Scholarly article on topic 'Cytochrome P450 enzymes in drug metabolism: Regulation of gene expression, enzyme activities, and impact of genetic variation'

Cytochrome P450 enzymes in drug metabolism: Regulation of gene expression, enzyme activities, and impact of genetic variation Academic research paper on "Clinical medicine"

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{Biotransformation / "Cytochrome P450 monooxygenase" / "NADPH:cytochrome P450 reductase" / Pharmacogenetics / Polymorphism / Xenobiotic}

Abstract of research paper on Clinical medicine, author of scientific article — Ulrich M. Zanger, Matthias Schwab

Abstract Cytochromes P450 (CYP) are a major source of variability in drug pharmacokinetics and response. Of 57 putatively functional human CYPs only about a dozen enzymes, belonging to the CYP1, 2, and 3 families, are responsible for the biotransformation of most foreign substances including 70–80% of all drugs in clinical use. The highest expressed forms in liver are CYPs 3A4, 2C9, 2C8, 2E1, and 1A2, while 2A6, 2D6, 2B6, 2C19 and 3A5 are less abundant and CYPs 2J2, 1A1, and 1B1 are mainly expressed extrahepatically. Expression of each CYP is influenced by a unique combination of mechanisms and factors including genetic polymorphisms, induction by xenobiotics, regulation by cytokines, hormones and during disease states, as well as sex, age, and others. Multiallelic genetic polymorphisms, which strongly depend on ethnicity, play a major role for the function of CYPs 2D6, 2C19, 2C9, 2B6, 3A5 and 2A6, and lead to distinct pharmacogenetic phenotypes termed as poor, intermediate, extensive, and ultrarapid metabolizers. For these CYPs, the evidence for clinical significance regarding adverse drug reactions (ADRs), drug efficacy and dose requirement is rapidly growing. Polymorphisms in CYPs 1A1, 1A2, 2C8, 2E1, 2J2, and 3A4 are generally less predictive, but new data on CYP3A4 show that predictive variants exist and that additional variants in regulatory genes or in NADPH:cytochrome P450 oxidoreductase (POR) can have an influence. Here we review the recent progress on drug metabolism activity profiles, interindividual variability and regulation of expression, and the functional and clinical impact of genetic variation in drug metabolizing P450s.

Academic research paper on topic "Cytochrome P450 enzymes in drug metabolism: Regulation of gene expression, enzyme activities, and impact of genetic variation"

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Pharmacology & Therapeutics

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Cytochrome P450 enzymes in drug metabolism: Regulation of gene expression, enzyme activities, and impact of genetic variation

Ulrich M. Zanger *, Matthias Schwab

Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, Auerbachstr. 112 D, 70376 Stuttgart, Germany Department of Clinical Pharmacology, The University ofTuebingen Medical Faculty, Tuebingen, Germany

ARTICLE INFO ABSTRACT

Cytochromes P450 (CYP) are a major source of variability in drug pharmacokinetics and response. Of 57 putatively functional human CYPs only about a dozen enzymes, belonging to the CYP1, 2, and 3 families, are responsible for the biotransformation of most foreign substances including 70-80% of all drugs in clinical use. The highest expressed forms in liver are CYPs 3A4,2C9,2C8,2E1, and 1A2, while 2A6,2D6,2B6,2C19 and 3A5 are less abundant and CYPs 2J2,1A1, and 1B1 are mainly expressed extrahepatically. Expression of each CYP is influenced by a unique combination of mechanisms and factors including genetic polymorphisms, induction by xenobiotics, regulation by cytokines, hormones and during disease states, as well as sex, age, and others. Multiallelic genetic polymorphisms, which strongly depend on ethnicity, play a major role for the function of CYPs 2D6, 2C19, 2C9, 2B6, 3A5 and 2A6, and lead to distinct pharmacogenetic phenotypes termed as poor, intermediate, extensive, and ultrarapid metabolizers. For these CYPs, the evidence for clinical significance regarding adverse drug reactions (ADRs), drug efficacy and dose requirement is rapidly growing. Polymorphisms in CYPs 1A1, 1A2, 2C8, 2E1, 2J2, and 3A4 are generally less predictive, but new data on CYP3A4 show that predictive variants exist and that additional variants in regulatory genes or in NADPH: cytochrome P450 oxidoreductase (POR) can have an influence. Here we review the recent progress on drug metabolism activity profiles, interindividual variability and regulation of expression, and the functional and clinical impact of genetic variation in drug metabolizing P450s.

© 2013 Elsevier Inc. All rights reserved.

Keywords:

Biotransformation

Cytochrome P450 monooxygenase

NADPH:cytochrome P450 reductase

Pharmacogenetics

Polymorphism

Xenobiotic

Contents

1. Introduction..............................................104

2. Factors that influence cytochromes P450 expression and function.....................104

3. Family CYP1: CYP1A1, CYPIA2, CYP1B1..................................104

4. Family CYP2..............................................105

5. Family CYP3: CYP3A4, CYP3A5, CYP3A7, CYP3A43 ............................105

6. NADPH:cytochrome P450 oxidoreductase (POR) .............................109

7. Conclusions and future perspectives...................................109

Conflict of interest statement.........................................110

Acknowledgments..............................................110

References .................................................111

Abbreviations: ADME, absorption, distribution, metabolism, excretion; ADR, adverse drug reaction; AhR, aromatic hydrocarbon receptor; CNV, copy number variant; CYP, cytochrome P450; DDI, drug-drug interaction; EM, extensive metabolizer; ERa, estrogen receptor alpha; FXR, farnesoid X receptor; GR, glucocorticoid receptor; GWAS, genome-wide association study; IM, intermediate metabolizer; LD, linkage disequilibrium; LXR, liver X receptor; MAF, minor allele frequency; NAFLD, non-alcoholic fatty liver disease; NASH, non-alcoholic steatohepatitis; NNRTI, non-nucleoside reverse transcriptase inhibitor; PBREM, phenobarbital-responsive enhancer module; PCR, polymerase chain reaction; PM, poor metabolizer; POR, NADPH:cytochrome P450 oxidoreductase; PPAR, peroxisome proliferator-activated receptor; SERM, selective estrogen receptor modulator; SLCO1B1, organic anion transporting polypeptide 1B1; UM, ultrarapid metabolizer; VDR, vitamin D receptor; XREM, xenobiotics-responsive enhancer module.

* Corresponding author at: Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, Auerbachstr. 112 D, 70376 Stuttgart, Germany. Tel.: +49 711 81 01 37 04; fax: +49 711 85 92 95.

E-mail address: uli.zanger@ikp-stuttgart.de (U.M. Zanger).

0163-7258/$ - see front matter © 2013 Elsevier Inc. All rights reserved. http://dx.doi.org/! 0.1016/j.pharmthera.2012.12.007

1. Introduction

Predicting the fate of a drug in a particular patient and his or her subsequent response is still a vision and far away from application in routine clinical practice. Recognizing the sources and understanding the factors that contribute to the extraordinary pharmacokinetic and pharmacodynamic variability within and between individuals remains a challenge of particular importance for drugs with narrow therapeutic index (Lin, 2007). The cytochromes P450 (CYPs) constitute the major enzyme family capable of catalyzing the oxidative biotransformation of most drugs and other lipophilic xenobiotics and are therefore of particular relevance for clinical pharmacology (Nelson, 2004; Guengerich, 2008; Zanger et al., 2008). In humans 57 putatively functional genes and 58 pseudogenes are encoded by various gene clusters distributed over most autosomal chromosomes, in comparison to 108 functional and 88 pseudogenes in the mouse (Nelson et al., 2004). Most of the human genes, which are grouped according to their sequence similarity into 18 families and 44 subfamilies (http://drnelson.uthsc.edu/ human.P450.table.html), have specific endogenous functions including the biosynthesis of steroid hormones, prostaglandins, bile acids, and others (Nebert & Russell, 2002). Only about a dozen enzymes belonging to the 1, 2, and 3 CYP-families are responsible for the metabolism of the majority of drugs and other xenobiotics. Despite the broad and overlapping substrate specificities of these enzymes, many drugs are metabolized at clinically relevant concentrations by one or few enzymes only, which limits the important redundancy of the phase I drug oxidation system. Knowledge of the intrinsic and extrinsic factors that influence expression and function of the responsible enzymes is thus a prerequisite for predicting variable pharmacokinetics and drug response. While monogenic polymorphisms explain a major part of the variability for only few enzymes (in particular CYP2D6), most enzymes are multifactorially controlled including additional polymorphisms in regulatory trans-genes and nongenetic host factors including sex, age, disease, hormonal and diurnal influences and other factors (Fig. 1). In this review we cover the CYPs of families 1 to 3 which

have been shown to be of major importance for the biotransformation of drugs. We review the recent progress on drug metabolism activity profiles, interindividual variability and regulation of expression, and the functional and clinical impact of genetic variation in drug metabolizing P450s, whereas epidemiological studies were only mentioned occasionally. Our intention was to provide basic knowledge for each CYP on all these aspects but to focus for the literature survey on the past ten years. In view of the enormous body of literature we could not cite all studies and we are aware that it is difficult to provide an entirely objective overview. In this sense, we would like to apologize to all authors of studies not mentioned in this review.

2. Factors that influence

cytochromes P450 expression and function

2.1. Genetic polymorphism

Heritable genetic variation in drug metabolizing enzyme genes has been studied for over 60 years and many intriguing examples of the genetic influence on drug biotransformation have been investigated at great detail and for some of them clinical relevance has been studied (Meyer, 2004). Interestingly, loss-of-function polymorphisms in CYP genes surprisingly often affect splicing and expression, rather than transcription or protein structure (Sadee et al., 2011). Gain-of-function variants include copy number variants (CNV) with an increased number of functional gene copies in CYP2D6 and CYP2A6 (Johansson & Ingelman-Sundberg, 2008), as well as promoter variants (e.g. in CYP2B6, CYP2C19) and amino acid variants with increased substrate turnover (e.g. in CYP2B6, CYP2C8). Surprisingly few polymorphisms affect clearly the substrate selectivity or the in-ducibility of drug metabolic pathways. Important polymorphisms of drug metabolizing CYPs with functional and clinical correlates are summarized in Table 1.

The CYP-specific drug oxidation phenotype can be determined in vivo using selective model substrates (Walsky & Obach, 2004; Fuhr et

Fig. 1. Fraction of clinically used drugs metabolized by P450 isoforms and factors influencing variability. A total of 248 drug metabolism pathways with known CYP involvement (Table 3; chemicals and endogenous substrates excluded) were analyzed. Each metabolic pathway was only counted once for the major contributing CYP isoform. Important variability factors are indicated by bold type with possible directions of influence indicated (|, increased activity; decreased activity; increased and decreased activity). Factors of controversial significance are shown in parentheses.

al., 2007). Different terms are in use for the associated pharmacokinetic phenotypes. In the case of the classical polymorphisms of the CYP2D6 and CYP2C19 genes, which were discovered by the phenotypic variation they elicit in drug-treated subjects,"poor metabolizer" (PM) refers to homozygous or compound heterozygous carriers of alleles with complete lack of function (null allele); "extensive metabolizer" (EM) refers to the "normal" phenotype, usually representing the major proportion of the population; "intermediate" metabolizers (IM) carry only one normal or functionally deficient allele, resulting in impaired drug oxidation capacity; and the "ultrarapid" metabolizer (UM) phenotype originates from gain-of-function variants (Fig. 2). Severe loss-of-function alleles and functional gene duplications are rare among CYPs 1A1, 1A2, 2C8, 2E1, 2J2, and 3A4, and terms like "slow metabolizer" and "rapid metabolizer" should here only be used in the context of phenotypic differences.

The clinical impact of polymorphism in a drug metabolising enzyme must be considered within its pharmacological context. Loss-of-function variants will lead to reduced clearance and increased plasma concentrations, while gain-of-function variants will lead to increased clearance and lower drug concentrations. If the drug is pharmacologically active, this results in increased and decreased drug effect, respectively, and potentially in drug-related toxicity due to overdosing. If the drug is metabolically activated (prodrug), the contrary is to be expected, and the pharmacological activity or toxic-ity of the metabolite(s) must be considered, as for example in the case of CYP2D6-dependent morphine formation from codeine. The influence of genetic polymorphisms on CYP expression and function, as well as their clinical impact will be discussed for each CYP below.

2.2. Epigenetic influences on drug metabolism

Some heritable changes in gene function are not based on DNA sequence variations, and the term epigenetics has been coined to describe such phenomena. Two important mechanisms are DNA meth-ylation and histone protein modification. Whereas DNA methylation is involved in normal cellular control of gene expression, histone modification affects the accessibility and transcriptional activity of the chro-matin within the cell. The term epigenetics further comprises gene regulatory mechanisms by microRNAs (miRNAs). Epigenetic patterns are principally reversible and may be tissue-specific and influenced by host factors (sex, age) and environmental factors. The influence of epigenetic processes on pharmacologically relevant genes and drug response is a rather new area of research that has recently been summarized (Ingelman-Sundberg & Gomez, 2010). A global analysis of differential gene expression in the human HepG2 hepatoma cell line following treatment with 5-aza-2'-deoxycytidine to inhibit DNA methylation and trichostatin A to inhibit histone deacetylation demonstrated widespread effects on more than 1500 and 500 genes, respectively, including induction of CYP3A genes and other cytochromes P450, as well as several transcription factors (Dannenberg & Edenberg, 2006). Examples studied in more detail include primarily CYP1 genes. CYP1A1 promoter methylation in human lung tissue was lowest among heavy tobacco smokers and highest in non-smokers, providing an example of environmental influence on DNA methylation patterns (Anttila et al., 2003). In CYP1A2 an inverse correlation between mRNA and extent of methylation at two CpG sites near the transcription start site was observed in human liver samples (Ghotbi et al., 2009). Promoter methylation at multiple CpG sites in the CYP1B1 gene promoter has been detected and associated with tamoxifen response (Widschwendter et al., 2004) and decreased inducibility (Beedanagari et al., 2010).

Recent research has also illuminated the impact of miRNAs on ADME gene expression. A current problem of miRNA information management relates to the large number of miRNAs, estimated at over 1000 different molecules per mammalian species and their poorly defined binding specificities, which allows for a vast number of

potential miRNA-target gene interactions (Friedman et al., 2009). Various databases have been developed, using different algorithms to predict potential binding sites (Lagana et al., 2009; Xiao et al., 2009; Rieger et al., 2011). However the integration of these datasets is a difficult task and dedicated software programs, e.g. MIRNA-DISTILLER, allow the compilation of miRNA predictions from different databases to facilitate data management (Rieger et al., 2011). Direct regulation by miRNAs was shown for CYP1B1 and CYP3A4 by miR-27b (Tsuchiya et al., 2006; Pan et al., 2009), and CYP2E1 by miR-378 (Mohri et al., 2010). Nuclear receptors are also targets of miRNAs as shown for the xenosensor pregnane X receptor (PXR, NR1I2), which was shown to be under control of miR-148a, thereby influencing CYP3A4 and CYP2B6 expression levels and the metabolism of xenobiotic drug substrates of these enzymes (Takagi et al., 2008). Hepatocyte nuclear factor 4 alpha (HNF4a) is regulated by miR-24 and miR-34a and overexpression of these miRNAs results in decrease of HNF4a and downregulation of its target genes (Takagi et al., 2010). The vitamin D receptor (VDR), another transcriptional regulator of CYP3A4, is also regulated by the CYP3A4-targeting miR-27b, resulting in an indirect and a direct mechanism for miRNA regulation of CYP3A4 (Pan et al., 2009). The same miRNA miR-27b also regulates the peroxisome proliferator-activated receptor PPARy (Karbiener et al., 2009; Jennewein et al., 2010) and the liver X receptor (LXR) was shown to be regulated by miR-613 (Ou et al., 2011), further supporting an important role of miRNAs in hepatic gene regulation.

Of special interest for pharmacogenetic aspects are SNPs in miRNAs and miRNA binding sites, as well as miRNA copy number variations, which may affect their expression and function and thus influence target gene expression (Schmeier et al., 2011; Wei et al., 2012). For example, the dihydrofolate-reductase (DHFR) gene harbors a socalled miRSNP in its 3'-UTR, which was found to cause loss of miR-24 binding leading to DHFR overexpression and association with methotrexate (MTX) resistance (Bertino et al., 2007; Mishra et al., 2007). Although the field of epigenetic regulation of drug metabolism and drug response genes is relatively new, these examples demonstrate an important impact of miRNAs on ADME gene regulation and potential relevance for drug response.

2.3. Nongenetic host factors

Sex influences a number of pharmacokinetically important parameters including body weight, fat distribution, liver blood flow, as well as expression of drug metabolizing enzymes and transporters (Beierle et al., 1999; Gandhi et al., 2004). Sex-specific expression of cyto-chromes P450 is common in laboratory animals including rats and mice and was found shown to be controlled by the different secretion profiles of growth hormone in female versus male animals (Dhir & Shapiro, 2003; Waxman & Holloway, 2009). In humans the differences are more subtle and their relevance for drug treatment is a matter of continuous concern (Gandhi et al., 2004; Schwartz, 2007; Waxman & Holloway, 2009). A recent genome-wide gene expression profiling study in 112 male and 112 female livers identified more than 1300 genes whose mRNA expression was significantly affected by sex, with 75% of them showing higher expression in females (Zhang et al., 2011). Among these were 40 ADME/ADME-related genes including CYP1A2, CYP3A4 and CYP7A1 showing female bias, and CYP3A5, CYP27B1, and UGT2B15 showing male bias. Most clinical studies indicate that women metabolize drugs more quickly than men. This is particularly the case for substrates of the major drug metabolizing cytochrome P450, CYP3A4 (e.g. antipyrine, alfentanil, erythromycin, midazolam, verapamil; Cotreau et al., 2005). Analyses of CYP3A4 in human liver have indeed shown ~2-fold higher levels of protein in female compared to male liver tissue (Schmidt et al., 2001; Wolbold et al., 2003; Lamba et al., 2010; Yang et al., 2010). This pronounced difference is also apparent on the mRNA level, and

Table 1

Selected genetic polymorphisms of human cytochromes P450 and POR.

CYP allele designation3 Key mutation(s)b rs number Location, protein effect

Allele frequenciesc

CYP1AT2C

CYP1A2'1C

CYP1A2'1F

2454A>G (rs1048943)

—3860G>A (rs2069514)

—163C>A (rs762551)

Promoter

Intron 1

gMAF 0.120 0.0-0.04 Af, AA 0.20-0.26 As 0.03-0.07 Ca 0.18-0.43 Hs 0.17 Pc gMAF 0.188 0.26-0.40 AA, Af 0.21-0.27 As, Pc 0.01-0.08 Ca 0.20-0.30 Hs gMAF 0.35 (A>C) 0.5-0.8 all ethnicities

CYPIBI'6

CYP2A6*2

CYP2A6*4A to *4H

CYP2A6V

CYP2AS*9

CYP2A6*17

CYP2B6*4

CYP2B6' 5

CYP2B6*6

CYP2B6'18

142C>G (rsl0012);

355G>T

(rsl056827);

4326C>G

(rsl056836)

1799T>A

(rsl801272)

Recombination

6558T>C (rs5031016)

—48T>G (rs28399433)

5065G>A (rs28399454)

18053A>G (rs2279343)

25505C>T (rs3211371)

15631G>T (rs3745274); 18053A>G (rs2279343)

21011T>C

(rs28399499)

R48G A119S L432V

CYP2A6 deleted

Promoter, TATA-box

K262R (isolated)

Q172HK262R

gMAF 0.32-39 0.5-0.85 AA, Af 0.09-0.13 As 0.23-0.40 Ca

gMAF 0.013 0.00-0.01 AA, Af 0.00-0.025 As 0.04-0.10 Ca 0.01-0.02 AA 0.05-0.24 As 0.01-0.04 Ca gMAF 0.04 0.00 AA, Af 0.06-0.13 As 0.00 Ca gMAF 0.13 0.04-0.12 AA, Af 0.16-0.27 As 0.04-0.05 Ca gMAF 0.025 0.04-0.50 AA, Af 0.00 As 0.00-0.02 Ca gMAF 0.26 0.00 AA, Af 0.05-0.12 As 0.04 Ca gMAF 0.05 0.01-0.04 AA, Af 0.01-0.04 As 0.09-0.12 Ca gMAF 0.27 0.33-0.5 AA, Af 0.10-0.21 As 0.14-0.27 Ca 0.62 Pc gMAF 0.02 0.04-0.08 AA 0.05-0.12 Af 0.01 Hs 0.00 As, Ca, Pc

Functional effect

Clinical correlations

| Activity (17ß-estradiol and estrone)

| Lung cancer risk in Chinese; | breast cancer risk in Caucasians; | prostate cancer risk

J. Inducibility (smokers)

May influence susceptibility to certain cancers

| Inducibility (smokers, omeprazole) | Susceptibility to cancer in Caucasians; | oral

clearance olanzapine; possible modifier for risk of coronary heart disease t Km, l Vmax (17ß-estradiol, recombinant) | Prostate cancer risk for L432V in Asians

No activity

Null allele

J. Activity

J. Activity

J. Activity

J. Nicotine metabolism & influence on cigarette consumption, nicotine dependence, smoking cessation response; | lung cancer risk in Caucasians; I oral clearance of tegafur

| Expression & activity

| Drug clearance (bupropion, efavirenz, cyclophosphamide)

J. Expression & activity

Possibly decreased drug clearance

J. Expression

I Activity (efavirenz, nevirapine) I Activity (cyclophosphamide)

J. Drug clearance & | adverse events including treatment discontinuation (efavirenz, nevirapine, S-methadone)

U Expression & activity

CYP2B6'22

CYP2C8'2

CYP2C8-3

CYP2C8'4

CYP2CT2

CYP2CT3 CYP2C1F2

CYP2C1F3 CYP2C1F17

CYP2D6-3 CYP2D6'4

CYP2D6'5 CYP2D6-6 CYP2D640

CYP2D647 CYP2D6*41

CYP2D6'Nxn

—82T>C (rs34223104)

11054A>T (rsll572103)

2130G>A

(rsll572080);

30411A>G

(rsl0509681)

11041C>G

(rs!058930)

3608C>T (rs!799853)

42614A>C (rs!057910)

19154G>A (rs4244285)

17948G>A (rs4986893)

—806C>T (rs!2248560)

2549delA (rs35742686) 1846G>A (rs3892097)

Recombination

1707delT (rs5030655) 100C>T (rs!065852)

1023C>T (rs28371706); 2850C>T (rs 16947) 2988G>A (rs28371725)

Recombination

Promoter, TATA-box I269F

R139K K399R

Splicing defect

W212X Promoter

Frameshift Splicing defect

Deletion

Frameshift

T107I R296C Splicing defect

Copy number variations

gMAF 0.012 0.00-0.025 AA, Af, As 0.024 Ca, Hs gMAF 0.039 0.10-0.22 AA Af 0.00 As, Ca gMAF 0.065 0.00 AA, Af, As 0.65-0.14 Ca, Hs

gMAF 0.026 0.00-0.01 AA Af, As, Pc

0.03-0.07 Ca, Hs gMAF 0.069 0.00-0.02 AA Af 0.00-0.02 As, Pc 0.10-0.17 Ca 0.065 Hs gMAF 0.043 0.00-0.01 Af, AA 0.02-0.06 As 0.06 Ca gMAF 0.199 0.10-0.17 AA Af 0.22-0.32 As, Pc 0.06-0.15 Ca 0.15 Hs gMAF 0.014 0.00-0.01 Af, Ca, Hs 0.03-0.07 As, Pc gMAF 0.15 0.15-0.27 AA Af 0.00-0.02 As 0.21-0.25 Ca gMAF 0.009 -0.01 all ethnicities gMAF 0.106 0.01-0.10 AA Af, As, Hs

0.15-0.25 Ca 0.03-0.06 all ethnicities gMAF 0.01 -0.01 all ethnicities gMAF 0.26 0.08-0.12 AA Af 0.40-0.70 As 0.02 Ca

gMAF 0.049 (for 1023C>T)

0.14-0.24 Af

0.00 As, Ca

gMAF 0.055

0.01-0.06 Af, As, Pc,

0.09 Ca

Up to 0.30 Af, Ar 0.01-0.09 Ca

I Expression & activity I Inducibility

I Activity

Possibly increased drug clearance

Controversial clinical effects

J. Activity (paclitaxel) I Activity (antidiabetics)

J. Activity (paclitaxel)

J. Activity

U Activity

J. Drug clearance & | risk of bleeding (anticoagulants warfarin, acenocoumarol, phenprocoumone); I drug clearance & | adverse events (sulfonylurea hypoglycemic drugs, NSAIDS); I drug clearance & | adverse events (sulfonylurea hypoglycemic drugs, NSAIDS)

Null allele

Null allele

J. Clearance & | efficacy of PPIs in Helicobacter pylori eradication therapy; j. anticoagulation effect of Clopidogrel 5 | cardiovascular events; j. clearance & | risk of ADRs for antidepressants (amitriptypine, Citalopram, clomipramine, moclobemide), antimalarials (proguanil), antifungals (voriconazole)

I Expression & activity

| Clearance of PPIs & risk of subtherapeutic concentrations; | risk of bleeding with Clopidogrel

Null allele Null allele

Null allele Null allele

I Expression & activity

J. Clearance & | risk of ADRs for many antiarrhythmics, antidepressants, antipsychotics; j. metabolic activation & analgesic effect of opioids (codeine, dihydrocodeine, oxycodone, tramadol); j. metabolic activation & efficacy of tamoxifen

J. Expression & activity

J. Expression & activity

I Expression & activity | Toxicity of opioids

(continued on next page) ^ o

Table 1 (continued)

CYP allele designation3 Key mutation(s)b rs number Location, protein effect

Allele frequencies'

Functional effect

Clinical correlations

CYP2J2*7 — 76G>T (rs890293) SP1-binding to promoter decreased gMAF 0.073 0.10-0.17 AA, Af 0.02-0.13 As 0.055-0.08 Ca | Expression & activity No conclusive clinical associations

CYP3A4*1B -392A>G (rs2740574) Promoter gMAF 0.20 0.50-0.82 AA, Af 0.00 As 0.03-0.05 Ca, Hs, SA Probably no effect on transcription | Prostate cancer disease progression

CYP3A4*22 15389 C>T (rs35599367) Intron 6 gMAF 0.021 0.043 AA 0.043 As 0.025-0.08 Ca | Expression & activity | Metabolism of simvastatin & | lipid-lowering response; | daily-dose requirement for tacrolimus

CYP3A5*3 6986A>G (rs776746) Intron 3, splicing defect gMAF 0.312 0.37 AA 0.12-0.35 Af 0.66-0.75 As, Hs 0.88-0.97 Ca H Expression & activity | Metabolism & dose requirements for selected drugs with a preference for metabolism by CYP3A5 over CYP3A4 (e.g., tacrolimus, saquinavir)

CYP3A5*6 1469oA>G (rs10264272) Exon 6, K208, splicing defect gMAF 0.045 0.15-0.25 Af 0.12 AA 0.00 As, Ca, His H Expression & activity

POR*28 31696C>T A503V gMAF 0.287 Various substrate- and CYP-dependent | Enzyme activity of major CYP enzymes in patients

(rs1o57868) 0.08-0.50 AA, Af 0.38-0.42 As 0.29-0.33 Ca 0.32-0.35 Pc effects in vitro with rare POR deficiency; | CYP3A4 enzyme activity with midazolam

a According to the CYPallele nomenclature homepage (http://www.cypalleles.ki.se). b Genomic positions are given with corresponding rs numbers in parentheses.

c gMAF, global allele frequency of the minor allele as reported in the 1000Genome phase 1 genotype data (released May 2011). Selected frequencies of individual ethnicities (AA, African American; Af African; As Asian; Ar, Arab; Ca Caucasian; Hs, Hispanic; In, Indian; Pc, Pacific; SA, South American) were compiled from dbSNP (build 137) at http://www.ncbi.nlm.nih.gov/projects/SNP/; from the Allele Frequency Database ALFRED at http://alfred.med.yale.edu/alfred/ index.asp; and from the references cited in the text.

translates into apparently substrate-dependent pharmacokinetic differences in the order of 20 to 50%. For other CYPs the issue of sex-biased expression has not been finally clarified and some contradictory data have been published from different studies, as discussed below.

Age is a well established influential factor for drug metabolism capacity, particularly at the extremes of life, where drug metabolism capacity appears to be substantially lower. In neonates, this is due to immaturity of several enzyme systems including cytochromes P450 (Kinirons & O'Mahony, 2004; Koukouritaki et al., 2004; Stevens, 2006; Stevens et al., 2008), which fully develop only during the first year of life. In addition, there are some peculiarities in the expression of specific isoforms, e.g. CYP3A7 is primarily a fetally expressed form of the CYP3A subfamily. In the elderly population, the ability to clear drugs is clearly decreased. This is particularly relevant for drugs with narrow therapeutic window, including anti-psychotics and antidepressants, anticoagulants, and betablockers. Clearance of paracetamol and benzodiazepines is also lower in older people. However, this does not seem to be a consequence of lower expression or activity of drug metabolizing enzyme systems. Studies in human liver have found a modest increase in expression and activity for most CYPs during life, particularly CYP2C9, which remained significant after correcting for multiple testing, while the influence of age on CYPs 1A2, 2A6, 2B6, 2C8, and 3A4 partially interacted with sex (Yang et al., 2010). Other reasons for limited drug clearance in the elderly are polypharmacy, i.e. inhibition of enzymes due to the concomitant intake of several potentially interacting drugs, as well as reduced liver blood flow and renal function (Cotreau et al., 2005). Age-associated changes in expression of genes involved in xenobiotic metabolism have also been identified in rats (Mori et al., 2007) and in long-lived mutant mice (Amador-Noguez et al., 2007).

Disease states generally have a negative effect on drug metabolism capacity. In liver cirrhosis changes in the architecture of the liver resulting in reduction of blood flow, loss of functional hepato-cytes, and decreased drug metabolizing enzymes contribute to decreased drug metabolism capacity and lower synthesis of serum proteins, leading on the one hand to decreased clearance but on

the other to increased unbound drug levels due to decreased plasma protein binding (Elbekai et al., 2004; Edginton & Willmann, 2008).

During infection, inflammation and cancer, circulating proinflammatory cytokines such as interleukin (IL)-1(3, TNF-a and 1L-6, which act as signaling molecules to elicit marked changes in liver gene expression profiles, lead to severe downregulation of many drug metabolizing enzymes (Slaviero et al., 2003; Aitken et al., 2006). The mechanism of these effects has been shown to be at least in part due to transcriptional suppression (Jover et al., 2002; Aitken et al., 2006; Aitken & Morgan, 2007). Alcoholic and non-alcoholic fatty liver disease (NAFLD) are conditions characterized by the abnormal retention of large amounts of triglycerides which are accumulated in fat vesicles. NAFLD is associated with diabetes, obesity and metabolic syndrome and in severe cases proceeds to steatohepatitis (NASH). While the majority of studies on drug metabolism in NAFLD were carried out in overweight rat or genetically obese db/db mouse models, human studies are rare. Available data show moderately reduced expression for most but not all CYP1-3 enzymes (Naik et al., 2013). Notably, CYP2E1 and fatty-acid metabolizing CYP4A proteins are upregulated and likely contribute to in the generation of free radicals, lipid peroxidation, mitochondrial damage and hepatic fibrosis (Gomez-Lechon et al., 2009; Buechler & Weiss, 2011). Overall the data suggest that drug metabolism is only moderately affected by steatosis, whereas in NASH it may be more strongly affected, comparable to other inflammatory conditions ( Lake et al., 2011 ). The influence of further disease states has been reviewed by others (Villeneuve & Pichette, 2004; Kim & Novak, 2007).

3. Family CYP1: CYP1A1, CYPIA2, CYP1B1

3.1. Regulation and variability of gene expression

The CYP1 family comprises three functional genes in two subfamilies. The highly conserved CYP1A1 and CYP1A2 genes consist of seven exons and six introns and are located on chromosome 15q24.1, whereas CYP1B1 consists of only three exons located on chromosome 2p22.2, which however encode the largest human P450 in terms of mRNA size

to ■g

Extensive Metabolizer

Ultrarapid metabolizer

duplicated gene

— — normal allele

partially defective allele

null allele

Intermediate Metabolizer

Poor Metabolizer

ñ? 1-1

r—Él I I 'I f I

Fig. 2. Sparteine oxidation phenotype and genotype distribution in a German population (n = 308). MRS: urinary metabolic ratio for sparteine (Raimundo et al., 2004; Zanger, 2008). Reproduced by permission of The Royal Society of Chemistry.

and number of amino acids (Murray et al., 2001; Nelson et al., 2004). In humans CYP1A2 is constitutively expressed at higher levels only in liver (Table 2). Estimations of the average abundance range from ~18 to 25 pmol per mg of microsomal protein determined by mass spectrometry (Kawakami et al., 2011; Ohtsuki et al., 2012) up to 65 pmol/mg determined immunologically (Shimada et al., 1994; Klein et al., 2010), representing ~4-16% of the total hepatic P450 pool (Table 2). In contrast, CYP1A1 and CYP1B1, both of which are primarily extrahepatically expressed enzymes (Ding & Kaminsky, 2003; Du et al., 2006; Paine et al., 2006; Bieche et al., 2007; Dutheil et al., 2008; Michaud et al., 2010), are found in liver at levels below 3 pmol/mg (Stiborova et al., 2005) or at undetectable levels (Chang et al., 2003), respectively.

The CYP1A1 and CYP1A2 genes are oriented head-to-head, sharing a 23 kb bi-directional promoter, which contains at least 13 Ah-receptor (AhR) response elements, some which appear to regulate transcription of both genes coordinately (Ueda et al., 2006; Jorge-Nebert et al., 2010). Consequently, both genes are highly inducible by numerous xenobi-otics which act as AhR ligands such as methylcholanthrene and other polycyclic aromatic hydrocarbons, dioxins, ß-naphthoflavone (Table 3; Nebert et al., 2004), as well as atypical inducers including omeprazole and primaquine which regulate transcription through the same response elements, but without binding to the AhR (Yoshinari et al., 2008). Typical exogenous sources of AhR activators are natural combustion products, dietary constitutents (e.g. in broccoli), and chemical manufacturing by-products (e.g. dioxins). Induction of CYP1A2 by smoking is well established and has been confirmed in recent in vivo studies (Ghotbi et al., 2007; Dobrinas et al., 2011). Numerous endogenous ligands such as eicosanoids have also been proposed to regulate expression of CYP1 genes (Nebert & Karp, 2008). Interestingly, both CYP1A1 and 1A2 are also inducible by phenobarbital, and transactivation of human CYP1A promoter constructs by the human constitutive androstane receptor (CAR) was shown to be mediated through a common ER8 cis-element (Yoshinari et al., 2010). In contrast, PXR-dependent regulation of CYP1A2 appears to be negligible as shown in vivo with the prototypical PXR ligand rifampin (Backman et al., 2006) and in human hepatocytes, where CYP1A2 was refractory to induction by statins (Feidt et al., 2010). Although CYP1B1 is principally regulated in similar ways, its promoter is distinct from that of the two related CYP1A genes, but also contains AhR binding sites as well as AhR-independent response elements, to which heterodimers between ARNT and the hypoxia response factor Hif-1a can bind (Schults et al., 2010).

A number of nongenetic factors influence CYP1A2 in vivo activity and/or hepatic expression levels. Decreased expression was found in liver donors with elevated liver function parameters, increased C-reactive protein (a marker of inflammation), and cholestasis (Klein et al., 2010). CYP1A2 activity in vivo was reported to be higher in men than in women for several substrates including the conversion of caffeine to paraxanthine (Relling et al., 1992; Ou-Yang et al., 2000). However recent studies failed to confirm such a difference in vivo (Ghotbi et al., 2007; Dobrinas et al., 2011) and in human liver tissue at the protein level (Klein et al., 2010) and at the mRNA level (Zhang et al., 2011). A possible explanation for the discrepant results may be that strong confounders of CYP1A2 activity, such as smoking and oral contraceptives (inhibition, see below), have not been consistently considered in all studies. The issue of a sex-bias in CYP1A2 expression and/or function thus remains controversial.

Evidence for epigenetic regulation of CYP1A2 expression was shown by measuring the extent of DNA methylation of a CpG island close to the translation start site, which was inversely correlated to hepatic CYP1A2 mRNA levels (Ghotbi et al., 2007; Dobrinas et al., 2011). Despite the common inducible regulation of CYP1A1 and 1A2, their ontogenic patterns differ substantially in that CYP1A1 is expressed earlier in embryogenesis and both CYP1A1 and CYP1B1 are primarily found in extrahepatic tissues (Table 2). Notably,

CYP1B1 is expressed in various cell types of the human and mouse eye, where it plays a still undefined role in the development of primary congenital glaucoma, an inheritable disease leading to blindness (Vasiliou & Gonzalez, 2008). CYP1B1 has furthermore been found at higher levels in tumorous compared to normal tissues and has been proposed as an early stage tumor marker (Murray et al., 1997, 2001). CYP1B1-null mice were resistant to cancerogenic toxicity by 7,12-dimethylbenz[a]anthracene because they lacked metabolic activation to the procarcinogenic 3,4-dihydrodiol metabolite in fibroblasts, emphasizing the importance of extrahepatic CYP1B1 for carcinogenesis (Buters etal., 1999).

3.2. Role of CYP! enzymes in drug metabolism

Catalytic activities of the CYP1 enzymes are overlapping and include hydroxylations and other oxidative transformations of many polycyclic aromatic hydrocarbons and other aromatic substances. Whereas CYP1A1 prefers planar aromatic hydrocarbons, CYP1A2 shows a preference for aromatic amines and heterocyclic compounds (Table 3). The crystal structure of CYP1A2 in complex with a-naphthoflavone revealed a rather compact active site with a cavity volume of 375 A3, which is larger than that of CYP2A6 (260 A3) but much smaller compared to CYPs 3A4 and 2C8 (~1400 A3; Sansen et al., 2007). Prototypical biotransformations catalyzed by CYP1A2 include 7-ethoxyresorufin O-deethylation, phenacetin O-deethylation, and caffeine N3-demethylation to paraxanthine, which are commonly used for in vitro or in vivo phenotype determination (Fuhr et al., 2007; Zhou et al., 2009). Due to its relatively high expression in liver, CYP1A2 plays a significant role in the metabolism of several clinically important drugs (Gunes & Dahl, 2008; Zhou et al., 2009). These include analgesics and antipyretics (acetaminophen, phenace-tin, lidocaine), antipsychotics (olanzapine, clozapine), antidepressants (duloxetine; Lobo et al., 2008), anti-inflammatory drugs (nabumetone; Turpeinen et al., 2009), cardiovascular drugs (propranolol, guanabenz, triamterene), the cholinesterase inhibitor tacrine used for the treatment of Alzheimer's disease, the muscle relaxant tizanidine (Granfors et al., 2004), the hypnotic zolpidem used in the short term treatment of insomnia, the drug riluzole used to treat amyotrophic lateral sclerosis, the 5-lipoxygenase inhibitor zileuton, among others (Table 3). Some drugs are bioactivated by CYP1A2, including the antiandrogen flutamide (Kang et al., 2008). Endogenous substrates include arachidonic acid, prostaglandins, oestrogens, melatonin and retinoic acid (Nebert & Dalton, 2006). In addition, benzo[a]pyrene and diverse other procarcinogens such as arylarenes, nitroarenes, and arylamines, present in charbroiled food and industrial combustion products are bioactivated by CYP1 enzymes to reactive and carcinogenic intermediates able to cause DNA damage. Drug treatment with CYP1A2 substrates is sensitive to drug-interactions caused by reversible or irreversible inhibition of the enzyme by several small molecule inhibitors that fit the active site, or by AhR-mediated gene induction (Table 3). Some of the most potent inhibitors are a-naphtoflavone (Ki<50 nM) and the selective serotonin reuptake inhibitor (SSRI), fluvoxamine (Ki~0.2 |jM; Hiemke & Hartter, 2000).

3.3. Genetic polymorphisms and functional impact

Common polymorphisms in the CYP! family have been found to be of limited impact on drug metabolism. Due to the role of all CYP1 enzymes in procarcinogen bioactivation many studies investigated their association to various forms of cancer. Rare variants at the CYPIB! locus result in primary congenital glaucoma although no consistent correlation has been observed between the severity of the glaucoma phenotype and the molecular CYP1B1 genotype (Vasiliou & Gonzalez, 2008).

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3.3.1. CYP1A1

Of four common variants originally termed m1 to m4, only the nonsynonymous m2 variant (CYP1A1*2C, 2454A>G, Ile462Val) has been clearly associated with 6- to 12-fold higher enzymatic activity towards 17(3-estradiol and estrone (Kisselev et al., 2005). This variant has a global minor allele frequency (gMAF) of 12% but it is more common among Asians and Hispanics than in other populations (Table 1).

3.3.2. CYP1A2

Br0sen and colleagues investigated the heritability of caffeine metabolic ratio as a CYP1A2 activity marker in a large cohort (n = 378) of mono- and dizygotic twins selected to exclude the influence ofsmoking, oral contraceptives and sex and they found indication for a strong overall heritability of 0.72 (Rasmussen et al., 2002). The CYPallele website (last accessed: December 2012) lists 21 defined alleles and numerous haplotype variants, some of which are associated with altered expression or inducibility, or code for proteins with altered enzyme activity. The Arg431Trp substitution (CYP1A2*6), which is located in the "meander" peptide, a region critical for maintenance of protein tertiary structure, was shown to result in nonfunctional protein (Zhou et al., 2004). However due to their rare occurrence this and other amino acid variants (Palma et al., 2010) are of limited clinical value. The 5'-upstream variant 3860G>A (CYP1A2*1C) was linked to decreased inducibility by smoking based on promoter analyses and to decreased caffeine 3-demethylation in Japanese smokers (Nakajima et al., 1999). The intron 1 polymorphism -163C>A (CYP1A2*1F) located downstream of the untranslated first exon was associated with increased enzyme inducibility in German (Sachse et al., 1999) and Swedish (Ghotbi et al., 2007) smokers and in Swedish and Serbian heavy coffee consumers (Djordjevic et al., 2010). The opposite effect of the two variants is consistent with the findings that carriers of the combined genotype CYP1A2*1C/*1F were not induced by omeprazole (Han et al., 2002). However there are also several controversial studies and thus it is so far unclear whether the SNP rs762551 (Table 1) represents the causal variant or whether linkage to other variants may be responsible for the different reported results across various populations. It should also be noted that the C-allele is considered as the reference allele, even though it is the rarer allele in most populations, and that some authors use the opposite allele designation. Additional CYP1A2 SNPs but so far no copy number variants were found in recent studies where CYP1A2 has been resequenced in individuals from different populations (Jiang et al., 2005; Browning et al., 2010).

Despite this multiplicity of CYP1A2 polymorphisms, attempts to explain the suggested heritability in CYP1A2 phenotype by relating common SNPs to phenotype remained disappointing and it was suggested that no single SNP or haplotype in the CYP1A2 gene has a clear predictive value (Jiang et al., 2006). Klein and coworkers investigated the influence of nongenetic factors and of 136 gene polymorphisms in the CYP1A locus (15 SNPs) and in 16 other candidate genes from different pathways on hepatic CYP1A2 mRNA and protein expression as well as phenacetin O-deethylase activity phenotypes (Klein et al., 2010). Remarkably, 10 SNPs in the ARNT, AhRR, HNF1a, IL1(3, SRC-1, and vitamin D receptor (VDR) genes, but none of the CYP1A-locus showed consistent associations by univariate analysis. Multivariate linear modeling indicated that genetic polymorphisms explained about 35% of hepatic CYP1A2 activity variation, whereas slightly more than 40% of the variation was explained by nongenetic and genetic factors together. This study indicated that additional genetic factors outside the CYP1A locus may have a greater influence on CYP1A2 phenotype than polymorphisms within the CYP1A locus.

3.3.3. CYP1B1

CYP1B1 was identified by genetic linkage analysis and mutation studies as causative gene of primary congenital glaucoma, an inheritable neurodegenerative disease leading to blindness (Vasiliou & Gonzalez, 2008). Over 80 mutations, mostly missense or nonsense mutations, deletions, insertions and/or duplications, were identified in patients with various forms of glaucoma. Some of them are

Chemical interaction profiles of human hepatic drug metabolizing cytochromes P450.

Substrates/pathwaysa

CYP isoform, substrate structural characteristics CYP1A2

Planar, aromatic, polyaromatic and heterocyclic amides and amines

CYP2A6

Nonplanar low MW molecules usually with 2 hydrogen bond acceptors; includes ketones and nitrosamines

Acetanilide 4-hydroxylation (2E1) Caffeine ¡V3-demethylation

Chlorpromazine N-demethylation and 5-sulfoxidation (CYP3A4) Clozapine N-demethylation (3A4/5) Estradiol 2-hydroxylation (3A4/5,1A1) 7-ethoxyresorufin O-deethylation (in vitro)

Flutamide 2-hydroxylation (3A4/5, 2C19) Genistein 3'hydroxylation (2E1) Guanabenz N-hydroxylation

Lidocaine N-deethylation and 3'-hydroxylation (3A4, 2B6) Melatonin 6-hydroxylation (1A1, 2C19)

Mianserin N-demethylation + N-oxidation (3A4/5)

Nabumetone, 6-methoxy-2-naphthylacetic acid formation

Naproxen O-demethylation (2C9, 2C8)

NNK (4-(methylnitrosamino)-l-(3-pyridyl)-l-butanone)

hydroxylation (most other CYPs)

Olanzapine N-demethylation + 2-/7-hydroxylation (2D6)

Perphenazine N-dealkylation (3A4/5, 2C9/19, 2D6)

Phenacetin O-deethylation (1A1, 2A13)

Propafenone N-dealkylation (3A4/5)

Propranolol N-desisopropylation

Riluzole N-hydroxylation

Tacrine 1-hydroxylation

Theophylline 8-hydroxylation and N3-demethylation Triamterene 4'-hydroxylation

Bilirubin oxidation to biliverdin Cotinine 3'-hydroxylation Coumarin 7-hydroxylation

1,7-dimethylaxanthine 8-hydroxylation

Efavirenz 7-hydroxylation (2B6)

Letrozole, carbinol formation (3A4/5)

Nicotine 5'-oxidation and N-demethylation (2B6)

Pilocarpine 3-hydroxylation

Tegafur, 5-fluorouracil formation (1A2, 2C8)

CYP2B6

Neutral or weakly basic, mostly lipophilic non-planar molecules with 1 to 2 hydrogen bond acceptors; includes anaesthetics, insecticides and herbicides

Artemether O-demethylation (3A4/5)

Artemisinin reductive cleavage (3A4/5)

Benzphetamine N-demethylation

7-Benzyloxyresorufin O-debenzylation (3A4/5)

Bupropion 4-hydroxylation

Chlorpyrifos desulfuration (3A4/5,1A2, 2C9/19)

Cyclophosphamide 4-hydroxylation (2C19, 3A4/5, 2C9, 2A6)

N,N-diethyl-m-toluamide (DEET) ring methyl hydroxylation

Efavirenz 8-hydroxylation (1A2,3A4/5)

Endosulfan S-oxidation (3A4)

7-ethoxy-4-trifluoromethylcoumarin (7-EFC) O-deethylation

Hexane 2-hydroxylation (2E1)

Ifosfamide 4-hydroxylation (3A4/5)

Ketamine N-demethylation (3A4/5, 2C9)

S-mephenytoin N-demethylation (2C9)

S-mephobarbital N-demethylation

Meperidine N-demethylation (3A4/5, 2C19)

Methadone N-demethylation (CYP2C, 3A4)

Methamphetamine 4-hydroxylation and N-demethylation

Inhibitors'5

Inducersc

Aminoglutethimide

Antipyrine

Bilirubin

Carbamazepine

Coffee

Cruciferous vegetables (e.g. broccoli)

Nelfinavir

Omeprazole

Phénobarbital and other barbiturates

a-naphthoflavone

Cimetidine

Ciprofloxacin

Disulfiram

Enoxacin

Fluvoxamine

Furafylline

Mexiletine

Moricizine

Oral contraceptives

Tolfenamic acid

Decursinol angelate

(R)-( + ) menthofuran

8-methoxypsoralen

Pilocarpine

Selegiline

Tranylcypromine

Bergamottin Clopidogrel Clotrimazole Imidazoles

Mifepristone (RU486) 2-phenyl-2- (1 -piperdinyl) propane

Raloxifene Sertraline thioTEPA

Ticlopidine Voriconazole

Phenytoin

Poly cyclic aromatic hydrocarbons (PAHs,

charbroiled meat, cigarette smoke)

Polychlorinated biphenyls

Primaquine

Rifampicine

Ritonavir

Sulfinpyrazone

2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD)

Artemisinin

Carbamazepine

Dexamethasone

Estrogens

Phénobarbital

Rifampicin

Artemisinine-type antimalarials

Baicalin

Carbamazepine

Cyclophosphamide

N,N-diethyl-m-toluamide (DEET)

Efavirenz

17-a-ethinylestradiol

Hyperforin

Metamizole

Nelfinavir

Nevirapine

Phénobarbital

Phenytoin

Rifampicin

Ritonavir

Statins (e.g. atorvastatin) Vitamin D St John's Wort

N-methyl-3,4-methylenedioxyamphetamine (MDMA, ecstasy) N-demethylation (1A2) Nevirapine 3-hydroxylation Propofol hydroxylation (CYP2C9)

Selegiline N-demethylation and N-depropargylation (1A2) Sertraline N-demethylation (2C19, 2C9, 3A4/5) Temazepam N-demethylation Testosterone 16a- and 16ß-hydroxylation (3A4/5) Tramadol N-demethylation (3A4/5)

CYP2C8 Amiodarone N-deethylation (3A4/5)

Relatively large and weakly acidic molecules; Amodiaquine ¡V-desethylation

includes antimalarials and oral antidiabetics Cerivastatin 6-hydroxylation and O-demethylation (3A4/5)

Chloroquine N-deethylation (3A4/5) Imatinib N-demethylationn (3A4/5) Montelukast 36-hydroxylation (3A4/5, 2C9) Nicardipine debenzylation (3A4/5) Paclitaxel 6a-hydroxylation Pioglitazone hydroxylation (1A2, 2D6) Retinoic acid 4-hydroxylation (3A7, 3A4, 2C9) Rosiglitazone N-demethylation and p-hydroxylation (2C9) Troglitazone oxidation (3A4/5)

CYP2C9

Weakly acidic molecules with a hydrogen bond acceptor; includes most NSAIDs

Acenocoumarol 6- and 7-hydroxylation (1A2, 2C19)

Candesartan O-deethylation

Chlorpropamide 2-hydroxylation (2C19)

Celecoxib methylhydroxylation (3A4/5)

Dapsone N-hydroxylation (2C8)

Diclofenac 4'-hydroxylation

Etodolac 6- and 7-hydroxylation

Fluoxetine N-demethylation (2D6, 2C19, 3A4/5)

Flurbiprofen 4'-hydroxylation

Glibenclamide hydroxylation

Glimepiride hydroxylation

Ibuprofen 2- and 3-hydroxylation (2C8, 3A4/5)

Indomethacine O-demethylation (2C8)

Irbesartan hydroxylation

Ketobemidone N-demethylation (3A4/5)

Lornoxicam 5'-hydroxylation

Losartan 2-step oxidation of-CH20H (3A4/5)

Meloxicam 5'-methylhydroxylation (3A4/5)

Phénobarbital p-hydroxylation (2C19)

Phenytoin 4-hydroxylation (2C19)

Piroxicam 5'-hydroxylation

Rosuvastatin N-demethylation

Sulfamethoxazole N-hydroxylation

S-warfarin 7-hydroxylation (4F2)

Tetrahydrocannabinol 11-hydroxylation (3A4/5)

Tienilic acid S-oxidation

Tolbutamide 4 -hydroxylation (2C19)

Valproic acid 4-hydroxylation (2B6, 2A6)

Valsartan 4-hydroxylation

Zaltoprofen sulfoxidation

CYP2C19

Neutral or weakly basic molecules or amides with 2 or 3 hydrogen bond acceptors; includes most proton pump inhibitors

Amitriptyline N-demethylation (2C8, 2C9) Clomipramine 8-hydroxylation (1A2) Clopidogrel, 2-oxo-clopidogrel formation (3A4/5, 2B6,1A2) Hexobarbital 3'-hydroxylation

Gemfibrozil Montelukast

Trimethoprim

Cyclophosphamide

Dexamethasone

Fibrates (e.g. gemfibrozil)

Hyperforin

Imatinib

Lithocholic acid

Nelfinavir

Paclitaxel

Phénobarbital

Phenytoin

Rifampicin

Ritonavir

Statins (e.g. atorvastatin)

Amiodarone

Fluconazole

Naringenin

Sulphaphenazole

Tienylic acid

Voriconazole

Aprepitant

Avasimibe

Barbiturates

Bosentan

Carbamazepine

Cyclophosphamide

Dexamethasone

Glutethimide

Hyperforin

Nelfinavir

Nifedipine

Norethindrone

Phénobarbital

Prednisone

Rifampicin

Ritonavir

Statins (e.g. atorvastatin) St John's Wort

(—)-N-3-benzyl-phenobarbital

Clopidogrel

Fluoxetine

Fluvoxamine

Acetylsalicylic acid Antipyrine

Artemisinin-type antimalarials Baicalin

(continued on next page)

CYP isoform, substrate structural characteristics

Substrates/pathwaysa

Inhibitorsb

Inducersc

Imipramine N-demethylation

Lansoprazole 5-hydroxylation (3A4/5)

Melatonin O-demethylation (1A2)

Nelfinavir tert-butylamide-hydroxylation (M8)

Omeprazole 5-hydroxylation (3A4/5)

Pantoprazole sulfoxidation (3A4/5)

Progesterone 21-hydroxylation (2C9, 3A4/5)

Proguanil isopropyl oxidation and cyclization to cycloguanil (3A4/5)

R-mephobarbital 4-hydroxylation (2B6)

Ranitidine N-demethylation (1A2, 2D6)

S-mephenytoin 4-hydroxylation

Venlafaxin N-demethylation (2C9, 3A4/5)

Voriconazole N-oxidation (3A4/5)

Naringenin

( + )-N-3-benzyl-nirvanol Omeprazole Ticlopidine Voriconazole

Barbiturates

Carbamazepine

Dexamethasone

Efavirenz

Hyperforin

Nelfinavir

Rifampicin

Ritonavir

St. John's Wort

CYP2D6 Amitriptyline 10-hydroxylation (3A4/5) Bupropion No significant induction by prototypical S S

Basic molecules with protonatable nitrogen atom Amphetamine 4-hydroxylation Celecoxib P450 inducers

4-7 A from the metabolism site; includes many Aprindine 5-hydroxylation Flecainide a

plant alkaloids and antidepressants Aripiprazole dehydrogenation (3A4/5) Fluoxetine

Atomoxetine 4-hydroxylation and N-demethylation Haloperidol

Bufuralol 1'-hydroxylation (1A2, 2C19) Methadone S

Carvedilol 4'- and 5'-hydroxylation Paroxetine S r> Sr

Chlorpromazine 7-hydroxylation (1A2) Quinidine w a

Clomiphene 4-hydroxylation (2B6) Cr

Codeine O-demethylation

Debrisoquine 4-hydroxylation r ac

Desipramine 2-hydroxylation (2C19,1A2)

Dextromethorphan O-demethylation (2C9/19) o

Dihydrocodeine O-demethylation y

Diphenhydramine N-demethylation (1A2, 2C9, 2C19) &

Dolasetron 6-hydroxylation (3A4/5) h r

Donepezil O-demethylation (3A4/5)

Duloxetine 4-, 5-, 6-hydroxylations (1A2) TO i

Flecainide meta-O-dealkylation (1A2) s

Galantamine N-demethylation 3

Hydrocodone O-demethylation (3A4/5) 00

Imipramine 2-hydroxylation (2C19,1A2) 2 o

Loratadine oxidation to desloratadine (3A4/5) )

Metoclopramide N-deethylation (3A4/5) o

Metoprolol a-hydroxylation and O-demethylation —

Mexiletine p-, m-, and 2-methylhydroxylation (1A2) o

Mianserin 8-hydroxylation (2B6, 3A4/5) Minaprine 4-hydroxylation Mirtazapine 8-hydroxylation (1A2, 3A4/5)

N-methyl-3,4-methylenedioxyamphetamine (MDMA, ecstasy) demethylenation (1A2)

Nortriptyline 10-hydroxylation (3A4/5)

Ondansetron 7- and 8-hydroxylation (3A4/5)

Oxycodone O-demethylation

Paroxetine demethylenation

Perhexiline hydroxylation

Procainamide N-hydroxylation

Promethazine hydroxylation

Propafenone 5-hydroxylation

Propranolol 4- and 5-hydroxylation (Masubuchi 1994) Risperidone 9-hydroxylation (3A4/5) Sparteine C-oxidation

Tamoxifen 4-hydroxylation (2C9) Thioridazine sulfoxidation (3A4/5)

Timolol hydroxylation (2C19)

Tramadol O-demethylation

Tropisetrone 5- and 6-hydroxylation (3A4/5)

Venlafaxin O-demethylation (2C19, 2C9)

Zuclopenthixol N-dealkylation (3A4/5)

CYP2E1

Small, generally neutral and hydrophilic, planar molecules; includes aliphatic alcohols and halogenated alkanes

Aniline 4-hydroxylation

Arachidonic acid o)-l-hydroxylation (1A1, 2D6)

Benzene hydroxylation and hydrochinone formation

Butadiene 1,2-epoxidation (2A6)

Chlorzoxazone 6-hydroxylation (1A2)

N,N-dimethylnitrosamine N-demethylation (2A6)

Enflurane oxidation and dehalogenation

Ethanol oxidation (major: ADH1)

Halothane hydroxylation (2A6, 3A4/5)

Isoflurane dehalogenation

Laurie acid (omega-l)-hydroxylation (4A11)

Para-nitrophenol 2-hydroxyIation

Salicylic acid 5-hydroxylation (3A4/5)

Sevoflurane hydroxylation

Styrene 7,8-epoxidation

Tetrachloromethane dehalogenation (3A4/5)

Toluene benzylic hydroxylation (2B6, 2C8,1A2)

Vinylchloride epoxidation

CYP2J2

Active site can accommodate large substrates similar to CYP3A4 but more restrictive catalytic function

Albendazole S-oxidation Amiodarone 4-hydoxyIation

Arachidonic acid epoxidation (mainly in heart; other CYPs) Astemizole O-demethylation

Cyclosporine A hydroxylation (major: 3A4/5) Ebastine t-butyl hydroxylation (3A4/5) Danazol hydroxylation Mesoridazine sulfoxidation (major: CYP3A4) Terfenadine hydroxylation

CYP3A4/5 L-a-acetylmethadol (LAAM) N-demethylation (2B6)

Large and lipophilic molecules of very diverse structures; Aflatoxin B1 3c«-hydroxylation and 8,9-epoxidation (1A2) includes over 50% of all clinically used drugs Alfentanil /V-dealkylation

Alprazolam a-hydroxylation Antipyrine 4-hydroxylation (1A2) Aprepitant N- and O-dealkylation (1A2, 2C19) Atorvastatin o- and p-hydroxylation Budesonide 6ß-hydroxylation Buprenorphine N-demethylation (2C8) Buspirone 6'-hydroxylation Carbamazepine 10,11-epoxidation (2C8) Cholesterol 4ß-hydroxylation Cisapride N-dealkylation (2A6) Citalopram N-demethylation (2C19, 2D6) Clarithromycin 14-(R)-hydroxylation and N-demethylation Clindamycin S-oxidation Codeine N-demethylation (2B6) Cortisol 6ß-hydroxylation Cyclobenzaprine (1A2, 2D6) Cyclophosphamide N-dechloroethylation Cyclosporine A hydroxylation (Ml and Ml 7 formation) Dasatinib N-dealkylation Dexamethasone 6-hydroxylation Dextromethorphan N-demethylation (2B6, 2C9/19))

Clomethiazole

Diet hyldithiocarbamate

Disulfiram

4-methylpyrazoIe

Orphenadrine

Acetone Ethanol Isoniazid Pyrazole

Various other solvents and chemicals

Arachidonic acid None known

a-naphthoflavone

Danazol

Hydroxyebastine

Ketoconazole

Terfenadone

Tranylcypromine

Troglitazone

Azamulin Amprenavir

Claritromycin Aprepitant

Diltiazem Artemisinine-type antimalarials

Erythromycin Avasimibe

Ethinylestradiol Baicalin

Grapefruit juice Baribiturates

Isoniazid Bosentan

Irinotecan Carbamazepine

Ketoconazole Dexamethasone

Mibefradil Efavirenz

Mifepristone (RU486) Etravirine

Naringenin Ginkgo biloba

Nicardipine Glucocorticoids

Ritonavir Hyperforin

Troleandomycin Imatinib

Verapamil Miconazole

Voriconazole Mitotane

Moricizine

Nafcillin

Nevirapine

Oxcarbazepine

Phénobarbital

Phenylbutazone

Phenytoin

(continued on next page)

Table 3 (continued)

CYP isoform, substrate structural characteristics Substrates/pathways3 Inhibitors6 Inducers'

Dextropropoxyphene N-demethylation Rifabutin

Diazepam 3-hydroxylation and N-demethylation (2C19) Rifampicin

Dihydrocodeine N-demethylation Rifapentin

Diltiazem N-demethylation (2C8, 2C9) Ritonavir

Docetaxel hydroxylation Statins

Dolasetron N-oxidation St. John's Wort

Donezepil O-dealkylation (2D6) Sulfinpyrazone

Ebastine N-dealkylation Topiramate

Erlotinib O-demethylation (1A1/2) Troglitazone

Erythromycin N-demethylation (2B6) Valproic acid

Eszopiclone oxidation and N-demethylation (2E1) Vinblastine

17a-ethinyl estradiol 2-hydroxylation

Etoposide catechol metabolite formation

Felodipine oxidation to pyridine

Fenofibrate deesterification

Fentanyl N-dealkylation

Fluticasone 17ß-carboxylic acid formation

Gefitinib O-demethylation (1A1/2D6)

Glyburide hydroxylation (2C9)

Granisetron N-demethylation

Haloperidol N-dealkylation (2D6)

Hydromorphone N-demethylation (2C9)

Ifosfamide N-dechloroethylation (2B6)

Ilaprazole sulfone formation

Irinotecan (CPT-11) oxidation to APC

Isotretinoin (13-cis- retinoic acid) 4-hydroxylation (2C8)

Lithocholic acid 6a-hydroxylation

Lopermide N-demethylation (2B6)

Midazolam 1-hydroxylation

Mifepristone (RU-486) N-demethylation

Mirtazapine N-demethylation and N-oxidation

Morphine N-demethylation (2C8)

Nevirapine 2-hydroxylation

Nifedipine oxidation to pyridine

Oxycodone N-demethylation

Paracetamol oxidation to NAPQI (2E1, 2A6,1A2, 2D6, 2C9/19) Quetiapine N-dealkylation Quinine 3-hydroxylation

Quinidine 3-hydroxylation and N-oxidation Sildenafil N-demethylation (2C9) Simvastatin 6'-ß-hydroxylation (2C8)

Sirolimus 16-O- and 39-O-demethylation and various hydroxylations

Tacrolimus O-demethylation

Teniposide O-demethylation

Testosterone 6ß-hydroxylation (2C9,1A1)

Tetrahydrocannabinol 7- and 8-hydroxylation

Tilidine N-demethylation

Tramadol N-demethylation (2B6)

Trazodone N-dealkylation to mCPP

Triazolam a- and 4-hydroxylation

Verapamil Nndemethylation to norverapamil (2C8)

Vincristine Ml-formation (3A5>3A4)

Ziprasidone sulfoxidation (2C19)

Zolpidem hydroxylation (1A2)

Data were compiled from Anzenbacher & Zanger, 2012, and from the references cited in the text.

a Substrates/pathways are grouped by the major metabolizing P450 enzyme in liver with other contributing enzymes given in parentheses. Drugs selectively metabolized by one form and used for phenotyping are printed in bold-type.

Endogenous substrates are underlined.

b Inhibitors listed have shown to inhibit the respective P450 significantly in vivo or in human liver microsomes; isoenzyme-selective inhibitors are shown in bold-type.

c Inducers that have been shown to induce the respective P450 significantly in vivo or in human hepatocytes; isoenzyme-selective inducers are shown in bold-type.

predicted to interrupt the open reading frame and some were shown to lead to severely compromised enzyme function (Jansson et al., 2001; Chavarria-Soley et al., 2008; Choudhary et al., 2008; Vasiliou & Gonzalez, 2008). Because these mutations are rare in the general population, their value as tumor markers or markers of altered metabolism of drugs appears to be of limited value. More common polymorphisms include five amino acid variants in different combinations. Functional analysis of variant proteins coexpressed with P450 reductase in E. co/i showed threefold increased Km towards 17(3-estradiol for the Leu432Val (CYP1B1*3) variant but little influence of the Arg48Gly, Ala119Ser and Asn453Ser variants (Li et al., 2000). This finding was however not reproduced in a yeast expression study using the same substrate, whereas decreased Vmax and increased Km towards 17(-estradiol was observed in combination alleles CYP1B1*6 and CYP1B1*7 (Aklillu et al., 2002). The CYP1B1.7 variant was also found to have lower benzo[a]pyrene hydroxylase activity. The Leu432Val variant was correlated to changed urinary estrogen metabolites, indicating in vivo contribution of CYP1B1 to estrogen catabolism (Napoli et al., 2009). These studies suggest that the common amino acid variations have moderate to low substrate-dependent effects on the catalytic properties of the enzyme.

3.4. Clinical impact of genetic variation

Because of the role of CYP1A and CYP1B enzymes in the metabolism of procarcinogens and cellular signalling molecules, their polymorphisms have been extensively studied as susceptibility factors in the context of various cancers (examples shown in Table 1). As this topic is not systematically covered here, the reader is referred to reviews and meta-analyses by others (Shi et al., 2008; Kaur-Knudsen et al., 2009b; Sergentanis & Economopoulos, 2009; Shaik et al., 2009; Economopoulos & Sergentanis, 2010; Yao et al., 2010a; Cui et al., 2012; Li et al., 2012a; Wang et al., 2012).

DNA damage caused by CYP1A-activated tobacco smoke mutagens may contribute to the development of coronary heart disease. Cornelis and colleagues investigated this hypothesis by genotyping 873 Costa Rican subjects with myocardial infarction (MI) for CYP1A1 and CYP1A2 genotypes (Cornelis et al., 2004). While CYP1A1 genotype had no influence, individuals homozygous for the low inducibility -163C allele had increased risk, but surprisingly this was independent of smoking status. The authors reasoned that activation of tobacco smoke mutagens by CYP1A2 may not play a significant role in CHD but that an unknown compound, e.g. a coffee ingredient detoxified by CYP1A2, may contribute to MI. This finding was however not reproduced in a Tunisian population (Achour et al., 2011). The controversial association between coffee intake and risk of MI was investigated with respect to the CYP1A2 -163C/A polymorphism by the same researchers (Cornelis et al., 2006). Intake of coffee was associated with an increased risk of MI only among individuals with the low-inducibility ( — 163C) allele in a dose-dependent way. This effect was independent of smoking status and the authors suggested that an unknown CYP1A2 substrate that is detoxified rather than activated may play a role in CHD.

As CYP1A2 is the rate-limiting enzyme for metabolism of caffeine, the most widely consumed psychoactive substance worldwide, CYP1A2 polymorphism could play a role in habitual caffeine intake, which is also regarded as a model for addictive behavior. Indeed, twin studies suggest heritability estimates for heavy caffeine use of up to 77% (Dvorak et al., 2003). Several genome-wide association studies (GWAS) and GWAS meta-analyses involving many thousands of subjects identified SNPs in the regulatory region of the CYPiA locus (rs2472304, rs2472297) as well as SNPs in the AhR gene regulatory region (rs4410790, rs6968865) along with genes related to addictive behavior to be associated with habitual coffee intake (Dvorak et al., 2003; Cornelis et al., 2011; Sulem et al., 2011). It would be highly interesting to identify the causal variants for these associations. The

CYP1A2 low-inducibility -163C allele may also modify the risk of hypertension in coffee drinkers, as suggested by one study (Palatini et al., 2009).

These data collectively support a role of CYP1A2 polymorphism in the metabolism and disposition of caffeine. Data concerning clinical drug use are generally less convincing although some studies reported a significant influence. CYP1A2*1F/*1F genotype was associated with a 22% reduction of olanzapine serum concentration, which was independent of any inducing agents, indicating a moderately increased activity of the *1F allele per se (Laika et al., 2010). In this study higher olanzapine concentrations were correlated to better improvement of paranoid and depressive symptoms in patients with schizophrenic disorders. In contrast, although CYP1A2 is the major P450 enzyme for the metabolism of the antipsychotic, clozapine, clinical studies did not confirm an influence of CYP1A2 genotype (Van der Weide et al., 2003; Jaquenoud Sirot et al., 2009). This was quite surprising since smoking behavior strongly influenced clozapine clearance and daily dose requirement.

In conclusion, the currently known polymorphisms in the CYP1A2 gene explain only a small fraction of the CYP1A2 variability in expression and function. GWAS and candidate gene studies indicate that genetic determinants in other genes contribute to CYP1A2 activity.

4. Family CYP2

The CYP2 family contains 16 full-length genes, which all have 9 exons and 8 introns. Several of the most important hepatic drug metabolizing CYPs but also extrahepatic enzymes and several "orphan" P450s (Guengerich & Cheng, 2011) with still unclear function are found in this family. The genes are spread over different chromosomes and organized in multi-gene clusters containing one or several subfamilies (Hoffman et al., 2001; Nelson et al., 2004). The three largest gene clusters are the CYP2ABFGST cluster on chromosome 19q13.2, which contains the CYP2A6 and CYP2B6 genes, the CYP2C cluster on chromosome 10q23.33 with the CYP2C8, CYP2C9, and CYP2C19 genes, and the CYP2D cluster on chromosome 22q13.1-2 with the only functional gene CYP2D6. In the evolution of rodents, many of the CYP2 subfamilies expanded tremendously, making the identification of true orthologues between mouse and human P450s especially challenging (Nelson et al., 2004). Most pharmacologically important CYP2 genes are highly polymorphic, in particular CYP2A6, CYP2B6, CYP2C9, CYP2C19, and CYP2D6. In this chapter, we focus on the description of those genes and enzymes that are of highest importance for xenobiotic metabolism.

4.i. Subfamily CYP2A: CYP2A6, CYP2A7, CYP2A13

4.1.1. Regulation and variability of gene expression

The three full-length human CYP2A genes 2A6,2A7, and 2A13 and a split pseudogene CYP2A18P are found on a 370 kb gene cluster on chromosome 19q13.2 that contains genes and pseudogenes of the CYP2A, 2B, 2F, 2G, 2S and 2T subfamilies (Hoffman et al., 2001; Nelson et al., 2004 Only CYP2A6 and CYP2A13 are functional whereas CYP2A7 apparently encodes a nonfunctional gene, although CYP2A7 cDNA generates an immunoreactive protein unable to incorporate heme (Ding et al., 1995; Hoffman et al., 2001). Human CYP2A6 is mainly expressed in liver (Table 2), where mean expression levels corresponding to ~4% of the hepatic P450 pool were found by Western blot analysis (Shimadaetal., 1994; Haberl etal.,2005) while mass spec-trometric studies found much higher levels of ~50 pmol/mg (Ohtsuki et al., 2012). Significantly increased protein and mRNA levels were observed in female liver donors (Al Koudsi et al., 2010) and decreased levels of protein and activity in Japanese compared to Caucasian liver donors (Shimada et al., 1996).

Transcriptional regulation of CYP2A6 involves several response pathways. In human hepatocytes, the PXR and CAR activators

rifampin and phenobarbital induce CYP2A6 mRNA via several DR4 elements (Itoh et al., 2006). Furthermore glucocorticoid receptor-mediated activation of CYP2A6 transcription by dexamethasone was reported to depend on HNF4a (Onica et al., 2008). In vivo, estrogen-containing oral contraceptives can increase CYP2A6 activity as measured by nicotine and cotinine clearance (Benowitz et al., 2006). In vitro data indicate transcriptional upregulation of CYP2A6 via an estrogen receptor-dependent pathway (Higashi et al., 2007). CYP2A13 codes for a catalytically active protein expressed at low levels predominantly in the respiratory tract including the lung, where expression levels decrease from nasal mucosa to peripheral lung tissues (Leclerc et al., 2010; Raunio & Rahnasto-Rilla, 2012).

4.1.2. Role of CYP2A enzymes in drug metabolism

Human CYP2A6 has been recognized as the major isoform involved in the oxidative metabolism of the psychoactive tobacco ingredient nicotine to the inactive cotinine (Raunio & Rahnasto-Rilla, 2012). Further metabolism of cotinine to 3'-hydroxycotinine is also exclusively catalyzed by CYP2A6 in humans and in agreement with higher expression of hepatic CYP2A6 in females, metabolism of nicotine to cotinine, and the trans-3'-hydroxycotinine/cotinine ratio were significantly higher in women than in men (Benowitz et al., 2006).

The 7-hydroxylation of coumarin is a selective marker activity for CYP2A6 (Pelkonen et al., 2000; Fuhr et al., 2007). Major racial differences exist, e.g. only 1% of white subjects but to up to 20% of Asians were characterized as poor metabolizers (Shimada et al., 1996; Raunio et al., 2001). CYP2A6 was identified as the main isoenzyme responsible for bioactivation of the cancer prodrug tegafur to 5-FU (Komatsu et al., 2000), efavirenz 7-hydroxylation, a minor pathway compared to the major 8-hydroxylation catalyzed by CYP2B6 (Desta et al., 2007), and the biotransformation of the aromatase inhibitor, letrozole, to its major, pharmacologically inactive carbinol metabolite, 4,4'-methanol-bisbenzonitrile (Murai et al., 2009). Furthermore, CYP2A6 contributes to the metabolism of a number of clinically used drugs such as disulfiram, fadrozole, halothane, osigamone, me-thoxyflurane, pilocarpine, promazine, and valproic acid (Di et al., 2009). An endogenous substrate of CYP2A6 that was only recently identified is bilirubin (Abu-Bakar et al., 2012). CYP2A13 has similar substrate specificity and also metabolizes coumarin and nicotine, but has been shown to be the most efficient metabolic activator of NNK (4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone), a major tobacco procarcinogen (Su et al., 2000).

4.1.3. Genetic polymorphisms and functional impact

Both increased and decreased gene expression and enzyme activity have been associated with genetic variation in the CYP2A6 gene. At present, 38 distinct star-alleles with deleted or duplicated genes, gene conversions, nucleotide deletions and insertions, as well as coding and non-coding SNPs are described on the CYPalleles website. Some of these variants were shown to change mRNA and/or protein expression levels or to affect the structure and function of the protein (Table 1). The prevalence of low-activity or loss-of-function CYP2A6 alleles was estimated to be ~9% in Caucasians, ~22% in Africans and up to 50% in Asians (Nakajima et al., 2006; di lulio et al., 2009). A detailed compilation of the known structural changes and their functional impact was presented by Mwenifumbo and Tyndale (2009).

Loss-of-function alleles include the CYP2A6*2[Leu160His] and the *4A-H deletion alleles, that have been shown to dramatically reduce enzyme activity in vivo in homozygous or hemizygous combination, resulting in the poor metabolizer phenotype in affected individuals (Table 1). The most frequent null alleles in Asians are the *4A to *4H hybrid-deleted alleles which consist of a CYP2A7-derived 5' part and a 3' part of CYP2A6 origin. The decreased gene copy number associated with these gene deletions appears to be correlated to decreased expression and activity (Haberl et al., 2005; Mwenifumbo et al.,

2010). The *4A deletion variant is rare in Caucasians and Africans but present at up to ~20% frequency in Asian populations (Table 1).

CYP2A6*2[Leu160His] encodes an unstable protein that fails to incorporate heme. Its frequency is ~1-5% in Caucasians and lower in Africans and Asians. In a study involving 156 liver samples from Caucasians, resequencing revealed 33 haplotypes of which two (*9B, containing a -48T>G TATA-box polymorphism, and the 2A7/2A6 recombination allele *12B) were major genetic determinants associated with decreased hepatic expression (Haberl et al., 2005). Additional alleles with functional impact include CYP2A6*7, *10, *17, and *35 associated with reduced enzyme activity in homozygous or hemizygous individuals. The *7[lle471Thr] and *10 [lle471Thr; Arg485Leu] alleles are Asian-specific and *17 [Val365Met] was only found in African Americans. The CYP2A6*5, *6, *11, *19, and *20 alleles were shown to result in reduced activity by heterologous expression. Further variants shown to affect expression or function include several promoter variants that result in decreased expression (Von Richter et al., 2004b). Apart from the low copy number variants with deleted CYP2A6, increased copy number variants termed *1X2A and *1X2B appear to be correlated to increased activity (Fukami et al., 2007).

4.1.4. Clinical impact of genetic variation

Most pharmacogenetic studies involving CYP2A6 were carried out to study the effect of genotype either on nicotine metabolism, smoking behavior, nicotine withdrawal symptoms or lung cancer risk (Benowitz et al., 2006; Nakajima et al., 2006; Mwenifumbo & Tyndale, 2007; Rossini et al., 2008). For example, compared to subjects with reference genotype (*1/*1), heterozygotes of alleles *9 and *12 had on average 80% of normal activity, whereas *4 heterozygotes or carriers of two low-activity alleles had ~50% nicotine C-oxidation activity (Benowitz et al., 2003). Because of its major contribution to nicotine metabolism, the enzyme has been proposed as a novel target for smoking cessation. In a nicotine-replacement clinical trial, the group with low-activity alleles had 50% reduced CYP2A6 activity before treatment and reached 44% higher steady-state plasma levels of nicotine (Malaiyandi et al., 2006). Moreover, severe nicotine withdrawal symptoms related to nicotine dependence during smoking cessation were higher in CYP2A6-genotyped Japanese high-activity subjects (50.0%) compared to the low-activity group (22.2%; (Kubota et al., 2006). Recent studies investigated CYP2A6 and the CHRNA5-CHRNA3-CHRNB4 nicotinic acetylcholine receptor gene polymorphisms in relation to smoking behavior and lung cancer risk. In ever-smokers of European ancestry (417 lung cancer patients) versus control subjects, increased cigarette consumption, nicotine dependence, and lung cancer risk were independently and additively associated with genetic variation in CYP2A6 and the acetylcholine receptor gene cluster. The combined risk group exhibited the greatest lung cancer risk with an odds ratio of ~2 (Wassenaar et al., 2011). CYP2A6 variants were also associated to smoking behavour (cigarettes smoked per day) at genome-wide significance (Thorgeirsson et al., 2010).

The significance of CYP2A6 polymorphism for drug therapy is emphasized by the following two examples. CYP2A6 is a major enzyme for the bioactivation of the cancer prodrug tegafur to 5-FU (Komatsu et al., 2000). A prospective pharmacokinetic study in Japanese patients with advanced cancers revealed that CYP2A6 variants determined oral clearance of tegafur as shown by multivariate regression analysis and that tegafur clearance was lowest in homozygous or compound heterozygous CYP2A6 variant allele carriers (Fujita et al., 2008). Another example is efavirenz, an antiretroviral drug with the major metabolic pathway catalyzed by CYP2B6, which leads to 8-hydroxy efavirenz. As initially shown in human liver microsomes, the minor 7-hydroxy-efavirenz metabolite is mainly formed by CYP2A6 (Desta et al., 2007). While the wide interindividual variability in efavirenz plasma exposure is largely explained by CYP2B6 polymorphisms, the CYP2A6-dependent accessory pathway becomes critically limiting for drug clearance in

patients with genetically impaired CYP2B6 (Di Iulio et al., 2009). As combined CYP2B6 and CYP2A6 genetic deficiency occurs at significant frequency in various human populations, the CYP2A6 polymorphism may be a clinically relevant determinant of extremely high efavirenz exposure in HIV patients. A more recent pharmacogenetic study found that CYP2A6 genotype is also a predictor for plasma letrozole concentrations in postmenopausal women with breast cancer (Desta et al., 2011).

4.2. Subfamily CYP2B: CYP2B6

4.2.0. Regulation and variability of gene expression

The functional CYP2B6 gene and the nonfunctional pseudogene CYP2B7P are located in a tandem head-to-tail arrangement within the large CYP2ABFGST gene cluster on chromosome 19 (Nelson et al., 2004). Although the genomic structures of the human and rodent CYP2B genes are very different with more functional genes expressed in the animals, strong inducibility by phenobarbital is a hallmark of CYP2B genes, and similar mechanisms apply in humans and animals. The major discovery in this field was the identification of the orphan nuclear receptor CAR (NR1I3) as the major regulator of CYP2B6 by drugs and other xenobiotics via a phenobarbital-responsive enhancer module (PBREM) at -1.7 kb of the CYP2B6 promoter (Sueyoshi et al., 1999). Later it was found that PXR (NR1I2) also contributes to CYP2B6 induction via a distal xenobiotics-responsive enhancer module (XREM, -8.5 kb; (Wang et al., 2003). CYP2B6 inducers (Table 3) are typically ligands of either of these receptors, such as rifampicin and barbiturates, or substances that activate both receptors (Faucette et al., 2004, 2006). In human hepatocytes inducibility of CYP2B6 was reported for cyclophosphamide (Gervot et al., 1999), artemisinine (Burk et al., 2005), carbamazepine, efavirenz and nevirapine (Faucette et al., 2007), metamizole (Saussele et al., 2007), the insect repellent N,N-diethyl-m-toluamide DEET; (Das et al., 2008), and several statins (Feidt et al., 2010).

In humans CYP2B6 belongs to the minor hepatic P450s, contributing on average ~2-5% to the total hepatic P450 pool (Table 2), but it exhibits ~300-fold variability of expression (Lang et al., 2001; Lamba et al., 2003; Desta et al., 2007; Hofmann et al., 2008; Ohtsuki et al., 2012). Expression in fetal liver appears to be considerably lower with up to 36% of pediatric liver samples having undetectable levels (Croom et al., 2009). According to this study CYP2B6 increases about 2-fold within the first month of life. Although most studies did not find a significant sex difference in CYP2B6 expression, the issue remains controversial as in some studies higher expression in females versus males was found, depending on ethnicity (Lamba et al., 2003). In another study 1.6-fold higher expression in female livers was observed in all samples but there was no difference when samples from drug-treated donors were excluded (Hofmann et al., 2008). Inflammation has also been recognized as an influential factor for CYP2B6 expression, as shown by downregulation in human hepatocytes in response to IL-6 and interferon y (Aitken & Morgan, 2007).

CYP2B6 was detected at the level of RNA and protein in human brain, where it was reported to be higher in samples of smokers and alcoholics (Miksys et al., 2003), a result that awaits confirmation. Low levels of CYP2B6 transcripts were found in several extrahepatic tissues including kidney (Aleksa et al., 2005), heart (Michaud et al., 2010), placenta (Wang et al., 2010a), and in various tissues of the respiratory tract including lung and nasal mucosa (Ding & Kaminsky, 2003). In contrast, expression in skin or keratinocytes (Du et al., 2006) and intestine (Paine et al., 2006) appears to be low or absent (Table 2).

4.2.2. Role ofCYP2B6 in drug metabolism

The CYP2B6 substrate selectivity comprises many diverse chemicals, including not only clinically used drugs but also a large number of environmental chemicals (Table 3). Therapeutically important drugs

metabolized mainly by CYP2B6 include the prodrug cyclophosphamide, which is converted to the direct precursor of the cytotoxic metabolites, phosphoramide mustard and acrolein, by 4-hydroxylation; the non-nucleoside reverse transcriptase inhibitor (NNRTI), efavirenz; the atypical antidepressant and smoking cessation agent bupropion; the antimalarial artemisinin; the anaesthetics propofol and ketamine; the ^-opioid agonist methadone, and others (Zanger et al., 2007; Turpeinen & Zanger, 2012). Suitable probe drugs for CYP2B6 include S-mephenytoin N-demethylation and bupropion hydroxylation, of which the latter one was shown to be more selective (Faucette et al., 2000; Fuhr et al., 2007) and is now most often used. Another potentially useful in vivo probe drug is efavirenz, based on in vitro investigations (Ward et al., 2003; Desta et al., 2007). CYP2B6 also metabolizes the N-demethylation of the recreational drug "ecstasy" (MDMA), which leads to potentially neurotoxic metabolites (Kreth et al., 2000), and it plays a minor role in nicotine metabolism (Yamazaki et al., 1999; Yamanaka et al., 2005). Numerous studies have furthermore documented an important role of CYP2B6 in the metabolism of pesticides and several other environmental chemicals and pollutants (Hodgson & Rose, 2007), in particular the activating oxidation of organophosphorus insecticides to their more toxic oxon metabolites. Examples are the organophosphorus insecticide, chlorpyrifos (Crane et al., 2012), the insecticide and endocrine disruptor methoxychlor, and the extensively used insect repellent DEET, among many others (Hodgson & Rose,

2007). Recent studies have revealed the crystal structure of CYP2B6 in complex with various inhibitors, providing first views into its active site and ligand binding properties (Gay etal., 2010; Shah etal., 2011).

4.2.3. Genetic po/ymorphisms and functional impact

The CYP2B6 gene is extensively polymorphic with numerous variants in the coding and noncoding regions of the gene (Lang et al., 2001; Lamba et al., 2003; Lang et al., 2004; Klein et al., 2005; Zukunft et al., 2005; Zanger et al., 2007). The CYPalleles website currently lists 29 distinct star-alleles with variant amino acid sequence or with proven functional effect. More than 30 SNPs code for amino acid changes which occur in different combinations and additional haplotype variants and SNPs not yet assigned to particular alleles exist. The most common allele is CYP2B6*6 with two amino acid changes Gln172His and Lys262Arg, in combination with other identified changes mainly in the promoter. The *6 allele occurs with frequencies between 15 and 60% across different populations (Table 1) and is associated with 50 to 75% decreased hepatic liver protein expression (Lang et al., 2001; Lamba et al., 2003; Desta et al., 2007). The major causal variant for decreased expression was identified as the 15631G>T [Gln172His] polymorphism, which was shown to cause incorrect splicing of the CYP2B6 pre-mRNA leading to a shorter mRNA that lacks exons 4 to 6 (Hofmann et al., 2008). This polymorphism represents an example of a nonsynonymous variant leading to major effects that are unrelated to the amino acid exchange but instead to changes on the mRNA level (Zanger & Hofmann, 2008; Sadee et al., 2011). Importantly, only appropriately designed RT-PCR assays will detect the qualitative and quantitative mRNA changes associated with this particular genetic variant (Hofmann et al., 2008). CYP2B6 genotyping by testing for 15631G>T as a marker of the *6 allele appears to be straightforward. However, it should not be forgotten that this results in the detection of a mixture of related complex hap-lotypes with additional upstream region SNPs (-1456T>C and -750T>C) and the second nonsynonymous variant 18053A>G [K262R]. Only marginal changes appear to be associated with the promoter variants (Lamba et al., 2003; Hesse et al., 2004; Hofmann et al.,

2008), but differential functional effects conferred by the K262R amino acid variant have been reported. The K262R variant (*4 allele, frequency ~2-6%) was associated with higher activity towards several substrates including bupropion (Kirchheiner et al., 2003), nicotine (Johnstone et al., 2006), efavirenz (Rotger et al., 2007), artemether (Honda et al., 2011), and selegiline (Watanabe et al., 2010).

However expressed K262R variant was unable to metabolize 17-a-ethynylestradiol (Bumpus et al., 2005) and further studies indicated altered active site topology of the variant resulting in more uncoupled reaction kinetics (Bumpus & Hollenberg, 2008). In contrast cyclo-phosphamide activation was found to be enhanced by the recombinant Q172H variant but decreased by the K262R variant (Xie et al., 2003; Ariyoshi et al., 2011; Raccor et al., 2012). These data strongly emphasize the need to perform detailed genotyping of the involved CYP2B6 variants in order to obtain conclusive results in particular from in vivo studies. Further differential effects of the amino acid variants were reported with respect to enzyme inactivation. In contrast to the wild-type enzyme the recombinantly expressed K262R variant was not inactivated by efavirenz, but both enzymes were irreversibly inhibited by 8-hydroxyefavirenz (Bumpus et al., 2006). Lower susceptibility to inhibition of the K262R variant and the *6 (Q172H/K262R) double variant was also found with respect to sertraline and clopidogrel and several other potent drug inhibitors of CYP2B6 (Talakad et al., 2009). Taken together, the low-expressor phenotype of the 15631G>T variant is partially compensated for by higher specific catalytic activity at least for some substrates, which explains some conflicting data regarding observations made in liver micro-somes, heterologous expression systems, or studies in vivo. This also appears to be the case for the *5 variant (25505C>T [R487C]) which expresses very low levels of an apparently highly active enzyme with bupropion (Lang et al., 2001) as well as efavirenz (Desta et al., 2007), which explains why it was not associated with efavirenz phar-macokinetics in patients (Burger et al., 2006).

Another important functionally deficient allele is CYP2B6*Î8 (21011T>C [1328T]), which occurs predominantly in African subjects with allele frequencies of 4-12% (Mehlotra et al., 2007; Li et al., 2012b). The variant cDNA did not form a functional protein in transfected mammalian cells tested with bupropione (Klein et al., 2005) and lack of activity was also found in vitro for 7-EFC and selegiline (Watanabe et al., 2010) as well as artemether (Honda et al., 2011). The *18 allele is thus phenotypically a null allele. At least 12 additional null or low-activity alleles have been described and analyzed with various substrates (Lang et al., 2004; Klein et al., 2005; Rotger et al., 2007; Watanabe et al., 2010; Honda et al., 2011). Although they are rather rare in all investigated populations they may have profound effects on drug metabolism if present in compound heterozygous genotypes e.g. in combination with *6 or *18 (Rotger et al., 2007). The CYP2B6*22 allele is a gain-of-function variant associated with increased transcription in vitro (Zukunft et al., 2005) and with increased activity in vivo (Rotger et al., 2007). It was shown that a -82T>C exchange alters the TATA-box into a functional CCAAT/enhancer-binding protein binding site that causes increased transcription from an alternative downstream initiation site (Zukunft et al., 2005). Interestingly, the -82T>C polymorphism also confers synergistically enhanced CYP2B6 inducibility by the PXR ligand rifampicin in human primary hepatocytes (Li et al., 2010).

4.2.4. Clinical impact of genetic variation

A major clinical role of CYP2B6 polymorphism was established in HIV therapy, following the identification of CYP2B6 as the major enzyme responsible for 8-hydroxylation of efavirenz and its further hydroxylation to 8, 14-dihydroxyefavirenz (Ward et al., 2003). The potent first-generation NNRTI of HlV-1is recommended as initial therapy with two NRTIs in highly active antiretroviral therapy (HAART) regimes, but patients with subtherapeutic plasma concentrations can develop resistance and treatment failure, whereas those with too high plasma levels are at increased risk of CNS side effects, which can lead to treatment discontinuation in a fraction of patients (King & Aberg, 2008). Several CYP2B6 low activity alleles have been associated with increased efavirenz plasma levels in numerous studies investigating HIV-infected adults and children. Homozygosity for the 15631T [Q172H] variant is associated with several-fold higher median efavirenz AUC compared to carriers of only one or no

T-allele as shown in numerous clinical studies (see reviews King & Aberg, 2008; Telenti & Zanger, 2008; Rakhmanina & van den Anker,

2010). Compound heterozygotes of 15631T and another low activity allele (e.g. *11, *18, *27, *28) also predict high efavirenz plasma levels (Rotger et al., 2007; Ribaudo et al., 2010). Three CYP2B6 polymorphisms, 15631G>T, 21011T>C and intron 3 rs4803419, were independently associated with efavirenz pharmacokinetics at genome-wide significance (Holzinger et al., 2012). In addition to CYP2B6 polymorphism, CYP2A6 and CYP3A4/5 variants also influence clearance, in particular when CYP2B6 is impaired (Arab-Alameddine et al., 2009). The 15631T [Q172H] variant was furthermore associated with increased neurotoxicity and other CNS side effects (Haas et al., 2004; King & Aberg, 2008; Lubomirov et al., 2010; Ribaudo et al., 2010; Maimbo et al., 2011) with highly active antiretroviral therapy (HAART)-induced liver injury (Yimer et al., 2012), and with efavirenz treatment discontinuation and the associated risk of developing drug resistance (Ribaudo et al., 2006; Lubomirov et al., 2011; Wyen et al.,

2011). In prospective, genotype-based dose adjustment studies the therapeutic dose of efavirenz could be successfully reduced and CNS-related side effects decreased (Gatanaga et al., 2007; Gatanaga & Oka, 2009). Using pharmacokinetic modelling and simulation it was suggested that a priori dose reduction in homozygous CYP2B6*6 patients would maintain drug exposure within the therapeutic range in this group of patients (Nyakutira et al., 2008).

In addition to efavirenz, CYP2B6 genotype also affects plasma levels of the antiretroviral drug nevirapine (Penzak et al., 2007; Mahungu et al., 2009). The impact of the 15631G>T polymorphism on nevirapine exposure was confirmed and quantified in a pharmacometric analysis of nevirapine plasma concentrations from 271 patients genotyped for 198 SNPs in 45 ADME genes and covariates (Lehr et al., 2011). Moreover, nevirapine-related cutaneous adverse events, which are most likely MHC class l-mediated, were significantly influenced by CYP2B6 polymorphism while hepatic side effects, most likely MHC class ll-mediated, were unaffected by CYP2B6 (Yuan et al., 2011).

While efavirenz pharmacokinetics appears to be largely explained by low expression of CYP2B6*6 in liver, other substrates may be additionally influenced by altered catalytic functions of CYP2B6 protein variants as outlined above. Bioactivation of the widely used anticancer and immuno-suppressant prodrug cyclophosphamide to 4-hydroxycyclophosphamide is highly variable in cancer patients and has been attributed mainly to CYP2B6 in vitro and in vivo with contributions from CYP2C19 and CYP3A4 (Chang et al., 1993; Raccor et al., 2012). There is growing evidence that cyclophosphamide bioactivation and response to therapy depend on CYP2B6 genotype (Xie et al., 2006; Nakajima et al., 2007; Rocha et al., 2009; Bray et al., 2010; Melanson et al., 2010; Yao et al., 2010b; Raccor et al., 2012). As mentioned above, allele-effects, particularly for *4 and *6, appear to differ for cyclophosphamide, most likely due to substrate-specific effects of amino acid substitutions (Ariyoshi et al., 2011; Raccor et al., 2012). Furthermore, the *5 (R487C) variant, not found to be related to efavirenz disposition, was significantly associated with lower rate of overall toxicity and higher rate of relapse in patients who received high dose CPA treatment (Black et al., 2012). However, data concerning cyclophosphamide from in vivo and in vitro studies are so far not consistent. ln part this may be explained by lack of consistency in allele definition and genotype information among studies (Helsby & Tingle, 2011).

CYP2B6 allele variants were also investigated in the context ofthe synthetic ц-opioid receptor agonist, methadone, which is metabolized by CYPs 3A4/5, 2B6, and 2D6, and used as a maintenance treatment for opioid addiction. In *6/*6 carriers (S)-methadone plasma levels were increased leading to potentially higher risk of severe cardiac arrhythmias and methadone associated deaths (Crettol et al., 2005; Eap et al., 2007; Bunten et al., 2011). Methadone dose requirement for effective treatment of opioid addiction was shown to be significantly reduced in carriers of this genotype (Levran et al., 2011).

In conclusion, the clinical impact of CYP2B6 pharmacogenetics has not yet been fully explored but an increasing number of studies suggest clinical relevance for different drug substrates. Importantly, the relationship between CYP2B6 genotype and in vivo drug metabolism function is complex due to the combined effects of the involved variants on both expression and catalytic properties, the latter of which additionally depend on the substrate.

4.3. Subfamily CYP2C: CYP2C8, CYP2C9, CYP2C18, CYP2C19

4.3.1. Regulation and variability of gene expression

The human CYP2C subfamily consists of the four highly homologous genes CYP2C18-CYP2C19-CYP2C9-CYP2C8 which are localized in this order (from centromere to telomere) in a ~390 kb gene cluster on chromosome 10q23.3. Although CYP2C18 mRNA is highly expressed in liver, the transcript is not efficiently translated into protein and does not make significant contributions to drug metabolism. CYP2C9 is the highest expressed member being expressed at similar or even higher protein levels compared to CYP3A4, while CYP2C8 and CYP2C19 are expressed at ~2-fold and 10-fold lower levels (Coller et al., 2002; Koukouritaki et al., 2004; Rettie & Jones, 2005; Naraharisetti et al., 2010; Ohtsuki et al., 2012). The dramatic expression difference between CYP2C9 and 2C19 was found to be at least in part due to the inability of the CYP2C19 promoter to be activated by HNF4a, despite the presence of similar HNF4a binding sites in both gene promoters (Kawashima et al., 2006). In addition to HNF4a, constitutive expression of the CYP2C genes in liver requires several other liver-enriched transcription factors including HNF3y and C/EBPa (Chen & Goldstein, 2009) and the liver-enriched factor GATA-4 (Mwinyi et al., 2010b). All three expressed CYP2C enzymes are inducible by ligands of the PXR/CAR, glucocorticoid (GR) and vitamin D (VDR) nuclear receptor pathways through different response elements in their 5'-flanking regions (Chen & Goldstein, 2009; Jover et al., 2009; Helsby & Burns, 2012). However, the genes show different relative inducibility. CYP2C8 is the most strongly inducible member, e.g. by statins (Feidt et al., 2010). Although distal PXR/CAR binding sites have been identified, the precise reason for the high inducibility of CYP2C8 has not been elucidated. Maximal induction of CYP2C9 requires cross-talk between distal CAR/PXR sites and proximal HNF4a binding sites in the CYP2C9 promoter which may be mediated by a mega-complex of HNF4a-associated cofactors including PGC-1a, SRC-1, and NCOA6 (Jover et al., 2009). Recently regulation of CYP2C19 (Mwinyi et al., 2010a) and CYP2C9 (Mwinyi et al., 2011) transcription by estrogen receptor alpha (ERa)-ligands was shown to be mediated by newly discovered response elements at ~150 bp upstream of the transcriptional start site, probably contributing to the known inhibition of these enzymes by oral contraceptives.

In a humanized CYP2C18/CYP2C19 transgenic mouse line only CYP2C19, but not CYP2C18 was expressed in liver to yield a catalyti-cally active enzyme. Interestingly, adult male mice expressed much higher CYP2C18 and CYP2C19 mRNA levels in liver and kidney compared with female mice (Lofgren et al., 2009). This appears to reflect the generally more pronounced sex effects on expression in mice compared to humans. Sex differences of human CYP2C9 and CYP2C19 have been investigated in only a few studies which so far do not seem to support the existence of marked sex-biased activity (Gandhi et al., 2004). At lower levels functional CYP2C enzymes are also expressed in extrahepatic tissues, e.g. in human small intestine and in cardiovascular tissues (Delozier et al., 2007). Together with CYP3A, the CYP2Cs represent the major intestinal CYPs, accounting for ~80% and 18%, respectively, of total immunoquantified CYPs (Paine et al., 2006; Thelen & Dressman, 2009).

4.3.2. Role of CYP2C enzymes in drug metabolism

Given the strong relatedness in DNAand protein sequence (>82%) and common mechanisms of transcriptional regulation of the CYP2C enzymes, it is surprising how unique each enzyme is in terms of

substrate specificity and role in drug metabolism (Table 3). The major enzyme CYP2C9 accepts weakly acidic substances including the anticoagulant warfarin, the anticonvulsants phenytoin and valproic acid, the angiotensin receptor blockers candesartan and losartan, oral antidiabetics like glibenclamide and tolbutamide, and most nonsteroidal anti-inflammatory drugs (NSAIDs; (Lee et al., 2002). Commonly used substrates for CYP2C9 phenotyping are diclofenac and tolbutamide. The CYP2C9 enzyme also metabolizes endogenous substances, in particular arachidonic acid and some steroids. The crystal structure of CYP2C9 has been resolved both unliganded and in complex with warfarin showing evidence for an additional substrate binding pocket (Williams et al., 2003). CYP2C9 is inhibited by several substances (Table 3) and this can be clinically important as in the case of warfarin treatment (Lu et al., 2008).

CYP2C8 is mainly responsible for the metabolism of the antidiabetics rosiglitazone and pioglitazone, the antiarrhythmic amiodarone, the natural anticancer drug paclitaxel, and the antimalarial amodiaquine, which is now commonly used as a selective marker activity. In addition to amodiaquine, CYP2C8 has a major role in metabolizing other antima-larials such as chloroquine and dapsone (Kerb et al., 2009). Additional drugs metabolized primarily by CYP2C8 include some retinoic acid drugs used in acne and cancer treatment. Some overlap in substrate specificity with CYP2C9 occurs, e.g. in the case of ibuprofen and others (Table 3; Goldstein, 2001; Lai et al., 2009). The clinical significance of CYP2C8 inhibition became apparent after its involvement in fatal drug interactions was described, which were in part due to its potent inhibition by gemfibrozil acyl-glucuronide, ultimately leading to rhabdomyol-ysis (Backman et al., 2002; Ogilvie et al., 2006). Additional potent CYP2C8 inhibitors at clinically relevant concentrations have meanwhile been described (Lai et al., 2009).

CYP2C19 was the first CYP2C enzyme to be discovered by its marked genetic polymorphism resulting in the S-mephenytoin PM and EM phenotypes (Kupfer & Preisig, 1984). The CYP2C19 enzyme was later shown to be the major enzyme for the inactivating metabolism of proton pump inhibitors (PPI) including omeprazole and pantoprazole, and for the metabolic activation of the anticoagulant clopidogrel to the active 2-oxo metabolite (Hulot et al., 2006; Kazui et al., 2010; Dansette et al., 2011; Boulenc et al., 2012). CYP2C19 has also a prominent role in the metabolism of several antidepressants of the first and second generation (Br0sen, 2004). Endogenous substrates of CYP2C19 include progesterone and melatonin (Table 3).

4.3.3. Genetic polymorphisms and functional impact

4.3.3.1. CYP2C8. Apart from rare variants with no (*5, *7), reduced (*8) or unknown activity, three alleles with amino acid changes, CYP2C8*2 and *3 (Dai et al., 2001), and *4 (Bahadur et al., 2002) are more common and have potential clinical relevance (Table 1). CYP2C8*2 [I269F] has been found practically only in Africans and African Americans at frequencies of ~10 to 22%. Slower conversion of the antimalarial drug amodiaquine to its metabolite N-desethylamodiaquine (DEAQ) in carriers of CYP2C8*2 has been reported (Li et al., 2002; Parikh et al., 2007). The heterologously expressed 2C8.2 variant had 2-fold lower intrinsic clearance for paclitaxel due to increased Km. CYP2C8*3 [R139K + K399R] occurs more frequently in white subjects but is almost absent in Africans and Asians. The CYP2C8.3 variant was initially shown to have reduced metabolic activity toward arachidonic acid (-40%) and paclitaxel (-80%) (Dai et al., 2001). A more recent comparison of protein variants expressed in yeast revealed lower activity of 2C8.2, 2C8.3 and 2C8.4 and altered IC50 values towards inhibitors compared to 2C8.1 (Gao et al., 2010). Some functional effects of CYP2C8*3 appear to be substrate dependent, e.g. for repaglinide and rosiglitazone higher metabolic capacity of this variant was observed (Daily & Aquilante, 2009). Similarly, a recent study showed 40% increased activity with pioglitazone as substrate of liver microsomes genotyped as CYP2C8*3/*3 compared to *1/*1 (Muschleretal., 2009).

A point of concern is that there is significant linkage disequilibrium (LD) between the CYP2C gene polymorphisms, due to their close distances. In particular CYP2C8*3 is in partial LD to CYP2C9*2, which may account in part for the association between CYP2C8*3 and CYP2C9*2 and *3 alleles with reduced clearance of ibuprofen (Garcia-Martin et al., 2004). CYP2C8 pharmacogenetics has recently been reviewed (Daily & Aquilante, 2009).

4.3.3.2. CYP2C9. Among the 35 distinct alleles listed on the CYPalleles website, the initially discovered alleles *2 [R144C] and *3 [1359L] are the best investigated ones (Goldstein, 2001; Rettie & Jones, 2005). The global MAFs of two variants are ~7% and ~4%, respectively, but their frequency in African and Asian populations is generally low, whereas *2 is present in >10% in some Caucasian populations (Table 1). The replacement of the positively charged arginine by cysteine in the 2C9.2 variant appears to affect the interaction of the protein with POR (Crespi & Miller, 1997). Expressed mutant CYP2C9.2 and CYP2C9.3 proteins have reduced intrinsic clearance, although the degree of activity reduction appears to depend on the particular substrate. For example, the Vmax/Km values of yeast-expressed 2C9.1 and 2C9.2 variants for seven different substrates including diclofenac, tolbutamide and piroxicam varied from 3.4-fold to 26.9-fold (Takanashi et al., 2000). The 359Leu variant had higher Km values than did the wild-type for all the reactions studied. Remarkably, the widely used marker substrate diclofenac was not a sensitive substrate to detect the *2/*3 activity difference either in this study or in vivo (Shimamoto et al., 2000). Compared to the *2 allele the *3 allele is more severely affected in its activity which can be 70-90% reduced for some substrates. In vivo, this results in clearance reductions of more than 70% in *3/*3 homozygotes and in about half of the clearance for heterozygotes. Other more rare alleles with decreased function include *5, *6, *8, and *U, although *8 was recognized as the most frequent CYP2C9 variant among African Americans (Scott et al., 2009). Promoter variants may contribute to expression variability as shown in a study which resequenced the upstream region and investigated numerous known and novel variants using a reporter gene assay (Kramer et al., 2008). On the other hand a large resequencing study in 400 Chinese subjects identified only 3 novel variants of unknown functional significance, indicating that probably most of the genetic variation in CYP2C9 is already known, at least among Asians (Xiong et al., 2011).

4.3.3.3. CYP2C19. The CYP2C19 or "S-mephenytoin polymorphism" was discovered during the early 1980s and was since then established as one of the clinically most relevant P450 polymorphisms (Kupfer & Preisig, 1984; Wrighton et al., 1993; De Morais et al., 1994a; Meyer & Zanger, 1997; Goldstein, 2001; Rosemary & Adithan, 2007). The existence of fairly common null alleles explains the large variability and strong phenotype-genotype correlations found for CYP2C19. About 2 to 5% of white and black populations but up to ~25% of Asians are CYP2C19 PMs (Table 1). The two most important null alleles are CYP2C19*2, which occurs almost exclusively in Caucasians, and CYP2C19*3, which occurs primarily in Asians. The causal mutation of *2 is located in exon 5 and leads to aberrant splicing (De Morais et al., 1994b), whereas that of *3 in exon 4 creates a premature stop codon (De Morais et al., 1994a). A further clinically relevant variant is the promoter variant -806C>T (*J7) which appears to increase expression and activity toward mephenytoin and omeprazole by a still unclear mechanism (Sim et al., 2006). Additional rare null alleles CYP2Cl9*4-*8 and variants with unknown phenotypic penetrance are listed on the CYPalleles nomenclature website.

4.3.4. Clinical impact of genetic variation

4.3.4.1. CYP2C8. Drug interaction studies support the importance of CYP2C8 for the hypoglycemic agent repaglinide. However, the impact of CYP2C8 pharmacogenetics on repaglinide pharmacokinetics and

clinical consequences is unclear. Some pharmacokinetic studies reported that individuals heterozygous for CYP2C8*3 showed higher drug clearance and up to 60% lower plasma levels of repaglinide compared with CYP2C8*1 carriers (Niemi et al., 2003; Rodríguez-Antona et al., 2008). Recently, a healthy volunteer study indicated that CYP2C8*3 did not affect the pharmacokinetics as well as pharmacody-namic parameters like changes in insulin and glucose concentration of repaglinide (Tomalik-Scharte et al., 2011). Confounding by genetic variants of the organic anion transporting polypeptide 1B1 (SLCO1B1), which mediates the uptake of repaglinide into cells, could be excluded in this study. CYP2C8 variants were reported to moderately alter pharmacokinetics and effects of PPAR-y agonists such as rosiglitazone and pioglitazone (Kirchheiner et al., 2006; Tornio et al., 2008; Yeo et al., 2011) but this contention is also controversially discussed by others (Pedersen et al., 2006).

The same conclusion is true for the anticancer drug paclitaxel frequently used in treatment of women with ovarian or breast cancer (Henningsson et al., 2005; Bergmann et al., 2011a, 2011b; Leskela et al., 2011). Whether subjects carrying CYP2C8 variants are at increased risk of amodiaquine-related severe ADRs is also still a matter of discussion (Kerb et al., 2009).

4.3.4.2. CYP2C9. Genetic variation in CYP2C9 is a recognized factor for ADRs as many of its drug substrates have a narrow therapeutic index. Numerous studies demonstrated the clinical significance of the CYP2C9*2 and *3 polymorphisms for most drug substrates mentioned above. Due to their common occurrence in Caucasians, there are about 1 to 2% of homozygous (*2/*2, *3/*3) and hemizygous (*2/*3) carriers that are at risk to experience more dramatic effects, but even for the heterozygous carriers higher incidence of ADRs were reported. This includes, for example, hypoglycaemia as a result from treatment with hypoglycaemic drugs (Holstein et al., 2005; Xu et al., 2009), gastrointestinal bleeding from NSAlDs (see below) and serious bleedings from anticoagulant treatment. Vitamin K antagonists such as warfarin, acenocoumarol, and phenprocoumone are commonly used to prevent thrombosis in several situations. The anticoagulant warfarin is probably the most thoroughly studied clinical application of CYP2C9 pharmacogenetics. In addition to the vitamin K epoxide reductase complex subunit 1 (VKORC1), variants of CYP2C9 affect warfarin dosing and response (Jonas & McLeod, 2009). However its contribution to anticoagulant response is rather low. In a study of 539 white patients on steady-state warfarin therapy 9% of variability in warfarin dose could be explained by CYP2C9 variants, compared to 25% by variants of VKORC1 (Rieder et al., 2005). Between 50 and 60% of the variability in maintenance dose of warfarin in Caucasians could be explained by variants of CYP2C9 and VKORC1 in combination with other patient factors (e.g. body size, age; (Klein et al., 2009). Different distribution of CYP2C9 and VKORC1 variants as a function of ancestry resulted in highest dose requirement of warfarin in Africans compared to Asians requiring lowest doses. In 2007 pharmacogenetic information on CYP2C9 and VKORC1 genetic polymorphisms as a predictor of response was included in the warfarin labeling by the FDA, and was revised in 2010 to include specific dosing recommendations. Currently various pharmacogenetics-guided warfarin dosing algorithms are available (Jonas & McLeod, 2009; Schwab & Schaeffeler, 2011). Interindividual dosage variation for the anticoagulants aceno-coumarol (Teichert et al., 2009) and phenprocoumon (Teichert et al., 2011) has also been associated with CYP2C9 genotype at genome-wide significance.

The influence of CYP2C9 polymorphisms on the metabolism of first- and second generation sulfonylurea hypoglycaemic drugs (e.g., glibenclamide, tolbutamide, glyburide, glimepiride) has been well-established by several studies with consequences on pharmaco-kinetics and in part on therapeutic response (e.g., HbA1C decrease, secondary failure of therapy, hypoglycemic events; (Manolopoulos et al., 2011 ). For example, type 2 diabetes mellitus patients responded

better to treatment with glibenclamide when they had variant alleles of CYP2C9 as compared to those with normal genotype (Surendiran et al., 2011).

CYP2C9 polymorphism affects the metabolism of several NSAIDs, which is of increasing importance in aging populations, due to dose-dependent ADRs (He et al., 2011). Association of NSAID exposure and risk for gastrointestinal bleeding with CYP2C9 genetic variants has been reported by several studies (Pilotto et al., 2007; Carbonell et al., 2010). However, due to limited data available and the fact that the published studies show substantial methodological limitations current evidence is still controversial (Estany-Gestal et al., 2011).

Finally, the fact that CYP2C9 catalyzes arachidonic acid epoxygenation and that it is expressed in endothelial cells suggested a role in ischemic heart disease and vascular homeostasis (Chehal & Granville, 2006). However in three independent studies totaling more than 52,000 individuals, no association between CYP2C9*2 and *3 polymorphisms and risk of atherosclerosis, ischemic vascular disease or death after ischemic heart disease was found (Kaur-Knudsen et al., 2009a).

4.3.4.3. CYP2C19. The clinical significance of CYP2C19 has been extensively documented (Goldstein, 2001; Desta et al., 2002; Furuta et al., 2007; Rosemary & Adithan, 2007; Lee, 2013). The effect of the CYP2C19 polymorphism on H. pylori eradication therapy in patients with ulcers is a special but intriguing example of clinical pharmacogenetics (Furuta et al., 2004; Klotz, 2006). The common eradication strategy involves application of different antibiotics (e.g., amoxicillin, clarithromycin, metronidazole), together with a proton pump inhibitor, which contributes to accelerated ulcer healing and increases effectiveness of the antibiotics. Since CYP2C19 contributes significantly to the metabolism of the PPIs omepra-zole, lansoprazole, and pantoprazole and to minor extent to the metabolism of rabeprazole, pharmacokinetics, the PPI-induced increase in intragastric pH and treatment outcome depend on CYP2C19 genotype (Furuta et al., 1998; Klotz et al., 2004; Furuta et al., 2007). For instance, PM subjects showed 4- to 15-fold increased AUC values for omeprazole and lansoprazole compared with EMs resulting in significantly higher H. pylori eradication rates compared to EMs. Accordingly, the PPI-induced increase in intragastric pH depends on the CYP2C19 polymorphism. In several studies with PPI based dual, triple and quadruple therapy in Asia and Europe it was shown that PM subjects benefit from their lower metabolism rate because their drug levels stay higher for a longer time resulting in a stronger acid inhibition and higher intragastric pH (Furuta et al., 2007; Yang & Lin, 2010). Since PPI are used also for the treatment of non-ulcer dyspepsia, reflux oesophagitis, gastroesophageal reflux disease (GERD), the Zollinger-Ellison syndrome, and prevention and treatment of NSAID-associated damage, healing rates of these diseases are also impacted by CYP2C19 genotype (Schwab et al., 2004, 2005). The CYP2C19 PM genotype does however not appear to be a risk factor for omeprazole-associated visual disorders (Lutz et al., 2002).

Pharmacokinetic effects associated with CYP2C19 genotype have also been reported for several antidepressants, including clomipra-mine (Nielsen et al., 1994), citalopram (Tai et al., 2002), amitriptyline (Steimer et al., 2005), moclobemide (Yu et al., 2001), as well as for benzodiazepines like diazepam (Bertilsson et al., 1989) and clobazam (Kosaki et al., 2004). Moreover, the CYP2C19 gene has substantial impact on the pharmacokinetics of the antifungal agent voriconazole and its interaction with other drugs (Mikus et al., 2011) and of the antimalarial drug proguanil (Kerb et al., 2009). Although pharmacoki-netic alterations were sometimes related to ADRs, an influence of CYP2C19 genotype on clinical pharmacodynamic outcome for most of these drugs remains so far controversial.

Important recent studies investigated CYP2C19 as a genetic determinant of the efficacy of the platelet aggregation inhibiting thienopyridine, clopidogrel (Plavix). Both, treatment and prevention of atherothrombotic events after percutaneous coronary revascular-ization in patients with coronary artery disease with clopidogrel is

complicated because a significant proportion of patients is resistant and experiences insufficient platelet inhibition resulting in cardiovascular (re-)events including stent thrombosis (Zuern et al., 2010). Numerous clinical studies have confirmed that CYP2C19 PMs have significantly lower anticoagulation effect ofclopidogrel, which is associated with an increased risk of major adverse cardiovascular events (Collet et al., 2009; Mega et al., 2009; Simon et al., 2009; Sofi et al., 2011). Already in 2009, the FDA added a Boxed Warning to the label for Plavix alerting patients and health care professionals that the drug can be less effective in genetically determined CYP2C19 PMs. However, recent meta-analyses drew an inconsistent picture. No significant or consistent influence of CYP2C19 genotype on the clinical efficacy of clopidogrel was found in one analysis (Bauer et al., 2011), association with clopidogrel metabolic concentration and platelet reactivity responsiveness but no overall association with cardiovascular events in another one (Holmes et al., 2011), while the most recent study, which analyzed 16 clinical studies, confirmed significant associations with major cardiovascular endpoints, i.e. increased risk of cardiac death, myocardial infarction, and stent thrombosis (Jang et al., 2012).

Several clinical studies investigated the clinical impact of the gain-of-function allele CYP2C19*17 (Zabalza et al., 2011). With clopidogrel, no association with platelet aggregation was found (Geisler et al., 2008) while another study reported increased risk of bleeding events (Sibbing et al., 2010). The homozygous *17/*17 genotype has been found to accelerate omeprazole metabolism resulting in subtherapeutic drug exposure (Baldwin et al., 2008). In another example, homozygous CYP2C19*17 genotype was associated with lower serum concentration of escitalopram, which might imply an increased risk of therapeutic failure (Tai et al., 2002). Moreover, treatment outcome of tamoxifen in postmenopausal breast cancer women has been associated with the CYP2C19*17 allele, although the magnitude of effect appears to be considerably smaller compared to loss of function alleles (Schroth et al., 2007; Li-Wan-Po et al., 2010). However, not all studies have identified a significant effect of CYP2C19*17 (Kurzawski et al., 2006; Ohlsson Rosenborg et al., 2008).

4.4. Subfamily CYP2D: CYP2D6

4.4.1. Regulation and variability of gene expression

CYP2D6 is the only protein-coding gene of the CYP2D subfamily. The CYP2D locus on chromosome 22q13.1also harbors two pseudogenes, CYP2D7 and CYP2D8P (Kimura et al., 1989; Heim & Meyer, 1992). CYP2D7 is expressed as mRNA in liver (Endrizzi et al., 2002), but the presence of an insertion in the first exon disrupts the reading frame, preventing expression of protein. In contrast, CYP2D8P is a true pseudogene which has accumulated several gene-disrupting mutations. Because CYP2D6 is considered to be essentially a noninducible gene not significantly influenced by smoking, alcohol consumption or sex (Bock et al., 1994; Glaeser et al., 2005), its transcriptional regulation has not been studied very thoroughly. It should be mentioned, however, that phenotypically determined induction by rifampicin in vivo was reported (Caraco et al., 1997). An initial promoter analysis revealed the presence of a positive DR-1 element which bound and responded to HNF4a which was antagonized by COUP-TF-I. It was concluded that the balance between HNF4a and COUP-TF-I may contribute to expression variability (Cairns et al., 1996). HNF4a also mediates downregulation of CYP2D6 during inflammation by nitric oxide through the same proximal DR-1 site (Hara & Adachi, 2002). The regulation of CYP2D6 by HNF4a was further investigated in a CYP2D6-humanized transgenic mouse line which expressed CYP2D6 protein in the liver, intestine, and kidney (Corchero et al., 2001). Conditional inactivation of the HNF4a gene in this mouse line decreased debrisoquine 4-hydroxylase activity by more than 50% demonstrating that HNF4a regulates CYP2D6 activity in vivo. The influence of HNF4a on CYP2D6 activity in vivo was further shown in a recent population-based polymorphism study which showed that the rare HNF4a variant G60D was unable to bind and activate the CYP2D6 promoter (Lee et al., 2008b).

Hepatic CYP2D6 protein content varies dramatically from person to person mainly due to its genetic polymorphism (Zanger et al., 2001). A recent quantitative comparison of hepatic CYP2D6 protein by Western blot and mass spectrometric analysis demonstrated comparability of the results which ranged from undetectable in genetic PMs up to ~70 pmol/mg of microsomal protein in carriers of three alleles (Langenfeld et al., 2009). CYP2D6 and the CYP2D7 pseudogene are found at low mRNA levels in most extrahepatic tissues (Table 2), and expression of protein has been shown in the gastrointestinal tract (Glaeser et al., 2005) and in different areas of the human brain (Table 2; Siegle et al., 2001; Miksys et al., 2002; Gaedigk et al., 2005; Dutheil et al., 2009; Ferguson & Tyndale, 2011). At the RNA level, CYP2D6 expression is characterized by the occurrence of numerous splice variants. However, with the exception of the effect of polymorphic splice variants (see below) the functional significance of alternative splicing of CYP2D6 pre-mRNA has not been clarified. In foetal liver CYP2D6 is virtually undetectable but expression surges within hours after birth (Cresteil, 1998). Investigation of CYP2D6 and CYP3A4 activity in healthy infants by using dextromethorphane as probe drug revealed that the CYP2D6-dependent O-demethylation activity was detectable and concordant with genotype by 2 weeks of age and showed no relationship with gestational age, whereas the CYP3A4-dependent N-demethylation developed more slowly over the first year of life (Blake et al., 2007). In a large (n = 222) set of pediatric liver samples it was found that gestational age influenced CYP2D6 protein expression and activity in prenatal samples whereas postnatal samples were influenced by age and genotype (Stevens et al., 2008).

4.4.2. Role of CYP2D6 in drug metabolism

The number of drugs metabolized primarily by CYP2D6 is very large in comparison to its relatively minor expression in liver and includes ~15-25% of all clinically used drugs from virtually all therapeutic classes, like antiarrhythmics (e.g. propafenone, mexiletine, flecainide), tricyclic and second generation antidepressants (e.g. amitriptyline, paroxetine, venlafaxin), antipsychotics (aripiprazole, risperidone), (3-blockers (bufuralol, metroprolol), as well as anti-cancer drugs, in particular the selective estrogen receptor modifier (SERM) tamoxifen, several opioid analgesics including codeine and tramadol, and many others (Table 3; Zanger et al., 2008; Stingl et al., 2012). Several highly selective test drugs have been used to determine the CYP2D6 drug oxidation phenotype, including debrisoquine, dextromethorphan, metoprolol, sparteine, and tramadol (Frank et al., 2007). Endogenous biotransformations include 5-methoxyindolethylamine O-demethylase (Yu et al., 2003a) and regeneration of serotonin from 5-methoxytryptamine (Yu et al., 2003b). CYP2D6 is also prone to inhibition by numerous compounds that need not be substrates but bind to the enzyme with high affinity, e.g. quinidine or methadone (Gelston et al., 2012; Table 3). Some of these inhibitors are strong enough to change the apparent phenotype of the patient, a phenomenon known as phenocopying. The structure-activity relationships for CYP2D6 substrates and inhibitors were useful to develop pharmacophore models (Lewis et al., 2004). The crystal structure of the CYP2D6 protein has been resolved yielding further insights into the active site and the chemical requirements for binding and catalysis (Rowland et al., 2006).

4.4.3. Genetic polymorphisms and functional impact

The CYP2D6 polymorphism is historically the best studied and one of the most intriguing examples of pharmacogenetics. CYP2D6 shows the greatest impact of genetic polymorphism among all major drug metabolizing CYPs, due to its wide spectrum of genetic variants (from null alleles to several-fold gene amplification), comparably little influence by environmental and nongenetic factors, and its extraordinarily broad substrate spelectivity. lnitial evidence for CYP2D6 genetic polymorphism came from population and family pharmacokinetic studies in the 1970's which showed that deficient

debrisoquine 4-hydroxylation (Mahgoub et al., 1977) and sparteine N-oxidation (Eichelbaum et al., 1979) occurs in 5 to 10% of European Caucasians as a monogenic recessive trait later shown to be the same for the two drugs and for many others that are polymorphically metabolized. ln ethnicities other than Caucasian the deficiency occurs at much lower frequency (Gaedigk, 2000). The molecular studies that followed were reviewed previously (Meyer & Zanger, 1997; Zanger et al., 2004; Ingelman-Sundberg, 2005). Currently 105 distinct alleles and a large number of allele variants are listed on the CYPalleles website, many of them leading to absent or nonfunctional protein, or to decreased or increased expression (Table 1). Genotype-phenotype correlation analysis studies in Caucasians phenotyped with the probe drugs sparteine or debrisoquine have clearly demonstrated the impact of genetic polymorphism on CYP2D6 function in vivo (Fig. 2; Sachse et al., 1997; Griese et al., 1998; Raimundo et al., 2004).

In Caucasians the most frequent null alleles are the CYP2D6*4 alleles, which all harbor a consensus splice site mutation (1846G>A) that leads to absence of detectable protein in the liver. The collective CYP2D6*4 allele frequency among Caucasians is about 20 to 25%, accounting for 70 to 90% of genetically determined PMs (Sachse et al., 1997; Griese et al., 1998). The virtual absence of the *4 allele in Asian, African, and South American populations explains the low incidence of the PM phenotype in these populations, whereas African-Americans have an intermediate frequency. The CYP2D6 gene deletion allele *5 is present at a frequency of 3 to 5% in most populations. The null alleles *3 and *6 are present at frequencies of ~1-3% in Caucasians whereas most other null alleles are even rarer. Nevertheless, collectively the low frequency alleles make a significant contribution to phenotype. Heterozygous carriers of one defective and one normal allele of CYP2D6 tend to have a lower median enzyme activity, which is overlapping with that of carriers of two functional alleles (Fig. 2).

A separate phenotype model has been termed intermediate metabolizer (IM), which occurs in all major races, but interestingly the causative mutations are different. About 10 to 15% of Caucasians are carriers of one partially defective allele (e.g. *4i, *9, *i0) in combination with another partially defective or null allele such as *4, resulting in a distinct phenotypic subgroup with compromised sparteine oxidation capacity (Fig. 2). The mechanism of the *41 allele, which is more common among Caucasians, has been studied in detail and an intron 6 SNP that leads to erroneous splicing resulting in only a fraction of correctly spliced mRNA has been shown to be responsible for the low activity in vitro and in vivo (Toscano et al., 2006). In Africans and Asians other partially defective alleles termed *17 and *i0, respectively, are prevalent. The *17 allele is present at frequencies of up to 30% in Africans (Wennerholm et al., 2002) and the *10 variant occurs at up to 50% in Asians (Sakuyama et al., 2008). Due to their high frequencies, the partial activity conferred by these alleles leads to a shift of the metabolic drug oxidation capacity towards lower values which has clinical relevance (Kitada, 2003).

A large number of structural variations exists at the CYP2D locus (Schaeffeler et al., 2003; Gaedigk et al., 2010). Unequal crossing over between the highly homologous genes involving a certain repetitive sequence also present in the c-myc gene lead to variants with deleted, duplicated, or otherwise recombined genes. CYP2D6 gene duplications were first identified in combination with the functional *2 allele (Bertilsson et al., 1993; Johansson et al., 1993; Johansson & lngelman-Sundberg, 2008) and later shown to occur also with other alleles including *i, *4, *6, *i0, *i7, *29, *35, *4i, *43, *45. This is important because not all duplications comprise functional genes, which complicates phenotype prediction (Schaeffeler et al., 2003; Gaedigk et al., 2012). The overall frequency of the gene duplications in Caucasians is between 1 and 5% whereas in some Arabian, Eastern African and Pacific populations it can reach 10 and even up to 50% and more. lt was hypothesized, therefore, that the striking preference of CYP2D6 for plant alcaloids found in food of some of these ethnicities

may play a role in recent selection processes (Ingelman-Sundberg, 2005).

The presence of the pseudogenes, structural variants, and numerous SNPs at the CYP2D locus requires particular cautiousness in the design of genotyping assays. Coamplification of pseudogenes, unexpected recombination events, and failure to account for important variants, for example due to ethnic variation can lead to erroneous interpretation of genotype. Numerous genotyping assays and strategies were developed which, due to the complexity of variants, usually identify only one functionally dominant key mutation per allele. This possibly reduces the predictive power of genotyping for certain haplotypes. The most comprehensive commercially available platform for CYP2D6 genotyping is the AmpliChip CYP450 test from Roche. This microarray has probes to identify 33 CYP2D6 alleles, including most confirmed variants responsible for absent or impaired enzyme activity and seven gene duplications, as well as two CYP2CÍ9 variant alleles (Jain, 2005; de Leon et al., 2009; Rebsamen et al., 2009).

4.4.4. Clinical impact of genetic variation

Numerous clinical studies document the clinical importance of the CYP2D6 polymorphism for response and/or ADRs to agents that are either inactivated or activated by this enzyme.

Several antiarrhythmic drugs including metoprolol, timolol, propafenone and others are metabolically inactivated by CYP2D6, leading to increased exposure and risk of adverse events for PMs/ IMs, although clinical effects are still controversial (Fux et al., 2005; Darbar & Roden, 2006; Klotz, 2007; Morike et al., 2008; Bijl et al., 2009; Rau et al., 2009). Many antidepressants and antipsychotics are substrates of CYP2D6 (Table 3) and oxidative metabolism usually leads to their inactivation and consequently a risk of overexposure in PMs/IMs and underexposure in UMs. Studies on clinical effects have however not been unanimous, in part due to inherently problematic dosing of these drugs. The area has been extensively reviewed (Kirchheiner et al., 2004; Bertilsson, 2007; Crisafulli et al., 2011; Stingl et al., 2012).

Several opioid drugs including codeine, dihydrocodeine, oxyco-done, and tramadol used in pain management are metabolically activated by CYP2D6 and genotype was shown to affect their efficacy and safety (Stamer et al., 2010; Leppert, 2011; Madadi et al., 2012). The prodrug codeine is O-demethylated by CYP2D6 to the pharmacologically active analgesic morphine. In CYP2D6 PMs there is no analgesic effect due to extremely low morphine plasma concentrations (Eckhardt et al., 1998). Conversely, increased effectiveness of codeine with sometimes life-threatening opioid intoxication was observed in patients with multiple CYP2D6 gene copies but also in neonates whose breastfeeding mothers were genetic CYP2D6 UMs, consistent with higher rates of conversion to morphine in patients with UM phe-notype (Gasche et al., 2004; Koren et al., 2006; Madadi et al., 2009). This scenario of morphine intoxication of neonates including further glucuronidation via UGT2B7 has been the subject of a computerized quantitative modeling study (Willmann et al., 2009). The physiologically based pharmacokinetic model was able to simulate the accumulation of morphine in the plasma of neonates during maternal codeine intake for different genotypes and to identify risk factors.

The selective estrogen receptor modulator (SERM) tamoxifen is extensively metabolized into at least 22 metabolites, two of which, 4-hydroxytamoxifen and the secondary metabolite endoxifen, are thought to be mainly responsible for the antiestrogenic effect because of their high affinity to the estrogen receptor (Johnson et al., 2004; Mürdter et al., 2011b). Because CYP2D6 is the major enzyme for the crucial 4-hydroxylation (Dehal & Kupfer, 1997; Coller et al., 2002; Johnson et al., 2004), CYP2D6 genotype should be expected to influence plasma concentrations of these active metabolites and hence treatment outcome, with patients having functionally impaired CYP2D6 producing lower levels of active metabolites and thus

profiting less from the treatment compared to patients with active enzyme (Brauch et al., 2009). This hypothesis has been scrutinized over the past decade, and initial retrospective studies found indeed that CYP2D6 PMs show less benefit from adjuvant treatment of postmenopausal breast cancer with tamoxifen monotherapy (Goetz et al., 2007; Schroth et al., 2007). However, while several more recent studies confirmed an association of CYP2D6 PM genotype with worse outcome (Schroth et al., 2009; Kiyotani et al., 2010; Madlensky et al., 2011) or demonstrated a beneficial therapeutic effect of genotype-guided treatment (Irvin et al., 2011; Kiyotani et al., 2012), other studies found no (Rae et al., 2012; Regan et al., 2012) or inconsistent allele-specific effects (Abraham et al., 2010). Possible explanations for inconsistent results include a number of confounding factors that have not been taken into account systematically, including previous chemotherapy, enzyme inhibition due to co-medication, meno-pausal status, but also differences in genotyping quality like limited CYP2D6 allele coverage or use of tumor instead of germline DNA for genotyping (see discussion comments by Brauch et al., 2013; Nakamura et al., 2012; Pharoah et al., 2012; Stanton, 2012). Despite existing evidence, well-planned prospective studies seem to be inevitable to clarify the real value of CYP2D6 predictive genotyping for clinical utility.

A recent study investigated the chemically related SERM clomi-phene, chemically closely related to tamoxifen, which is used as first line infertility treatment in women. Like tamoxifen, clomiphene is a prodrug and requires bioactivation by 4-hydroxylation. CYP2D6 was shown to be the key enzyme in the conversion of clomiphene to its active metabolites (E)-4-hydroxy-clomiphene and (E)-4-hydroxy-desethyl-clomiphene by human liver microsomes (Ghobadi et al., 2008; Murdter et al., 2011a). A strong gene-dose effect was found for the formation rate of both metabolites in microsomes and in a pharmacokinetic study including healthy women genotyped for CYP2D6. Furthermore, both metabolites were shown to mediate clo-miphene action through oestrogen receptor binding. Clinical studies are therefore warranted to prove the validity of CYP2D6 genetics for individualization of clomiphene therapy in infertility.

In addition the gene has been studied as a risk factor for numerous diseases. Most of the epidemiological studies revealed conflicting results concerning pharmacogenetic association and the reader is referred to specialized articles, for example, regarding Parkinson's disease (Christensen et al., 1998), schizophrenia and other psychiatric diseases (Patsopoulos et al., 2005; Stingl et al., 2012), Alzheimer's disease (Scordo et al., 2006), and several forms of cancer (Agundez, 2004; Rodriguez-Antona et al., 2010).

4.5. Subfamily CYP2E: CYP2E1

4.5.1. Regulation and variability of gene expression

CYP2E1 is the only gene of the CYP2E subfamily located at chromosome 10q26.3. Its expression is undetectable in fetal liver. Within several hours after birth protein and activity rise considerably but independently of mRNA which remains quite low (Vieira et al., 1996). Stabilization of CYP2E1 protein by ketone bodies could explain the early neonatal rise at the protein level. During the following periods from one month to one year, accumulation of CYP2E1 mRNA is correlated with the methylation status of CpG residues in the 5' flanking region. Transcriptional activators suggested to participate in the regulation of CYP2E1 expression include HNF1 a and ( -catenin (Gonzalez, 2007). Expression in liver is rather high (Table 2) and variability of CYP2E1 protein between individuals appears to be considerable and correlated to catalytic activity (Ohtsuki et al., 2012; Tan et al., 2001). One study reported one third greater clearance values for the probe drug, chlorzoxazone, in males compared to females (Kim & O'Shea, 1995). CYP2E1 transcript levels are not well or not at all correlated to protein, and this has been suggested

to be the consequence of strong regulation via translational repression by miR-378 (Mohri et al., 2010).

The CYP2E1 enzyme is inducible by many of its substrates (Table 3) as well as several hormones by complex mechanisms involving transcriptional, translational and posttranslational effects (Gonzalez, 2007). In humans induction by ethanol was shown to involve increased transcription which primarily takes place in perivenous hepatocytes (Takahashi et al., 1993). Significant and rapid induction of CYP2E1 activity was shown to occur already at moderate alcohol consumption which was rapidly reversed following alcohol withdrawal (Oneta et al., 2002). CYP2E1 is furthermore induced under diverse pathophysiological conditions including diabetes, obesity, fasting, alcohol and non-alcoholic liver disease where it is believed to play a pathophysiological role (Caro & Cederbaum, 2004; Aubert et al., 2011). Extrahepatic expression of CYP2E1 has been found at lower levels in brain, nasal mucosa, kidney cortex, tes-tis, ovaries, the gastrointestinal tract and at somewhat higher levels in cardiac tissue (Lieber, 1997; Joshi & Tyndale, 2006; Thelen & Dressman, 2009; Michaud et al., 2010; Ferguson & Tyndale, 2011).

4.5.2. Role of CYP2E1 in drug metabolism and toxicology

CYP2E1 displays a substrate preference for low molecular weight molecules, including ethanol, acetone and other organic solvents, narcotics like halothane, and some drugs including chlorzoxazone and paracetamol (Table 3). Many known industrial chemicals and occupational and environmental toxicants as well as procarcinogens are also CYP2E1 substrates (Bolt et al., 2003; Lu & Cederbaum, 2008). Endogenous substrates of CYP2E1 are lauric acid and acetone, a product of fatty acid oxidation, which is oxidized to acetol and further to 1,2-propanediol in the propanediol pathway of gluconeogenesis. The role of CYP2E1 in ethanol oxidation depends on the conditions. The major enzyme for ethanol oxidation to acetaldehyde is alcohol dehydrogenase (ADH), whereas CYP2E1 plays a more important role at elevated concentrations and after chronic consumption due to induction (Caro & Cederbaum, 2004).

An important feature of ethanol oxidation via CYP2E1 is the generation of reactive oxygen species (ROS) which contributes to damage of liver cells. CYP2E1 is an effective generator of ROS such as the superoxide anion radical and hydrogen peroxide as a result of uncoupling of oxygen consumption with NADPH oxidation (Caro & Cederbaum, 2004). The fact that significant levels of CYP2E1 are located within the mitochondria could contribute further to its deleterious effects (Knockaert et al., 2011). A number of studies therefore implicated CYP2E1 as a causative player in alcoholic liver disease as well as nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH) probably through enhancement of hepatic lipid peroxidation (Cederbaum, 2006; Aubert et al., 2011). Furthermore, chronic alcohol consumption has been recognized as a major risk factor for esophageal cancer, probably due to carcinogenic and genotoxic effects of acetaldehyde and oxidative stress (Wang et al., 2009; Millonig et al., 2011).

4.5.3. Genetic polymorphisms and pharmacogenetics

The CYPalleles website lists only 7 distinct CYP2E1 star-alleles and few variants compared to other CYP2 genes, and for none of them could an important functional impact be demonstrated. Some studies suggested effects on expression for several 5'-upstream region polymorphisms, including an insertion of several repeats (CYP2E1*1D; McCarver et al., 1998) and polymorphic Pstl (-1293G>C) and Rsa I ( —1053C>T) sites (Neafsey et al., 2009; Tan et al., 2001). The latter two polymorphisms occur together on alleles *5A and*5B, the latter of which lacks an additional nucleotide change in intron 6 (7632T>A), which can be detected as a Dral RFLP. CYP2E1*5A has an approximate frequency 5% in Caucasians and up to 38% in Asians, whereas *5B appears to be present only in Asians at ~2 to 8% frequency. Only few coding-region variants have been reported with little or no impact

on enzyme function. A recent study analyzed 11 polymorphisms and their haplotypes in over 2600 individuals from population samples representing the major geographical regions of the world (Lee et al., 2008a). Similar to other genetic loci, CYP2E1 haplotype diversity was much higher among African populations (6-10 common haplotypes) than in other parts of the world (about 1-6 common haplotypes).

Because of the particular relevance of CYP2E1 for toxicology due to its role in the metabolic activation of procarcinogens and chemical carcinogenesis, pharmacogenetic studies mostly focused on association with various cancers. For example, genetic polymorphisms in CYP2E1 have been associated with altered susceptibility to hepatic cirrhosis induced by ethanol and with increased risk of development of esophageal cancer and other malignant tumours. Likewise, long-term intake of CYP2E1 inducers has also been recognized as a risk factor for malignancy, in particular for carriers of certain variant CYP2E1 alleles. As these studies are complex and often contradictory in their results, the reader is referred to some recent specialized reviews and meta-analyses on CYP2E1 and risk of chemically induced cancers (Trafalis et al., 2010), association with lung cancer risk (Wang et al., 2010b), and alcohol-related cancers (Druesne-Pecollo et al., 2009).

4.6. Subfamily CYP2J: CYP2J2

4.6.1. Regulation and variability of gene expression

The human CYP2J subfamily has only a single gene, CYP2J2, which encodes a 502 amino acid microsomal P450 protein. Like all other CYP2 members, the CYP2J2 gene comprises 9 exons and 8 introns, which are spread over a region encompassing about 40 kb on chromosome 1. The CYP2J2 gene promoter lacks a TATA-box and its basal activity is regulated predominantly by Sp1, for which at least 4 different binding sites are present within the first 100 bp of 5'-up-stream sequence (King et al., 2002). This promoter arrangement is consistent with a "housekeeping" nature of this P450 gene, which does not seem to be inducible by typical P450 inducers. CYP2J2 is expressed at high levels in the heart, especially in cardiac myocytes and in endothelial cells (Wu et al., 1996; Delozier et al., 2007; Michaud et al., 2010), and at lower levels it is also expressed in lung, gastrointestinal tract, and pancreas, as well as in selected brain regions (Table 2). In the liver CYP2J2 abundance is less than 1% of total P450 content, and similar estimations have been made regarding expression in small intestine (Paine et al., 2006; Yamazaki et al.,

2006). Prenatal expression of CYP2J2 mRNA and protein was also observed in these tissues (Gaedigk et al., 2006).

4.6.2. Role of CYP2J2 in drug metabolism and toxicology

The role of CYP2J2 in drug metabolism has not been fully evaluated. Several antihistamine drugs including terfenadine, ebastine, and astemizole, have been identified as efficient substrates but the contribution of CYP2J2 to overall clearance is not clear and may strongly depend on the tissue (Table 3; Matsumoto et al., 2002; Lafite et al.,

2007). One recent study screened 139 marketed drugs from different therapeutic classes and identified eight novel CYP2J2 substrates including amiodarone, cyclosporine, and other drugs typically metabolized by CYP3A4, however with different regioselectivity (Lee et al., 2010).

CYP2J2 is one of the major P450 enzymes to metabolize arachidonic acid (AA) predominantly via NADPH-dependent olefin epoxidation to 20-HETE and all four regioisomeric cis-epoxyeicosatrienoic acids, i.e. the 5,6-, 8,9-, 11,12-, and 14,15-EETs (Node et al., 1999; Xu et al., 2011). These AA metabolites play important roles in the regulation of renal, pulmonary, cardiac and vascular functions. ln the heart some of the CYP2J2-generated products have anti-inflammatory and vascular-protective effects. For example the 11,12-EET exerts antiinflammatory effects by inhibiting endothelial nuclear factor-KB, a

transcription factor that plays a key role in eliciting inflammatory responses in the vascular wall. The CYP2J2 products 8,9- and 11,12-EETs were also shown to activate anti-inflammatory functions by binding as ligands to PPARa (Wray et al., 2009).

Besides its cardioprotective functions, CYP2J2 is also being investigated for its role in cancer as it was found to be highly and selectively expressed in different human tumor tissues and cancer cell lines (Chen et al., 2009). Interestingly, CYP2J2 inhibitors structurally related to the drug terfenadine decreased EET production and inhibited proliferation and neoplastic phenotypes of human tumor cells and in murine xenograft models.

4.6.3. Genetic polymorphisms and pharmacogenetics

Considerable interindividual variation in CYP2J2 expression has been observed and investigated in relation to genetic polymorphism. The CYPalleles website lists 10 distinct star alleles, eight of which carry nonsynonymous SNPs. Some of these variants (*2: T143A; *3: R158C; *4: I192N; *6: N404Y) were shown to have decreased catalytic activity towards AA when tested recombinantly in an insect cell system (King et al., 2002). The significance of these amino acid variants for drug substrates of CYP2J2 has not been investigated, to our knowledge. The most common CYP2J2 allele variant with functional relevance is CYP2J2*7, which occurs at frequencies of ~2-17% in different populations (Table 1). The key SNP rs890293 is located in the proximal promoter at (— 76G>T) and disrupts one of the SP1 binding sites, which results in ~50% reduced promoter-activity relative to the wild-type promoter (Spiecker et al., 2004). Because of the assumed role of CYP2J2 in physiological processes of the heart, most pharmacogenetic studies analyzed CYP2J2*7 in cohorts of patients with coronary artery disease, coronary heart disease, myocardial infarction, or hypertension. So far these studies did not result in conclusive associations, with many of them showing either no or controversial associations (Berlin et al., 2011; Xu et al., 2011). The in vivo relevance of CYP2J2 polymorphism thus remains to be established.

5. Family CYP3: CYP3A4, CYP3A5, CYP3A7, CYP3A43

5.Í. Regulation and variability of gene expression

The human CYP3 family consists only of one subfamily, CYP3A, which is located on chromosome 7q22.1 and has a size of 231 kb. It comprises the four CYP genes 3A4, 3A5, 3A7, and 3A43. The mouse Cyp3a cluster contains 7 full length genes but there are no orthologous pairs between mouse and human, suggesting that a single CYP3A gene present in the common ancestor existed, which independently expanded during the last 75 MY (Nelson et al., 2004). CYP3A4 is in the majority of individuals abundantly expressed in liver but population variability is extremely high (>100-fold), although complete absence of expression has not been definitively proven to our knowledge. Average microsomal content has been estimated between ~60 pmol per mg of microsomal protein (Ohtsuki et al., 2012) to 110 pmol/mg (Klein et al., 2012) to ~146 pmol/mg (Westlind-Johnsson et al., 2003), representing on average about ~14-24% to the microsomal P450 pool (Shimada et al., 1994; Lin et al., 2002; Wolbold et al., 2003; Ohtsuki et al., 2012). Expression of the three minor isoforms, CYP3A5, CYP3A7, and CYP3A43 is generally lower compared to CYP3A4, although the contribution of CYP3A5 in carriers of CYP3A5*Í may be substantial in low expressors of CYP3A4 (Hustert et al., 2001; Kuehl et al., 2001; Koch et al., 2002; Westlind-Johnsson et al., 2003; Daly, 2006). According to a recent mass-spectrometric quantification, the mean fractions of CYPs 3A4, 3A5, 3A7, and 3A43 proteins of the total microsomal CYP3A protein amount measured in 17 adult samples were 85.4% (range, 6.2-270 pmol/mg), 5.4% (2.5-17.1), 3.4% (<9.4), and 5.8% (<6.4), respectively (Ohtsuki et al., 2012). If one sample with high CYP3A5 expression (genotype was not determined) was omitted, CYP3A5 expression ranged from 2.5 to 4.3 pmol/mg. CYP3A7 is more abundantly expressed in fetal liver than

in adult liver but the mechanism for this has not been studied in detail (Cresteil, 1998; Leeder et al., 2005). CYP3A4 is the major expressed P450 in intestinal enterocytes, with levels uncorrelated to those of liver, and contributes substantially to the first-pass metabolism of orally administered drugs (Ding & Kaminsky, 2003; von Richter et al., 2004a; Daly, 2006). In other extrahepatic tissues including the respiratory tract, brain, lung, and kidney, CYP3A5 expression appears to be predominant or similar to CYP3A4 (Table 2; Raunio et al., 2005; Daly, 2006; Dutheil et al., 2008; Pavek & Dvorak, 2008; Bolbrinker et al., 2012). Multiple signaling pathways contribute to the complex regulation of the CYP3A genes. Constitutive transcriptional regulation includes both positive and negative regulators, in particular C/EBPa and C/EBPß (Jover et al., 2002; Rodriguez-Antona et al., 2003; Martinez-Jimenez et al., 2005), HNF1a and HNF3y (Rodriguez-Antona et al., 2003), HNF4a (Tirona et al., 2003; Tegude et al., 2007; Jover et al., 2009), and USF (Biggs et al., 2007). Induc-ible transcriptional regulation in response to numerous xenobiotics (Table 3) is due to at least three major cis-acting modules: the proximal PXR responsive element prPXRE, the distal (-7.2 to -7.8 kb) xenobiotic-responsive enhancer module XREM, and the constitutive liver enhancer module CLEM4 (-11.4 to -10.5 kb; Matsumura et al., 2004; Jover et al., 2009; Qiu et al., 2010). The xenosensors PXR and CAR, which bind to these regions, translocate to the nucleus upon binding of structurally diverse drug ligands, including barbiturates, rifampicin, statins, and many other drugs and then heterodimerize with RXR to enhance transcription several-fold (Timsit & Negishi, 2007; Liu et al., 2008; Pascussi et al., 2008). These nuclear receptors were also shown to induce CYP3A5 in liver and intestine (Burk et al., 2004), which explains the observed coregulation between hepatic CYP3A4 and CYP3A5 (Lin et al., 2002). Additional ligand-dependent transcriptional regulators of CYP3A4 include the bile acid receptor FXR (Gnerre et al., 2004), the glucocorticoid receptor (Pascussi et al., 2008), the oxysterole receptor LXR (Duniec-Dmuchowski et al., 2007), and the vitamin D receptor (Matsubara et al., 2008). Recent studies indicate that PPARa also contributes to constitutive and inducible regulation of CYP3A4. In a genetic candidate gene approach (see below), PPARa SNPs were found to affect hepatic CYP3A4 phenotypes including atorvastatin hydroxylase activity (Klein et al., 2012) and in a systems biology approach genome-wide time-resolved data from human hepatocytes challenged with statins identified, among others, PPARa as an influential factor for CYP3A4 expression (Schröder et al., 2011). Validation experiments in these studies demonstrated ~3-fold induction of CYP3A4 in human hepatocytes by the potent PPARa agonist, WY14,643, while the antagonist MK886 and shRNA-mediated PPARa knock-down lead to marked repression. These studies together with an earlier transcriptional profiling study (Rakhshandehroo et al., 2009) clearly demonstrate an impact of the lipid homeostase regulator PPARa on CYP3A4.

Also of considerable importance is cytokine-mediated down-regulation of CYP3A4 in the course of the inflammatory response via JAK/STAT pathway (Jover et al., 2002). This is clinically relevant for example in cancer patients because tumors can be a source of systemically circulating cytokines which then lead to substantial down-regulation of CYP3A4 and other drug metabolizing enzymes and transporters (Slaviero et al., 2003; Aitken et al., 2006). Moreover, as summarized in chapter 3.3, CYP3A4 shows significant activity and expression differences in females versus males (Wolbold et al., 2003; Cotreau et al., 2005; Lamba et al., 2010). Recently, higher activity in females was also confirmed in women from Tanzania (P< 0.001) and Korea (P< 0.00001) by measuring the proposed endogenous CYP3A4/5 metabolite-marker 4ß-hydroxycholesterol (Diczfalusy et al., 2011). The mechanistic basis for sex-biased expression of CYP3A4 and other CYPs has not been elucidated but may involve different growth hormone/Stat5b signaling (Waxman & Holloway, 2009). A recent investigation in human hepatocytes from female and male donors found more efficient hormone-dependent activation, greater extent ofnuclear translocation, and stronger binding to DNA motifs of HNF4a and PXR in female compared to male hepatocytes (Thangavel et al., 2011).

5.2. Role ofCYP3A enzymes in drug metabolism

The CYP3A subfamily enzymes play a major role in the metabolism of ~30% of clinically used drugs from almost all therapeutic categories (Fig. 1; Table 3; Bu, 2006; Liu et al., 2007; Zanger et al., 2008). The active site of CYP3A4 is large and flexible and can accommodate and metabolize many preferentially lipophilic compounds with comparatively large structures (Scott & Halpert, 2005; Hendrychova et al., 2011). Typical large substrates are immunosuppressants like cyclosporin A and tacrolimus, macrolide antibiotics like erythromycin, and anticancer drugs including taxol, but smaller molecules are also accepted including ifosfamide, tamoxifen, benzodiazepines, several statins, antidepressants, opioids and many more (Table 3). CYP3A4 is also an efficient steroid hydroxylase with an important role in the catabolism of several endogenous steroids including testosterone, progesterone, androstenedione, cortisol and bile acids. Although several probe drugs that measure general CYP3A activity are available, e.g. midazolam, erythromycin, alprazolam, and dextromethorphan (Fuhr et al., 2007; Liu et al., 2007), phenotyping data obtained with different CYP3A substrates are not generally well correlated to each other, a CYP3A4-typical feature that may be related to the occurrence of several overlapping substrate binding regions and the well-known allosteric regulation of CYP3A4 enzyme activity (Niwa et al., 2008; Foti et al., 2010; Roberts et al., 2011).

The high sequence similarity between the CYP3A isozymes (CYP3A4 and CYP3A5 share >85% primary amino acid sequence identity) leads to highly similar substrate selectivity between the isoforms (Williams et al., 2002). Nevertheless, some limited substrate and regioselectivity differences were observed. For example, aflatoxin B1 (AFB1) 3a-hydroxylation to AFQ1 is solely catalyzed by CYP3A4 and results in detoxification and subsequent elimination of AFB1, whereas CYP3A5 converts it to the genotoxic exo-8,9-epoxide AFBO (Kamdem et al., 2006). Another example is the aromatic ortho-hydroxylation of atorva-statin, which is 16-fold more efficiently catalyzed by CYP3A4 compared to CYP3A5 (Feidt et al., 2010).

5.3. Genetic polymorphisms and functional impact 5.3.1. CYP3A4

Drug oxidation phenotypes of CYP3A4 are strongly variable but unimodally distributed. Nevertheless there is indication of substantial heritability. For example, antipyrine 4-hydroxylation rate, which is mainly catalyzed by CYP3A4 (Engel et al., 1996), was reported to be largely inherited as shown in early twin studies (Penno et al., 1981). Moreover, a repeated drug administration approach lead Kalow and colleagues to conclude a high degree of heritability for CYP3A4 drug oxidation capacity towards several of its substrates (Ozdemir et al., 2000). The genetic basis for these observations remained obscure even though several resequencing and haplotype tagging studies have recently been carried out at the CYP3A locus in ethnically diverse populations (Thompson et al., 2006; Schirmer et al., 2007; Perera et al., 2009; Perera, 2010). A genome-wide association study carried out in 310 twins, who had been induced with St John's Wort, also failed to identify significant associations (Rahmioglu et al., 2012). The apparent "missing heritability" of CYP3A4 drug oxidation pheno-type is thus an intriguing genetic problem (Sadee, 2012).

One of the more common and frequently studied polymorphisms is the proximal promoter variant CYP3A4*1B [-392A>G] which occurs in white populations at ~2 to 9% but at higher frequencies in Africans (Table 1). This SNP was initially found to be associated with higher tumor grade and stage in prostate cancer and showed higher nifedi-pine oxidase activity in human livers (Rebbeck et al., 1998). Association of CYP3A4*1B with markers of advanced disease was confirmed by some but not all further studies (Keshava et al., 2004; Perera, 2010). A functional effect of this variant could however not be established and remained controversial, despite several studies in

vitro and in human liver (Wandel et al., 2000; Spurdle et al., 2002; Rodriguez-Antona et al., 2005; Klein et al., 2012).

A notable recent discovery is the intron 6 C>T variant rs35599367 (CYP3A4*22) which was found by a systematic screen for SNPs showing allelic expression imbalance in human liver and with decreased mRNA expression in cultured cells transfected with minigenes (Wang et al., 2011). These authors found that the effect of the variant was not confounded by sex or other variables and that it accounted for 7% of the mRNA expression variability in a cohort of 93 liver samples. In another recent liver study this variant was associated with decreased protein levels by univariate and multivariate analysis (Klein et al., 2012).

Another intronic variant rs4646450, located in the CYP3A5 gene, has recently been associated with reduced tacrolimus dosage requirement in Japanese patients (Onizuka et al., 2010) and with reduced serum dihydroepiandrosterone sulfate concentrations (Zhai et al., 2011). This variant was also associated with decreased protein and activity of CYP3A4 in human liver, explaining about 3-5% of hepatic variability (Klein et al., 2012). This indicates that SNPs at the CYP3A locus exist that are likely to influence expression, and possibly there are more to be discovered.

Another possibility to explain the "missing heritability" of CYP3A4 variability is that polymorphic trans-acting genetic factors could account in part. Initial studies indicated that PXR variants may contribute to CYP3A4 expression differences (Lamba et al., 2005, 2008). Recently this hypothesis was explored more systematically using liver tissue banks. Several genome-wide association studies with a total number of >800 human liver samples failed however to identify novel significant markers of CYP3A4 expression and function (Schadt et al., 2008; Yang et al., 2010; Innocenti et al., 2011; Schroder et al., 2013; Glubb et al., 2012). This disappointing result is probably due to lack of power due to the statistical correction for the large number of tests. In contrast, two recent studies applied candidate-gene approaches to detect cis-and trans-SNPs influencing expression phenotypes. Lamba and colleagues phenotyped 128 livers by quantitative real-time PCR for expression of CYP3A genes and identified a functional CCT-repeat polymorphism in the FoxA2 gene to be associated with higher expression of FoxA2 mRNA and its targets PXR and CYP3A4 (Lamba et al., 2010). Polymorphisms in FoxA2, HNF4a, FoxA3, PXR, ABCB1, and the CYP3A4 promoter together explained 24.6% of the variation in hepatic CYP3A4 mRNA expression. However the study lacked information of the relevance of these variations for CYP3A4 protein and activity.

In a liver cohort study mentioned earlier, Klein and colleagues phenotyped 149 Caucasian liver samples for CYP3A4 mRNA and protein levels and for verapamil N-demethylase and atorvastatin hydroxylase activities (Klein et al., 2012). They identified SNPs in the Ah-receptor nuclear translocator (ARNT), glucocorticoid receptor (GR), progesterone receptor membrane component 2 (PGRMC2), and peroxisome proliferator activated receptor alpha (PPARa) to be consistently associated with CYP3A4 phenotype. Validation in an atorvastatin-treated volunteer cohort confirmed decreased atorvastatin-2-hydroxylation in carriers of PPARa SNP rs4253728. Moreover, homozygous carriers expressed significantly less PPARa protein in liver and shRNA-mediated PPARa gene knock-down in primary human hepatocytes decreased expression levels of CYP3A4 by more than 50%. Multivariate analysis revealed that two linked PPARa SNPs alone explained ~8-9% of the atorvastatin hydroxylase activity variation, whereas all genetic and nongenetic factors together accounted for ~33% of atorvastatin 2-hydroxylase variation (Klein et al., 2012).

5.3.2. CYP3A5

Expression of CYP3A5 in liver is polymorphic as only a fraction of about 5 to 10% of Caucasians, but 60% or more of Africans or African Americans have appreciable amounts expressed in their liver. These ethnic differences are largely explained by two alleles that result in aberrant splicing and deficient expression of the functional transcript.

The most common deficient allele CYP3A5*3 harbors a mutation in in-tron 3 that leads to aberrant splicing and a truncated protein, and occurs in all ethnic groups studied, with large frequency differences between major races (Table 1; Kuehl et al., 2001). CYP3A5*6 with an exon 7 mutation that also leads to an aberrantly spliced mRNA lacking exon 7 was only detected in populations of African origin (Kuehl et al., 2001; Roy et al., 2005). Another null allele, CYP3A5*7, includes a frame-shift mutation (Hustert et al., 2001). Taken together, only a small percentage of Caucasian subjects but considerably larger fractions of Asians and Africans have a functional copy of the CYP3A5 gene (*1). In these individuals, CYP3A5 could make a significant contribution to drug metabolism, particularly for substrates with preferential metabolism by CYP3A5 over CYP3A4 (e.g., tacrolimus) and in individual with a low expression of CYP3A4.

5.3.3. CYP3A7

The fetal-predominant form accounts for up to 50% of total P450 content in fetal livers (Cresteil, 1998; Leeder et al., 2005). Although expression shifts after birth from CYP3A7 to CYP3A4, it remains poly-morphically expressed in some adult livers and in intestine (Burk et al., 2002). Most of the CYP3A7 mRNA high expressor phenotypes could be explained by the CYP3A7*1C promoter variant, which harbors a recombined promoter starting with -188G>T and extending to -129, which has been replaced by the corresponding CYP3A4 promoter from -210 to -250 bp. The mutant gene is more effectively transcribed due to increased binding and transactivation by HNF4a, PXR/RXR and CAR/RXR heterodimers to a polymorphic ER6 motif (Burk et al., 2002). Further studies with a specific antibody estimated the relative content of CYP3A7 to the total CYP3A pool to be between 9 and 36% for the ~10% of high expressors and about 10-fold lower in the low expressors (Sim et al., 2005). Additional alleles of the CYP3A7 gene can be found on the CYPalleles website. Due to the large overlap in substrate specificity with other CYP3A enzymes the clinical significance CYP3A7 polymorphism has not been well studied.

5.4. Clinical impact of genetic variation

5.4.1. CYP3A4

In agreement with a decreasing effect of the *22 variant on CYP3A4 expression, patients treated with atorvastatin, simvastatin, or lovastatin who were carriers of the T allele required 1.7- to 5-fold reduced statin doses compared to non-T carriers for optimal lipid control (Wang et al., 2011). Significant association of CYP3A4*22 with decreased 2-OH-atorvastatin/atorvastatin AUC0_^ ratio was observed in atorvastatin-treated volunteers (Klein et al., 2012). The association of CYP3A4*22 with simvastatin lipid-lowering response was also shown in another clinical study (Elens et al., 2011a). Furthermore, renal transplant recipients who were carriers of the low-expressor T-allele had a 33% reduced mean daily-dose requirement to reach the same tacrolimus blood concentration compared to homozygotes for the wild type allele (Elens et al., 2011b) and 1.6 to 2.0-fold higher dose-adjusted trough blood levels of tacrolismus and cyclosporine in stable renal transplant patients (Elens et al., 2011c). Despite these consistent reports, the rather low frequency of this intron 6 variant (global minor allele frequency 2.1%, Caucasians 3-8%; Table 1) limits its contribution to overall CYP3A4 variability.

5.4.2. CYP3A5

Associations with CYP3A5 genotype were reported, for example, for the immunosuppressant tacrolimus (Hesselink et al., 2003; Anglicheau et al., 2007; Elens et al., 2011b); the antihypertensive verapamil (Jin et al., 2007), and the HIV protease inhibitor saquinavir (Josephson et al., 2007). Tacrolimus-related nephrotoxicity is clinically highly relevant and dose adjustment of tacrolimus by therapeutic drug monitoring is common clinical practice in renal transplant patients as recommended by the European consensus conference report

(Wallemacq et al., 2009). To elucidate underlying mechanisms several studies investigated the contribution of CYP3A5 genetics demonstrating a strong association between the deficient allele CYP3A5*3 and lower tacrolimus clearance, higher blood concentrations, and lower dose requirements (Staatz et al., 2010a, 2010b). In a large cohort of 'real-world' kidney transplant recipients (n = 446) on tacrolimus steady state concentrations, CYP3A5*3 alone explained 39% of the variability of tacrolimus blood concentration to dose (C/D) ratio, compared to 46% explained by clinical covariates and CYP3A5*3 together (Birdwell et al., 2012). In another trial a predictive model that included age, ethnicity and concomitant use of medications explained ~30% of the variability in tacrolimus dosing, which increased to 58% by including CYP3A5*3 genotype (Wang et al., 2010c). Thus, clinical variables and CYP3A5 pharmacogenetics explain approximately one-half of the interindividual variability in tacrolimus C/D ratio, suggesting that algorithms for pharmacogenetics-guided tacrolimus dosing should be helpful for clinical practice.

6. NADPH:cytochrome P450 oxidoreductase (POR)

NADPH:cytochrome P450 oxidoreductase (POR) is a microsomal flavoprotein which constitutes an essential component of several ox-ygenase enzyme complexes including heme oxygenase, squalene monooxygenase, 7-dehydrocholesterol reductase, and in particular all 50 microsomal cytochrome P450 (CYP) monooxygenases. The FMN/FAD flavogroups of the protein mediate the transfers of two single electrons originating from NADPH to the P450 prosthetic heme iron. Furthermore, POR can directly metabolize a number of drugs by 1-electron reduction reactions. This is particularly the case for drugs with quinone moieties, including several anticancer prodrugs such as menadione, mitomycin C, tirapazamine, and E09 used for the treatment of solid tumors (Hart et al., 2008).

In contrast to the multiplicity of CYPs, mammals have only a single POR gene. In humans the gene is located on chromosome 7q11.2 and spans about 72 kb, coding for a 680 amino acid protein which uses flavin adenine dinucleotide (FAD) and flavin mononucleotide (FMN) as prosthetic groups. Complete deletion of the por gene in mouse is embryoni-cally lethal most likely due to deficient adrenocortical steroidogenesis (Shen et al., 2002; Otto et al., 2003). In contrast, liver-specific knockout of por leads to phenotypically and reproductively normal mice that accumulate hepatic lipids and have a drastically diminished capacity for hepatic drug metabolism (Gu et al., 2003; Finn et al., 2007).

The amount of POR in human liver is stoichiometrically ~5 to 10-fold lower compared to the microsomal CYP pool, raising the possibility that it represents a limiting factor for monooxygenase function (Hart et al., 2008; Gomes et al., 2009). Although interindividual variability of POR expression in human liver is lower compared to most drug metabolizing microsomal CYP enzymes, it was shown to be significantly correlated to several CYP monooxygenase activities, supporting a possible rate-limiting role in catalysis (Backes & Kelley, 2003). POR expression variability is in part due to the inducibility of the POR gene by both PXR and CAR (Maglich et al., 2002), as well as hormonal influences on transcription (Tee et al., 2011), whereas other nongenetic factors including sex, age, smoking and drinking habits do not appear to be ofsig-nificant influence in liver (Hart et al., 2008; Gomes et al., 2009). Following the discovery of an untranslated first exon, the human POR promoter was located and binding of transcription factors Smad3/ Smad4, as well as thyroid hormone receptor TRa and TR( was confirmed by chromatin immunoprecipitation (Tee et al., 2011).

In recent years rare POR missense mutations in humans were discovered that cause disordered steroidogenesis, ambiguous genitalia, and Antley-Bixler syndrome (Fluck et al., 2004; Huang et al., 2005; Fluck et al., 2008). The CYPalleles website currently lists 41 distinct star-alleles, most of which represent vary rare variants identified in patients with clinical manifestations of POR deficiency (Sim et al., 2009). The causative mutations in these alleles either result in

amino acid changes that severely damage POR function or in erroneous splicing or translation. The clinical and developmental aspects of these rare POR variants have been recently reviewed ( Fluck & Pandey, 2011).

Using a cocktail phenotyping approach in a family with genetic POR deficiency, subnormal activities of CYP1A2, CYP2C9, CYP2D6 and CYP3A4 were observed in a heterozygous patient with congenital adrenal hyperplasia, demonstrating that POR activity can be limiting in vivo at least for these P450 enzymes (Tomalik-Scharte et al., 2010).

Common POR polymorphisms also exist in addition to the rare disease-causing mutations (Huang et al., 2005, 2008a). In particular a A503V variation (POR*28) is present at high frequencies ranging from 19% to 37% in all major ethnicities and has been studied most extensively (Table 1). In a recombinant system the variant retained >50% of the wild type activity towards several CYPs (Huang et al., 2008b; Sandee et al., 2010), while in an analysis of 150 human liver microsome samples it was not associated with significant changes in any of 11 measured CYP activities (Gomes et al., 2009). Nevertheless an in vivo study found that POR*28 TT genotype was associated with a 1.6-fold increase in CYP3A midazolam 1'-hydroxylase activity compared with POR*28 C carriers, a finding that could be replicated in an independent cohort (Oneda et al., 2009). Similarly, in a cohort of allograft recipients under tacrolimus therapy, POR*28 T-allele carriers had significantly higher tacrolimus dose requirements compared to noncarriers but only if they were genotypic CYP3A5 expressors (i.e. presence of at least one CYP3A5*i allele; (DeJonge et al., 2011). Thus, the effect of this common POR variant appears to depend on both CYP and substrate analyzed.

Numerous additional missense mutants that influence P450 activities in a CYP- and substrate specific manner have been identified more recently (Hart et al., 2008; Huang et al., 2008a; Kranendonk et al., 2008; Marohnic et al., 2010). In a large scale sequencing study, Miller and colleagues sequenced the POR gene in 842 normal persons from 4 ethnic groups and detected 140 SNPs of which 43 occurred at >1% frequency and 13 were novel nonsynonymous variants (Huang et al., 2008a). Moreover, polymorphisms in the promoter and in in-trons could affect transcription and/or splicing, but have not yet been investigated systematically. In the above-mentioned study, twelve promoter variants were detected. One polymorphism at -152 bp was described to result in decreased transcription in cell lines (Tee et al., 2011). In addition, two common 5'-flanking region SNPs ( —173C>A, -208C>T) and the intron 2 SNP rs2868177 were significantly associated with variations in warfarin maintenance dose, in addition to several known factors, in a study of 122 male patients, who had attained a stable warfarin dose. A study in human liver microsomes identified three intronic POR variants that affected several CYP activities as determined by multivariate analysis, in concert with the donor's sex (Gomes et al., 2009). An effect of intron 11 variant rs2302429G>A on CYP1A2 activity but not inducibility was recently demonstrated in an in vivo study (Dobrinas et al., 2012). Taken together these recent advances indicate that POR variants are a complex but potentially relevant source of genetic variation for steroid hydroxylation and drug oxidation (Miller et al., 2011).

7. Conclusions and future perspectives

The scientific literature cited in this review, and many more articles we could not mention, demonstrate the tremendous progress that has been made in understanding the drug metabolizing cytochromes P450 with respect to their functional properties and differences, regulation of gene expression, population variability, genotype-phenotype correlation, and clinical impact. Regulation of all CYPs is clearly multifactorial with sex, age, hormonal and disease states and inhibition or induction-type drug-drug interactions contributing to inter- and intraindividual variability. Nevertheless, genomic markers have a confirmed impact

on several CYPs, approximately in the order CYP2D6>CYP2C19~ CYP2A6 > CYP2B6 > CYP2C9 > CYP3A4/5.

Future directions should include basic as well as clinical aspects. In basic research it will be interesting to see how many rare mutations exist in these genes in various populations, and what their contribution to the total variability is. In this review we focused on common variants, but because rare variants often have stronger impact on phenotype, their collective role for complex phenotypes may be significant. Rare variants with strongly decreased function can be particularly important for phenotype if present in combination with another allele or haplotype of moderately decreased function, as shown for example in the case of efavirenz-treated HIV patients (Rotger et al., 2007). Although earlier studies have shown that genotyping of only few common variants is sufficient to predict the major phenotypes (Sachse et al., 1997; Griese et al., 1998), it should be remembered that even in the case of CYP2D6 this conclusion was based on very few probe drugs (i.e. debrisoquine, sparteine) that have no clinical significance today. For other substrates the situation may be different.

Current progress in the 1000-genomes project and by targeted re-sequencing suggests that the number of unknown rare polymorphisms and "private" mutations can make significant contributions to interindividual as well as interethnic variability, because these mostly unknown variants show increased population-specificity and are enriched for functional variants (Gamazon et al., 2009; Marth et al., 2011; MacArthur et al., 2012). However, the task is a difficult one, because available in vitro test systems for CYP enzyme and gene variants are time-consuming and unreliable and in vivo testing on the other hand is impractical due to the low SNP frequencies. In silico prediction tools to filter out potentially functional SNPs for further study may thus be a promising approach, although functional effects of intronic and promoter variants remain especially difficult to predict (Pang et al., 2009).

Polymorphisms in trans-acting genes, for example genes that influence monooxygenase activity (e.g., NADPH:cytochrome P450 re-ductase, cytochrome b5) or in the numerous regulatory genes involved in transcriptional, posttranscriptional and posttranslational regulation have only been occasionally investigated so far but these studies have shown potential to develop predictive multigenetic gene signatures for CYP1A2 (Klein et al., 2010) and CYP3A4 (Klein et al., 2012; Lamba et al., 2010). Furthermore, the contribution of CNVs for drug metabolic phenotypes has also not been evaluated in a systematic way, although several examples with functional impact, including CYPs 2A6 and 2D6, as well as several phase II genes are known (Johansson & Ingelman-Sundberg, 2008; Gamazon et al., 2011). These directions may ultimately reveal the true genetic contribution to variable CYP-dependent drug metabolism phenotype.

Concerning clinical aspects, it must be noted that translation of this knowledge into clinical practice has been slow and not on a broad front. The reasons for this are manifold and mostly not directly related to P450 research but rather to organizational, medical, ethical and legal issues which have been discussed in other reviews (Frueh & Gurwitz, 2004; Woelderink et al., 2006; Pirmohamed, 2009; Lunshof & Gurwitz, 2012). Future directions should focus on the proper evaluation of clinical outcomes and properly designed clinical studies to assess the clinical utility as well as practicality of CYP genotyping. A pharmacogenetic test is considered clinically useful when it can be shown to improve drug therapy in terms of efficacy or safety, whereas practicality requires development of a suitable infrastructure, including testing facility, instructed personel, and incorporation into the general health care system. It is further necessary to realize that CYP genotyping alone can not be the answer. For each drug, the relevant genes have to be defined and tested, along with other factors (sex, age, health and nutritional condition, and many more), in order to exploit the full potential of pharmacogenetics for drug therapy. Systems biology approaches, in particular physiology-based pharmacokinetic

and pharmacodynamic modeling of the complex interplay between the many levels and facets of drug-organism interactions should also be expected to make major contributions in the future towards implementing pharmacogenetic testing in personalized medicine (Rostami-Hodjegan & Tucker, 2007; Eissing et al., 2011; Holzhutter et al., 2012).

Conflict of interest statement

U.M. Zanger named as coinventor of several patent applications directed to the detection of specific CYP polymorphisms for diagnostic purposes and is entitled to share in any net income derived from licensing these patent rights under standard academic institutional policies. M. Schwab declares no conflict of interest.

Acknowledgments

Work in the authors' laboratory is supported by the German Federal Ministry of Education and Research (Virtual Liver Network grant 0315755 and grant 03 IS 2061C), the Deutsche Forschungsgemeinschaft (grant SCHW 858/1-1), the FP7 EU Initial Training Network Program "FightingDrugFailure" (grant PITN-GA-2009-238132), and the RobertBosch Foundation, Stuttgart, Germany. These funding agencies had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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