors.A potent inhibitor of CYP2C19, benzbromarone has been

reported by Locuson et al. [78]. Benzbromarone analogs canbe modified to show selectivity for the CYP2C9 enzyme.The most important feature for these compounds to bind toCYP2C19 was low acidity or non-ionizability and hydro-phobic substituents adjacent to the phenol moiety. FourCoMFA models (0.5 < q2 < 0.7) were derived based on thebenzbromarone analogues coupled with phenobarbital ana-logues because models on benzbromarone analogues only,were too limited to give models. The authors were not ableto determine which model was correct and as they suggestthis may be because the dataset is limited or there is no sin-gle conformation that is greatly favoured over another forthese analogues.

There has also been significant progress in computationalapproaches to predicting sites of metabolism and many ofthese have subsequently become available commercially asdatabases and software. MetaSite developed by Cruciani etal. utilises the 3D structure of human CYPs and substrate-haem proximity together with the inherent reactivity ofligand atoms to generate a probability of site of metabolismfor each substrate atom [79]. This approach does not rely onlarge training sets of data for a prediction and is typicallyable to predict the sites of metabolism with a predictive ac-

curacy of 85%. Alternative methodologies have involvedrule-based expert systems like METEOR [80] and machinelearning (kernel partial least squares) using databases of hu-man drug metabolism data (MetaDrug) [81]. Built on largeknowledge- and data-bases, these models provide predictionsof CYP and non-CYP mediated metabolism albeit in a clas-sification manner.

QSAR Modelling for UDP-Glucuronosyltransferases(UGT) Metabolism

A general reaction mechanism for glucuronidation isshown in Fig. (3). UGT enzymes catalyse the displacementof uridine di-phosphate by a nucleophilic centre on the sub-strate leading to a more water soluble glucuronide. The de-velopment of models for predicting metabolism and forcharacterising structural features of substrates for UGT iso-forms is less advanced relative to the CYP family. Only re-cently has the substrate profile of UGT isoforms begun toapproach an interpretable level. cDNAs encoding 18 UGTproteins have been isolated to date and these have been clas-sified in two families, UGT1 and UGT2, based on aminoacid sequence identity [82]. Of the various human UGTs,substrate selectivities now approach interpretable levels forUGT 1A1, 1A3, 1A4, 1A6, 1A7, 1A8, 1A9, 1A10, 2B4,2B7, 2B15 and 2B17. An X-ray crystal structure of a UGT isnot yet available and few structurally or catalytically relevantamino acids have been identified. Contemporary QSARmodelling for the major UGT isoforms is discussed.

UGT1A1

UGT1A1 is a polymorphic enzyme that is solely respon-sible for the glucuronidation of bilirubin, and additionallyhas the capacity to metabolise hydroxyoestrogens, thyroidhormones and numerous drug and non-drug xenobiotics [83-85]. An understanding of UGT1A1 substrate and inhibitorselectivities and binding affinities becomes important for the

identification of compounds whose elimination may be im-paired in subjects with var iant genotypes, and for the predic-tion of potentially inhibitory interactions involving xenobiot-ics and endogenous compounds metabolised by UGT1A1.

Sorich et al. [86] have generated two- and three-dimensional QSAR models for 23 known UGT1A1 sub-strates with diverse structure and binding affinity. Initially asimple procedure was developed to determine apparent inhi-bition constants (Ki,app) for these compounds. Eighteen sub-strates were subsequently used to construct models and the

Fig. (2). Structure for Benzbromarone.

Fig. (3). Glucuronidation and the SN2 Mechanism.

O

O

Br

OH

Br

HOO

HOOH

O

P

O

-O UMP (uridine mono-phosphate)

HO2CO

H

-B-Enzyme

H-B-Enzyme

DeprotonationNucleophilic

SN2 attack

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remaining five to validate the predictive ability of the mod-els. A 3D-QSAR using a common features pharmacophoreto align the substrates was constructed. The Ki,app for fourout of the five test substrates predicted within one log unitand an r2 = 0.71 was obtained. A 2D-QSAR using PLS re-gression was also built. The optimal model was a six de-scriptor, two-component model with an r2 = 0.92 and a

leave-one-out cross-validated r2 = 0.77. The test set sub-strates were predicted within one log unit of their observedvalue. The common features pharmacophore demonstratedthe importance of two hydrophobic domains separated fromthe glucuronidation site by 4 and 7 , respectively.

UGT1A4

UGT1A4 has the ability to metabolise a structurally di-verse group of compounds, which includes drugs, nondrugxenobiotics, and hydroxysteroids. Of particular interest is thecapacity of UGT1A4 to catalyse the glucuronidation of pri-mary (e.g. 4-aminobiphenyl), secondary (e.g. desmethylclo-zapine) and tertiary amine (e.g. clozapine) containing com-pounds [87,88]. Smith et al. [89] have used 2D and 3DQSAR modelling techniques to test models capable of ra-tionalizing and predicting human UDP-glucuronosyltrans-ferase 1A4 (UGT1A4) substrate selectivity and binding af-finity (Km,app). The data set included 24 structurally diverseUGT1A4 substrates, with 18 of these comprising the trainingset and six as an external prediction set. A common fea-tures pharmacophore was generated with the program Cata-lyst (Accelrys, Inc., San Diego, CA) after overlapping thesites of conjugation using a novel, user-defined glucuroni-dation feature. Pharmacophore based 3D-QSAR (r2 = 0.88)and molecular-field-based 3D-QSAR (r2 = 0.73) modelswere developed using Catalyst and self-organising molecularfield analysis (SOMFA) software, respectively. In addition, a2D-QSAR (r2 =0.80, rcv

2 = 0.73) was generated using PLSregression and variable selection using an unsupervised for-

ward selection (UFS) algorithm. Both UGT1A4 pharma-cophores included two hydrophobic features and the glucu-ronidation site. The 2D-QSAR showed the best overall pre-dictivity and highlighted the importance of hydrophobicity(as Log P) in substrate-enzyme binding.

UGT1A9

A successful 2D-QSAR model for UGT1A9 has beenobtained for a set of simple phenols [90]. In light of this suc-cess Smith et al. [45] assembled a more structurally diversedata set of UGT1A9 substrates and their Ki values deter-mined using the same procedure as reported previously forUGT1A1 [86]. The same variable selection, PLS and PCAapproaches reported for the UGT1A1 and UGT1A4 sub-strates [86,89] were applied to the UGT1A9 dataset. The

leave-one-out cross-validation r2

statistic (

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as a percentage of total clearance) in human [96]. Volsurfand MolConn-Z descriptors were used with three modellingapproaches; PLS (partial least squares) for quantitative pre-diction and SIMCA (soft independent modelling by classanalogy) and recursive partitioning (RP) for classification.Validation was via an external test set of 20 compounds, andthe models of percent renal clearance were reasonably suc-

cessful. However, these models were extrapolated to the pre-diction of oral bioavailability (Foral) and although renal clear-ance provides an insight into Foral they are not directly com-parable. The presence of drug in the urine following an oraldose does give a measure of Foral, however unless a com-pound has an intrinsic renal clearance no estimate of Foral canbe obtained. Furthermore, the predictive ability of the modelwas based on assumptions that Foral is inversely correlated topercent renal clearance v ia descriptors such as logP.

As mentioned earlier, Turneret al. using a small trainingset of 50 compounds built computational models for plasmaprotein binding, Vss, and clearance (total and renal) in hu-man using an artificial neural network methodology [38].The error in prediction was encouraging; RMSE for renal

clearance was 0.6. Although somewhat greater deviationbetween observed and predicted values occurred for renalclearance than Vss and plasma protein binding.

The ease with which human renal excretion data can becollected non-invasively has aided the study of such QSARof this PK property. This is in stark contrast to that of biliaryelimination data in human, where measurements in vivo arechallenging. Although faecal analysis (preferably followingintravenous administration) is feasible in the clinical setting,to obtain estimates of biliary clearance, data can be con-founded by the potential for intestinal secretion, enterohe-patic recycling and drug or metabolite lability following pro-longed exposure in the colonic contents. Cannulated animalmodels of biliary clearance are the obvious alternative, but

species differences in transporter expression and activity,cast doubt on the relevance to human. Studies in isolatedperfused rat liver preparations have been used to build SARfor specific series of drug molecules and this was demon-strated by Proost and colleagues investigating the hepaticuptake and biliary excretion of 16 neuromuscular blockingagents [97]. They were able to show the importance of lipo-philicity as a factor driving the degree of plasma proteinbinding and hepatic uptake rate, whilst biliary excretion ratewas not correlated with logP. Compartmental PK analysishighlighted a considerable fraction of the dose amassed inthe liver, unavailable for biliary clearance, in line with earlierfindings showing accumulation of these compounds in mito-chondria and lysosomes. More than 30 years ago, Hirom etal. reported the molecular weight dependency of biliary ex-

cretion in preclinical species [98, 99]. These findings wereobserved on a range of organic non-drug like compoundsand there have been few reports since, able to corroborate orexpand this into a global (Q)SAR in a drug discovery setting.

The dearth of biliary clearance data in man has led to fewreports on the computational prediction of this PK propertyin its flow rate, clearance form and as such, efforts have fo-cussed on deconvoluting the impact of the various transportproteins and their role in the hepatic sinusoidal and canalicu-lar membranes (as well as the blood-brain barrier, intestinalmucosa and renal brush border). This transport protein

QSAR has been made possible by datasets generated from invitro assays like Caco-2. Furthermore, in vitro models ofbiliary elimination have been developed over the last fewyears firstly in preclinical species [100] and more recently insandwich-cultured cryopreserved human hepatocytes [101].These cultures develop functional bile canaliculi making itpossible to study intrinsic biliary clearance. And with such

models becoming easily available, validated and robust, thecollation of datasets will allow in silico models to be built.

The role of transporters is key in drug absorption anddisposition and has led to increased efforts to develop effec-tive models, both in vitro and in silico. P-gp is one suchtransporter, a member of the ATP-binding cassette family ofproteins, acting as an energy-dependent efflux pump at var i-ous physiological barriers. Recently, Crivori et al. made pro-gress in the computational prediction of potential P-gp sub-strates and inhibitors, developing two models [102]. Partialleast squares discriminant analysis (PLS-DA) for differenti-ating P-gp substrates and non-substrates based on calculatedmolecular descriptors (VolSurf) was developed using a train-ing set of 53 diverse drugs. The classification as P-gp sub-

strate or non-substrate was based on the efflux ratio fromCaco-2 permeability measurements. The resultant model wasable to correctly predict 72% of an external validation set of272 proprietary compounds. Although a fully-interpretablemodel based on VolSurf descriptors was achieved, it pro-vided a qualitative classification rather than a quantitativeprediction of the permeability coefficient, Papp (basolateralto apical). Data from the Caco-2 model may also be com-plex, involving other transporters in the efflux mechanism.

To address the need for quantitative predictions, P-gpinhibition was modelled using data from the calcein-AM(CAM) assay. This functional data was analysed using aPLS-DA approach with GRIND pharmacophore based de-scriptors (3D conformation-dependent QSAR). The model

was able to discriminate between 69 substrates and 56 in-hibitors taken from the literature with an average accuracy of82%. Although it was not possible to define the mechanismof action (i.e. competitive vs. non-competitive inhibition),the model was able to identify some key molecular features,including H-bonding donors and acceptors and regions ofhydrophobicity, that differentiate a substrate from an inhibi-tor. The 3D pharmacophore is graphically represented in Fig.(4). A similar approach has been employed to the QSAR ofthe human ATP-binding cassette transporter, ABCB11 (orBSEP, bile salt export pump) [103]. Plasma membrane vesi-cles were prepared from insect cells over-expressingABCB11 and used to measure the transport of the radiola-belled substrate, 14C taurocholate (Fig. 5). The inhibition oftaurocholate transport was assessed for a diverse selection of

40 compounds and modelled using Markush TOPFRAGdescriptors and multiple linear regression analysis. A goodcorrelation between observed and predicted inhibitory po-tency was obtained (r2 = 0.95) and a unique combination ofchemical fragments were identified as important positive andnegative determinants of an inhibitory interaction withABCB11. This study exhibited a novel and successful appli-cation of fragment-based QSAR.

There have been some recent reports on the crystal struc-tures of transport proteins where, like the mammalian CYPisoforms, high resolution crystallography has been challeng-

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ing due to issues concerning expression, solubilisation, puri-fication and stabilisation of these integral membrane pro-teins. The crystal structure of AcrB, a principal multi-drugefflux transporter in E. coli, has been reported with andwithout substrates bound. Elucidation of both mechanism ofaction and detail about the drug binding sites was accom-plished [104]. Recent efforts have focussed on homologymodelling of mammalian transporters using these bacterialcrystal structures as a template, and reports have includedmodels of rabbit PEPT1 [105], rabbit OCT2 [106] and hu-man OAT [107]. Future efforts hold a lot of promise with

ligand SAR exploration of these and other homology mod-els, as well as the expectation for human crystal structuresfor the myriad of transporters involved in drug disposition.

FUTURE DIRECTIONS

Over the last few years there has been increasing pressureon the pharmaceutical industry to produce a continuous pipe-line of beneficial drugs, in the absence of a parallel expan-sion in the number of medicinal chemists. Hence, there isnow increased emphasis on designing out unwanted proper-ties before a test compound is synthesised. Successful insilico models offer the industry a more informed choice oftarget to be selected for synthesis. Good predictive modelsfor ADME parameters depend on selecting the right statisti-cal approach, the appropriate molecular descriptors, and a

sufficiently large set of experimental data. Many of the insilico ADME models published in the literature are localmodels based upon limited data and successful models canbe obtained with simple linear 2D techniques. The holy grailof developing global models for ADME is fraught with theinnate complexity of the physiological processes involvedand the enormity of the chemical space to be investigated.The pharmaceutical industry has the resources to generatelarge sets of experimental data and efficient use of this datain the form of global models is one of the challenges the in-dustry currently faces.

While a great deal of progress has been made with pre-dictive in silico ADME models there is still much to do.Many of the human intestinal absorption models have beenshown to be successful for the prediction of large data sets,with some debate remaining over the ability to predict drugsthat are actively transported across the intestinal wall.

Very recently the first in silico models for predictingsteady-state volume of distribution (Vss) have been pub-lished. It is also apparent that active transport can affect thedistribution of a drug and the modelling of this phenomenonwill be a challenge for the future.

A plethora of models have been generated for the predic-tion of drug metabolism, especially the CYP P450 system

and less so for the UGT system of enzymes. An obviouschallenge for UGT modelling is to connect the complemen-tary roles of ligand-based and protein-based modelling.Hence, obtaining a representation of the 3D structure ofUGTs, either by homology modeling or by meeting the chal-lenge of crystallising human UGTs, is paramount. Ad-vancements in crystallisation technology, especially towardsmembrane-bound enzymes, may result in a 3D structure ofUGTs. Also, increasing studies of the structural biology ofother glycosyltransferases [108] may allow the homologymodelling of UGTs to be achieved.

Fig. (4). A graphical representation of the most important 3D-pharmacophoric GRIND features for the astemizole inhibitor, as referencecompound (A) and for the cimetidine substrate, as reference compound (B). The colored areas around the molecule are the GRID MIF calcu-lated with the DRY (yellow), N1 (blue) and O (red) probes. From reference [102], and reproduced courtesy of Dr. P. Crivori and ACS Publi-cations.

Fig. (5). Chemical structure of taurocholic acid.

HO

H

OH

H

OH

H

H NH

O

S

OO

HO

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Although 90% of hepatic metabolism involves CYP andUGT there is limited QSAR modelling for other metabolis-ing enzymes. For example, glutathione S-transferases(GSTs) constitute an important class of phase 2 detoxifyingenzymes, catalysing the conjugation of glutathione (GSH)with electrophilic compounds. However, the QSAR model-ing is mainly limited to rat GSTs and 2-substituted 1-chloro-

4-nitrobenzenes [109]. There are still many important me-tabolizing enzymes systems that require further developmentin the field of QSAR.

Despite the complex nature of renal clearance the first insilico models for the prediction of renal clearance, albeit as apercentage of clearance as opposed to absolute clearance, arestarting to emerge. Due to the limited data of biliary clear-ance measurements in man there is a lack of QSAR modelsfor this PK parameter at present. However, with new non-invasive technologies such as positron emission tomography,large data sets of human biliary clearance could be availableas early as the next few years.

The ultimate future for ADME in silico modelling is tobe able to predict the substrate and inhibition potential ofdrugs to protein transporters. We have seen throughout thisreview that protein transporters affect absorption, distribu-tion, metabolism and excretion. Indeed several QSAR mod-els have been generated for a modicum of transporters andwith the onward increase in transporter cloning and het-erologous expression, one can expect the number of in silicomodels to grow exponentionally.

This review has highlighted the advancement over thelast few years in developing QSAR models for ADME.ADME represents only a part of the story in lead optimisa-tion to produce a beneficial drug. Often compromises have tobe made in the molecular structure; balancing potency,metabolic stability and protein binding is a common para-digm. Moreover, medicinal chemists have to be persuaded

that the QSAR models developed are indeed predictive. Me-dicinal chemists tend to feel most comfortable with the sim-plest QSAR model for modulating a biological property. Insome cases, this will be disparate, as the QSAR models be-come more black box, where interpretation of molecularproperties driving ADME becomes more complex. The cur-rent challenge for in silico ADME in drug discovery is tointegrate the catalytic triad of medicinal chemist, computa-tional chemist and ADME scientist.

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Received: May 07, 2007 Revised: October 29, 2007 Accepted: January 16, 2008

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