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ISBN 978-952-62-1218-0 (Paperback)ISBN 978-952-62-1219-7 (PDF)ISSN 0355-3191 (Print)ISSN 1796-220X (Online)

U N I V E R S I TAT I S O U L U E N S I SACTAA

SCIENTIAE RERUM NATURALIUM

U N I V E R S I TAT I S O U L U E N S I SACTAA

SCIENTIAE RERUM NATURALIUM

OULU 2016

A 674

Toni Lassila

IN VITRO METHODS IN THE STUDY OF REACTIVE DRUG METABOLITES WITH LIQUID CHROMATOGRAPHY / MASS SPECTROMETRY

UNIVERSITY OF OULU GRADUATE SCHOOL;UNIVERSITY OF OULU,FACULTY OF SCIENCE AND FACULTY OF MEDICINE;MEDICAL RESEARCH CENTER OULU;OULU UNIVERSITY HOSPITAL

A 674

ACTA

Toni Lassila

A C T A U N I V E R S I T A T I S O U L U E N S I SA S c i e n t i a e R e r u m N a t u r a l i u m 6 7 4

TONI LASSILA

IN VITRO METHODS IN THE STUDY OF REACTIVE DRUG METABOLITES WITH LIQUID CHROMATOGRAPHY / MASS SPECTROMETRY

Academic dissertation to be presented with the assent ofthe Doctoral Training Committee of Technology andNatural Sciences of the University of Oulu for publicdefence in the Wetteri auditorium (IT115), Linnanmaa, on27 May 2016, at 12 noon

UNIVERSITY OF OULU, OULU 2016

Copyright © 2016Acta Univ. Oul. A 674, 2016

Supervised byDocent Sampo MattilaDocent Miia TurpeinenDocent Ari Tolonen

Reviewed byProfessor Risto KostiainenProfessor Seppo Auriola

ISBN 978-952-62-1218-0 (Paperback)ISBN 978-952-62-1219-7 (PDF)

ISSN 0355-3191 (Printed)ISSN 1796-220X (Online)

Cover DesignRaimo Ahonen

JUVENES PRINTTAMPERE 2016

OpponentDoctor Risto Juvonen

Lassila, Toni, In vitro methods in the study of reactive drug metabolites with liquidchromatography / mass spectrometry. University of Oulu Graduate School; University of Oulu, Faculty of Science and Faculty ofMedicine; Medical Research Center Oulu; Oulu University HospitalActa Univ. Oul. A 674, 2016University of Oulu, P.O. Box 8000, FI-90014 University of Oulu, Finland

Abstract

Reactive metabolites are believed to be responsible for rare but serious idiosyncratic adverse drugreactions (IADRs) that have led to the withdrawal of numerous drugs from the market. This hasresulted in major harm to patients, economic losses for the pharmaceutical companies andrepresents a serious problem in drug development. Reactive metabolites can be studied by trappingthem with suitable nucleophiles, most commonly with glutathione. The glutathione conjugatesformed in these reactions can be analyzed with liquid chromatography mass spectrometry (LC/MS) techniques. In this study, new in vitro methods for the detection and analysis of reactivemetabolites were developed. The suitability for reactive metabolite screening of different enzymesources commonly used in vitro were compared. It was found that sub-cellular fractions yieldedsignificantly larger amounts of glutathione-trapped reactive metabolites as compared to theamounts obtained from intact hepatocytes. Additionally, different metabolites were detected insome cases. Biomimetic metalloporphyrin catalysts were tested for their ability to produce largeramounts of glutathione-trapped metabolites relative to liver S9 fraction incubations. An increasein reactive metabolite production was observed with biomimetic models, but not all of themetabolites produced by liver S9 were observed. The glutathione conjugates of pulegone and ofits metabolite menthofuran were analyzed with LC/MS/MS, and the fragmentation spectra of N-and S-/N- di-linked glutathione conjugate were interpreted in detail for the first time. These resultswill enable more efficient screening of reactive metabolites of furan-containing compounds. Acylglucuronides are metabolites produced from carboxylic acid-containing compounds and can bereactive. A good correlation was found between the acyl migration half-life and the tendency of adrug to cause IADRs. The carboxylic moiety can also be metabolized to yield acyl coenzyme A(CoA) conjugates that may be more reactive than their corresponding acyl glucuronides. Theformation of CoA conjugates and additional conjugates formed from them was found to be morelikely with drugs that cause IADRs.

Keywords: acyl glucuronide, glutathione trapping, liquid chromatography, massspectrometry, reactive metabolites

Lassila, Toni, Kemiallisesti reaktiivisten metaboliittien in vitro -tutkimuksianestekromatografia / massaspektrometrisillä menetelmillä. Oulun yliopiston tutkijakoulu; Oulun yliopisto, Luonnontieteellinen tiedekunta jaLääketieteellinen tiedekunta; Medical Research Center Oulu; Oulun yliopistollinen sairaalaActa Univ. Oul. A 674, 2016Oulun yliopisto, PL 8000, 90014 Oulun yliopisto

Tiivistelmä

Reaktiivisten metaboliittien uskotaan olevan syypää tietyntyyppisiin harvinaisiin, mutta vakaviinidiosynkraattisiin lääkehaittavaikutuksiin, jotka ovat johtaneet useiden lääkeaineiden poistami-seen markkinoilta. Ne ovat aiheuttaneet merkittäviä haittoja potilaille, tappioita lääkeyhtiöille jaovat vakava ongelma lääkekehityksessä. Reaktiivisia metaboliitteja voidaan tutkia vangitsemal-la niitä sopivilla nukleofiileillä, yleisimmin glutationilla. Muodostuneet glutationikonjugaatitvoidaan sitten analysoida nestekromatografia / massaspektrometrisin tekniikoin. Tässä tutkimuk-sessa kehitettiin uusia in vitro tapoja havaita ja analysoida reaktiivisia metaboliitteja. Tavallisim-min käytettyjen entsyymilähteiden soveltuvuutta testattiin reaktiivisten metaboliittien seulon-taan. Solufraktioiden havaittiin tuottavan huomattavasti suurempia määriä glutationi-vangittujareaktiivisia metaboliitteja kuin elävät solut. Lisäksi eri metaboliitteja havaittiin joillekin aineilleeri entsyymilähteissä. Biomimeettisen metalliporfyriinikatalyytin kykyä tuottaa suurempia mää-riä glutationilla vangittuja reaktiivisia metaboliitteja testattiin vertaamalla sitä maksan S9 frakti-oon. Vaikka katalyytillä pystyi tuottamaan suurempia määriä reaktiivisia metaboliitteja, kaikkiaS9 fraktiossa havaittuja metaboliitteja se ei tuottanut. Pulegonin ja menthofuraanin glutationi-konjugaatteja analysoitiin LC/MS/MS tekniikalla ja N- sekä S-/N- sitoutuneiden glutationikon-jugaattien pilkkoutumisspektrit tulkittiin tarkasti ensimmäistä kertaa. Tulokset mahdollistavatfuraanirenkaan sisältävistä yhdisteistä syntyvien reaktiivisten metaboliittien tehokkaamman seu-lonnan. Asyyliglukuronit ovat karboksyylihapporyhmän sisältämien yhdisteiden metaboliitteja,jotka voivat olla reaktiivisia. Asyyliglukuronien vaeltamisen puoliintumisajan ja idiosynkraattis-ten lääkehaittavaikutusten välillä havaittiin selvä korrelaatio. Karboksyylihapporyhmän kanssavoi muodostua myös asyyli koentsyymi A konjugaatteja, jotka voivat olla reaktiivisempia kuinvastaavat asyyliglukuronit. Koentsyymi A ja siitä edelleen syntyviä muita konjugaatteja havait-tiin pääasiassa lääkeaineille, joiden todennäköisyys aiheuttaa idiosynkraattisia lääkehaittavaiku-tuksia oli suurempi.

Asiasanat: asyyliglukuroni, glutationi, massaspektrometria, nestekromatografia,reaktiiviset metaboliitit

7

Acknowledgements

This work was carried out in the Faculty of Science, Structural chemistry, and the

Faculty of Medicine, Research Unit of Biomedicine, University of Oulu, during the

years 2014-2016. Laboratory work was performed at the Admescope Ltd.

laboratory.

I am most thankful for my supervisors Sampo Mattila, Miia Turpeinen and

especially Ari Tolonen. His expertise and ideas have been essential to this work and

have helped me a great deal to complete this thesis. He has spent a lot of time

reviewing and writing the original articles and helped during problems and in the

interpretation of results. I also want to thank my principal supervisor Sampo Mattila

and Miia Turpeinen for their support and making this thesis possible. I want to

thank Orion Research Foundation for their grant.

I thank Professor Risto Kostiainen and Professor Seppo Auriola for their pre-

examination and valuable comments. I also thank Joshua Ward for the careful

revision of the English language and Päivi Joensuu at the Laboratory of Mass

Spectrometry for her support.

I want to thank my co-authors Juho Hokkanen, Sanna-Mari Aatsinki, Olavi

Pelkonen, Timo Rousu and Christophe Chesné. Their work and ideas have been

extremely valuable. I also want to thank the laboratory staff on Admescope, who

have helped me with the experiments and other people working in the Admescope

for their support and advice and generally for the opportunity to use the laboratory.

Oulu, April 2016 Toni Lassila

8

9

Abbreviations

AA acetic acid

ACN acetonitrile

ADR adverse drug reaction

AKR aldo-keto reductase

AOX aldehyde oxidase

APCI atmospheric pressure chemical ionization

API atmospheric pressure ionization

APPI atmospheric pressure photoionization

BMO biomimetic oxidation

CoA coenzyme A

CYP cytochrome P450

DDI drug-drug interaction

dG 2’-deoxyguanosine

DILI drug-induce liver injury

DME drug metabolizing enzyme

DMSO dimethyl sulfoxide

ESI electrospray ionization

FA formic acid

FMO flavin-containing monooxygenase

FWHM full width at half maximum

GSH glutathione (reduced form)

GST glutathione S-transferase

HLA human leukocyte antigen

HLM human liver microsome

HPLC high-performance liquid chromatography

HRMS high-resolution mass spectrometry

HSAB hard and soft acids and bases

IADR idiosyncratic adverse drug reaction

KCN potassium cyanide

LC liquid chromatography

LLE liquid-liquid extraction

MAO monoamine oxidase

MS mass spectrometry

MS/MS tandem mass spectrometry

m/z mass-to-charge ratio

10

NADPH nicotinamide adenine dinucleotide phosphate (reduced form)

NSAID nonsteroidal anti-inflammatory drug

NL neutral loss scanning

PAPS 3’-phosphoadenosine 5’-phosphosulfate

PDA photodiode array

PI product ion scanning

PP protein precipitation

Q quadrupole mass spectrometer

QqQ triple quadrupole mass spectrometer

Q-TOF quadrupole time-of-flight mass spectrometer

QLIT quadrupole linear ion trap mass spectrometer

RAM restricted access media

rCYP recombinant cytochrome P450

RP reverse phase

SIM selected ion monitoring

SPE solid phase extraction

SRM selective reaction monitoring

SULT sulfotransferase

TOF time-of-flight (mass spectrometer)

tR retention time

UDPGA uridine 5’-diphospho-glucuronic acid

UGT UDP-glucuronosyltransferase

UHPLC ultra-high-performance liquid chromatography

11

List of original publications

This thesis is based on the following publications, which are referred throughout

the text by their Roman numerals:

I Lassila T, Rousu T, Mattila S, Chesne C, Pelkonen O, Turpeinen M & Tolonen A (2015) Formation of GSH-trapped reactive metabolites in human liver microsomes, S9 fraction, HepaRG-cells, and human hepatocytes. J Pharmceut Biomed Anal 115: 345–351.

II Lassila T, Mattila S, Turpeinen M & Tolonen, A (2015) Glutathione trapping of reactive drug metabolites produced by biomimetic metalloporphyrin catalysts. Rapid Commun Mass Spectrom 29: 521–532.

III Lassila T, Mattila S, Turpeinen M, Pelkonen O & Tolonen A (2016) Tandem mass spectrometric analysis of S- and N-linked glutathione conjugates of pulegone and menthofuran and identification of P450 enzymes mediating their formation. Rapid Commun Mass Spectrom 30: 917–926.

IV Lassila T, Hokkanen J, Aatsinki S-M, Mattila S, Turpeinen M & Tolonen A (2015) The toxicity of carboxylic acid-containing drugs: the role of acyl migration and CoA conjugation investigated. Chem Res Toxicol 28: 2292–2303.

The present author is the primary author in all articles and responsible for the

majority of the laboratory work and writing together with Tolonen, A. In

publication I, some experiments and data analysis were performed with Rousu, T.

In publication IV, certain experiments, data analysis, and writing were performed

by Hokkanen, J. and Aatsinki, S.-M.

12

13

Table of contents

Abstract

Tiivistelmä

Acknowledgements 7 Abbreviations 9 List of original publications 11 Table of contents 13 1 Introduction 15 2 Literature review 17

2.1 Drug metabolism ..................................................................................... 17 2.1.1 Phase I metabolism ....................................................................... 18 2.1.2 Phase II metabolism ..................................................................... 18

2.2 Drug toxicity ........................................................................................... 19 2.3 Reactive metabolites ............................................................................... 23

2.3.1 Reactive metabolite trapping ........................................................ 24 2.3.2 Glutathione ................................................................................... 25 2.3.3 Acyl glucuronides ......................................................................... 27 2.3.4 CoA conjugates ............................................................................. 30

2.4 Enzyme sources used in vitro .................................................................. 31 2.4.1 Subcellular fractions and recombinant enzymes .......................... 32 2.4.2 Liver slices, hepatocytes and immortal cell lines ......................... 33 2.4.3 Drug metabolite production .......................................................... 34

2.5 Liquid chromatography-mass spectrometry ............................................ 34 2.5.1 Sample preparation ....................................................................... 34 2.5.2 Liquid chromatography ................................................................ 35 2.5.3 Mass spectrometry ........................................................................ 36

3 Aims of the research 39 4 Materials and methods 41

4.1 Materials ................................................................................................. 41 4.2 Incubations .............................................................................................. 41 4.3 Instrumentation ....................................................................................... 42

5 Results and discussion 43 5.1 Enzyme source comparison [I] ................................................................ 43 5.2 Glutathione-trapped reactive metabolites with biomimetic

metalloporphyrins [II] ............................................................................. 49

14

5.3 S- and N-linked glutathione conjugates of pulegone and

menthofuran [III] ..................................................................................... 52 5.4 Identification of CYP P450 enzymes mediating the formation of

reactive metabolite from menthofuran [III] ............................................. 58 5.5 Reactive metabolites of carboxylic acid-containing drugs [IV] .............. 61

6 Summary and conclusions 71 References 75 Original articles 93

15

1 Introduction

Drug metabolism is essential for the removal of drugs and other foreign substances

from the body. The process usually produces metabolites that are less active and

more easily excreted relative to the parent compound, but occasionally reactive and

toxic compounds may also be formed. Reactive metabolites may react with

macromolecules, forming covalent bonds with proteins or DNA. DNA damage can

result in carcinogenicity, and damaged proteins can cause toxicity. Damage to

important regulatory components may cause toxicity directly, or it can trigger an

immune reaction that causes the damage, such as hepatotoxicity. Reactive

metabolites are a serious problem in drug development and several drugs have been

withdrawn from the market due to adverse effects, most likely caused by reactive

metabolites, although the definite mechanism of action is not clear and cannot be

fully explained.

The role of reactive metabolites in carcinogenesis was found first, and reactive

metabolites were later used to explain drug-induced liver injury (DILI) caused by

paracetamol. [1-4] The toxicity of paracetamol is observed only after the

recommended dosage is greatly exceeded, but in the cases of many other drugs, the

toxicity caused by reactive metabolites is not predictable, and it often appears only

very rarely in certain individuals. This type of idiosyncratic adverse drug reaction

(IADR) is difficult to observe during clinical trials in which a limited number of

people are taking the drug. It is often detected only after the drug has been

introduced to the market and potentially millions of subjects have been exposed to

it. This has led pharmaceutical companies to develop preclinical testing strategies

intended to minimize the formation of reactive metabolites from drug candidates.

The potential of the drug candidate to cause toxicity through its reactive metabolites

is screened with various techniques, such as covalent binding, nucleophilic trapping,

CYP inactivation, toxicological screens, and tissue binding. If the risk is assessed

to be too high, the structure of the compound needs to be modified to reduce its

reactivity. However, these screens are not perfect, and potentially dangerous drugs

can still pass the screens while at the same time safe drugs are unnecessarily

abandoned. [5]

This work investigates various in vitro techniques in the study of reactive

metabolites, mostly focusing on glutathione trapping. Different in vitro enzyme

sources were compared based on their ability to produce glutathione-trapped

reactive metabolites. The production of reactive metabolites with a biomimetic

catalyst was also investigated. A new type of N-linked glutathione conjugate was

16

characterized with tandem mass spectrometry. Reactive acyl glucuronides and

coenzyme A conjugates produced from some carboxylic acid-containing drugs

were also investigated.

17

2 Literature review

2.1 Drug metabolism

Xenobiotics are substances that do not occur naturally in the body, but are received

form the surrounding environment, primarily via absorption by the digestive track,

but also via other locations such as the lungs or the skin. Large numbers of

xenobiotics are found in foods and natural products, but the body is also exposed

to a variety of synthetic xenobiotics, such as drugs, pesticides and other

environmental pollutants. Some xenobiotics may be excreted directly to bile or

urine without metabolism, but most are too lipophilic to be cleared from the body

directly. The biological purpose of drug metabolism is to convert them into more

hydrophilic forms that are easier to excrete. [6, 7]

Drug metabolism can be divided into phase I and phase II metabolism. In phase

I metabolism, the xenobiotic is oxidized or hydrolyzed, forming or exposing new

functional groups, thus preparing the molecule to phase II metabolism, while

making it slightly more hydrophilic as well. In the conjugative phase II metabolism,

large hydrophilic molecules that make the xenobiotic significantly more

hydrophilic are attached to suitable functional groups. The molecule may undergo

both phase I and II metabolism or only one of them, depending on its structure,

before it is excreted. [7, 8]

Many xenobiotics may be bioactive and cause diverse biological effects in the

body, which can be beneficial or harmful, depending on compound. In most cases,

the biological activity of the xenobiotic is diminished by metabolism, but

metabolites may also retain activity. Common examples are prodrugs that are

designed to be metabolized to the active substance, and some drugs and xenobiotics

that are metabolized to yield more toxic substances or reactive metabolites. [9-11]

The most important site of drug metabolism is the liver, as it contains the

highest concentrations of numerous drug-metabolizing enzymes, especially the

cytochrome P450 (CYP) enzymes. Other metabolic organs include the gut lumen,

lungs, kidney, brain and skin, which all contain some drug metabolizing enzymes

(DMEs), some of which are not present in the liver. The liver is also the most

important site of metabolism because compounds absorbed via the gastrointestinal

tract must first pass through the liver and can be metabolized extensively in a

process called first-pass metabolism, before they can enter rest of the body. [12]

18

2.1.1 Phase I metabolism

A large majority of phase I metabolic reactions are catalyzed by cytochrome P450

enzymes. Other important phase I metabolic enzymatic systems include the Flavin-

containing monooxygenases (FMO), monoamine oxidases (MAO), aldehyde

oxidases (AOX) and aldo-keto reductase (AKR). [13] The reactions catalyzed by

these enzymes include various oxidation reactions, the most common being

hydroxylation, deamination, dehalogenation, heteroatom oxidation and

dealkylation. [8] Phase I metabolism also involves hydrolysis reactions catalyzed

by esterases, amidases, and epoxide hydrolases, from which carboxyesterases

(CES1 and CES2) are especially important for drug metabolism. [14] The CYP

enzymes can be divided into several families and subfamilies. At least 57 CYPs

have been identified to date. Many are involved in the metabolism of endogenous

substances, but the those chiefly responsible for the xenobiotic metabolism in

humans are CYPs 1A2, 2A6, 2B6, 2C8, 2C9, 2C19, 2D6, 2E1, and 3A4. Of these,

CYPs 2C9, 2C19, 2D6 and 3A4 are the most important, accounting for

approximately 80% of the oxidative metabolism of commonly used drugs. [15, 16]

The expression levels for some these enzymes are highly variable due to genetic

and environmental factors. Certain enzymes can be expressed in much lower or

higher levels in some individuals than in others, and allelic variants have different

levels of activity. [17-20] Certain chemicals and drugs can induce the expression

of some CYPs, and other chemicals can inhibit them, leading to drug-drug

interactions (DDIs) if used together with drugs metabolized by an enzyme that is

inhibited or induced. Genetic polymorphisms and DDIs lead to a large variability

in the rate of drug metabolism, which can result in elevated or reduced

concentrations of the affected drug in the blood circulation or tissues, causing

adverse side effects or reduced efficacy. [21, 22]

2.1.2 Phase II metabolism

In conjugative phase II metabolism, a large, hydrophilic biomolecule is conjugated

to a heteroatom of the xenobiotic. This usually results in deactivation of the

xenobiotics biological activity, increased water solubility and finally excretion

from the body. The most common phase II metabolic reactions include

glucuronidation, sulfation, glutathione conjugation, methylation, acetylation and

amino acid conjugation. These reactions are catalyzed by their respective

transferases, the genetic variability of which can affect their activity in a similar

19

way as discussed above for CYPs. However, the transferases are not as easily and

strongly induced as some CYP isoforms. [19, 23] The most important phase II

reaction is glucuronidation, as it has high capacity and glucuronide conjugates are

important metabolites of many drugs. [14, 23] Glucuronidation is catalyzed by

microsomal UDP-transferases, requires the cofactor uridine diphosphate

glucuronic acid (UDPGA) and generally results in inactive drug metabolites. [24]

Exceptions include some reactive acyl glucuronides and biologically active

morphine-6-glucuronide. [25, 26] Sulfation can be an important reaction in some

cases and can compete with glucuronidation, but it is limited by the availability of

the required cofactor3’-phosphoadenosine-5’-phosphosulfate(PAPS).[27,28] Sulfation is catalyzed primarily by cytosolic sulfotransferases (SULTs) and, like

glucuronidation, generally yields inactive drug metabolites. [29] However, certain

sulfate conjugates can be reactive: for example, certain benzylic and allylic

alcohols can yield carcinogenic sulfate conjugates, and skin reactions associated

with nevirapine are believed to be mediated by its reactive sulfate conjugate. [30-

33] Additionally, the sulfate metabolite of minoxidil is the active compound that

stimulates hair follicles. [34] Glutathione can react with many electrophilic

molecules without enzymatic catalysis, but the reaction is also catalyzed by

glutathione-S-transferases. Glutathione conjugation represents an important

detoxification reaction, as it inactivates many reactive species. [35] Methylation,

which is catalyzed by methyltransferases (MT) and requires S-adenosyl methionine

(SAM) as a cofactor, acetylation, which is catalyzed by N-acetyltransferases (NAT)

and requires acyl coenzyme A, and amino acid conjugation reactions are generally

only of minor importance, but can have a significant effect on the metabolism of

certain drugs. Unlike other phase II metabolic reactions, methylation and

acetylation result in decreased water solubility. [15, 36]

2.2 Drug toxicity

Drug safety is a serious concern in drug development, and toxicity is the single

most important reason for halting the development of a drug candidate, accounting

for approximately 30% of all drugs terminated. [37] Drug toxicity can be classified

into several categories depending on the classification used. [38, 39] On-target,

mechanism-based or type A1 (augmented) toxicity is associated with non-optimal

interactions between the drug and its target receptor, such as excessive inhibition

or activation of the intended target receptor in the case of overdose, or interaction

with an unintended location. [38, 39] For example, it has been proposed that

20

myopathy caused by statins is induced by inhibition of HMG-CoA reductase in the

muscle, while the intended target of the drug is in the liver. [40] Off-target or type

A2 toxicity occurs when a drug interacts with a receptor that it was not intended to

target, due to limited selectivity of the drug or its metabolites for the target receptor.

[38, 39] A typical example is the antihistamine terfenadine, whose primary target

is the histamine H1 receptor, but it also binds to the hERG channel, causing

arrhythmias if the blood concentration is too high. [41] Type B (bizarre) or

idiosyncratic toxicity is unpredictable, rare and cannot be fully explained.

Hypersensitivity reactions can sometimes be classified as idiosyncratic, but some

have classified them as a separate group. [38, 39] Halothane is an example of a

drug with idiosyncratic toxicity, as it is metabolized to yield reactive metabolites

that bind to liver proteins and cause liver injury in some patients. [42] Penicillin

allergy is a typical example of a hypersensitivity reaction and is most likely caused

by the formation of covalent bonds between the reactive β-lactam ring and proteins.

[43] Biological activation or type C (chemical) toxicity results from the activation

of the drug to yield chemically reactive species that cause damage to cells. [38, 39]

The most common example is paracetamol, which is metabolized mostly to

glucuronide and sulfate conjugates, but also to reactive NAPQI in the liver. NAPQI

reacts with glutathione, but if the glutathione reserves are depleted, it can start

reacting with liver proteins, leading to hepatotoxicity. [1] Type B and type C

reactions are closely related, as reactive metabolites are believed to be the primary

underlying cause of both types of reactions. The difference is that type C reactions

are predictable, and their effects are dose-dependent and typically have a rapid

onset, whereas type B reactions are not predictable, and their effects have no

apparent correlation to dose and can have a long latency of onset. It has been

proposed that these types of toxic reactions may have the same basic mechanism

but differ only in their rate of occurrence, and type B reactions might behave like

type C reactions if the drug is administered in much higher doses. [44] This view

is supported by the observation that the innate immune system, usually associated

with idiosyncratic reactions, is involved in the toxicity of paracetamol. [45] Type

D (delayed) toxicity is associated with reactions occurring long after the treatment

has ended. A typical example is tumor relapse after treatment with

chemotherapeutic agents because reactive metabolites have damaged the cellular

DNA. Type E reactions are associated with the end of the treatment, such as

withdrawal. [39]

Type B or idiosyncratic adverse drug reactions (IADRs) are particularly

problematic in drug development because they are difficult to detect, and have

21

caused withdrawals of several drugs such as benoxaprofen, bromfenac, tienilic acid

and troglitazone from the market, resulting in great financial losses for the

pharmaceutical companies and serious adverse effects to the patients. [46, 47] By

definition, IADRs occur very rarely; around 1 patient in 10,000 or 100,000 develop

them. Toxicity does not appear in a majority of the patients at any dose, it is not

related to the pharmaceutical effect of the drug and there can be a long latency

period between the start of the drug therapy and onset of toxicity. [48] Common

toxic side effects include liver injury, skin rashes, hypersensitivity and hematologic

adverse reactions such as agranulocytosis. The severity of these effects varies from

mild rash or asymptomatic rise of serum aminotransferase levels to toxic epidermal

necrolysis or liver failure which can result in death. [49]

The exact mechanism of IADRs has not been elucidated, but the first step is

believed to be the formation of reactive metabolite intermediates from the drug

molecule, which form covalent adducts with biological macromolecules such as

proteins. If important regulatory proteins are modified, toxicity can occur directly.

Alternatively, modified proteins are recognized by the immune system as foreign

components, which initiates an immune response that eventually leads to toxic

effects. This is called “hapten theory”, as haptens are compounds that are able to

elicit immune response after they have conjugated with large biomolecules.

Reactions with DNA can lead to carcinogenic effects. Relationships between drug

toxicity and reactive metabolites are summarized in Fig. 1. In addition to “hapten

theory”, the “danger theory” has also been suggested. In this theory, an additional

danger signal, resulting for example from cell injury, is required to activate the

immune system. The danger signal can arise from the injury caused by the drug

itself, or it can be generated by other component such as infection. Additionally,

the “pharmacological interactions theory” has been proposed, in which the

generation of reactive metabolites is not required, but the immune response is

activated directly by the drug molecule. These theories are not exclusive and it is

possible that different drugs cause IADRs via different mechanisms. [49, 50]

The protein targets of several drugs have been identified and include DMEs

such as CYP and GST enzymes as well as other microsomal proteins and serum

albumin. [51] The most heavily investigated drug is paracetamol, for which 29

protein targets have been identified. [52] Interestingly, the metabolism of

paracetamol and its much less hepatotoxic meta isomer, 3’-hydroxyacetanilide,

yielded different profiles of covalently modified proteins, though the overall level

of covalent binding was comparable for both molecules. [53] That two compounds

can exhibit differing levels of toxicity but comparable levels of covalent

22

modification of proteins illustrates that not all covalent binding leads to toxicity,

and it has even been speculated that some critical proteins need to be modified, the

cellular site of the modification is important or cell damage is also required in order

to generate the danger signal. [54]

Fig. 1. The role of metabolism in drug toxicity.

Generally, it is not possible to predict which individuals will experience IADRs,

but it is believed that it is caused by a complex combination of genetic

polymorphisms and environmental effects. For example, patients with pre-existing

liver disease or HIV infection are predisposed to DILI. Polymorphism of drug

transporters, decreased activity of detoxifying glutathione S-transferases and N-

acetyltransferases and increased activity of cytochrome P450 enzymes, which can

all contribute to the increased amounts of reactive metabolites, have been

associated with, but cannot fully account for, DILI caused by certain drugs. Genetic

variability in human leukocyte antigen (HLA) and cytokine expression can play an

important part in causing IADRs in the case of certain drugs. [45, 55] For example,

a strong correlation has been found between hypersensitivity to the antiretroviral-

drug abacavir and the HLA-B*5701 allele, which is present in 78% of patients that

23

experience hypersensitivity, but present in only 2% of abacavir-tolerant patients.

[56]

2.3 Reactive metabolites

Reactive metabolites are associated with most drugs that can cause DILI, but the

formation of reactive metabolites does not necessarily mean that a drug will cause

them. [46] An example of this is raloxifene, which is extensively detoxified by first-

pass metabolism to glucuronide, which prevents the formation of reactive

metabolites as observed in vitro. [57] Another example is paroxetine, whose

reactive metabolites are effectively detoxified by GSH-conjugation and also

blocked by methylation. Paroxetine can be safely administered at a low daily dose

(20 mg). [58] There is additional evidence that some drugs might be able to cause

IADRs without the generation of reactive metabolites, as in the case of

ximelagatran. Reactive metabolites have not been identified in its metabolic profile,

but a strong genetic association has been established between elevated levels of

serum alanine aminotransferase and some major histocompatibility complex alleles,

suggesting an immunological mechanism, i.e. pharmacological interaction. [59] An

important observation is that drugs given in low doses (< 10 mg/day) are less likely

to cause IADRs than drugs given in high doses. [60] A statistically significant

correlation has been found between the daily dose of oral medications and DILI, as

only a few cases of DILI have been associated with drugs taken at 11-49mg or ≤10

mg/day (14% and 9% of all cases, respectively). [61] This indicates that a certain

threshold of reactive metabolites is required to initiate a toxic response. [62]

A common method for studying reactive metabolites is to measure the level of

covalent binding to proteins. A radioactively labeled drug is incubated with

microsomal proteins, and proteins are extracted and repeatedly washed to remove

non-covalently bound drug molecules. Residual radioactivity is taken as a measure

of covalent binding to the proteins: it has been suggested that covalent binding of

over 50 pmol per mg of protein is a potential problem, and advancing drug

candidate should be assessed carefully in the light of qualifying considerations. [63]

A higher level of covalent binding has been observed to be more likely with drugs

that can cause IADRs than with drugs that do not cause them. [64] However, in a

study of 9 hepatotoxic and 9 non-hepatotoxic drugs in incubations with liver

microsomes, liver S9 fractions and hepatocytes, it was not possible to separate the

hepatotoxic from the non-hepatotoxic compounds based on covalent binding. The

separation was improved somewhat by using hepatocytes and estimating the total

24

body burden of covalent binding, which is calculated from the covalent binding and

daily dose, but even with this approach the separation was incomplete. [65, 66]

Better separation has been achieved in other studies, although some overlap

remained even after the daily dose was taken into account. [67] When the covalent

binding of 42 drugs to human hepatocytes was plotted against their daily dose,

excellent separation of safe drugs from warning and withdrawn drugs was achieved.

[68] Covalent binding experiments have also been combined with in vitro toxicity

panels including transporter inhibition, mitochondrial toxicity and cellular toxicity

to categorize drugs into different hazard zones. Good separation was achieved with

high specificity (78%) and selectivity (100%) in a test set of 36 drugs, categorized

as “severe”, “marked” and “low” based on their IADR risk. [69]

The problem with covalent binding assays is that they require radioactively

labeled drugs, which are usually not available during the early stages of drug

development. To solve this problem, glutathione-trapping methods have been

developed. A correlation between GSH conjugate production and covalent binding

has been demonstrated. [70] An improved correlation was achieved by combining

GSH conjugate production with time-dependent inhibition of cytochrome P450

enzymes. [71] GSH conjugates are much more likely to be produced by the

metabolism of drugs that can cause IADRs than drugs that do not cause them, but

both false positives and negatives were observed. [72] Combining daily dose with

metabolism-dependent inhibition, GSH adduct formation and covalent binding

were shown to be good predictors of hepatoxicity (>80%) in a large study

containing over 200 compounds. [73] A decision tree with metabolism dependent

inhibition, GSH adduct formation and daily dose was able to predict 45% of the

hepatoxic drugs, and predicted 10% of the non-hepatoxic drugs to be toxic. [73]

2.3.1 Reactive metabolite trapping

As most reactive metabolites are highly labile and react rapidly in solution, it is

generally not possible to directly observe these intermediates by analytical

techniques. In order to detect these electrophilic reactive metabolites, they can be

trapped with nucleophilic trapping agents to form stable conjugates that can be

detected. [63] Glutathione (GSH) is an effective trapping agent against many soft

electrophiles. [63, 74, 75] These include many quinones, imines, epoxides, arene

oxides and nitrenium ions, which are classified as “soft” based on the hard and soft

acids and bases (HSAB) concept as they have large radii and are easily polarized.

[76] Alternatively cysteine [77], N-acetylcysteine [78, 79] or tailor-made peptides

25

[80-82] can be used. Potassium cyanide is more effective at trapping hard

electrophiles, such as iminium ions, which have smaller radii and are harder to

polarize. [75, 83-85] A potential problem with cyanide is that it can generate

methylated metabonates which are not true metabolites, but rather in vitro artifacts.

These metabonates can be distinguished from true metabolites by LC/MS, but this

can be problematic if only radioactivity detection is used. [86] Reactive aldehydes

are effectively trapped with semicarbazide [75, 85, 87] or methoxylamine, [88,89]

which form Schiff base adducts with aldehydes.

2.3.2 Glutathione

Glutathione is often used in trapping experiments as it is able to trap many types of

reactive metabolites. Glutathione modifications have been used to improve the

sensitivity and selectivity of experiments, for example by using a brominated or

ethyl ester analogs of glutathione. [90-92] Quantitative analysis can be facilitated

by the addition of fluorescence tags [93] and semi-quantitative analysis with

quaternary ammonium glutathione analogues. [94] LC/MS analysis of GSH

conjugates can be prepared more easily using a mixture of isotope-labeled and non-

labeled glutathione as the trapping agent, as this mixture yields an easily

identifiable double peak pattern in the mass spectrum. [95-98] A common method

for screening glutathione conjugates is to measure the neutral loss of 129 Da by

LC/MS/MS, which indicates the loss of pyroglutamate (Fig. 2a). However, not all

GSH conjugates can be detected by this method, and false positives are also

possible. These problems can be alleviated by the additional detection of the neutral

loss of glutathione at 307 Da and using high-resolution mass spectrometers. [99]

Glutathione conjugates can also be screened in negative ionization mode by

observing a fragment at m/z 272 or 254 Da (Fig. 2b). This method has high

sensitivity, but MS/MS fragmentation data is generally not useful in the

characterization of the conjugate as most fragments are associated with the

glutathione moiety. [100] Therefore it is recommended to also perform additional

MS/MS experiments in positive ionization mode if the negative ionization

screening method is used. [101, 102]

Glutathione is a tripeptide (γ-glutamyl-cysteine-glycine) that occurs naturally

in animal cells at high concentrations, up to 10 mM in liver cells. It has many

important biological functions, such as the detoxification of free radicals, reactive

oxygen species and electrophilic xenobiotics. It also has other vital biological

functions, for example in the metabolism of some important compounds such as

26

leukotrienes. [103, 104] The nucleophilic site of glutathione is the sulfhydryl group

of cysteine, and it forms S-glutathione conjugates with electrophiles. However,

with furan compounds glutathione can also react via the amino group of the

glutamate to form N-conjugates. [105] S-conjugation is catalyzed by glutathione

S-transferases, (GSTs), but it can also occur non-enzymatically. [35]

Fig. 2. Commonly used fragments to screen glutathione conjugates a) in positive

ionization and b) in negative ionization modes.

Seven distinct classes of human cytosolic GSTs (α,μ,π, σ, θ, κ, ω and ζ) have been

identified in addition to microsomal and mitochondrial enzymes. [106] GSTs are

dimeric enzymes present in many tissues, but at their highest concentrations in the

liver and testis. [35, 107] Genetic polymorphism has been reported and can affect

the ability to detoxicate reactive metabolites, leading to an increased risk of

27

developing drug induced liver injury. [108, 109] Recently, several cytosomal

isoforms were found to catalyze the conjugation of GSH with clozapine and

diclofenac. The formation of some GSH conjugates was dependent on GSTs, and

formation of the conjugates was catalyzed by several GST isoforms. [110, 111]

Additionally, the conjugation of GSH to reactive metabolites of acetaminophen

[112], valproic acid [113], felbamate [114], zileuton [115] and troglitazone [116]

has been observed to be catalyzed by several GSTs. GSH conjugates are further

metabolized to glutamyl-cysteine, cysteine, and N-acetylcysteine conjugates (NAC,

mercapturates). [35] These are particularly important in vivo, and many urinary

metabolites are NAC-conjugates. Glutathione conjugates are transported out of the

cells by multidrug resistance protein (MRP), a transmembrane transport protein.

[35]

2.3.3 Acyl glucuronides

The formation of acyl glucuronide is an important excretion pathway in mammals

for many drugs containing carboxylic acid moieties. [117] Most common these

types of drugs are nonsteroidal anti-inflammatory drugs (NSAIDs) such as

ibuprofen or diclofenac, but other drugs such as clofibrate (fibrate), furosemide

(diuretic) and valproic acid (antiepileptic) may contain the carboxylic acid group

as well. [118] In general, glucuronidation decreases the bioactivity and lipophilicity

of the xenobiotic and increases its affinity to export pumps, resulting in increased

excretion. [118] In most cases, glucuronidation is considered to be a detoxification

pathway, but conjugation to the carboxylic acid forms an acyl glucuronide, which

may be reactive. [119, 120] Several carboxylic acid-containing drugs are associated

with rare but severe drug toxicity, and some, such as benoxaprofen and zomepirac,

have been withdrawn from the market. [121] It has been suggested that reactive

acyl glucuronides are causing the toxicity, but direct evidence of this is lacking.

[122]

As shown in Fig. 3, acyl glucuronides may be reactive towards macro-

molecules by two different mechanisms: transacylation and glycation. [123-125] In

the transacylation reaction, a nucleophile such as an S-, N- or O-containing

functional group replaces the glucuronic acid in a direct substitution reaction. This

forms a covalent bond between the xenobiotic compound and the macromolecule,

for example a protein, leading to toxic effects. In the glycation reaction, the 1-O-β-

acyl glucuronide initially formed in the conjugation reaction undergoes an

intramolecular rearrangement, an acyl migration reaction, which produces a

28

mixture of 2-, 3- and 4-O-acyl glucuronides. [122] The acyl migration reaction

exposes the hemiacetal group of the glucuronic acid moiety, which can undergo

anomerization to α and β forms and is exposed to nucleophilic attack. Acyl

migration isomers may form covalent adducts with macromolecules, and their

structure is different from transacylation products, as the glucuronic acid is

contained in the conjugate. If the nucleophile is an amine, it can undergo an

Amadori rearrangement reaction, in which a proton is transferred from the adjacent

hydroxyl to the anomeric carbon to yield a more stable form. This reaction has been

demonstrated by trapping with cyanoborohydride or sodium cyanide in a reaction

with human serum albumin. [126, 127]

Fig. 3. Reactivity of acyl glucuronides.

The rate of acyl migration is much faster at higher pH and occurs rapidly with some

aglycone structures, whereas some structures are resistant to acyl migration. Acyl

migration isomers are resistant to hydrolysis and it is not catalyzed by β-

glucuronidases, and this fact has been used to distinguish the 1-O-β isomer from

other isomers. [128] The relative importance of transacylation and glycation

reactions is not clear, but some evidence has been found that the acyl migration

pathway is likely predominant at the concentrations of the compound found in vivo

compared to transacylation. [129] Glycation is considered to be more important

than direct transacylation in the case of probenecid, diclofenac and zomepirac. [130,

131]

29

Acyl glucuronides can undergo hydrolysis to generate the parent compound.

This reaction is catalyzed by β-glucuronidases and unspecific esterases, and occurs

more rapidly at higher pH values. Serum albumin can both inhibit hydrolysis by

binding acyl glucuronide to protective sites and catalyze it by binding to catalytic

sites. [25] Hydrolysis is important for many compounds, because if the glucuronide

metabolite is excreted to the bile, it is exposed to bacterial β-glucuronidases in the

gut that will regenerate large amounts of the parent compound which can be

reabsorbed. This is called a systemic cycle or enterohepatic recirculation, and may

be an important aspect of the pharmacokinetic properties of many compounds,

increasing the concentration in the blood and slowing down excretion. If acyl

glucuronide excretion is impaired by renal or liver disease, hydrolysis can

significantly increase the half-life of carboxylic acid drugs, leading to toxicity. [25,

118]

The formation of acyl glucuronides is catalyzed by UDP-glucuronosyl-

transferases (UDPGTs), which conjugate the cofactor uridine diphosphate

glucuronic acid (UDPGA) to carboxylic acids, forming 1-O-β-isomers. [132] These

enzymes are active in the endoplasmic reticulum and nuclear envelope, mostly in

the liver and gastrointestinal track, but also in other tissues such as the kidneys and

brain. [24] Several isoforms have been found to glucuronidate carboxylic acid-

containing drugs. [132]

A correlation between acyl glucuronide degradation rates and the covalent

binding of radioactively labeled drugs to serum albumin has been found. [133]

However, a stronger correlation was found between covalent binding and the

aglycone release rate (hydrolysis rate) weighted by the percentage of isomerization

(acyl migration). [134] It has been demonstrated that it is possible to predict the

risk of IADRs from the acyl glucuronide degradation rate with higher values

predicting higher risk. The IADR risk of drugs and the half-lives of their acyl

glucuronides were found to correlate better in buffer than in human serum albumin

or in human plasma. [135] Another technique that can be used to estimate the

reactivity of acyl glucuronides is to trap them with lysine-phenylalanine dipeptide

and construct a reactivity scale based on the conjugates that are formed. [136]

Recently, the acyl migration rate was used to assess the reactivity of acyl

glucuronides. [137] Acyl glucuronide stability tests have typically required

authentic acyl glucuronides, which can be difficult to obtain, but new techniques

have been introduced to produce these metabolites directly with human liver

microsomes. [134, 137-139] Stability studies have revealed a pattern in the

reactivity of acyl glucuronides; acetic acid derivatives have been the most unstable,

30

whereas propionic acid and benzoic acid derivatives are more resistant to acyl

migration and are less reactive, probably due to steric hindrance, but electronic

properties caused by other groups can affect reactivity as well. [133, 136, 140, 141]

Acyl glucuronides have been observed to react in vitro with various proteins,

such as serum albumin [129, 142-144], UDP-glucuronosyltransferases [145],

tubulin [146, 147] and superoxide dismutase. [142] Lysine residues are the most

common amino acid to be modified covalently in serum albumin, but covalent

bonds to serine, arginine and aspartic acid have been observed as well. [129, 143,

144] In vivo, a major target has been identified as dipeptidyl peptidase IV, also

known as CD26, which is localized to the apical bile canalicular membrane in

hepatocytes and is exposed to acyl glucuronides during biliary excretion. [148-150]

Acyl glucuronides are concentrated in the liver and bile, but they can also be

transported around the body causing toxic effects elsewhere.

2.3.4 CoA conjugates

Coenzyme A conjugates are related to acyl glucuronides because they both may be

formed from carboxylic acid-containing drugs. [151] CoA conjugates are formed

in lower quantities as compared to acyl glucuronides, but can be much more

reactive. [152-154] The formation of CoA conjugates is catalyzed by acyl-CoA

synthases (ACSs) and is initiated by activating the carboxylic acid by ATP to acyl

adenylate (AMP) intermediate, which further reacts with CoA, forming the

conjugate. [151, 155] CoA and AMP conjugates can react to taurine and glycine

amides and carnitine esters and reactions are catalyzed by N-acyltransferases. [151,

156, 157] CoA and AMP conjugates also readily react with glutathione and N-

acetyl cysteine, forming thioesters. CoA reactivity towards glutathione correlates

with the hydrolysis rate of the conjugate. [158] AMP conjugates have been

observed to be more reactive towards the amino groups of glycine and taurine,

whereas CoA conjugates are more reactive towards the thiol groups of GSH and

NAC. [159, 160] These reactions are summarized in Fig. 4. It has been proposed

that the glutathione thioester can also be formed from the reaction between acyl

glucuronide and GSH. It can be reactive and the glutathione thioester of diclofenac

was able to react with N-acetylcysteine more extensively than acyl glucuronide.

[161] The role of CoA conjugates in covalent modifications seems to be important

for some drugs, but significant variation has been observed between different

compounds. [162] CoA conjugates can induce toxicity by direct acylation of

proteins in a similar way as acyl glucuronides or by interfering with mitochondrial

31

function by disrupting β-oxidation system and lipid metabolism. [163] This can

occur via several mechanisms, such as depletion of CoA and carnitine levels,

inhibition of mitochondrial fatty acid oxidation enzymes, inhibition of

mitochondrial DNA replication, or damage or uncoupling of oxidative

phosphorylation and enzymes of the mitochondrial respiratory chain. [151, 163]

Additionally, hybrid triglycerides can be formed from carboxylic acid-containing

xenobiotics. [164]

Fig. 4. CoA conjugates and CoA derived conjugates.

2.4 Enzyme sources used in vitro

As part of the preclinical stage in the development of a new drug, its

biotransformation properties are investigated. This process includes experiments to

determine metabolic stability and the metabolic profile, which are commonly

performed by incubating compounds with suitable enzyme sources in vitro. Other

experiments commonly performed in vitro are enzyme inhibition and induction

studies, which are performed to predict drug-drug interactions. Enzyme sources

used in these experiments include subcellular fractions and whole cell-containing

preparations such as liver slices or hepatocytes. Each enzyme source has its

32

advantages and disadvantages and is preferred in different types of experiments.

[165-169]

2.4.1 Subcellular fractions and recombinant enzymes

Subcellular fractions are prepared by homogenization and differential

centrifugation of liver samples to extract drug metabolizing enzymes (DME). [170,

171] Liver fractions from various animal species and human organ donors are

commercially available. Extensive centrifugation of liver homogenate is employed

to produce the microsomal fraction, which contains proteins only from the

endothelial reticulum. Less extensive centrifugation yields the S9 fraction, which

contains cytosolic proteins as well. CYPs, FMOs and UGTs are located in the

endothelial reticulum, but many phase II enzymes are located in the cytosol.

Therefore, the S9 fraction contains the conjugative phase II proteins, such as

cytosomal GSTs, NATs and SULTs, which catalyze glutathione conjugation, N-

acetylation and sulfation reactions, and it offers a wider metabolic profile than

microsomes. [165, 168] One drawback of the S9 fraction is that it usually has lower

enzyme activity per unit than microsomes, which decreases the sensitivity of the

assay. [165, 168] A disadvantage of both systems is that as the cellular structure is

lost, some drug metabolism pathways requiring multiple cellular components might

no longer be intact. Additionally, coenzymes, such as NADPH, GSH, UDPGA and

PAPS need to be added to the incubation mixture, in order to facilitate metabolism.

Only short incubation periods can be used, which limits the usage of subcellular

fractions. [166] Microsomes are widely used to study aspects of drug metabolism

such as metabolic stability, metabolite identification and CYP inhibition due to their

availability, ease of use and reproducibility.[165] One reason for these favorable

properties is that microsomes can be stored at -80°C for extended periods with little

loss of enzyme activity. [172] Additionally, microsomes from different population

pools are available, making it possible to study different population groups, for

example based on age and gender, and reduce interindividual variability. [165]

Purified DMEs can be produced in bacterial, yeast, insect or mammalian cells

by gene technology. [173] For example, supersomes are insect cells in which the

gene of the DME enzyme is transferred via a baculovirus vector. Purification of the

enzyme is easier than from other sources, as the expression level of the enzyme is

high, and insect cells lack endogenous P450 or UDPGA activity. [168] These

purified enzymes are especially useful in the identification of the enzyme isoform

metabolizing the xenobiotic in question, but extrapolation to in vivo is not always

33

straightforward. Several purified isoforms of CYP, UGT, SULT, AOX, FMO, NAT,

MAO and CES are commercially available. [174]

2.4.2 Liver slices, hepatocytes and immortal cell lines

Precision-cut liver slices represent the opposite approach to the subcellular fraction,

as the cellular structure and heterogeneous structure of the liver are preserved,

which makes slices suitable for transportation and induction studies. [172, 175, 176]

Liver slices can remain active for longer than microsomes, but can be difficult to

maintain, as oxygen and nutrient transport to the cells needs to be secured, which

requires specific techniques and equipment. [177] Additionally, uptake, clearance

and metabolism appear to be lower in liver slices than in hepatocytes due to reduced

penetration deeper in the slice, and slices have thus acquired less popularity

compared to hepatocytes. [175, 178, 179]

Hepatocytes are intact liver cells and contain all the phase I and phase II DMEs

and cellular compartments, giving access to the entire metabolic profile. This

makes them the best system to extrapolate to the in vivo situation and important

tools for assessing drug metabolism. [180, 181] They are especially used in CYP-

induction studies, as the enzyme activity can be induced. [182] Cultured

hepatocytes can remain viable for several weeks, but the metabolic capability is

reduced with time. [183] Their low availability, high price and large variability

between different batches from different individuals are also problematic,

especially with human hepatocytes. However, cryopreservation has improved their

availability, and variation can be reduced by using mixtures of hepatocytes from

different individuals.

In order to solve problems associated with hepatocytes, several immortal cells

lines such as HepG2 and BC2 from hepatocellular carcinoma and hepatoma cells

have been developed. [168, 184, 185] Problems with these cell lines have included

very low levels of DMEs, but the recently developed HepaRG cell line has been

found to express these rather well and are inducible. [186-188] They are easier to

culture, more affordable and less variable, with more stable expression of DMEs

over time compared to hepatocytes. [168] However, there are some differences in

the DME activity between HepaRG cells and hepatocytes. [189]

34

2.4.3 Drug metabolite production

Various methods have been utilized to produce larger, mg quantities of drug

metabolites. These include chemical synthesis, large-scale microsomal incubations,

expression in microorganisms and synthesis using biomimetic catalysts. The

metabolites produced using these methods are useful for more accurate structural

determination by NMR, for analytical standards or for pharmacological screening.

[190, 191] The problem with using microsomes and enzymes to produce

preparative-scale amounts of metabolites is that they are easily saturated, leading

to low yields in addition to the difficult process of isolation from complex

biological mixture and high cost. To address these problems, biomimetic catalysts,

metalloporphyrins which are able to catalyze various oxidation reactions

selectively, can be employed. The selectivity and other properties of the catalyst

can be tuned by modifying the porphyrin structure and by the selection of single

oxygen donors, solvents and co-catalysts. [191, 192]

2.5 Liquid chromatography-mass spectrometry

Liquid chromatography-mass spectrometry (LC/MS) has seen significant advances

during the last decade. New, improved LC and MS equipment with increased

sensitivity, accuracy and throughput has been introduced. This is important in the

study of reactive metabolites, as they are usually produced in very low

concentrations and thus require very sensitive analytical methods. Common

analytical procedures can be divided into four phases: sample preparation,

separation of components by liquid chromatography, detection by mass

spectrometry and data analysis.

2.5.1 Sample preparation

Before samples can be introduced to the LC/MS system, sample preparation is

required to remove sample matrix components such as salts, lipids, proteins and

other endogenous and background compounds that are present in many biological

samples. These can block the HPLC column or interfere with the ionization process,

reducing sensitivity and repeatability. [193] The removal of these compounds can

be achieved in a number a ways. The most commonly used methods include solid-

phase extraction (SPE), liquid-liquid extraction (LLE) or protein precipitation (PP).

Some other examples include filtration, dialysis, liquid-liquid microextraction,

35

solid-phase microextraction and restricted access media (RAM). [194, 195] Liquid-

liquid extraction is the traditionally used method, in which analytes are extracted

from the sample matrix with a water-immiscible organic solvent. Traditionally, the

drawback of LLE has been low throughput and high solvent consumption, but this

can be improved with microextraction techniques. However, LLE has been largely

replaced with SPE methods, which are readily automated. In SPE, analytes are

bound to the extraction cartridge which is filled with sorbent material, and non-

bound materials such as metal salts are eluted with water. After this, analytes are

eluted with a stronger solvent, such as methanol, and larger, strongly retained

components such as proteins and most lipids are retained. Different packaging

materials are available, suitable for different applications such as ion exchange,

which can achieve high selectivity for ionizable compounds. [196] In the protein

precipitation method, proteins are precipitated with the addition of an organic

solvent, such as methanol or acetonitrile, and subsequently removed with

centrifugation. The advantages of protein precipitation are its simplicity, ease of

use and suitability for rapid method development. Both SPE and LLE can achieve

good removal of interfering components and extraction efficiency in many cases.

They are used most commonly when clean samples are required and specific

components are being targeted. [194] In the case of trapped reactive metabolites,

the problem with these methods is that some compounds can be lost during the

sample preparation process. SPE methods require optimization for each component,

which is not practical in the screening type experiments that are used to search

reactive metabolites. Additionally, glutathione conjugates are typically highly

hydrophilic and may not be effectively retained by the SPE sorbent material or

extracted from the water phase. This inefficiency is not a problem in protein

precipitation, but precipitation may not effectively remove all of the protein and

lipid components that can cause interference. [194]

2.5.2 Liquid chromatography

The purpose of the LC system is to separate different compounds from each other

and to enable selective and reliable detection. Especially isobaric metabolites and

labile metabolites, which can degrade during ionization process back to their

original compounds need to be separated from each other. Separation of analytes

from background matrix ions is required for reliable quantitation due to ion

suppression effects. The most commonly used columns in LC/MS applications are

reverse phase (RP) columns, which are usually made with silica or hybrid particles

36

containing methyl groups or ethyl bridges in addition to silica and have a particle

diameter that is commonly between 2-5 μm. [197, 198] These particles possess a

hydrophobic coating, usually consisting of hydrocarbon chains with chain length

varying between 4 and 18 carbons. Other coatings have also been developed, such

as phenyl, fluorophenyl, amino, and cyano, which offer a different selectivity of

separation for certain components. [199] Retention depends on the degree of

interaction between the component and column material, and more strongly

retained compounds elute later. The eluent pH, gradient program and the choice of

column material need to be optimized, in order to achieve good retention,

selectivity, and peak shape. The pH of the eluent is adjusted with volatile additives

that are compatible with mass spectrometric detection, such as acetic acid, formic

acid, ammonium acetate, formate or ammonium hydroxide. Generic separation

methods often use C18 columns and the elution program is initiated with a high

ratio of high aqueous phase containing 0.1% formic or acetic acid, and organic

content (methanol or acetonitrile) is gradually increased. [200]

The introduction of sub-2 μm particles has dramatically improved the

performance of liquid chromatography, enabling faster analyses and narrower

peaks. The drawback is that smaller particles produce higher backpressure, and this

ultra-high-performance liquid chromatography (UHPLC) requires new

instrumentation capable of producing high pressures of over 10000 psi in

comparison to conventional HPLC, which operates under 6000 psi.[201-203]

Semiporous, fused-core particles have been introduced, which can offer similar

performance as UHPLC at lower backpressures and can even be operated with

normal HPLC equipment. [204]

2.5.3 Mass spectrometry

Mass spectrometry can be coupled with liquid chromatography via atmospheric

pressure ionization (API) techniques. The most commonly used API is electrospray

ionization (ESI). Others include atmospheric pressure chemical ionization (APCI)

and atmospheric pressure photoionization (APPI). ESI is widely used, as it is able

to ionize most organic molecules that contain polar functional groups. Less polar

compounds lacking easily ionizable groups are more effectively ionized by APCI

or APPI. [205, 206] API techniques, especially ESI, are susceptible to ion

suppression due to matrix effects, which can especially interfere with quantitative

analysis. [207, 208] Ionization can be performed in either negative or positive

ionization mode. The preferred mode depends on the nature of the compound to be

37

ionized. Acidic compounds are generally more easily detected as negative ions, and

bases more easily detected as positive ions, but many can be detected as both ions.

[200] ESI is a soft ionization technique, as usually very few fragment ions are

observed and compounds are detected as protonated or alkali metal adducts in the

positive ionization mode or as deprotonated ions in the negative mode. In the ESI

source, eluent from the LC is directed to a capillary needle, which is at a high

electric potential. When the eluent is passed through the capillary, it becomes

electrically charged and forms fine, charged droplets as it exits the needle. These

droplets vaporize, aided by heated gas flow, and disintegrate to ever-smaller

droplets due to electric repulsion. Finally, individual ions are produced by an ion

vaporization mechanism, in which ions are evaporated from the droplet surface due

to a high electric field or charge residue mechanism, in which solvent is evaporated

until only one solute molecule is left with electric charge. The ions formed are then

guided to the mass spectrometer by ion lenses and differential pumping. [209, 210]

The most commonly used mass analyzer types in modern mass spectrometers

include quadrupoles, time-of-flight, orbitrap and iontrap instruments, each having

specific advantages and disadvantages. [211, 212] Quadrupole (Q) type mass

analyzers have low resolution and low sensitivity in the scanning mode, but their

sensitivity is improved in selected ion monitoring mode (SIM). When quadrupoles

are combined to triple quadrupoles and operated in selective reaction monitoring

(SRM) mode, they provide excellent sensitivity, selectivity and linear range. SRM

mode is commonly used when the quantitation of low-abundance components is

priority, but it does not provide qualitative data and is not optimal for screening

tasks. Triple quadrupole spectrometers can be operated in neutral loss (NL) or

precursor ion scanning (PI) modes for the screening of specific metabolites, such

as glucuronide or glutathione conjugates, but these methods are less sensitive than

SRM. Time-of-flight (TOF) instruments are high-resolution mass spectrometers

(HRMS) that are able to archive high resolution (>10000, full width half maximum,

FWHM) and mass accuracy (<5 ppm) and enable the analysis of all the compounds

form the sample in a single run without prior information. Hybrid Q-TOF

instruments also provide MS/MS data and accurate mass data on the fragments.

Additional advantages of TOF include a wide mass range, high sensitivity and high

scanning speed, making it highly suitable for UHPLC analysis. Traditionally TOF

have not been used in quantitative work due to their limited linear range, but it has

been improved to cover 4-5 orders of magnitude. [213] Orbitraps can provide even

greater mass resolution and mass accuracy, but are more expensive and older

generation equipment have been limited in scanning speed, making them unsuitable

38

for UHPLC, but this problem has been largely solved in the latest generation

instruments. Ion traps can provide excellent MSn capabilities, but have limited

resolution and scanning rate. The quadrupole-linear ion trap (QLIT) has been

shown to be very useful due to its sensitivity and acquisition methods. [206, 212]

The major bottleneck in LC/MS analysis is usually data processing, especially

in the case of data-rich high-resolution mass data obtained from QTOF and orbitrap

instruments. [214] Manually searching all the possible combinations of possible

metabolites is labor-intensive, and automated methods have been developed for

metabolite screening. Software automatically calculates the mass of all possible

metabolites, searches for them in the high-resolution mass data and compares

results to the control sample in order to identify metabolites. [215] This automated

process is not completely reliable, and manual inspection of the results is still

required. [216] Various data filtering techniques, such as mass defect filtering

(MDF) [217- 221] background subtraction [222-225] and isotope-pattern filtering

[226, 227] have also been used to reduce the heavy background present in many

biological matrices. A generic dealkylation tool has been used to automatically

identify potential metabolites generated by cleavage reactions and improve the

MDF filter. [228] Data-dependent acquisition methods can also be very useful in

the characterization of metabolites. When a predefined ion reach trigger level or

specific isotopic pattern is detected, additional MS/MS experiments can be

performed simultaneously in the same experiment, increasing the throughput of the

analysis. [229, 230] Alternatively, it is possible to use the MSE acquisition method,

in which nonspecific fragmentation data is measured simultaneously with accurate

molecular mass by varying the collision energy in the collision cell. [231, 232]

Fragment ion data can be used to identify biotransformation sites, and software has

been used to automate this task as well. [233, 234]

39

3 Aims of the research

The general aim of this research was to develop better in vitro techniques for the

production and analysis of reactive drug metabolites. Trapping reactive metabolites

with glutathione was a particular focus as it is the most commonly employed

method and is able to trap large variety of different kinds of reactive metabolites.

Results from this study can improve the early stages of drug development by

helping to identify compounds that produce reactive metabolites and can

potentially cause idiosyncratic drug toxicity. The specific aims were:

– to compare different enzymes sources (microsomes, S9 fraction with and

without conjugative cofactors UDPGA, SULT, cryopreserved hepatocytes and

HepaRG cells) for the detection of glutathione-trapped metabolites with

several drugs that are known to produce reactive metabolites and not known to

produce reactive metabolites;

– to test biomimetic metalloporphyrins for the large-scale-production of

glutathione-trapped reactive metabolites and compare metabolites produced by

metalloporphyrin to metabolites produced by hepatic enzymes;

– to analyze glutathione-trapped metabolites of pulegone and menthofuran by

MS/MS and identify CYP enzymes responsible for the bioactivation of

menthofuran;

– to improve the technique of estimating the reactivity of acyl glucuronides by

separating hydrolysis from the degradation constant and isolating the acyl

migration (several carboxylic-acid containing drugs that are known to cause

IADRs and not known to cause them were selected as test compounds) and

– to estimate the significance of coenzyme A conjugates compared to acyl

glucuronides by searching all coenzyme A related conjugates from incubation

with carboxylic acid-containing drugs.

40

41

4 Materials and methods

4.1 Materials

BMO kits were a gift from HepatoChem Inc. (Beverly, USA). HepaRG cells and

human hepatocytes were acquired from Biopredic international (Rennes, France).

Human liver microsomes used in [III] were from Bioreclamation IVT (Brussels,

Belgium) and human liver microsomes and human liver S9 fraction used in [II]

were acquired as surplus from organ donors at the Oulu University Hospital.

Baculovirus-insect cell-expressed recombinant human CYP enzymes (rCYPs) were

purchased from BD Biosciences Discovery Labware (Bedford, MA). Compounds

and cofactors were acquired from Sigma-Aldrich (Helsinki, Finland). HPLC grade

acetonitrile was purchased from Merck (LiChorosolv GG, Darmstadt, Germany).

Acetic acid, formic acid and ammonium formate were purchased from BDH

Laboratory Supplies (Poole, England). Laboratory water was distilled and purified

with a Direct-Q water purifier (Millipore, Molsheim, France).

4.2 Incubations

A basic incubation mixture with liver sub-fractions consisted of 1.5-2 mg/ml of S9

fraction or 1.0 mg/ml microsomal protein. The incubation concentrations of

NADPH, UDPGA, and PAPS were 1 mM when used. The concentration of GSH

was 1 mM or 4 mM, depending on the experiment. Substrates were initially

dissolved in DMSO, and the final concentrations in the incubations were 20 – 100

μM with 0.5-1% DMSO. Each reaction mixture was preincubated for 2 minutes at

+37 C in a shaking incubator block (Eppendorf Thermomixer 5436, Hamburg,

Germany). Reactions were started by the addition of cofactors, and were terminated

after 60 min of incubation time by adding an equivalent volume of ice-cold

acetonitrile and cooling in an ice bath. Vials were centrifuged, then the contents

were transferred to analysis vials and analyzed or stored at -25°C until analysis.

In incubations with hepatocytes, the viable cell density was 1 million/ml as

determined by a trypan blue test. The GSH concentration used was 4 mM. More

details about the incubations can be found in the original articles I - IV.

42

4.3 Instrumentation

The first MS instrument system used in the study was a Thermo Ultimate 3000

UHPLC with autosampler, vacuum degasser, photo-diode-array (PDA) detector

and column oven connected to a Q-Exactive orbitrap mass spectrometer with

electrospray ionization source. MS/MS data were acquired in data-dependent-MS2

measurement mode, which performed a full mass spectral scan and triggered a

further MS/MS experiment for specified target ions. Full scan spectra were

acquired with a resolution of 35,000 and MS/MS spectra with a resolution of 17,500

(FWHM, at m/z 200). The mass spectrometer and UHPLC system were operated

with the Xcalibur software program.

The second MS instrument system used in the study was a Waters Aquity

UHPLC system with an autosampler, vacuum degasser and column oven connected

to a Waters Xevo G2 quadrupole-time-of-flight (QTOF) high-resolution mass

spectrometer equipped with a LockSpray electrospray ionization source. It was

operated with a resolution of 22,500 (FWHM, at m/z 556). To obtain MS/MS data,

the instrument was operated with two parallel data acquisition functions (MSE-

mode) with collision energies of 3 eV for molecular ions and a ramp of 10-40 eV

for fragment ions.

The third MS instrument system used in the study was a Water Acquity ultra-

performance liquid chromatographic UPLC system with an autosampler, vacuum

degasser and column oven connected to a photo-diode-array detector and LCT

Premier XE-time-of-flight high-resolution mass spectrometer equipped with a

Lockspray electrospray ion source.

With the second and third systems, leucine encephalin was used as the lock

mass compound ([M+H]+ m/z 556.2771) for accurate mass measurements and was

infused into the ion source via a separate ionization probe using a syringe pump.

The mass spectrometer and UPLC system were controlled with the MassLynx 4.1

software program. More details about the LC conditions and columns used can be

found in original articles I - IV.

43

5 Results and discussion

5.1 Enzyme source comparison [I]

Different enzymes sources, human liver microsomes, human liver S9 fraction (with

and without UDPGA and PAPS) HepaRG cells and human hepatocytes were

compared for their ability to produce reactive metabolites trapped as glutathione

conjugates. Ten test compounds were selected, six (clozapine, diclofenac, pulegone,

ethinyl estradiol, ticlopidine, and ciprofloxacin) of them were compounds with

known adverse reactions while four (montelukast, losartan, citalopram, and

caffeine) were not associated with it and were negative controls. At least clozapine,

diclofenac, pulegone, ethinyl estradiol, and ticlopidine are known to produce GSH-

trapped metabolites in human liver microsomes. [72, 75] Each compound was

incubated with each enzyme source in the presence of glutathione. Glutathione was

used in a 1:1 ratio of stable isotope-labeled to non-labeled glutathione, which

yielded an easily identifiable isotope pattern. The Metabolynx XS subroutine of

MassLynx 4.1 was used to screen the data for metabolites. S-cysteinyl-glycine, S-

cysteine and N-acetylcysteine were considered to be GSH conjugates, as they are

formed from GSH.

The most important finding was that cell-based incubations produced a

significantly lower abundance of glutathione conjugates compared to subcellular

fractions. Microsomes and S9 fractions with or without all cofactors yielded similar

results, and hepatocytes and HepaRG cells yielded mostly similar results; however,

there were some differences observed between HepaRG cells and human

hepatocytes, as different main GSH conjugates were observed in these incubations

for some compounds, such as diclofenac and ticlopidine. Overall, the differences

were minor, and the amount of GSH conjugates produced and disappearance of the

parent compound were similar in these incubations. The addition of cofactors for

glucuronic acid and sulfonic acid, UDPGA and PAPS to S9 fractions slightly

reduced the abundance of formed GSH conjugates compared to incubation of S9

fractions without these cofactors. The combined abundance of GSH conjugates

relative to the LC/MS peak area of the parent at 0 min is represented in Table 1.

Calculations performed assuming a similar ESI-MS response for all compounds,

which is not accurate, especially for pulegone and ethinyl estradiol, for which

glutathione conjugation significantly increased sensitivity, leading to very high

44

values in Table 1. Because of this, the values cannot be compared between

compounds, but only between enzyme sources.

45

Ta

ble

1. Q

ua

nti

tati

ve

co

mp

ari

so

n o

f o

bs

erv

ed

GS

H-c

on

jug

ate

s f

rom

ten

te

st

co

mp

ou

nd

s i

ncu

ba

ted

in

hu

man

liv

er

mic

ros

om

es

,

hu

ma

n l

ive

r S

9 f

rac

tio

ns

wit

h t

wo

dif

fere

nt

se

t o

f co

fac

tors

, H

ep

aR

G-c

ells

wit

h s

up

ple

men

ted

GS

H,

an

d h

um

an

he

pato

cy

tes

wit

h

su

pp

lem

en

ted

GS

H. T

he

pe

rce

nta

ge

va

lue

s r

efe

r to

re

lati

ve

sh

are

s o

f co

mb

ined

LC

/MS

peak a

reas o

f a

ll G

SH

-co

nju

gate

s t

o L

C/M

S

pe

ak

are

a o

f th

e p

are

nt

co

mp

ou

nd

at

0 m

in s

am

ple

(%

of

0 m

in p

are

nt)

.

Com

poun

d M

icro

som

es

S9

S9

with

all

cofa

ctor

s H

epaR

G

Hum

an h

epat

ocyt

es

Rel

ativ

e sh

ares

of c

ombi

ned

LC/M

S p

eak

area

s of

all

GS

H-c

onju

gate

s to

LC

/MS

pea

k ar

ea o

f the

par

ent c

ompo

und

at 0

min

sam

ple

(% o

f 0 m

in p

aren

t)

Com

poun

ds w

ith k

now

n ad

vers

e re

actio

ns o

n liv

er o

r bon

e m

arro

w

Clo

zapi

ne

6.5

4.3

8.1

0.6

0.3

Dic

lofe

nac

4.2

2.7

0.4

0 0.

5

Pul

egon

e 21

00

2200

24

6 48

6 13

0

Eth

inyl

est

radi

ol

6400

37

00

1200

13

7 76

Ticl

opid

ine

8.9

4.9

0.1

0.1

0.9

Cip

roflo

xaci

n 0

0 0

0 0

Com

poun

ds n

ot a

ssoc

iate

d w

ith h

epat

o- o

r mye

loto

xici

ties

Mon

telu

kast

0.

04

0.1

0.08

0

0

Losa

rtan

1.4

0.8

0 0

0

Cita

lopr

am

0 0

0 0

0

Caf

fein

e 0

0 0

0 0

46

All compounds with known adverse reactions, except ciprofloxacin, produced

GSH conjugates in all incubations. This is probably due to the fact that the reactive

metabolite of ciprofloxacin is more easily trapped with cyanide. [75] Montelukast

and losartan, which are not associated with these types of reactions, produced small

amounts of GSH conjugates in microsomes and S9 fractions, but not in cells. This

is most likely explained by the lower amount of GSH conjugates produced in cells,

resulting in the concentration of these conjugates remaining below the detection

limit. Another possibility is that other metabolic pathways present only in the cells

blocked the production of these conjugates. When the combined LC/MS peak areas

of the GSH conjugates were divided by the total amount of metabolites produced,

lower proportions were observed for cell-based incubations compared to

incubations with sub-cellular fractions, which can be seen in Fig. 5. Also, the

number of detected GSH conjugates was lower in cell-based incubations compared

to sub-cellular incubations.

Fig. 5. Relative shares of combined LC/MS peak areas of all GSH-conjugates to the

combined LC/MS peak areas from all metabolites (stable phase I and phase II

metabolites + GSH-conjugates), in % of metabolism.

Some of the main glutathione metabolites formed were different in cells and in

subcellular incubations, as represented in Table 2. For example, completely

different glutathione conjugates were produced from ethinyl estradiol in

hepatocytes compared to subcellular fractions.

47

Ta

ble

2. Q

ua

lita

tiv

e c

om

pa

ris

on

of

ob

se

rve

d m

ajo

r G

SH

-co

nju

gate

s f

rom

te

n t

est

co

mp

ou

nd

s i

n h

um

an

liv

er

mic

ros

om

es

, h

um

an

liv

er

S9

fr

ac

tio

ns w

ith

tw

o d

iffe

ren

t s

et

of

co

fac

tors

, H

ep

aR

G-c

ell

s w

ith

s

up

ple

me

nte

d G

SH

, an

d h

um

an

h

ep

ato

cyte

s w

ith

su

pp

lem

en

ted

GS

H.

“nd

” d

en

ote

s “

no

t d

ete

cte

d”.

“R

M”

de

no

tes

re

ac

tiv

e m

eta

bo

lite

s d

ete

cte

d a

s d

iffe

ren

t g

luta

thio

ne

co

nju

gate

s

for

ea

ch

co

mp

ou

nd

. F

or

iden

tifi

ca

tio

n o

f th

e m

eta

bo

lite

s, p

lea

se

re

fer

to t

he

su

pp

lem

en

tary

ma

teri

al

of

the

art

icle

[I]

.

Com

poun

d M

icro

som

es

S9

S9

with

all

cofa

ctor

s H

epaR

G

Hum

an h

epat

ocyt

es

Com

poun

ds w

ith k

now

n ad

vers

e re

actio

ns o

n liv

er o

r bon

e m

arro

w

Clo

zapi

ne

RM

2 R

M2

RM

2 R

M2

RM

2

Dic

lofe

nac

RM

1, R

M2

RM

1, R

M2

RM

3 nd

R

M3

Pul

egon

e R

M5,

RM

8 R

M5,

RM

8 R

M5,

RM

8 R

M5

RM

5

Eth

inyl

est

radi

ol

RM

4, R

M5

RM

4, R

M5

RM

4, R

M5

RM

8, R

M9

RM

9

Ticl

opid

ine

RM

1, R

M2

RM

1, R

M2

RM

1, R

M2

RM

4 R

M1,

RM

2

Cip

roflo

xaci

n nd

nd

nd

nd

nd

Com

poun

ds n

ot a

ssoc

iate

d w

ith h

epat

o- o

r mye

loto

xici

ties

Mon

telu

kast

R

M1

RM

1 R

M1

nd

nd

Losa

rtan

RM

1 R

M1

nd

nd

nd

Cita

lopr

am

nd

nd

nd

nd

nd

Caf

fein

e nd

nd

nd

nd

nd

48

The lower amounts of formed glutathione conjugates in cells can probably be

explained by binding of the reactive metabolites to other nucleophiles and proteins

present in the cells in higher amounts compared to subcellular fractions. Cells

commonly have lower metabolic conversion compared to microsomes, but this was

compensated by using longer incubation time, and the disappearance of the parent

compound and formation of phase I metabolites were quite similar in different

incubations, as seen in Table 3. The only exception was diclofenac, the

disappearance of which was clearly higher in sub-cellular fractions compared to

cells. The proportion of the GSH conjugates compared to all metabolites was lower

in the cell-based incubations, which supports the theory that reactive metabolites

also bonded with nucleophiles other than glutathione. Another explanation is that

some other metabolic pathways present only in the cells prevented the formation

of reactive metabolites leading to glutathione conjugates, which is supported by the

observation of different main reactive metabolites in different incubations. It should

be noted that microsomal fractions contain only microsomal glutathione S-

transferases (GSTs), but S9 fractions and cells also contain cytosolic GSTs.

Therefore, higher amounts of GSH conjugates would be expected in S9 fractions

and cells, but this was not observed. It is possible that cytosolic GSTs do not play

an important role in catalyzing GSH conjugation to these compounds. Alternatively,

the usage of high concentration of GSH masks the effect of GSTs, as high

concentrations are known to reduce the GST-dependency of conjugate formation.

Another interesting observation was that almost no GSH conjugates were detected

without the addition of GSH to the incubation medium in the cell-based incubations,

which is in agreement with some earlier results. [235] This may be due to low GSH

concentration in the cryopreserved cells and suggests that at least some reactive

metabolites are excreted out of the cell.

49

Table 3. Parent compound remaining at the end of the incubation. Values are reported

as relative LC/MS peak areas in comparison to 0 min (%).

Microsomes S9 S9 with all

cofactors

HepaRG Human

Hepatocytes

Clozapine 66 66 81 83 76

Diclofenac 4 13 37 86 62

Pulegone 8 16 31 5 3

Ethinyl estradiol 28 33 45 59 39

Ticlopidine 36 48 56 70 55

Montelukast 75 85 82 83 82

Losartan 95 99 93 97 86

Ciprofloxacin 100 100 100 100 98

Citalopram 100 100 95 99 100

Caffeine 100 100 99 100 100

5.2 Glutathione-trapped reactive metabolites with biomimetic

metalloporphyrins [II]

Three test compounds (clozapine, ticlopidine and citalopram) were incubated both

with human liver S9 fractions and with biomimetic metalloporphyrin catalysts in

the presence of glutathione. The study utilized a BMO kit, (BioMimetic Oxidation

kit), a commercial biomimetic catalyst kit, based on metalloporphyrins from

HepatoChem Inc. Clozapine and ticlopidine were selected as test compounds, as

they are known for their idiosyncratic toxicity and are known to produce

glutathione conjugates in vitro. [236, 237] Citalopram was selected as a control

compound, as it is not known to yield glutathione conjugates in vitro with human

liver microsomes. Incubation mixtures were analyzed with LC/MS and LC/MS/MS,

and the BMO incubations were analyzed for the presence of metabolites identified

in S9 fraction incubations. The main focus of this study was on glutathione-trapped

reactive metabolites, but phase I metabolites were also examined in order to better

characterize the overall metabolism.

A total of six glutathione conjugates (RM1-RM6) were detected for clozapine

in incubations with human liver S9 fractions. The structure of these metabolites is

presented in Fig. 6, together with the analysis of their fragmentation pathways.

MS/MS spectra were used to confirm that these metabolites were clozapine GSH

conjugates. A characteristic fragment ion was, for example, the loss of

50

pyroglutamate and fragments from the the methylpiperazine ring, similar to those

observed for clozapine.

Fig. 6. Clozapine GSH conjugates.

51

However, MS/MS spectra could not be used to accurately determine the site of

GSH conjugation, and more exact identification was based on the comparison of

measured data to published literature data in which structures had been determined

by LC-MS and NMR. [238, 239] RM1 was a cysteine conjugate, RM2-RM4 were

glutathione conjugates and RM5-RM6 were glutathione conjugates with an

additional oxygen atom. The site of the oxygen was determined to be on the

methylpiperazine ring, most likely being N-oxide. N-oxide was also the most

abundant phase I metabolite detected in the BMO incubations. GSH conjugates

RM3-RM6 were detected in the BMO incubation, some of them in much higher

abundance compared to S9 incubations. RM1 was not detected in the BMO

incubation, most likely because it is formed by degradation of the glutathione

conjugate, which requires specific enzymes that are not present in the BMO

incubation. RM2, which was detected only in the S9 fraction incubation, was a

glutathione conjugate formed by the loss of a chlorine atom. RM2 likely requires

glutathione S-transferases to catalyze the conjugation reaction, as this metabolite

was detected only after addition of these enzymes to the microsomal incubation in

a recent study. [111] Five phase I metabolites were detected in the S9 incubation,

with four of these were also detected in the BMO incubation.

For ticlopidine, four glutathione conjugates were detected in the S9 incubation,

but none of these were observed in the BMO incubation. These are presented in

Fig. 7. All of these conjugates had the same accurate mass, which corresponded to

oxidation and hydrogenation combined with glutathione conjugation, but the

MS/MS spectra were different for each one. Each conjugate lost pyroglutamate,

which is characteristic for glutathione conjugates, and RM1 and RM2 lost SO,

indicating that they were S-oxides. Based on the fragmentation spectra and

literature data, GSH conjugates RM1 and RM2 were most likely generated by the

reaction of glutathione with ticlopidine S-oxide, where as RM3 and RM4 were

generated by the reaction of glutathione with ticlopidine epoxide. [237, 240] It was

unexpected that no glutathione conjugates of ticlopidine were detected in the BMO

incubations, as metalloporphyrins should be able to perform S-oxidation in a

similar manner as N-oxidations that were observed in good abundance for all

compounds. It is possible that glutathione deactivates the catalyst, preventing the

formation of reactive intermediates. In the S9 incubation, a total of nine phase I

metabolites were detected, 8 of which were also detected in the BMO incubation.

For the control compound citalopram, no glutathione conjugates were detected

in S9 or BMO incubations, which was expected. The same three phase I metabolites

were instead detected in both BMO and S9 incubations.

52

Fig. 7. Ticlopidine GSH conjugates.

5.3 S- and N-linked glutathione conjugates of pulegone and

menthofuran [III]

(S)-(+)-pulegone, (R)-(-)-pulegone and menthofuran were incubated with human

liver S9 fractions with glutathione as a trapping agent for reactive metabolites. GSH

conjugates were searched for with LC/MS and LC/MS/MS in both positive and

negative polarities. In addition to GSH conjugation, hydrogenation,

dehydrogenation, hydroxylation and combinations of these reactions were included

when calculating the m/z values for the possible conjugates. A total of six major

glutathione conjugates were detected in the incubations with pulegone and

53

menthofuran, which are presented in Table 4. M1-M3 were detected both in the

incubation with pulegone and menthofuran, but the measured peak area was

approximately 10 times higher in the incubation with menthofuran compared to

(+)-pulegone and they were identified as glutathione conjugates of menthofuran

based on their accurate mass. These conjugates were detected also in the incubation

with (-)-pulegone, but their abundance was 20-50 times lower relative to the

menthofuran incubation. Metabolite M4 was detected only in the incubation with

menthofuran in low abundance. Conjugates M5 and M6 were detected only in the

incubations with pulegone, and their formation was not catalyzed by CYP enzymes,

as equal amounts were formed in the presence of NADPH and without it.

The MS/MS spectra for the compounds M1-M3 is presented in Figs 8-10.

Analysis of these spectra revealed that M1 was a glutathione S-conjugate, but the

MS/MS spectra of M2 could only be rationalized as a glutathione N-conjugate. The

accurate mass and MS/MS spectra of M3 corresponded to glutathione S-/N-di-

conjugate of menthofuran. In positive ionization mode, the neutral loss of glycine

(C2H5NO2, 75.0320) was a common fragment for all metabolites M1-M3, but its

abundance was relatively low. Fragments commonly used to screen glutathione

conjugates, such as the neutral losses of pyroglutamate (C5H7NO3, 129.0426 Da)

and glutathione (C10H17N3O6S, 307.0838 Da) were detected only for the S-

conjugate M1, but not for M2 or M3. Instead, the characteristic neutral losses for

both of these conjugates were C5H10N2O3S (178.0412 Da) and C10H14N2O6S

(290.0573 Da) in the positive ionization. In negative ionization mode, fragments at

m/z 272.0888 and 254.0782 are commonly used to screen for glutathione S-

conjugates, and were observed in high abundance for M1 but not for M2 or M3. A

fragment at m/z 143.0462 was instead observed for all conjugates M1-M3. For M2,

the neutral losses of SH2 (33.0988 Da) and C10H14N2O6S (290.0573 Da) were

abundant fragment ions in the negative ionization mode. For M3, the characteristic

neutral loss was C10H12N2O6 (256.0695 Da). M4 was identified as a glutathione di-

conjugate similar to M3, but the second conjugation reaction had occurred with

another glutathione molecule, not with the same as in M3. Due to its low abundance,

only one fragment, corresponding to the neutral loss of glutathione C10H17N3O6S

(307.0838 Da), a characteristic loss for glutathione S-conjugates, was detected in

the positive ionization mode. The fragmentation spectra of M5 and M6 were largely

similar to M1, and they were identified as S-glutathione conjugates of pulegone.

54

Ta

ble

4. E

xa

ct

ma

ss

data

fo

r th

e o

bs

erv

ed

glu

tath

ion

e c

on

jug

ate

s o

f m

en

tho

fura

n a

nd

pu

leg

on

e.

“nd

” d

en

ote

s t

ha

t u

nc

on

jug

ate

d

pu

leg

on

e a

nd

me

nth

ofu

ran

we

re n

ot

de

tecte

d in

ne

gati

ve

io

niz

ati

on

mo

de

. “D

iffe

ren

ce

” d

en

ote

s t

he d

iffe

ren

ce

be

twe

en

ca

lcu

late

d

an

d e

xp

eri

men

tal

mas

s.

Fo

rmul

a C

alc.

[M+H

]+ D

iffer

ence

[mD

a]

Cal

c. [M

-H]-

Diff

eren

ce [m

Da]

t R

P

uleg

one

Men

thof

uran

Pul

egon

e C

10H

16O

15

3.12

74

0.4

151.

1128

nd

. 3.

78

Men

thof

uran

C

10H

14O

15

1.11

17

0.3

149.

0972

nd

. 4.

55

M1

C20

H29

N3O

7S

456.

1799

1.

4 45

4.16

53

-0.5

2.

86

+ +

M2

C20

H29

N3O

7S

456.

1799

1.

0 45

4.16

53

0.3

2.72

+

+

M3

C20

H27

N3O

6S

438.

1693

0.

9 43

6.15

48

-0.1

2.

68

+ +

M4

C30

H44

N6O

12S

2 74

5.25

31

0.8

743.

2386

-1

.2

2.54

-

+

M5

C20

H31

N3O

7S

458.

1955

0.

7 45

6.18

10

-0.5

2.

32

+ -

M6

C20

H33

N3O

7S

460.

2112

1.

1 45

8.19

66

-0.3

2.

39

+ -

55

Fig. 8. MS/MS spectra for M1 in a) positive and b) negative ionization mode.

56

Fig. 9. MS/MS spectra for M2 in a) positive and b) negative ionization mode.

57

Fig. 10. MS/MS spectra for M3 in a) positive b) negative ionization mode.

To our knowledge, only one N-linked conjugate [241] and few N-/S-di-linked

conjugates [242] have been reported previously, but thorough MS/MS analysis has

not been performed for these kinds of conjugates or their fragmentation spectra

compared to S-conjugates. However, it seems that N- and N-/S- conjugates are

limited to furan-containing compounds, and are not as common as S-conjugates.

The reactions involved between pulegone and menthofuran with glutathione are

summarized in Fig. 11. Pulegone can react directly with glutathione at the α,β-

unsaturated ketone in to form M6 in a Michael addition type reaction, and this type

of C-8 cysteine metabolite has been identified in rats. [243] The reaction

mechanism of the similar metabolite M5 could not be rationalized, nor could the

58

site of conjugation be identified. Alternatively, pulegone can be metabolized to

menthofuran in a CYP mediated reaction. Menthofuran can be further activated by

CYP enzymes to the reactive metabolite, most likely an epoxide, which can then

rearrange to γ-ketoenal. [244] S-linked M1 is formed when the epoxide or γ-

ketoenal is attacked by the sulfhydryl group of glutathione, followed by

dehydration. A likely mechanism for the formation of the N-linked glutathione

conjugate M2 involves nucleophilic attack of the glutamic amine to the γ-ketoenal,

followed by dehydration and Schiff base formation, which then cyclizes. [245] M3

is likely formed by initial S-conjugation to the γ-ketoenal intermediate, followed

by N-conjugation, in a similar way as with M2. [242]

Fig. 11. Metabolic activation of pulegone and menthofuran to reactive intermediate

trapped by glutathione.

5.4 Identification of CYP P450 enzymes mediating the formation of

reactive metabolite from menthofuran [III]

Menthofuran was incubated with recombinant CYP enzymes and glutathione to

determine the CYP enzymes responsible for the activation of menthofuran to its

59

reactive metabolite. Varying amounts of glutathione conjugates M1-M3 were

observed in the incubations of all CYP isoforms, as can be seen in Fig. 12a. The

relative abundances of conjugates M1-M3 were similar in all incubations, with M1

being 6-6.5 more abundant than M2 or M3. The highest abundance of conjugates

was produced by CYPs 1A2, 2B6 and 2C19, but all isoforms produced some

conjugates. These results were extrapolated to the in vivo situation by correcting

values with the amounts of different isoenzymes in human liver. [246]

60

Fig. 12. Relative abundances of M1, M2 and M3 in incubations with different

recombinant CYP enzymes a) LC/MS abundances, b) in vivo prediction of relative roles

of each CYP isoform, based on mean relative abundances of CYPs in Caucasian liver.

This extrapolation suggested that 1A2 and 2B6 would have the largest role in the

formation of M1-M3, followed by 3A4 and 2E1 (Fig. 12b). These results were

somewhat different compared to earlier results, which indicated that isoforms 1A2,

2B6, 2C19 and 2E1 were responsible for the formation of 2-hydroxymenthofuran

61

and mintlactones form menthofuran, metabolites that were shown to be formed via

the reactive intermediate. [247] The different results are most likely due to

differences in experimental setup, as different metabolites were observed. In this

study, a more direct method was employed, as trapped reactive metabolites were

detected, whereas in the other study connections to reactive intermediates were

made indirectly.

5.5 Reactive metabolites of carboxylic acid-containing drugs [IV]

A total of 13 carboxylic acid-containing drugs were selected for this study and were

classified as safe (montelukast, telmisartan, repaglinide, furosemide), warning

(gemfibrozil, valproic acid, ibuprofen, indomethacin, naproxen, tolmetin,

diclofenac) or withdrawn (zomepirac, isoxepac) based on their FDA labeling on

hepatoxicity and market withdrawals. The study can be divided into three different

sections: half-lives of acyl glucuronides, in vitro cytotoxicity and mitochondrial

toxicity experiments and CoA or derived conjugates, with the intention of

integrating results from these experiments to give a better picture of the toxicity of

carboxylic acid-containing drugs.

The acyl glucuronide degradation rate was determined in a two-step incubation

introduced by Chen et al. [138] Acyl glucuronides were produced in a microsomal

incubation, and after the first incubation period the incubation was stopped with

UDP, which inhibits the glucuronidation reaction. After this, the second incubation

was performed to determine the acyl migration rate by observing the abundance of

the different acyl migration isomers with LC/MS. Acyl glucuronides were

identified based on their accurate mass and the cleavage of the glucuronic acid

moiety in the MS/MS experiments. Esterases from the microsomal incubation are

present in the second incubation, catalyzing hydrolysis of the acyl glucuronides.

The hydrolysis rate might not represent the reactivity of the acyl glucuronide very

well in these conditions. This is why both observed half-lives and relative half-lives,

in which the hydrolysis has been separated from the acyl migration, were

determined.

While total half-lives correlated with safety classification, in accordance to

other studies, [135] the correlation was stronger with relative half-lives, as seen in

Table 5. Clearly, longer half-lives, and especially relative half-lives, were observed

for safe drugs compared to drugs in non-safe categories.

62

Fig. 13. MS peak areas of a) Telmisartan acyl glucuronides and b) Isoxepac acyl

glucuronides relative to the peak area of 1-O-β-acyl glucuronide at 0 min in incubations

with liver microsomes.

63

Fig. 14. Extracted ion chromatograms of indomethacin acyl glucuronides at different

incubation times. The peak area of 1-O-β-acyl glucuronide decreases and other peak

areas increase over time.

64

This was primarily due to the low acyl migration rate of drugs in the safe category,

as a majority of the acyl glucuronide disappearance appeared to be due to

hydrolysis. This is demonstrated in Fig. 13, in which the degradation of two

different acyl glucuronides is presented. Isoxepac is a withdrawn drug which

undergoes rapid acyl migration, while the acyl migration observed for the safe drug

telmisartan is negligible, and hydrolysis is instead the determining factor of the

observed half-life. There were some exceptions, as the safe drug furosemide had

short half-lives, and the warning drugs gemfibrozil and valproic acid had very long

half-lives. Furosemide was classified as a warning drug and gemfibrozil as a safe

drug in a recent study, and their safety classification is somewhat ambiguous. [135]

The acyl glucuronide of valproic acid is known to be very stable, and the adverse

reactions and warning classifications are most likely due to reactive metabolites

generated by oxidative metabolism. [248] Other reactive metabolites besides acyl

glucuronides can contribute to the adverse effects of other drugs as well, as reactive

metabolites formed by oxidative metabolism have been detected at least for

zomepirac, tolmetin, and diclofenac. [249, 250]

The total acyl migration at 6 hours had some correlation to the safety

classification, but the difference was not as clear, and surprisingly there were some

differences between liver microsomes and hepatocytes. The total acyl glucuronide

formation in human liver microsomes did not correlate with the safety classification.

The correlation between safety classification and relative acyl migration half-life

was further improved when the half-life was divided by the maximum daily dose

of the drug, as seen in Table 6. This was due to many drugs in the safe category

being taken in much lower doses compared to drugs in the warning category.

Mitochondrial toxicity and cytotoxicity experiments were also performed, but

they did not reveal any correlation to drug safety, as many safe drugs showed

significant toxicity and many warning drugs did not. Interestingly, when the

toxicity was compared to human bile salt export pump inhibition (BSEPi) data from

the literature, an excellent correlation was observed, as all compounds toxic to

mitochondria were also BSEP inhibitors, as seen in Table 5. It is possible that at

least some of the toxicity observed in these in vitro experiments is caused by BSEP

inhibition of the parent compound.

65

Ta

ble

5. A

cy

l m

igra

tio

n, fo

rmati

on

of

ac

yl g

luc

uro

nid

es

, c

on

jug

ate

s a

nd

cell

to

xic

ity.

Cat

egor

y C

ompo

und

Hal

f-liv

es, h

A

G m

igra

tion

at 6

hc To

tal A

G fo

rmat

ion,

%

of p

aren

td C

ell t

oxic

ity

O

bsa

Rel

b H

LM

Hep

aRG

M

itoto

x C

ytot

ox

BS

EP

ie

Saf

e M

onte

luka

st

4.3

> 40

0.

0 0

1.0

**

**

**

Saf

e Te

lmis

arta

n 3.

7 32

11

.0

2 1.

0 *

* **

Saf

e R

epag

linid

e 9.

2 18

22

.6

30

2.9

**

0 *

Saf

e Fu

rose

mid

e 2.

6 3.

1 66

.2

35

0.4

0 0

0 W

arni

ng

Gem

fibro

zil

6.3

> 40

0.

0 7

2.8

0 0

0 W

arni

ng

Val

proi

c ac

id

4.4

30

11.7

0

NA

0 0

0 W

arni

ng

Ibup

rofe

n 2.

6 2.

4 77

.4

69

0.5

0 0

0 W

arni

ng

Indo

met

haci

n 1.

2 2.

2 76

.6

29

2.0

* 0

* W

arni

ng

Nap

roxe

n 1.

5 1.

4 59

38

0.

2 0

0 0

War

ning

To

lmet

in

0.5

0.4

97.8

13

0.

03

0 *

NA

War

ning

D

iclo

fena

c 0.

2 0.

3 10

0 22

2.

2 **

0

* W

ithdr

awn

Zom

epira

c 1.

1 1.

4 91

.5

22

0.1

0 0

NA

With

draw

n Is

oxep

ac

0.6

0.6

100

15

0.04

0

0 N

A

+ =

Con

juga

te d

etec

ted,

- =

Con

juga

te n

ot d

etec

ted

** =

Tox

ic; A

TP/v

iabi

lity

<70%

at 1

00 µ

M c

once

ntra

tion

com

pare

d to

veh

icle

, dos

e de

pend

ent l

oss

of A

TP/v

iabi

lity

* =

Wea

kly

toxi

c; A

TP/v

iabi

lity

<70%

at 1

00 µ

M c

once

ntra

tion

only

com

pare

d to

veh

icle

0 =

Not

toxi

c; A

TP/v

iabi

lity

>70%

, at 1

00 µ

M c

once

ntra

tion

com

pare

d to

veh

icle

BS

EP

toxi

city

was

cla

ssifi

ed a

s:

**

= S

trong

inhi

bito

r of B

SE

P (>

70%

inhi

bitio

n at

50

µM c

once

ntra

tion)

* =

Mod

erat

e in

hibi

tor o

f BS

EP

(30

- 70%

at 5

0 µM

con

cent

ratio

n)

0 =

No

BS

EP

inhi

bitio

n (<

30%

at 5

0 µM

con

cent

ratio

n)

66

Cat

egor

y C

ompo

und

Hal

f-liv

es, h

A

G m

igra

tion

at 6

hc To

tal A

G fo

rmat

ion,

%

of p

aren

td C

ell t

oxic

ity

O

bsa

Rel

b H

LM

Hep

aRG

M

itoto

x C

ytot

ox

BS

EP

ie

NA

= D

ata

not a

vaila

ble

a D

egra

dati

on r

ate

of f

orm

ed 1

-O-β-AG

b C

alcu

late

d va

lue

excl

udin

g th

e ef

fect

of

hydr

olys

is, r

efer

to th

e te

xt

c Pea

k ar

ea o

f th

e m

igra

ted

AG

s co

mpa

red

to 1

-O-β-AG

at t

he b

egin

ning

of

the

seco

ndar

y in

cuba

tion

d P

eak

area

of

all t

he A

Gs

divi

ded

by p

aren

t pea

k ar

ea a

t 0 m

in. M

easu

red

by U

V, s

ame

UV

res

pons

e as

sum

ed f

or p

aren

t and

all

AG

s e D

ata

coll

ecte

d fr

om li

tera

ture

, met

abol

ical

ly in

com

pete

nt s

yste

m [

251,

252

]

67

Ta

ble

6. R

es

ult

s c

orr

ela

ted

wit

h m

ax

imu

m d

ail

y d

os

e.

Cat

egor

y C

ompo

und

Max

imum

Dai

ly d

ose/

mg

HLM

H

epaR

G

Dos

e-co

rrec

ted

toxi

city

R

el. h

alf-

life/

dose

a A

G M

igra

tion

at 6

h

(%) ×

dos

ea, b

AG

Mig

ratio

n (%

) ×

dose

a,b

H

epaR

G T

otal

AG

× do

sea,

c M

itoto

x C

ytot

ox

BS

EP

id S

afe

Mon

telu

kast

10

23

45

0 0

0.2

* *

**

Saf

e Te

lmis

arta

n 80

20

6 2

0.3

1.5

* *

**

Saf

e R

epag

linid

e 16

50

9 1

1 1.

0 *

0 *

Saf

e Fu

rose

mid

e 12

0 9

24

13

1.6

0 0

0 W

arni

ng

Gem

fibro

zil

1200

8

32

32

134.

6 0

0 0

War

ning

V

alpr

oic

acid

20

00

2 16

2 N

A

0 0

0 0

War

ning

Ib

upro

fen

2400

0.

2 90

1 80

3 54

.7

0 0

0 W

arni

ng

Indo

met

haci

n 20

0 4

43

16

11.2

**

0 *

War

ning

N

apro

xen

1000

0.

3 25

6 16

6 6.

9 0

0 0

War

ning

To

lmet

in

1800

0.

1 68

4 87

2.

1 0

**

NA

War

ning

D

iclo

fena

c 20

0 0.

4 68

15

14

.8

**

0 *

With

draw

n Zo

mep

irac

100

4 31

7

0.4

0 0

NA

With

draw

n Is

oxep

ac

200

0.8

75

11

0.3

0 0

NA

Dos

e-co

rrec

ted

toxi

city

cla

ssifi

catio

n w

as c

alcu

late

d fro

m v

iabi

lity

(ATP

) and

mito

chon

dria

l tox

icity

and

cla

ssifi

ed a

s:

** =

Tox

ic; A

TP/v

iabi

lity

<70%

at 1

00 µ

M c

once

ntra

tion

com

pare

d to

veh

icle

, sho

win

g hi

gh d

ose-

corr

ecte

d to

xici

ty (m

ax d

aily

dos

e ×

toxi

city

, at 1

00 µ

M)

* =

Mild

Tox

icity

; ATP

/via

bilit

y <7

0% a

t 100

µM

con

cent

ratio

n on

ly c

ompa

red

to v

ehic

le, s

how

ing

som

e do

se-c

orre

cted

toxi

city

(max

dai

ly d

ose

× to

xici

ty, a

t

100

µM)

0 =

Not

toxi

c, A

TP/v

iabi

lity

>70%

, at 1

00 µ

M c

once

ntra

tion

BS

EP

toxi

city

was

cla

ssifi

ed a

s:

** =

Stro

ng in

hibi

tor o

f BS

EP

(> 7

0% in

hibi

tion

at 5

0 µM

con

cent

ratio

n)

* =

Mod

erat

e in

hibi

tor o

f BS

EP

(30

- 70%

at 5

0 µM

con

cent

ratio

n)

68

Cat

egor

y C

ompo

und

Max

imum

Dai

ly d

ose/

mg

HLM

H

epaR

G

Dos

e-co

rrec

ted

toxi

city

R

el. h

alf-

life/

dose

a A

G M

igra

tion

at 6

h

(%) ×

dos

ea, b

AG

Mig

ratio

n (%

) ×

dose

a,b

H

epaR

G T

otal

AG

× do

sea,

c M

itoto

x C

ytot

ox

BS

EP

id 0

= N

o B

SE

P in

hibi

tion

(< 3

0% a

t 50

µM c

once

ntra

tion)

NA

= D

ata

not a

vaila

ble

a M

axim

um d

aily

dos

e w

as c

onve

rted

to m

illim

oles

b A

G m

igra

tion

at 6

hou

rs fr

om T

able

5 m

ultip

lied

by m

axim

um d

aily

dos

e

c Tot

al A

G fo

rmat

ion

% fr

om p

aren

t fro

m T

able

5, m

ultip

lied

by m

axim

um d

aily

dos

e

d Dat

a co

llect

ed fr

om li

tera

ture

, met

abol

ical

ly in

com

pete

nt s

yste

m [2

51, 2

52]

69

Test compounds were incubated with HepaRG cells and AMP, CoA, CoA derived

taurine, glycine and carnitine conjugates, and GSH thioester conjugates were

searched for with LC/MS. The results are presented in Table 7. Most of the

conjugates were detected for drugs in the warning category, with the exception of

telmisartan, a drug in the safe category, for which glycine and glutathione thioester

conjugates were detected. Taurine and glutathione thioester conjugates were the

most common conjugates detected, while a CoA conjugate was detected only for

valproic acid and ibuprofen. No AMP conjugates were detected and a carnitine

conjugate was detected only for ibuprofen. Not all drugs in the warning category

produced these conjugates, which is probably explained by the potential instability

of these conjugates. While AMP and CoA are required for the formation of taurine,

glycine and carnitine conjugates, they were not detected, probably because they

were unstable or reacted completely to other conjugates. The detection of

glutathione thioester indicates that the compound can undergo a transacylation

reaction either via the CoA or glucuronic acid route.

Table 7. Detected CoA-route conjugates and GSH transacylation products (SG).

Category Compound SG AMP CoA Taurine Glycine Carnitine Safe Montelukast - - - - - - Safe Telmisartan + - - - + - Safe Repaglinide - - - - - - Safe Furosemide - - - - - - Warning Gemfibrozil - - - - - - Warning Valproic acid + - + + - - Warning Ibuprofen + - + + - + Warning Indomethacin + - - - - - Warning Naproxen + - - - - - Warning Tolmetin - - - + - - Warning Diclofenac + - - + - - Withdrawn Zomepirac - - - + - - Withdrawn Isoxepac - - - - - -

+ = Conjugate detected

- = Conjugate not detected

70

71

6 Summary and conclusions

Significant differences in the production of glutathione conjugates were observed

between different enzymes sources. Sub-cellular fractions microsomes, S9 fraction

and S9 fractions with additional UDPGA and PAPS were similar to each other, and

human hepatocytes and HepaRG cells were similar to each other. Sub-cellular

fractions produced significantly higher amounts of glutathione conjugates

compared to cells. The proportion of reactive metabolites was lower in cells when

compared against amount of parent compound at 0 min and also against the total

metabolite formation, even when the disappearance of the parent compound was

similar in each incubation. Additionally, the number of detected conjugates was

higher and the main glutathione conjugates detected were different in sub-cellular

fractions when compared to cells. These differences are most likely due to the

binding of reactive metabolites with nucleophiles other than GSH or alternative

metabolic pathways present only in cells that can reduce the significance of

pathways leading to reactive metabolites. In general, these results show that results

from different enzyme sources are not comparable, as different results are obtained.

As assays using sub-cellular fractions are much more sensitive in detecting GSH

conjugates, their usage is justified. On the other hand, the cell might offer a more

realistic picture of drug metabolism, as they contain all the drug metabolizing

enzymes. S9 fractions with added cofactors for glucuronidation and sulfation might

be a good compromise between sensitivity and taking account different metabolic

pathways. The LC/MS method used with the utilization of a 1:1 ratio of stable

isotope-labeled and non-labeled glutathione was observed to be a sensitive and

useful technique in the screening of reactive metabolites.

The study showed that metalloporphyrins are able to produce glutathione

trapped reactive metabolites in selected cases, but limitations were observed. The

reactive metabolite of clozapine, the iminium ion was generated and successfully

trapped with glutathione. All glutathione metabolites of clozapine were not

produced in the BMO assay, as it lacked the required enzymes, such as glutathione

S-transferases and degradation enzymes. It is not clear which glutathione

conjugates require glutathione S-transferases to be formed, but if they are required,

BMO cannot be used to produce them. That ticlopidine glutathione conjugates were

not detected in the BMO incubation, indicates that the reactive metabolites of

ticlopidine, S-oxides and epoxides were not generated by the BMO catalysts. If this

is caused by the deactivation of the catalyst by glutathione, it represents a

significant drawback for the assay. However, due to the limited number of

72

compounds tested, it is not possible to make general assumptions based on these

results.

N-linked and N- and S-di-linked glutathione conjugates were analyzed in detail

with tandem mass spectrometry for the first time. Significant differences were

observed between common S-linked and uncommon N- or N-/S- linked conjugates

of pulegone and menthofuran. Most importantly, the most common fragments used

to screen GSH conjugates, such as the neutral loss of pyroglutamate or glutathione

in positive ionization mode were not observed for N- or N-/S- linked conjugates.

For these, the primary fragments lost were C5H10N2O3S (178.0412 Da) and

C10H14N2O6S (290.0573 Da) in positive ionization mode. In negative ionization

mode, fragments derived from glutathione, commonly used to screen GSH

conjugates, were observed only for the S-linked GSH conjugate. For the N-linked

conjugate, the neutral loss of SH2 (33.0988 Da) and C10H14N2O6S (290.0573 Da)

were abundant fragment ions in negative ionization mode. For the N-linked

conjugate, the neutral losses of SH2 (33.0988 Da) C10H14N2O6S (290.0573 Da)

were abundant fragment ions in negative ionization mode. For S-/N-di-linked

conjugates, the characteristic neutral loss was C10H12N2O6 (256.0695 Da) and a

fragment at m/z 143.0462 was observed for all conjugates. It is recommended to

use at least some of these fragments in the screening of glutathione conjugates for

furan-containing compounds, in order to capture all potential GSH conjugates. The

results presented here can be valuable in these screening tasks. As only one

compound was studied, the applicability of the results to other compounds is

uncertain, but as most fragments originated from the glutathione moiety, it seems

likely that these results are generally applicable to other similar compounds.

Recombinant enzymes were used to determine the most important CYP P450

isoforms responsible for the bioactivation of menthofuran. CYP enzymes 1A2, 2B6

and 2C19 were found to produce the highest abundance of menthofuran GSH

conjugates, and correlation with relative in vivo levels of each CYP isoform also

stressed the role of CYPs 2E1 and 3A4.The relative half-lives, which are a measure

of the acyl migration rate of acyl glucuronides, were found to correlate well with

the drug safety categories. The correlation was even better than the observed half-

lives that also included the hydrolysis of the acyl glucuronide. This was because

drugs in the safe category degraded primarily through hydrolysis and acyl

migration was minor, whereas drugs in the warning or withdrawn categories

degraded primarily through acyl migration. As the safe drugs were taken in low

doses, the correlation was improved when the daily dose was taken into account.

This resulted in improved separation between safe drugs and drugs causing IADRs

73

compared to total acyl degradation half-lives used by others. [135] Mitochondrial

toxicity and cytotoxicity of the tested compounds did not correlate with the safety

categories, but correlated well with human bile salt export pump inhibition data

collected from literature. The test compounds were also incubated with HepaRG

cells and CoA conjugate and conjugates derived from CoA were searched.

Conjugates were primarily detected in the warning or withdrawn categories,

although not for all of them. Also, conjugates were detected form one compound

in the safe category. The most commonly detected conjugates were glutathione

thioester and taurine conjugates, whereas CoA were detected only for two

compounds. This is likely due to the instability of the CoA conjugates. Taken

together, very short acyl migration half-life and the detection of CoA or derived

conjugates can give indication on the potential of the drug to cause hepatotoxicity.

The reasons behind the different levels of reactive metabolites produced in

cells and sub-cellular fractions deserve further attention. The measurement of

covalent binding of radioactively labeled drug molecules could be used to

determine whether the formed reactive metabolites are bound with proteins or if

they are formed in lower quantities in cells, as both theories could be used to

explain the observed differences between enzyme sources. If a similar quantity of

covalent binding is observed in different enzyme sources, it would indicate that

different reaction pathways are responsible for the observations. Higher covalent

binding in cells would indicate that reactive metabolites are produced, but are not

effectively trapped with GSH, as they are bound to proteins.

The sample size in the research on the production of reactive metabolites with

the biomimetic metalloporphyrin catalyst was very small and could be expanded to

include other compounds with different kinds of reactive metabolites to see which

kind of reactive metabolites could be produced and trapped with glutathione. The

theory that glutathione is able to inhibit the catalyst could be tested by comparing

the amounts of metabolites produced in incubations with and without trapping

agents and by searching differences in metabolites profiles.

Other furan containing compounds could be screened for their GSH conjugates

to see if they behave in a similar way as the GSH conjugates of menthofuran, if

their fragmentation patterns are similar and if there are some differences in their

preference to form glutathione N- or S-conjugates.

In the study of carboxylic-acid containing drugs, a glutathione thioester

compound was found for several compounds, but it is not clear if this metabolite is

formed via the acyl glucuronide or the CoA route. This could be tested by

incubating test compounds with liver microsomes, providing only UDPGA or acyl-

74

CoA as cofactor and measuring the resulting level of glutathione thioester.

Alternatively, inhibition of glucuronidation would provide same results.

75

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Original articles

I Lassila T, Rousu T, Mattila S, Chesne C, Pelkonen O, Turpeinen M & Tolonen A (2015) Formation of GSH-trapped reactive metabolites in human liver microsomes, S9 fraction, HepaRG-cells, and human hepatocytes. J Pharmceut Biomed Anal 115: 345–351.

II Lassila T, Mattila S, Turpeinen M & Tolonen, A (2015) Glutathione trapping of reactive drug metabolites produced by biomimetic metalloporphyrin catalysts. Rapid Commun Mass Spectrom 29: 521–532.

III LassilaT,MattilaS,TurpeinenM,PelkonenO&TolonenA(2016)TandemmassspectrometricanalysisofS-andN-linkedglutathioneconjugatesofpulegoneandmenthofuranandidentificationofP450enzymesmediatingtheirformation.RapidCommunMassSpectrom30: 917–926. IV Lassila T, Hokkanen J, Aatsinki S-M, Mattila S, Turpeinen M & Tolonen A (2015) The

toxicity of carboxylic acid-containing drugs: the role of acyl migration and CoA conjugation investigated. Chem Res Toxicol 28: 2292–2303.

Reprinted with permission from Elsevier (I), John Wiley and Sons (II-III) and

American Chemical Society (IV).

Original publications are not included in the electronic version of the dissertation.

94

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659. Prokkola, Hanna (2015) Biodegradation studies of recycled vegetable oils,surface-active agents, and condensing wastewaters

660. Halkola, Eija (2015) Participation in infrastructuring the future school : a nexusanalytic inquiry

661. Kujala, Sonja (2015) Dissecting genetic variation in European Scots pine (Pinussylvestris L.) : special emphasis on polygenic adaptation

662. Muilu-Mäkelä, Riina (2015) Polyamine metabolism of Scots pine under abioticstress

663. Pakanen, Minna (2015) Visual design examples in the evaluation of anticipateduser experience at the early phases of research and development

664. Hyry, Jaakko (2015) Designing projected user interfaces as assistive technologyfor the elderly

665. Varanka, Sanna (2016) Multiscale influence of environmental factors on waterquality in boreal rivers : application of spatial-based statistical modelling

666. Luukkonen, Tero (2016) New adsorption and oxidation-based approaches forwater and wastewater treatment : studies regarding organic peracids, boiler-water treatment, and geopolymers

667. Tolkkinen, Mari (2016) Multi-stressor effects in boreal streams : disentangling theroles of natural and land use disturbance to stream communities

668. Kaakinen, Juhani (2016) Öljyllä ja raskasmetalleilla pilaantuneita maita koskevanympäristölainsäädännön ja lupamenettelyn edistäminen kemiallisella tutkimuksella

669. Huttunen, Kaisa-Leena (2016) Biodiversity through time : coherence, stability andspecies turnover in boreal stream communities

670. Rönkä, Nelli (2016) Phylogeography and conservation genetics of waders

671. Fucci, Davide (2016) The role of process conformance and developers' skills inthe context of test-driven development

672. Manninen, Outi (2016) The resilience of understorey vegetation and soil toincreasing nitrogen and disturbances in boreal forests and the subarcticecosystem

673. Pentinsaari, Mikko (2016) Utility of DNA barcodes in identification anddelimitation of beetle species, with insights into COI protein structure across theanimal kingdom

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IN VITRO METHODS IN THE STUDY OF REACTIVE DRUG METABOLITES WITH LIQUID CHROMATOGRAPHY / MASS SPECTROMETRY

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