Effect of Glucuronides on Metabolic Enzymes and Active Hepatic Uptake: in Vitro Assessment and Prediction of Drug-Drug Interaction Risk

Effect of glucuronides on metabolic enzymes and active hepatic uptake: in vitro assessment and prediction of drug-drug interaction risk

A thesis submitted to the University of Manchester for the degree of Doctor of Philosophy in the Faculty of Medical and Human Sciences

2015

Rebecca Alice Sullivan

Manchester Pharmacy School

Contents

CONTENTS ...... 2

LIST OF FIGURES ...... 6

LIST OF TABLES ...... 12

ABSTRACT ...... 16

DECLARATION ...... 17

LIST OF ABBREVIATIONS...... 18

ACKNOWLEDGEMENTS ...... 20

THE AUTHOR ...... 21

CHAPTER 1 INTRODUCTION ...... 22

1.1 Overview and relevance of drug-drug interactions and glucuronide metabolites . 22

1.2 Metabolising enzymes and drug-drug interactions ...... 22 1.2.1 Cytochrome P450 enzymes ...... 23 1.2.2 Uridine di-phospho-glucuronosyltransferase enzymes ...... 24 1.2.3 Classes of drug-drug interactions - enzyme induction and inhibition ...... 25

1. 3 Transporter membrane proteins – importance to drug-drug interactions ...... 27 1.3.1 The OATP1B1 transporter ...... 28 1.3.2 The role of OATP1B1 in drug-drug interactions ...... 29 1.3.3 In vitro investigation of inhibition of OATP1B1 ...... 30

1.4 Investigation of drug-drug interactions in vitro ...... 33 1.4.1 Investigating enzyme inhibition in vitro ...... 33 1.4.2 In vitro systems for investigation of metabolic drug-drug interactions ...... 34 1.4.3 Investigation of inhibition of OATP1B1 in vitro ...... 35 1.4.4 In vitro systems for investigation of OATP1B1 inhibition ...... 35 1.4.5 In vitro systems suitable for investigation of metabolising enzymes and OATP1B1 inhibition ...... 36 1.4.6 Quantification of drug-drug interactions in vitro ...... 36 1.4.7 Prediction of drug-drug interactions ...... 37

1.5 The contribution of metabolites to metabolic drug-drug interactions ...... 40

1.6 The contribution of metabolites to transporter drug-drug interactions ...... 42

1.7 The contribution of glucuronide metabolites to drug-drug interactions ...... 42 1.7.1 Inhibition of metabolising enzymes by glucuronides ...... 43 1.7.2 Glucuronides and parent drugs inhibiting the OATP1B1 transporter ...... 49

1.8 Project Aims ...... 52

1.9 Compounds selected for investigation ...... 53 1.9.1 Gemfibrozil acyl-β-D-glucuronide...... 53 1.9.2 Clopidogrel acyl-β-D-glucuronide...... 54 1.9.3 Raloxifene-4’-glucuronide ...... 54 1.9.4 Ezetimibe phenoxy-β-D-glucuronide ...... 55 1.9.5 Mefenamic acid acyl-β-D-glucuronide ...... 55 1.9.6 Mycophenolic acid β-D-glucuronide ...... 56 1.9.7 Repaglinide acyl-β-D-glucuronide ...... 56 1.9.8 Diclofenac acyl-β-D-glucuronide ...... 57 1.9.9 Telmisartan acyl-β-D-glucuronide ...... 57 1.9.10 Raltegravir β-D-glucuronide ...... 58

CHAPTER 2 IN VITRO ASSESSMENT OF INHIBITION OF METABOLISING ENZYMES BY GLUCURONIDE METABOLITES ...... 59

2.1 Introduction ...... 59

2.2 Aims...... 60

2.3 Methods ...... 60 2.3.1 Selection of repaglinide as a probe substrate ...... 60 2.3.2 Reagents ...... 62 2.3.3 Assessment of CYP2C8 and CYP3A4 inhibition by repaglinide glucuronide ...... 62 2.3.4 Assessment of CYP2C8, CYP3A4 and UGT1A1 inhibition by reference inhibitors . 63 2.3.5 Assessment of CYP2C8, CYP3A4 and UGT1A1 inhibition by glucuronides ...... 64 2.3.6 Analysis of inhibitor concentrations and glucuronide stability ...... 65 2.3.7 LC/MS-MS Analysis of samples ...... 65 2.3.8 Data analysis ...... 66 2.3.9 Correction of IC50 data for nonspecific binding ...... 66

2.4 Results ...... 68 2.4.1 Inhibition of CYP2C8 and CYP3A4 by repaglinide glucuronide ...... 68 2.4.2 Inhibition studies in human liver microsomes – reference inhibitors ...... 69 2.4.3 CYP2C8, CYP3A4 and UGT1A1 inhibition by glucuronide metabolites ...... 71 2.4.4 CYP2C8 and CYP3A4 inhibition studies in human liver microsomes – P450 co- factors ...... 76 2.4.5 Comparison of the enzyme inhibitory potency between glucuronides and parent drugs ...... 80 2.4.6 Impact of pre-incubation on P450 and UGT inhibition ...... 82 2.4.7 Impact of co-factor selection on enzyme inhibitory potential ...... 83 2.4.8 Monitoring of inhibitor concentrations ...... 86

2.5 Discussion ...... 89 2.5.1 Inhibitory effects of reference inhibitors on CYP2C8, CYP3A4 and UGT1A1 ...... 89 2.5.2 Inhibition of CYP2C8, CYP3A4 and UGT1A1 by glucuronides – combined co-factor conditions ...... 90 2.5.3 IC50 experiments using P450 only co-factors – impact of co-factor conditions on the assessment of the inhibitory potential of glucuronides ...... 92 2.5.4 Comparison of CYP2C8 and CYP3A4 inhibitory potential between glucuronides and parent drugs ...... 93 2.6.5 Comparison of glucuronide and parent compound P450 inhibitory potency ...... 94 2.5.6 Investigation of time-dependent enzyme inhibition ...... 95 2.5.7 Conclusion ...... 96

CHAPTER 3 IN VITRO INVESTIGATION OF OATP1B1 INHIBITION BY GLUCURONIDES ...... 97

3.1 Introduction ...... 97

3.2 Aims...... 98

3.3 Methods ...... 98 3.3.1 Selection of OATP1B1 probe substrates ...... 98 3.3.2 Reagents ...... 99 3.3.3 Source and preparation on HEK293-OATP1B1 cells ...... 99 3.3.4 Assessment of OATP1B1 inhibitory potential and effect of pre-incubation using E17βG as a prototypical probe substrate...... 100 3.3.5 Assessment of OATP1B1 inhibitory potential and effect of pre-incubation using pitavastatin as a probe substrate ...... 101 3.3.6 LC-MS/MS ...... 101 3.3.7 Data analysis ...... 101 3.3.8 Prediction of physicochemical properties of OATP1B1 inhibitors and correlation with inhibitory potency ...... 103

3.4 Results ...... 104 3.4.1 Selection of a prototypical OATP1B1 probe substrate ...... 104 3.4.2 Selection of a clinically relevant OATP1B1 probe substrate – contribution of OATP1B1 to pitavastatin uptake ...... 108 3.4.3 Effect of glucuronides on OATP1B1 using E17βG as a probe ...... 112 3.4.4 Effect of parent drugs on OATP1B1 using E17βG as a probe ...... 115 3.4.5 Effect of glucuronides and parent drugs on OATP1B1 using pitavastatin as a probe ...... 117 3.4.6 Comparison of glucuronide and parent drugs OATP1B1 inhibitory potency ...... 121 3.4.7 OATP1B1 inhibition by reference inhibitors ...... 125 3.4.8 Effect of pre-incubation with inhibitor on inhibition of OATP1B1 ...... 127 3.4.9 Comparison of E17βG and pitavastatin OATP1B1 inhibition data ...... 129 3.4.10 Investigation of the correlation of physicochemical properties and CYP2C8 and OATP1B1 inhibitory potential ...... 133

3.5 Discussion ...... 135 3.5.1 Inhibitory effects of glucuronides on OATP1B1 ...... 135 3.5.2 Inhibitory effects of parent drugs on OATP1B1 ...... 136 3.5.3 Comparison of OATP1B1 inhibitory potential between glucuronides and parent drugs ...... 137

3.5.4 Inhibitory effects of reference compounds on OATP1B1 ...... 138 3.5.5 The effect of pre-incubation with inhibitor on OATP1B1 inhibition ...... 139 3.5.6 Comparison of E17βG and pitavastatin sensitivity to OATP1B1 inhibition ...... 141 3.5.7 Conclusions and future directions ...... 142

CHAPTER 4 COLLATION OF METABOLITE CLINICAL EXPOSURE DATA AND PREDICTION OF THE CLINICAL RISK OF DRUG-DRUG INTERACTIONS ...... 143

4.1 Introduction ...... 143

4.2 Aims...... 144

4.3 Methods ...... 144 4.3.1 Collation of clinical exposure data for P450 and glucuronide metabolites ...... 144 4.3.2 Assessment of the clinical relevance of in vitro enzyme inhibition by glucuronides ...... 146 4.3.3 Prediction of clinical relevance of in vitro OATP1B1 inhibition ...... 151 4.3.4 Prediction of Gemfibrozil and gemfibrozil glucuronide DDI with repaglinide using SimCYP ...... 154

4.4 Results ...... 157 4.4.1 Metabolite exposure of potent P450 inhibitors ...... 157 4.4.2 Metabolite exposure data of potent OATP1B1 inhibitors ...... 162 4.4.3 Collation of glucuronide metabolite exposure data ...... 163 4.4.4 Investigation of drug-drug interaction risk associated with inhibition of metabolising enzymes by glucuronide metabolites in vitro ...... 168 4.4.5 Investigation drug-drug interaction risk associated with in vitro inhibition of OATP1B1 ...... 182 4.4.6 Prediction of the drug-drug interaction between gemfibrozil and repaglinide using SimCYP ...... 193

4.5 Discussion ...... 194 4.5.1 Clinical exposure of metabolites ...... 194 4.5.2 Evaluation of the clinical DDI risk of in vitro inhibition of metabolising enzymes by glucuronides ...... 196 4.5.3 Evaluation of the clinical drug-drug interaction risk of in vitro inhibition of OATP1B1 by glucuronides ...... 198 4.5.4 Investigation of the gemfibrozil – repaglinide drug-drug interaction using dynamic modelling approach ...... 199 4.5.5 Conclusions ...... 200

CHAPTER 5 FINAL DISCUSSION ...... 202

5.1 Inhibition of metabolising enzymes by glucuronides in vitro ...... 202

5.2 Investigation of the OATP1B1 inhibitory potential of glucuronides in vitro ...... 205

5.3 Clinical exposure of glucuronides ...... 208

5.4 Prediction of the risk of glucuronide mediated drug-drug interactions ...... 208

5.5 Concluding remarks ...... 211

REFERENCES ...... 212

CHAPTER 6 APPENDICES ...... 245

Word count: 79,531

List of figures Figure 1.1 (A) Scheme describing glucuronidation where R-X is the substrate (R is a C group and X is a functional group susceptible to glucuronidation), UDP-GA is uridine diphosphate glucuronic acid and RX-GA is the glucuronide metabolite. (B) Biosynthesis of an O-acyl glucuronide reproduced from Stachulski et al., (2006) (64) ...... 24

Figure 1.2 Illustration of DDI resulting in increased exposure of atorvastatin (victim drug) in the presence of boceprevir (perpetrator). Mean (with standard deviation) plasma concentration-time profiles of atorvastatin (40 mg) when administered to 20 healthy volunteers alone or with boceprevir (800 mg) (79) ...... 26

Figure 1.3 Human transport proteins present in hepatocytes. Figure taken from (Giacomini et al., (2010) (88) ...... 28

Figure 1.4 FDA Metabolic DDI Decision Tree. General Scheme of Model-Based Prediction: The Investigational Drug (and Metabolite Present at ≥25% of Parent Drug AUC) as an Interacting Drug of P450 Enzymes (13) ...... 41

Figure 1.5 Figure taken from from Vasilyeva et al., (2015) (204) illustrating recirculation of sorafenib glucuronide which as well as being secreted into the bile by MRP2 (ABCC2) is also secreted into blood by MRP3 (ABCC3) and at least one other transporter. From the blood, sorafenib glucuronide can be taken up again into downstream hepatocytes via OATP1B1-type carriers (Oatp1a and Oatp1b in mice) ...... 43

Figure 1.6 Metabolising enzymes inhibited in vitro by glucuronides (A) and parent drugs (B). A total of 40 and 382 IC50 or Ki values were reported for glucuronides and their parent drugs inhibiting metabolising enzymes, respectively. Full details of study design and references are provided in Appendix Table 6.1 ...... 44

Figure 1.7 P450 enzymes inhibited in vitro by glucuronides (A) and parent drugs (B). A total of 26 and 239 IC50 or Ki values were reported for glucuronides and their parent drugs inhibiting P450 enzymes, respectively. Full details of study design and references are provided in Appendix Table 6.1 ...... 44

Figure 1.8 Comparison of IC50 values of glucuronide and parent drugs collated from the literature and classified per metabolising enzyme. Data for parent and glucuronide were obtained from the same study. All data were obtained in HLM or recombinant enzymes, without pre-incubation with inhibitor. Full details of study design and references are provided in Appendix Table 6.1 ...... 45

Figure 1.9 Chemical structure of gemfibrozil acyl-β-D-glucuronide ...... 53

Figure 1.10 Chemical structure of clopidogrel acyl-β-D-glucuronide ...... 54

Figure 1.11 Chemical structure of raloxifene-4-glucuronide ...... 54

Figure 1.12 Chemical structure of ezetimibe phenoxy-β-D-glucuronide ...... 55

Figure 1.13 Chemical structure of mefenamic acid acyl- β-D-glucuronide ...... 55

Figure 1.14 Chemical structure of mycophenolic acid β-D-glucuronide ...... 56

Figure 1.15 Chemical structure of repaglinide acyl-β-D-glucuronide ...... 56

Figure 1.16 Chemical structure of diclofenac acyl-β-D-glucuronide ...... 57

Figure 1.17 Chemical structure of telmisartan acyl-β-D-glucuronide ...... 57

Figure 1.18 Chemical structure of raltegravir β-D-glucuronide ...... 58

Figure 2.1 Structure of repaglinide and its metabolic pathways showing the enzymes responsible for the conversion of repaglinide and the proposed mechanism for the formation of M2. Figure taken from Sall et al., (2012) ...... 61

Figure 2.2 IC50 profiles for repaglinide glucuronide against CYP2C8 (A) and CYP3A4 (B) in pooled HLM with P450 co-factors. Data represent mean ± sd of 3 separate experiments without () and with () pre-incubation with inhibitor ...... 68

Figure 2.3 IC50 profiles for ketoconazole against formation of repaglinide M4 (A), repaglinide M1 (B) and repaglinide glucuronide (C) by CYP2C8, CYP3A4 and UGT1A1, respectively. Data represent mean ± sd of at least 3 separate experiments conducted in pooled HLM with combined co-factors without () and with () pre-incubation with inhibitor ...... 69

Figure 2.4 IC50 profiles for trimethoprim against formation of repaglinide M4 (A), repaglinide M1 (B) and repaglinide glucuronide (C) by CYP2C8, CYP3A4 and UGT1A1, respectively. Data represent mean ± sd of at least 3 separate experiments conducted in pooled HLM with combined co-factors without () and with () pre-incubation with inhibitor ...... 70

Figure 2.5 IC50 profiles for rifamycin SV against formation of repaglinide M4 (A), repaglinide M1 (B) and repaglinide glucuronide (C) by CYP2C8, CYP3A4 and UGT1A1, respectively. Data represent mean ± sd of at least 3 separate experiments conducted in pooled HLM with combined co-factors without () and with () pre-incubation with inhibitor ...... 70

Figure 2.6 Comparison of rifamycin SV CYP2C8, CYP3A4 and UGT1A1 IC50 values obtained using repaglinide as a probe substrate in HLM and P450, UGT or combined co- factors without (A) and with (B) pre-incubation of the inhibitor ...... 71

Figure 2.7 IC50 profiles for 9 glucuronides against CYP2C8 in combined co-factor conditions in HLM. Repaglinide M4 formation was investigated in the presence of gemfibrozil glucuronide (A), clopidogrel glucuronide (B), diclofenac glucuronide (C), telmisartan glucuronide (D), mefenamic acid glucuronide (E), ezetimibe glucuronide (F), raltegravir glucuronide (G), mycophenolic acid glucuronide (H) and raloxifene 4’ – glucuronide (I). Data represent mean ± sd of at least 3 separate experiments performed without () and with () pre-incubation with inhibitor ...... 73

Figure 2.8 Comparison of IC50 values obtained against CYP2C8, CYP3A4 and UGT1A1 without (A) and with (B) pre-incubation for telmisartan and mefenamic acid glucuronides. All IC50 values were corrected for predicted fu,mic, as stated in Table 2.2 ...... 74

Figure 2.9 IC50 profiles for telmisartan glucuronide against CYP3A4 mediated formation of repaglinide M1 (A) and UGT1A1 mediated formation of repaglinide glucuronide (B). Experiments were performed in combined co-factor conditions in pooled HLM. Data represent mean ± sd of at least 3 separate experiments without () and with () pre- incubation with inhibitor ...... 75

Figure 2.10 IC50 profiles for mefenamic glucuronide against CYP3A4 mediated formation of repaglinide M1 (A) and UGT1A1 mediated formation of repaglinide glucuronide (B). Experiments were performed in combined co-factor conditions in pooled HLM. Data represent mean ± sd of at least 3 separate experiments without () and with () pre- incubation with inhibitor ...... 75

Figure 2.11 IC50 profiles for 7 glucuronides against CYP2C8 in pooled HLM obtained using P450 co-factor conditions. Repaglinide M4 formation was investigated in the presence of gemfibrozil glucuronide (A), clopidogrel glucuronide (B), diclofenac glucuronide (C), mefenamic glucuronide (D), telmisartan glucuronide (E), ezetimibe glucuronide (F) and mycophenolic acid glucuronide (G). Data represent mean ± sd of at least 3 separate experiments without () and with () pre-incubation with inhibitor ...... 78

Figure 2.12 IC50 profiles for 5 parent drugs against CYP2C8 in pooled HLM with P450 co- factor conditions. Repaglinide M4 formation was investigated in the presence of gemfibrozil (A), clopidogrel (B), diclofenac (C), mefenamic acid (D) and telmisartan (E). Data represent mean ± sd of at least 3 separate experiments without () and with () pre-incubation with inhibitor ...... 79

Figure 2.13 IC50 profiles for 5 parent drugs against CYP3A4 in pooled HLM with P450 co- factor conditions. Repaglinide M1 formation was investigated in the presence of gemfibrozil (A), clopidogrel (B), diclofenac (C), mefenamic acid (D) and telmisartan (E). Data represent mean ± sd of at least 3 separate experiments without () and with () pre-incubation with inhibitor ...... 80

Figure 2.14 Inhibitory effects of mefenamic acid (A, B) gemfibrozil (C, D) and telmisartan (E, F) glucuronides () and respective parent drugs () on CYP2C8-mediated repaglinide M4 formation. Data represent mean ± sd of at least 3 separate experiments conducted in pooled HLM with P450 co-factors without (A, C, E) and with (B, D, F) pre-incubation with inhibitor 81

Figure 2.15 Comparison of IC50 data, corrected for fu,mic, obtained without and with pre- incubation. Data were available against CYP2C8 (), CYP3A4 () and UGT1A1 () in combined (purple), P450 (green) or UGT (blue) co-factors. IC50 values were obtained for rifamycin SV (1), telmisartan glucuronide (2), telmisartan (3), clopidogrel (4), mefenamic acid glucuronide (5), mefenamic acid (6), clopidogrel glucuronide (7), gemfibrozil glucuronide (8), diclofenac glucuronide (9), gemfibrozil (10), diclofenac (11), trimethoprim (12). For clarity, this figure excludes ketoconazole where IC50 values of ~ 0.02 µM obtained both without and with pre-incubation ...... 83

Figure 2.16 Comparison of IC50 data, corrected for fu,mic, obtained in P450 or UGT and combined co-factor experiments without (purple data points) or with pre-incubation (green data points). Data were available against CYP2C8 (), CYP3A4 () and UGT1A1 () for telmisartan glucuronide (1), gemfibrozil glucuronide (2), diclofenac glucuronide (3), mefenamic glucuronide (4), clopidogrel glucuronide (5) and rifamycin SV (6) ...... 84

Figure 2.17 Inhibitory effects of clopidogrel glucuronide (A, B) and mefenamic acid glucuronide (C, D) on CYP2C8-mediated formation of repaglinide M4 in combined () and P450 () co-factor conditions. Data represent mean ± sd of at least 3 separate experiments

performed in pooled HLM following a 30-minute pre-incubation without (A, C) or with (B, D) inhibitor ...... 85

Figure 2.18 Nominal vs. measured inhibitor concentrations monitored during CYP2C8, CYP3A4 and UGT1A1 IC50 experiments in pooled HLM with combined co-factors. Inhibitor concentrations were monitored at the end of 30-minute pre-incubation with inhibitor (), 10- minute co-incubation with repaglinide following 30-minute pre-incubation with inhibitor () and 10-minute co-incubation with repaglinide without pre-incubation with inhibitor (). Data represent mean ± sd of at least 3 separate experiments. Inhibitor concentrations were monitored for gemfibrozil glucuronide (A), clopidogrel glucuronide (B), ezetimibe glucuronide (C), telmisartan glucuronide (D), raloxifene 4’-glucuronide (E), raltegravir glucuronide (F) and mycophenolic glucuronide (G) in combined P450 and UGT co-factor experiments. Repaglinide glucuronide concentrations (H) were monitored in P450 co-factor experiments ...... 87

Figure 2.19 Nominal vs. measured clopidogrel parent drug concentrations monitored during CYP2C8 and CYP3A4 IC50 experiments in pooled HLM with P450 co-factors. Inhibitor concentrations were monitored in the inhibitor stock solutions in PB (), at the end of 30- minute pre-incubation with inhibitor (), 10-minute co-incubation with repaglinide following 30-minute pre-incubation with inhibitor () and 10-minute co-incubation with repaglinide without pre-incubation with inhibitor (). Data represent mean ± standard deviation of at least 3 separate experiments ...... 88

Figure 2.20 Clopidogrel parent drug IC50 profiles obtained without (A, C) and with (B, D) pre- incubation with inhibitor. IC50 profiles were plotted using the nominal inhibitor concentration () or inhibitor concentrations measured at the end of 30-minute pre-incubation with inhibitor (), 10-minute co-incubation with repaglinide following 30-minute pre-incubation with inhibitor or 10-minute co-incubation with repaglinide without pre-incubation with inhibitor (). Data represent mean ± standard deviation of at least 3 separate experiments ...... 88

Figure 3.1 In vitro cell systems used to explore OATP1B1 inhibition in the literature database collated using the UWDIDB. Cell systems included in the database were human embryonic kidney cells (HEK293), Chinese hamster ovary cells (CHO), Madin-Darby canine kidney cells (MDCK), Xenopous Leavis Oocytes (XLO), HeLa cells (HeLa) and human hepatocytes. Individual details are listed in Appendix Table 6.2 ...... 107

Figure 3.2 In vitro OATP1B1 IC50 and Ki data collated using the UWDIDB for cyclosporine with a range of probe substrates at different concentrations. All studies were performed in HEK293 cells expressing OATP1B1 without a pre-incubation step...... 108

Figure 3.3 In vitro OATP1B1 IC50 data collated for rifampicin using HEK293 cells with a range of concentrations of statins and E17βG at as probe substrates. Full study details are provided in Appendix Table 6.2 ...... 109

Figure 3.4 Inhibitory effects of 10 glucuronides on OATP1B1-mediated uptake of E17βG in stably transfected HEK293-OATP1B1 cells. E17βG uptake was investigated in the presence of gemfibrozil glucuronide (A), telmisartan glucuronide (B), repaglinide glucuronide (C), clopidogrel glucuronide (D), diclofenac glucuronide (E), mefenamic acid glucuronide (F), ezetimibe glucuronide (G), raloxifene 4’ – glucuronide (H), mycophenolic acid glucuronide (I) and raltegravir glucuronide (J). Data represent mean ± SD of at least 3 separate experiments without () and with () pre-incubation with inhibitor ...... 114

Figure 3.5 Inhibitory effects of 5 selected parent drugs on OATP1B1-mediated uptake of E17βG in stably transfected HEK293-OATP1B1 cells. Data represent mean ± SD of at least

3 separate experiments without () and with () pre-incubation with inhibitor. E17βG uptake was investigated in the presence of increasing concentrations of gemfibrozil (A), telmisartan (B), repaglinide (C), diclofenac (D) and ezetimibe (E) ...... 116

Figure 3.6 Inhibitory effects of 5 glucuronides on OATP1B1- mediated uptake of pitavastatin in stably transfected HEK293-OATP1B1 cells. Data represent mean ± SD of at least 3 separate experiments without () and with () pre-incubation with inhibitor. Pitavastatin uptake was investigated in the presence of repaglinide glucuronide (A), telmisartan glucuronide (B), gemfibrozil glucuronide (C), diclofenac glucuronide (D) and ezetimibe glucuronide (E) ...... 119

Figure 3.7 Inhibitory effects of 5 parent drugs on OATP1B1-mediated uptake of pitavastatin in stably transfected HEK293-OATP1B1 cells. Data represent mean ± SD of at least 3 separate experiments without () and with () pre-incubation with inhibitor. Pitavastatin uptake was investigated in the presence of repaglinide (A), telmisartan (B), gemfibrozil (C), diclofenac (D) and ezetimibe (E) ...... 120

Figure 3.8 Inhibitory effects of ezetimibe (A, B), gemfibrozil (C, D) and telmisartan (E, F) glucuronides () and respective parent drugs () on OATP1B1-mediated uptake of E17βG in stably transfected HEK293 cells. Data represent mean ± SD of at least 3 separate experiments following a 30-minute pre-incubation with buffer alone (A, C, E) or buffer containing inhibitor (B, D, F)...... 122

Figure 3.9 Inhibitory effects of diclofenac (A, B), ezetimibe (C, D) and repaglinide (E, F) glucuronides () and respective parent drugs () on OATP1B1-mediated uptake of pitavastatin in stably transfected HEK293 cells. Data represent mean ± SD of at least 3 separate experiments following a 30-minute pre-incubation with buffer (A, C, E) or inhibitor (B, D, F) ...... 124

Figure 3.10 Inhibitory effects of 4 reference inhibitors on OATP1B1-mediated uptake of E17βG (A-D) and pitavastatin (E-H) in stably transfected HEK293-OATP1B1 cells. Data represent mean ± SD of at least 3 separate experiments without () and with () pre- incubation with inhibitor. Probe substrate uptake was investigated in the presence of cyclosporine (A, E), rifampicin (B, F), erythromycin (C, G) and rifamycin SV (D, H) ...... 126

Figure 3.11 Comparison of OATP1B1 IC50 data obtained without and with a 30-minute pre- incubation with inhibitor using E17βG (A) or pitavastatin (B) as a probe substrate. The dashed line represents the line of unity. Data were available for rifamycin SV (1), cyclosporine (2), rifampicin (3), telmisartan (4), telmisartan glucuronide (5), repaglinide (6), repaglinide glucuronide (7), erythromycin (8), diclofenac glucuronide (9), diclofenac (10), ezetimibe glucuronide (11), gemfibrozil glucuronide (12), raloxifene glucuronide (13), clopidogrel glucuronide (14), mefenamic acid glucuronide (15), ezetimibe (16) and gemfibrozil (17) ...... 128

Figure 3.12 Comparison of E17βG and pitavastatin OATP1B1 IC50 data without (A) and with (B) a 30-minute pre-incubation. The dashed line represents the line of unity. Rifamycin SV (1), cyclosporine (2), rifampicin (3), telmisartan (4), telmisartan glucuronide (5), repaglinide (6), repaglinide glucuronide (7), erythromycin (8), diclofenac glucuronide (9), diclofenac (10), ezetimibe glucuronide (11), gemfibrozil glucuronide (12), ezetimibe (13), gemfibrozil (14) 131

Figure 3.13 Inhibitory effects of diclofenac glucuronide (A), diclofenac (B) and repaglinide glucuronide (C) on OATP1B1-mediated uptake of E17βG () and pitavastatin () in stably transfected HEK293 cells. Data represent mean ± SD of at least 3 separate experiments following a 30-minute pre-incubation with inhibitor ...... 132

Figure 3.14 Comparison of the physicochemical properties of OATP1B1 inhibitors to IC50 values obtained in HEK293 cells using E17βG () or pitavastatin () as a probe without (blue) and with (red) pre-incubation for all inhibitors investigated (A, C, E, G) and the glucuronides on their own (B, D, F, H) ...... 134

Figure 3.15 Comparison of the –fold increase in cyclosporine OATP1B1 IC50 following pre- incubation with inhibitor between the probe substrates investigated in this study and those reported in the literature ...... 140

Figure 4.1 Overview of the therapeutic indications of 25 potent P450 inhibitors listed in the FDA 2012 guidance ...... 157

Figure 4.2 Metabolite : parent AUC (blue) and Cmax (red) ratios for itraconazole (A), and telithromycin (B) across multiple doses of the parent drug. Dashed line represents the FDA recommended metabolite exposure limit for in vitro investigation of metabolites P450 inhibitory potential. Itraconazole and telithromycin data across multiple doses were reported by Uno et al., (2006) (405) and Namour et al., (2001) (406), respectively ...... 159

Figure 4.3 Metabolite : parent AUC (blue) and Cmax (red) ratios for quinidine across multiple doses of inhibitor (1 – 100 mg). Dashed line represents the FDA recommended metabolite exposure limit for in vitro investigation of metabolites P450 inhibitory potential. Qunidine and its metabolites exposure data across multiple doses were reported by Maeda et al., (2011) (407) ...... 160

Figure 4.4 Metabolite : parent AUC (blue) and Cmax (red) ratios for metabolites of itraconazole following oral administration of 100 mg of the parent compound up to 7 days (A) and voriconazole following oral or iv administration of 400 mg of the parent (B). Dashed line represents the FDA recommended metabolite exposure limit for in vitro investigation of metabolites P450 inhibitory potential. Itraconazole and its metabolite exposure data following repeated administration up to 7 days were reported by Templeton et al, (2008) (197). Voriconazole and its metabolite exposure data were reported by Scholz et al., (2008) (408) ...... 161

Figure 4.5 AUC and Cmax exposure ratios (%) for cyclosporine and its metabolites in vivo. References for in vivo exposure data are presented in Appendix Table 6.3 ...... 162

Figure 4.6 Therapeutic indications of 38 compounds reported to have glucuronide metabolites ...... 163

Figure 4.7 AUC (blue) and Cmax (red) metabolite : parent ratios for glucuronide metabolites based on total plasma concentrations. Metabolite : parent ratios for glucuronide metabolites based on Cmax,u data are shown in green for glucuronide-parent pairs selected for the current study. The dashed line indicates 25% AUC or Cmax ratio. References and details of study design are provided in Appendix Table 6.4 ...... 165

Figure 4.8 CYP2C8 R values (1+ Cmax/Ki) calculated for gemfibrozil and its glucuronide using Ki and IC50 values, respectively, obtained following pre-incubation with inhibitor in HLM with either combined or P450 co-factors. The dashed line represents the FDA limit of 1.1 after which further investigation of enzyme inhibitory potential is recommended. Inhibitor exposure values used for prediction of DDI potential at the 600 mg dose were obtained following multiple administrations of gemfibrozil (2.5 days) before co-administration with repaglinide. All other values were predicted using inhibitor exposure data obtained following a single dose of gemfibrozil, details are listed in Table 4.8 ...... 172

Figure 4.9 Predicted repaglinide AUC’/AUC ratios for gemfibrozil () and clopidogrel (), inhibitor parent drugs (red), glucuronides (green) and parent and glucuronide in combination (blue). Repaglinide AUC’/AUC ratios were predicted using a mechansitic static model incorporating fmCYP2C8 of either 0.49 (A) or 0.92 (B). For clopidogrel and gemfibrozil AUC’/AUC ratios were predicted using inhibitor exposure data obtained for different doses of the parent drug. CYP2C8 inhibition data were generated in HLM using repaglinide as a probe substrate in the presence of P450 co-factors. The dashed line represents the FDA limit indicating a potential risk of clinical DDI ...... 177

Figure 4.10 Comparison of predicted and observed repaglinide AUC ratios calculated using predictive equations including: 1 + Cmax/Ki (A), AUC’/AUC fmCYP2C8 0.49 (B) or AUC’/AUC fmCYP2C8 0.92 (C) for gemfibrozil, gemfibrozil glucuronide, clopidogrel, clopidogrel glucuronide (alone or in combination) and trimethoprim. Predictions were made for gemfibrozil doses ranging from 30 – 900 mg, clopidogrel doses 75 – 300 mg and trimethoprim at 160 mg. The solid line represents the line of unity and the dashed lines represent the 2-fold limit in prediction accuracy ...... 181

Figure 4.11 Comparison of Cmax/ OATP1B1 IC50 ratios calculated using E17βG (A) or pitavastatin (B) as a probe substrate using IC50 data obtained without or with a 30-minute pre-incubation with inhibitor. Cmax data were collated from the literature and references are provided in Table 4.8. The dashed line represents the line of unity ...... 182

Figure 4.12 Comparison of predicted and observed changes in pitavastatin AUC in the presence of inhibitor calculated using either the R (1 + I,in,max,u or Cmax,u/IC50) (A, C) or the static mechanistic model (AUC’/AUC) approach (B, D). Inhibition data were obtained in HEK293 cells expressing OATP1B1 using either E17βG (A, B) or pitavastatin (C, D) as a probe substrate. References and details of clinical DDI data are provided in Table 4.4, input inhibitor concentration data for prediction of DDI are provided in Table 4.8. Comparisons were made for gemfibrozil, gemfibrozil glucuronide, rifampicin, cyclosporine and erythromycin. For the static mechanistic model (AUC’/AUC) approach (B, D) ftOATP1B1 values of 0.68 () and 0.86 () were used. The solid line represents the line of unity and the dashed lines represent the 2-fold limit in prediction accuracy ...... 193

Figure 5.1 Comparison of glucuronide and parent drug enzyme inhibition data collated from the literature (red) or obtained in this study (purple) without pre-incubation for CYP2C8 () and CYP3A4 (X). The solid line represents the line of unity and the dashed lines represent 2-fold difference between glucuronide and parent inhibition data ...... 204

List of tables Table 1.1 Examples of compounds associated with DDIs relating to P450 enzymes (35-37) ...... 23

Table 1.2 Examples of compounds associated with DDIs relating to inhibition of OATP1B1 29

Table 1.3 Effect of pre-incubation with inhibitor followed by co-incubation of inhibitor with probe substrate on the OATP1B1 inhibitory potential of cyclosporine in HEK293 cells ...... 32

Table 1.4 Mechanistic equations describing changes in hepatic and intestinal clearance (CL) used to incorporate multiple inhibitors or interaction mechanisms into mechanistic pharmacokinetic models taken from Houston and Galetin., (2010) (11) ...... 39

Table 1.5 Summary of literature IC50 or Ki data for glucuronide metabolites and their respective parent drugs against metabolising enzymes without or with () pre-incubation with inhibitor. Inhibition data were collated from studies where both glucuronides and parent drugs were investigated in vitro in HLM or recombinant enzymes. Details of enzyme inhibition and study design are provided in Appendix Table 6.1...... 47

Table 1.6 Collation of literature IC50 or Ki data for glucuronide metabolites and their respective parent drugs against OATP1B1 in vitro where both parent and glucuronide were investigated in a single study ...... 50

Table 2.1 Demographics of human hepatic pooled microsomes used for CYP2C8, CYP3A4 and UGT1A1 IC50 assays...... 62

Table 2.2 IC50 values for inhibition of repaglinide M4, M1 and glucuronide metabolism by CYP2C8, CYP3A4 and UGT1A1, respectively. Data represent mean ± sd of at least 3 separate experiments performed in pooled HLM with combined co-factor conditions without (IC50 (0)) and with (IC50 (30)) a pre-incubation with inhibitor. Values in brackets represent IC50 corrected for fu,mic ...... 72

Table 2.3 IC50 values for inhibition of repaglinide M4 and M1 metabolism by CYP2C8 and CYP3A4, respectively. Data represent mean ± standard deviation of at least 3 separate experiments performed in pooled HLM with CYP co-factor conditions (IC50 (0)) and with (IC50 (30)) a pre-incubation with inhibitor. Values in brackets are corrected for fu,mic ...... 77

Table 3.1 Summary of probe substrates used in in vitro investigations of OATP1B1 inhibition in a range of in vitro systems described in Figure 3.1. Data were collated using the UWDIDB. Full study details are provided in Appendix Table 6.2...... 105

Table 3.2 Summary details for the SLCO1B1 polymorphism studies used for the assessment of pitavastatin fT,OATP1B1 ...... 111

Table 3.3 Summary details for clinical DDI studies used for the assessment of pitavastatin fT,OATP1B1 ...... 111

Table 3.4 IC50 values for OATP1B1-mediated uptake of E17βG in stably transfected HEK293-OATP1B1 cells. Data represent mean ± SD of at least 3 separate experiments. Data were obtained without (0) or with (30) a 30-minute pre-incubation with inhibitor ...... 113

Table 3.5 IC50 values for OATP1B1-mediated uptake of pitavastatin in stably transfected HEK293-OATP1B1 cells. Data represent mean ± SD of at least 3 separate experiments. Data were obtained without (0) or with (30) a 30-minute pre-incubation with inhibitor. Ki values were calculated from mean IC50 values using Equation 3.3 ...... 118

Table 4.1 List of strong OATP1B1 and P450 inhibitors as classified by the FDA (13) and the P450 enzyme or transporter which they inhibit. All inhibitors listed cause an increase in the AUC of victim drug ≥ 5-fold ...... 145

Table 4.2 Reported in vivo repaglinide DDIs with inhibitors investigated in this study where both victim and perpetrator concentrations were monitored. Details on administered repaglinide and inhibitor dose, number of subjects and change in repaglinide AUC are included ...... 148

Table 4.3 Summary of in vitro and in vivo assessment of the contribution of OATP1B1 to the hepatic uptake of pitavastatin. Full study details are provided in Section 3.4.2 ...... 153

Table 4.4 In vivo DDIs of pitavastatin with inhibitors investigated in this study where changes in victim drug AUC were reported, including details of pitavastatin and inhibitor dose, number of subjects and change in pitavastatin. Inhibitor concentrations were not reported ...... 153

Table 4.5 Summary of drug input parameters for PBPK modelling and simulations ...... 155

Table 4.6 Parameter values used for gemfibrozil and gemfibrozil glucuronide DDI simulations with repaglinide. In vitro CYP2C8 inhibition data were obtained in HLM using repaglinide as a probe substrate (Chapter 2). For gemfibrozil glucuronide CYP2C8 inhibition data obtained following a 30-minute pre-incubation with inhibitor were used to account for time-dependent inhibition. OATP1B1 inhibition data were obtained using E17βG or pitavastatin (shown in brackets) as a probe substrate following a 30-minute pre-incubation with inhibitor (Chapter 3) ...... 156

Table 4.7 Summary of AUC and Cmax ratios calculated for potent P450 inhibitors listed by the FDA guidelines. All clinical exposure data was collated from literature and the full database is provided in Appendix Table 6.3 ...... 158

Table 4.8 Clinical data collated for glucuronides, parent drugs and reference inhibitors and used for prediction of DDI risk ...... 166

Table 4.9 AUC ratio values for CYP2C8 calculated using Ki data determined from in vitro experiments in HLM, without pre-incuabtion and where clinical exposure data was available. Experiments were performed with combined (P450 + UGT), P450 or UGT co-factors. Cmax was used as the input inhibitor concentration. Details of clinical DDI studies and references for the observed repaglinide AUC ratios are provided in Table 4.2 ...... 169

Table 4.10 Repaglinide AUC’/AUC ratios calculated for inhibitors of CYP2C8 using fmCYP2C8 values of 0.49 or 0.92. Ratios were calculated using inhibition data obtained in in vitro experiments in HLM using repaglinide as a probe substrate in the presence of combined (P450 + UGT) or P450 co-factors. Inhibitor concentration input data are provided in Table 4.8, I, in,max,u values were used for parent drugs and reference inhibitors and Cmax u values were used for glucuronides. Details of DDI studies and references for the observed repaglinide AUC ratios are provided in Table 4.2 ...... 175

Table 4.11 Predicted repaglinide AUC’/AUC ratios calculated using a mechanistic static model to assess the synergistic DDI potential of parent drugs and glucuronides. CYP2C8 inhibition data were generated in HLM using repaglinide as a probe substrate in the presence of P450 co-factors. Inhibitor exposure data reported in DDI studies with repaglinide at a range of inhibitor doses were used to predict the DDI risk. Repaglinide AUC’/AUC ratios were calculated for inhibitors of CYP2C8 using fmCYP2C8 values of either 0.49 or 0.92. Details of DDI studies and references for the observed repaglinide AUC ratios are provided in Table 4.2 ...... 178

Table 4.12 Assessment of the number of predicted changes in repaglinide AUC in the presence of inhibitors of CYP2C8 in comparison to observed values in vivo. Predicted R values were calculated using a basic model (R = 1+I/Ki) where the inhibitor concentration was the maximum total (bound and unbound) systemic concentration. AUC’/AUC values were calculated using mechanistic static models (Equations 4.3 and 4.4) for parent drugs and glucuronides individually or in combination...... 180

is 1.25. Cmax/IC50 ratios for ezetimibe and its glucuronide were < 0.001 in all cases, results are provided in Appendix Table 6.11. Details of DDI studies for the observed pitavastatin AUC ratios are provided in Table 4.4 ...... 185

Table 4.14 Predicted pitavastatin AUC ratios calculated for gemfibrozil glucuronide, gemfibrozil and reference inhibitors calculated using a mechanistic static model. In vitro OATP1B1 inhibition data were generated in OATP1B1 expressing HEK293 cells using pitavastatin or E17βG as a probe substrate following 30-minute pre-incubation with inhibitor. For all other inhibitors pitavastatin AUC ratios were < 1.25 (Appendix Table 6.12). I,in,max,u values and Cmax u values were used as inhibitor concentration inputs for parent drugs and glucuronides, respectively (Table 4.8). Details of DDI studies and references for the observed pitavastatin AUC ratios are provided in Table 4.4 ...... 188

Table 4.15 Assessment of the number of predicted changes in pitavastatin AUC in the presence of inhibitors of OATP1B1 in comparison to observed values in vivo. Predicted R values were calculated using a basic model (R = 1+I/Ki) where the inhibitor concentration was the maximum total (bound and unbound) systemic concentration. AUC’/AUC values were calculated using mechanistic static models (Equations 4.8) for parent drugs and glucuronides individually ...... 192

Abstract The potential contribution of drug metabolites to drug-drug interactions (DDIs) is increasingly recognised. The latest FDA guidance recommends investigation of the effect of metabolites on CYP450 enzymes if present at ≥25% of parent systemic exposure. In addition, the clinical relevance of transporter mediated DDIs is highlighted in the latest EMA and FDA guidelines. Glucuronidation is a major conjugation pathway and increasing numbers of glucuronides have been reported to inhibit metabolic enzymes and/or uptake transporters e.g., gemfibrozil glucuronide inhibits both CYP2C8 and OATP1B1. However, their inhibitory effects on a range of metabolic enzymes and transporters has not been characterised in a systematic manner and their potency in relation to parent compounds is unknown. The aim of this Thesis was to comprehensively investigate the inhibitory potential of 18 compounds in total including 10 glucuronides, parent compounds and reference inhibitors of interest against CYP2C8, CYP3A4 and UGT1A1 in human liver microsomes, using repaglinide as a probe substrate. Similar studies were performed on OATP1B1 in HEK293 cells, using estradiol 17β glucuronide (E17βG) as an initial probe substrate. For a subset of glucuronides and parent drugs, additional studies were conducted using pitavastatin as a clinically relevant OATP1B1 probe. A pre-incubation with inhibitor was included in both transporter and metabolism experiments to assess potential for time-dependent inhibition. In addition, the clinical exposure of glucuronides was assessed in relation to FDA guidance exposure limits. The in vitro data generated and inhibitor clinical exposure data collated were used to predict glucuronide DDI risk initially using basic models (1 + I/Ki). Subsequently, the extent of DDI was predicted using static mechanistic models incorporating the fraction of victim drug metabolised (fmCYP2C8) or transported (ftOATP1B1) by the enzyme or transporter of interest using repaglinide and pitavastatin as relevant victim drugs. CYP2C8 IC50 values were characterised for 5/10 glucuronides (IC50 8.6 – 54.1 µM for telmisartan and diclofenac glucuronides, respectively) which demonstrated similar or greater inhibitory potency to their parent compounds except for mefenamic acid which was 3-fold more potent than its glucuronide. The choice of enzyme co-factor conditions influenced the CYP2C8 inhibitory potential of mefenamic acid and clopidogrel glucuronides, no changes were seen for other glucuronides. Time-dependent increase in potency was observed for clopidogrel and gemfibrozil glucuronides resulting in 3 to 10-fold more potent inhibition than parent compounds. Minimal inhibition of CYP3A4 and UGT1A1 by glucuronides was observed. OATP1B1 IC50 values with E17βG as a probe substrate were obtained for 8/10 glucuronides (IC50 1.2 – 55.1 µM for telmisartan and raloxifene glucuronides, respectively) and all 5 parent compounds investigated (IC50 0.73 – 47.8 µM). For repaglinide, diclofenac and gemfibrozil glucuronides, OATP1B1 IC50 values were comparable to parents. Ezetimibe glucuronide was 4-fold more potent than its parent but the opposite was observed for telmisartan. The OATP1B1 inhibitory potency of parents and glucuronides was in the same rank order; however, differential pre-incubation effects were seen between glucuronide-parent pairs. Using pitavastatin as a probe, IC50 values were within 2-fold of those obtained using E17βG except for gemfibrozil, diclofenac and ezetimibe where up to 12-fold more potent OATP1B1 inhibition was observed using E17βG. A mean increase in OATP1B1 inhibitory potency of 1.7 and 2.3-fold was observed following pre-incubation with inhibitor using E17βG and pitavastatin as probe substrates, respectively. In conclusion, glucuronides were found to inhibit both CYP2C8 and OATP1B1 in vitro causing comparable or more potent inhibition than their parent drugs in the majority of cases. Inclusion of a pre-incubation step with inhibitor is recommended for both OATP1B1 and CYP2C8 inhibition studies to obtain the most conservative estimate and assess the mechanism of inhibition. The inhibition of metabolising enzymes or OATP1B1 by glucuronides was not predicted to cause a clinically relevant DDI using the 1 + I/Ki approach, except in the case of gemfibrozil and clopidogrel glucuronides and the reference inhibitors. Using static mechanistic equations, only gemfibrozil glucuronide and reference inhibitors were predicted to cause clinically relevant inhibition of OATP1B1 and CYP2C8. The paucity of glucuronide clinical exposure data limited the prediction of the DDI risk of glucuronides. However, where clinical exposure data were available, glucuronides exceeded 25% of parent drug exposure. Improved understanding of glucuronide DDI risk resulting from inhibition of multiple clearance mechanisms (CYP2C8 and OATP1B1) by glucuronides and their parent drugs in conjunction with glucuronide exposure at the site of inhibition is required to more accurately predict DDI risk associated with glucuronides.

Declaration

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List of abbreviations AUC Area under plasma concentration-time curve

CLint Intrinsic clearance

Cmax Maximum plasma concentration

CHO Chinese hamster ovary cells

D Dose

DDI Drug-drug interaction

EDTA Ethylenediaminetetraacetic acid

EMA European Medicines Agency

E17βG Estradiol 17 β glucuronide

Fa Fraction of dose absorbed

FDA U.S. Food and Drug Administration

FG Fraction escaping metabolism in the gut fmCYP2C8 Fraction metabolised by CYP2C8 fmCYP Fraction metabolised by P450 enzymes fup Fraction unbound in plasma fTOATP1B1 Fraction transported by OATP1B1 h Hour

HEK293 Human embryonic kidney 293 cells

HLMs Human liver microsomes

I Inhibitor concentration

IC50 Inhibitor concentration required to produce 50% control activity iv Intravenous

IVIVE In Vitro-In Vivo Extrapolation

JMHLW Japanese Ministry of Health, Labour and Welfare ka Absorption rate constant

Ki Reversible inhibition constant KI Concentration of inhibitor at half of the inactivation rate constant

KI Concentration of inhibitor at half of the inactivation rate constant

kinact Inactivation rate constant

Km Michaelis-Menten constant

LogD7.4 Log of the octanol-water distribution coefficient at pH 7.4

MDCK Madin-Darby canine kidney

NADPH/ NADP β-Nicotinamide adenine dinucleotide phosphate reduced/oxidised

OATP Organic anion transporting polypeptideOATP1B1 Organic anion transporting polypeptide 1B1

P450/CYP450 Cytochrome P450

PBPK Physiologically-based pharmacokinetic

PKC Protein kinase C

Qh Hepatic blood flow

R Ratio of victim drug AUC in the presence and absence of inhibitor

S substrate concentration

SAL Saccharic acid lactone

SD Standard deviation

SLCO1B1 Human gene which encode OATP1B1 transporters

SNPs Single nucleotide polymorphisms

UDPGA Uridine di-phosphate glucuronic acid

UGT Uridine di-phospho-glucuronosyltransferase

UW DIDB University of Washington Drug Interaction Database

Vmax Maximum velocity

Acknowledgements

Firstly, I would like to thank my supervisors Dr Aleksandra Galetin and Dr Helen Rollison for their support and encouragement throughout the PhD and writing of this thesis. I have greatly benefited from the advice and mentoring provided and I am extremely appreciative of their guidance.

Thanks to everyone at CAPkR for their help, support and friendship. I would particularly like to thank Sue Murby for assistance with the LC-MS/MS analysis. I would also like to thank AstraZeneca (AZ) for their financial support and all the people at AZ who let me share their labs and spent time helping me with my experiments during my visit.

I would like to thank Natty, Sarah and Suzi for their constant friendship and believing in me throughout yet another degree. I would also like to thank my ‘PhD come dine on me group’ for all the fun and excellent food they have contributed to my time in Manchester. Finally, I would like to thank Robyn, for always making me smile and giving me perspective.

The author

The author graduated with an MSci (Hons) Degree in Pharmacology from The University of Glasgow in 2011. During these studies the author spent one year (2009 - 2010) in the drug metabolism and pharmacokinetics department at Sanofi Aventis, Alnwick, under the supervision of Gill Morrison. During this placement the author worked as part of the pharmacokinetics and drug metabolism teams and also carried out a project performing pharmacokinetic and pharmacodynamic investigations with SAR103168, a novel treatment for acute myeloid leukaemia.

In September 2011, the author joined the Centre for Applied Pharmacokinetics Research at the University of Manchester to begin her PhD under the supervision of Dr Aleksandra Galetin and Dr Helen Rollison. The research was funded by the Biotechnology and Biological Sciences Research Council (UK) and AZ. The PhD program included a yearlong stay (2013 – 2014) at AZ in the Drug Safety and Metabolism group, AZ, Alderely Edge under the supervision of Dr Helen Rollison. The PhD research was completed in September 2015.

Chapter 1 Introduction

1.1 Overview and relevance of drug-drug interactions and glucuronide metabolites The co-administration of multiple drugs is crucial for the effective treatment of many diseases and is increasing in incidence. However, it is often associated with complications such as drug- drug interactions (DDIs) which can result in severe adverse effects and toxicity at normally therapeutic doses (1-3). Adverse effects resulting from DDIs have led to the withdrawal of some drugs, for example mibefradil and sorivudine, while others, such as furafylline, have been halted during development as a consequence of their propensity to cause DDIs (4-7). As a result of their influence on the potency and safety of therapeutic compounds, DDIs are a key factor considered during both the development and clinical use of a compound (8).

Pharmacokinetic drug-drug interactions result in altered exposure to a drug, or its metabolite, in the presence of another compound and are clinically important if the efficacy or toxicity of one or both drugs is altered (9, 10). The pharmacokinetics of co-administered drugs may be modified as a result of interactions with the enzymes or transporters involved in metabolism (9, 11, 12). In vitro DDI studies investigating enzyme and transporter inhibitory potential are recommended by the American Food and Drug Administration (FDA), European Medicines Agency (EMA) and Japanese Ministry of Health, Labour and Welfare (JMHLW), as part of a compounds development, in order to determine the potential for DDIs and guide further investigations (13-15).

Increasing attention is being paid to identifying the circulating metabolites of therapeutic compounds and determining their role in DDIs (16-19). In this area, the role of glucuronide metabolites, produced by hepatic Uridine 5’-diphospho-glucuronsyltransferase (UGT) enzymes, requires elucidation due to their high prevalence and potential pharmacologic effect (20-22). Examination of the contribution of glucuronides and the effect of multiple inhibitory mechanisms, including enzyme and transporter interactions, is of increasing importance in the evaluation of glucuronide drug-drug interactions because of the potential to avoid toxicity and optimise drug development (21, 23).

1.2 Metabolising enzymes and drug-drug interactions Drug metabolism occurs predominantly in the hepatocyte cells of the liver (24, 25). Hepatocytes contain a range of metabolising enzymes including those belonging to the cytochrome P450 (P450) and UGT super families. These super families display overlapping and wide substrate specificities and as a result they are responsible for the metabolism of a broad range of both endogenous and exogenous compounds. A drug may be subject to sequential P450 oxidative metabolism (Phase I) followed by conjugation (Phase II), for example glucuronidation by UGTs. This is a major route of hepatic clearance, possibly a result

of the close physical association of both classes of enzymes in the endoplasmic reticulum of hepatocytes (24, 26, 27).

1.2.1 Cytochrome P450 enzymes The P450 enzyme family have been extensively studied with over 8500 enzymes identified to date (28-30). FDA guidelines suggest investigation of a novel compounds P450 metabolism and inhibitory potential especially in relation to CYP3A4, CYP2C8, CYP1A2, CYP2D6 and CYP2C9 due to their major contribution to hepatic metabolism. Compounds removed from the market due to DDIs associated with CYP450 enzymes include astemizole and cerivastatin as a result of heart palpitations and rhabdomyolysis, respectively; further examples of DDIs relating to P450 enzymes are listed in Table 1.1 (31-34).

Table 1.1 Examples of compounds associated with DDIs relating to P450 enzymes (35-37)

Enzyme Perpetrator Victim drug Result of DDI inhibited Telithromycin CYP3A4 Verapamil Hypotension and QT prolongation Diltiazem CYP3A4 Simvastatin Myopathy Mibefradil CYP3A4 Terfenadine Severe cardiac arrhythmia Fluoxetine CYP2D6 Risperidone Increased risk of extrapyrimidal side effects Metronidazole CYP2C9 Warfarin Increased risk of bleeding Terbinafine CYP2C6 Amitriptyline Dry mouth, dizziness and cardiac toxicity

Ketoconazole CYP3A4 Terfenadine Ventricular arrhythmias

CYP3A4 has been reported to be the major metabolising enzyme in the liver and intestine, contributing to the metabolism of ~45 of the top 200 drugs cleared by metabolism (38-40). As a result of its dominant role in metabolism, CYP3A4 is often involved in clinical DDIs and its importance to this area has been extensively reported (38, 40-42). Other P450 enzymes recognised as predominant contributors to drug metabolism in humans include CYP1A2, CYP2C9, CYP2C19, CYP2D6 and CYP2C8. The broad substrate specificity of these enzymes increases their susceptibility to inhibition; this can result in significantly altered substrate pharmacokinetics and is of major importance to DDI investigations (5, 30, 43, 44).

1.2.1.1 CYP2C8 The CYP2C8 enzyme is involved in the metabolism over 60 clinically prescribed drugs, including verapamil and cerivastatin, as well as of endogenous compounds, for example, arachidonic acid (45). CYP2C8 has a large active site which can accommodate a broad range of substrates including large, polar compounds such as glucuronides, for example estradiol and diclofenac glucuronides (46-48). This enzyme has been reported to account for ~7% of total hepatic P450 content and is also located extrahepatically in the kidney, brain, adrenal gland and duodenum (45, 49). Multiple allelic variants of CYP2C8 have been reported;

CYP2C8*1 is the wildtype enzyme, however, other allelic variants include CYP2C8*3, which has a frequency of 10 to 23% in Caucasians and is the most studied in vivo (50-52). The effect of CYP2C8 polymorphisms on the pharmacokinetics of drugs metabolised by this enzyme in vivo has been reported to vary depending on the probe substrate. For instance, a 45% reduction in the area under the plasma concentration-time curve (AUC) of repaglinide was reported in subjects with the CYP2C8*3 genotype in comparison to wildtype (53). However, the half-life of (R)-ibuprofen was increased for subjects with the CYP2C8*3 genotype (9 h) in comparison to individuals with the CYP2C8*1 (2 h) genotype (50). Clinical DDIs associated with inhibition of CYP2C8 include a 600% increase in cerivastatin AUC in the presence of gemfibrozil (34) and a 40% increase in rosiglitazone AUC following co-administration with trimethoprim (54).

1.2.2 Uridine di-phospho-glucuronosyltransferase enzymes Glucuronidation accounts for approximately 35% of phase II drug metabolism with 1 in 10 of the 200 most prescribed drugs partially cleared by this route (20, 38, 55). Phase II metabolism increases the molecular weight and water solubility of a compound, increasing its susceptibility for excretion (25, 56). 18 UGT enzyme isoforms have been identified to date and categorised in to two families (UGT1 & 2) according to amino acid sequence; however, the UGT enzymes are less well characterised than the P450 enzyme family (22, 57-59).

Glucuronidation occurs in the endoplasmic reticulum of the liver, kidney and intestines and requires the co-factor UDP-glucuronic acid (Figure 1.1) (25, 60-63). Exposure to this enzyme system is dependent on substrate uptake into cells where the glucuronic acid group is transferred from the cofactor to the substrate in a nucleophilic attack. As observed with P450 enzymes, there is overlap in substrate specificity between UGT enzymes, however, this is far more pronounced within the UGT super family. A broad range of drugs containing nucleophilic functional groups act as substrates producing N, O, sulphur or acyl glucuronide metabolites; in humans O-glucuronides are the most common products of glucuronidation (20, 25, 60).

(A) R-XH + UDP-GA → RX-GA + UDP +H2O

(B)

CO H CO H 2 UDPGT 2 HO HO O O + RCO2H

HO OUDP HO O R OH OH O

Figure 1.1 (A) Scheme describing glucuronidation where R-X is the substrate (R is a C group and X is a functional group susceptible to glucuronidation), UDP-GA is uridine diphosphate glucuronic acid and RX-GA is the glucuronide metabolite. (B) Biosynthesis of an O-acyl glucuronide reproduced from Stachulski et al., (2006) (64)

The UGT enzymes considered most important to hepatic drug metabolism are UGT1A1, 1A3, 1A4, 1A6, 1A9, 2B7 and 2B15 (21). Many polymorphisms have been reported for the UGT enzymes; some result in altered functionality of the enzyme while others reduce expression (65). These polymorphisms have been reported to influence the pharmacokinetics of UGT substrates. For example, in vivo and in vitro studies using ezetimibe and raloxifene (UGT1A1 substrates) have shown significantly reduced glucuronide formation in volunteers or HLM that were homozygous for UGT1A1*28, when compared to the wild type (UGT1A1*1) or heterozygous genotypes (66, 67). Similarly, the pharmacokinetics of valproic acid, morphine and mycophenolic acid are reported to be influenced by polymorphisms in UGT2B7 (*1 and *2) (68-70). However, data for the effects of these polymorphisms and those of various other UGT enzymes explored both in vitro and in vivo are often conflicting and are not as well characterised as those of P450 enzymes (65, 71).

As a result of the contribution of UGT to xenobiotic metabolism, investigation and prediction of their contribution to drug elimination is of major importance to drug discovery (72). There is also growing interest in the identification of probe substrates which enable glucuronidation phenotyping in vitro as these would facilitate prediction of drug clearance by this route as well as investigation of DDIs (22). Examples of potential probe substrates used for the investigation of UGT enzyme driven metabolism include 4-methylumbelliferone glucuronidation for UGT1A1 and zidovudine glucuronidation for UGT2B7 (73, 74).

1.2.3 Classes of drug-drug interactions - enzyme induction and inhibition Metabolism based DDIs lead to either decreased (inhibition) or increased enzyme activity (induction) (11, 75, 76). The induction of drug metabolising enzymes is a result of increased enzyme synthesis or decreased degradation (11, 77). The decreased enzyme activity resulting from inhibitory interactions can be attributed to loss of cofactor supply, enzyme destruction, suppression of enzyme synthesis or direct inhibition of the enzyme; the latter is the most commonly observed interaction in clinical practice (76). Inhibitory DDIs are categorised as either reversible or irreversible and are discussed in more detail below; both may mediate toxicity as a result of increased exposure to a compound (75, 78).

The extent of a DDI in vivo is expressed as the change in the area under the plasma concentration-time curve in the presence and absence of an inhibitor as illustrated for boceprevir in Figure 1.2, which reversibly inhibits CYP3A4 and increases exposure to atorvastatin (79). The AUC is a function of the clearance of a victim drug and changes in AUC in the absence and presence of a range of potential inhibitor (perpetrator) concentrations indicate the extent of enzyme inhibition, calculated using Equation 1.1 (11, 80). The classification of an inhibitors strength was developed based on orally administered midazolam as a probe for CYP3A4 inhibitors and this system is applied widely to classify pharmacokinetic DDIs (5). Changes in the AUC of a victim drug ≥ 5-fold are described as a strong interaction, changes ≥ 2-fold but < 5-fold indicate moderate interaction while changes ≥ 1.25-fold but < 2- fold are considered a weak interaction (5, 13). 25

푨푼푪′ 푭푮′ 푪푳 Equation 1.1 = ∗ 푨푼푪 푭푮 푪푳′

Where the prime superscript indicates the parameter in the presence of the inhibitor, CL is enzyme mediated clearance, FG is the fraction of administered drug dose escaping metabolism by the intestine and it is assumed that the change in P450-mediated clearance is not accompanied by any effect on intestinal absorption or plasma protein binding

1.2.3.1 Reversible inhibitory drug-drug interactions Reversible inhibitory interactions occur as a result of reduced enzyme activity in the presence of a drug. In this case, enzyme activity is fully recovered once administration of the perpetrator drug is stopped. The inhibition may be of a competitive or non-competitive nature (11, 81). Competitive inhibition occurs following co-administration of drugs which directly compete for the active site of a metabolising enzyme. The inhibitor prevents victim drug metabolism by the enzyme as observed in the case of imipramine, a CYP2D6 substrate, whose blood concentrations are increased in the presence of fluoxetine, a CYP2D6 inhibitor (11, 37). Non- competitive inhibition results from inhibitor binding to a separate site of the enzyme to the substrate and inducing a conformational change which inhibits enzyme-substrate interaction. Further forms of reversible inhibition are less common but include uncompetitive inhibition, where inhibitor binds to the enzyme-substrate complex and prevents metabolism, and mixed- type inhibition which involves elements of both competitive and non-competitive mechanisms (37, 78). 26

1.2.3.2 Irreversible inhibitory drug-drug interactions Irreversible enzyme inhibition, also referred to as time-dependent (TDI) or mechanism-based inhibition is caused by the production of a reactive metabolite which binds to an enzyme causing a loss in that enzymes activity. The activity of the enzyme can be regained only by re-synthesis of the protein (16, 76). The resulting inhibition is both concentration- and time- dependent and can involve reactive intermediates generated from the metabolism of inhibitor covalently reacting with an active site of the enzyme. Alternatively, inhibition can be non- covalent, involving reactive intermediates of the inhibitor coordinating with the heme prosthetic group of the P450 enzyme and leading to the formation of a catalytically inactive metabolite– inhibitor complex (41, 82). The extent of inhibition is largely dependent on the rate of metabolism of the inhibitory metabolite from the parent compound and is relatively long-lasting as synthesis of new enzyme is required for recovery of activity (78, 83, 84). Irreversible inhibitory interactions are considered to cause the most clinically significant DDIs and, though there are recognised examples of mechanism based inhibitors such as tamoxifen, isoniazid and ritonavir, the actual reactive metabolites responsible for inhibition have not been determined in many cases (16, 76). Examples of metabolites which have been characterised as inhibitors include hydroxyitraconazole which inhibits CYP3A4 (85) and norfluoxetine which inhibits a number of P450 enzymes including CYP2D6 (86).

1. 3 Transporter membrane proteins – importance to drug-drug interactions Membrane transporter proteins mediate the efflux and uptake of a compound and its distribution to tissues by active processes. The transport of compounds to and away from their sites of metabolism directly affects the pharmacokinetics of a drug, influencing efficacy and toxicity profiles and providing a further site for DDI’s (87-90). The contribution of transporters to DDIs and their synergistic effect in combination with other inhibitory mechanisms is becoming more widely acknowledged. In recognition of this, current FDA guidelines recommend in vitro assessment of a novel drugs inhibitory potential against a range of uptake and efflux transporters for which clinical DDIs have been reported. Namely: P-gp, BCRP, OATP1B1, OATP1B3, OCT2, OAT1 and OAT3 transporters (1, 13). Figure 1.3 illustrates the multiple efflux and uptake transporters present in human hepatocytes.

Figure 1.3 Human transport proteins present in hepatocytes. Figure taken from (Giacomini et al., (2010) (88)

1.3.1 The OATP1B1 transporter The Organic Anion Transporter Protein family (OATP) are important hepatic uptake transporters, illustrated in Figure 1.3. OATP transporters form part of the detoxification process of xenobiotics, alongside P450 enzymes and efflux transporters, by mediating the active transport of many exogenous molecules from the systemic circulation into the hepatocytes. Substrates of OATPs generally have a high molecular weight (> 450 KD) but vary in their physicochemical properties and include bile salts and thyroid hormones as well as glucuronide metabolites such as mycophenolic acid glucuronide. Substrate transport is proposed to be through a positively charged central pore; though the precise mechanism is not fully understood translocation of substrates in an electro-neutral, rocker-switch type mechanism, independent of membrane potential, adenosine triphosphate levels or sodium, potassium or chloride gradient has been proposed (90-95).

The OATPs belong to the solute carrying family of transporters and are categorised according to amino acid sequence homology (12, 24, 90, 96). OATPs have a 12 membrane spanning domain structure with multiple binding sites facilitating broad substrate specificity but increasing the risk of DDIs due to competition. OATP1B1 and 1B3 have been identified as the predominant isoforms present in the liver; no crystal structure has yet been identified (87, 88, 96, 97). The genes coding for OATP1B1 and OATP1B3, which share an amino acid identity of 80%, are located on the short arm of chromosome 12 (12p12) (98, 99). SLCO1B1 is a polymorphic gene for which 49 variants have been identified in humans (100). SCLO1B1 single-nucleotide polymorphisms (SNP) c.521T > C in exon 5 and c.388A > G in exon 4 are the two most commonly reported. Of the four haplotypes formed from combinations of these two variants (SCO1B1*1a, *1b, *5 and *15), SLCO1B1*1a is recognised as the wildtype haplotype (101-103). Variation in SNPs is associated with altered transport activity of OATP1B1 substrates in vitro and in vivo. For example, in transiently transfected Human

Embryonic Kidney 293 cells (HEK293) expressing SCO1B1*1b5 and *15, atorvastatin, simvastatin, cerivastatin and pravastatin uptake were reduced in comparison to uptake by HEK293 cells expressing SLCO1B1*1a (104). In vivo, the c.521T > C SNP, present in both SLCO1B1*5 and *15 haplotypes, is associated with increased plasma concentrations of a range of drugs, including repaglinide, pitavastatin and pravastatin, in comparison to the wildtype variant due to reduced transport (105-107).

1.3.2 The role of OATP1B1 in drug-drug interactions In addition to the importance of OATP1B1 to the hepatic uptake of many compounds, clinical DDIs attributed to inhibition of OATP1B1 have been reported. For example, in kidney transplant patients cyclosporine caused a 5- and 20-fold increase in the AUC of pravastatin and lovastatin, respectively (108). Similarly, rosuvastatin exposure was doubled in the presence of ritonavir/lopinavir in healthy volunteers (109). Further examples of clinical DDIs involving the OATP1B1 transporter are presented in Table 1.2. Inhibition of OATP1B1 has been reported between compounds which are both substrates of this transporter, such as gemfibrozil and cerivastatin, though it is also possible for compounds which are not substrates for a transporter to cause inhibition, for example telmisartan (33, 103, 110, 111). In vivo, investigation of OATP1B1 inhibition and resulting DDIs is complicated by a lack of known transporter specific probes which aren’t subject to metabolism (112). The FDA guidance lists a total of 15 compounds as example in vivo substrates of OATP1B1, many of which are also substrates of metabolising enzymes and other transporters. For example, pravastatin is a substrate of CYP3A4, CYP3A5, and OATP1B3 all of which may contribute to its in vivo pharmacokinetics alongside OATP1B1 (13, 113, 114). Similarly, OATP1B1 specific inhibitors are also lacking. Although 9 compounds are listed in the FDA guidance as in vivo inhibitors of OATP1B1, these compounds also inhibit other hepatic transporters. For example atazanavir inhibits P-gp (115), and some also inhibit metabolising enzymes, for example saquinavir inhibits CYP3A in addition to OATP1B1(116).

Table 1.2 Examples of compounds associated with DDIs relating to inhibition of OATP1B1

Inhibitor Probe substrate Mean fold change in (dose (mg)) (dose (mg)) probe substrate AUC Reference Fimasartan (80) Atorvastatin acid (240) 1.3 (117) Quercetin (40) Pravastatin (500) 1.2 (118) Rifampin (40) Atorvastatin acid (600) 7.3 (119) Sildenafil (125) Bosentan (80) 1.3 (120) Asunaprevir (200) Rosuvastatin (10) 1.4 (120) Boceprevir (800) Atorvastatin (40) 2.3 (79) Clarithromycin (500) Bosentan (125) 3.7 (121) Erythromycin (250) Bromocriptine (5) 3.7 (122) Telaprevir (750) Maraviroc (150) 9.3 (123)

1.3.3 In vitro investigation of inhibition of OATP1B1 Evaluation of the OATP1B1 inhibitory potential of a novel drug in vitro is complicated by factors such as substrate-dependent inhibition and pre-incubation and long-lasting inhibitory effects (124-126). A range of compounds are successfully used as probe substrates to assess in vitro OATP1B1 inhibition, for example prototypical probe substrates such as estradiol-17-β- glucuronide (E17βG) and estrone-sulphate and therapeutically used drugs such as atorvastatin and repaglinide (127-130). However, the in vitro potency of an increasing number of inhibitors of OATP1B1 has been reported to be influenced by the probe substrate used. For example, rifampicin IC50 values, which are half the maximal inhibitory concentration of a compound, ranged from 0.4 to 7 µM with glibenclamide and estrone-sulphate as probes, respectively, in HEK293 cells (127, 128, 131). This highlights that careful selection of in vitro probe substrates is necessary as the results may affect the accuracy of prediction of DDIs and decisions about the requirement for clinical studies. Multiple binding sites on the OATP1B1 transporter have been suggested, i.e., the existence of a high and a low affinity site. This may complicate the interactions of different substrates and inhibitors, potentially resulting in substrate-dependent inhibition (131, 132). Therefore, it has been proposed that multiple inhibitor-substrate combinations including prototypical as well as clinically relevant probe substrates are assessed in vitro to investigate clinical OATP1B1 DDI risk (131-133).

The effect of pre-incubation with inhibitor prior to co-incubation of inhibitor with probe substrate on OATP1B1 inhibitory potency in vitro is increasingly explored and cyclosporine is the most studied inhibitor in this area (Table 1.3). Pre-incubation with inhibitor has been reported to increase cyclosporine OATP1B1 inhibitory potency with all probes for which data were available in comparison to co-incubation of probe substrate with inhibitor alone. The increase in inhibitory potency for cyclosporine following pre-incubation ranged from 4 – 22-fold when E17βG and atorvastatin were used as probes, respectively. The precise mechanism of this pre-incubation effect is unknown although it has been observed for other OATP1B1 inhibitors e.g., simeprevir and asunaprevir (134) and similar reports exist for other OATP transporters e.g., the OATP2B1 transporter with apple juice (126).

An additional observation is that the inhibition effect of some inhibitors on OATP1B1 in vitro is long lasting (125, 126, 134). For example, cyclosporine has been reported to inhibit the uptake of estrone-sulphate by OATP1B1 in HEK293 cells for up to 18 hours after its removal from the incubation (135). This effect has also been reported in vivo in rat with decreased bromosulphopthalein clearance observed up to 21 h after administration of cyclosporine (136). However, there is currently no in vivo data for this effect in man. The long lasting inhibitory effect of cyclosporine on OATP1B1 has been attributed to reduced transporter activity and not reduced expression of the transporter, and may be mediated from an intracellular position which could possibly contribute to the enhanced inhibition following pre-incubation (136). Long lasting inhibition effects have been observed for other OATPs including OATP1B3, OATP2B1 and OATP1A2; indirect inhibition by activation of protein kinase C (PKC) has been reported

as a potential underlying mechanism (137-139). For OATP1B3, activation of PKC reduced transport activity by post-translational regulation of OATP1B3 without affecting expression of the transporter in sandwich cultured human hepatocytes (137). The precise mechanism through which OATP1B3 function decreases as a result of PKC activation remains unclear but may be a result of increased phosphorylation (137, 140). Inhibition of OATP1A2 and OATP2B1 in a time and concentration-dependent manner by indirect PKC activation has also been reported in COS-7 and Madin-Darby Canine Kidney (MDCK) cell lines, respectively (138, 139). Transport activity was reduced as a result of decreased cell surface expression of the transporters. PKC modulated transporter internalisation has been reported for OATP1A2 (139). However, mutation of putative OATP2B1 phosphorylation sites in MDCK cells did not reduce inhibition of OATP2B1 transport indicating that phosphorylation of these amino acids is not the mechanism of OATP2B1 internalisation and other mechanisms should be considered (138). Currently, no literature data detailing the in vitro effects of PKC on OATP1B1 inhibition have been reported. In addition, Furihata et al., (2014) (134) reported distinctive long-lasting inhibitor effects of direct acting antiviral agents on OATP1B1 and OATP1B3. For example, OATP1B1 functional levels at 1 h after removal of simeprevir from the incubation were found to be 65% and complete recovery of OATP1B1 function was observed 3 hours after simeprevir exposure. Contrastingly, residual OATP1B3 activity was similar (~ 50 %) 1 and 3 hours after simeprevir exposure. For another direct acting antiviral agent, sofosbuvir, no long-lasting pre-incubation inhibitory effects on OATP1B1 function were observed. These reports show that long-lasting inhibition is not a universal factor of OATP inhibition and that the mechanisms responsible for this effect may vary between OATP transporters. The clinical relevance of pre-incubation and long-lasting OATP1B1 inhibitory effects are currently unknown and have not been explored in vitro with a range of clinically relevant probe substrates.

Table 1.3 Effect of pre-incubation with inhibitor followed by co-incubation of inhibitor with probe substrate on the OATP1B1 inhibitory potential of cyclosporine in HEK293 cells

Cyclosporine Length of IC Length of IC 50 concentration co-incubation 50 (pre-incubation Probe substrate pre-incubation (pre-incubation Reference range with probe with inhibitor) with inhibitor with buffer) (µM) substrate (µM)

0 – 3 µM Atorvastatin (0.5 µM) 1 hr 1 min 0.47 0.021 (141)

0 – 6 µM E17βG (0.02 µM) 30-45 min 3 min 0.198 0.019 (124)

0 – 10 µM E17βG (0.1 µM) 1 hr 2 min 0.046 0.014 (128)

0 – 10 µM Estrone-sulphate (0.01 µM) 1 hr 1 min 0.13 0.026 (128)

0 – 10 µM Bromosulphopthalein (0.01 µM) 1 hr 2 min 0.25 0.079 (128)

0 – 10 µM Pitavastatin (0.1 µM) 1 hr 2 min 0.1 0.026 (128)

0 – 10 µM Atorvastatin (0.1 µM)) 1 hr 1 min 0.11 0.026 (128)

1.4 Investigation of drug-drug interactions in vitro Current guidelines from governing bodies require investigations of toxicity, carcinogenicity and DDIs for a parent compound and in vitro studies investigating the propensity of a compound to cause DDIs are routine in order to avoid the occurrence of toxic drug-drug interactions (1, 11, 13). Investigation of the potential toxicity and carcinogenicity of a parent drug and metabolites which account for >30% of the elimination of the parent is recommended, as is investigation of a metabolites P450 inhibitory potential if metabolite total systemic exposure exceeds 25% of that of the parent drug. In vitro studies investigating a compound or metabolites potential to cause DDIs are typically conducted prior to the initiation of in vivo investigations and the information gained aids optimisation of further studies and compound progression (5, 13, 23, 24, 42). It is key that appropriate in vitro experiments are selected which account for physiological conditions and provide accurate and reliable data. The primary aim of interaction studies completed in these systems is to produce kinetic parameters which can be incorporated into predictive equations used to evaluate a drug or metabolites enzyme or transporter inhibitory properties and predict DDIs.

1.4.1 Investigating enzyme inhibition in vitro The enzyme inhibitory potential of a drug or metabolite is evaluated in vitro by conducting experiments assessing the depletion of a parent probe substrate or the formation of specific probe metabolites with time (5, 82, 84). This enables a linear rate to be determined and metabolism of the probe to be assessed in the absence and presence of a potential inhibitor over a range of concentrations; determining the potency of enzyme inhibition (5, 58, 84). Probe substrates must be selective for the enzymes of interest, commercially available and have a simple metabolic scheme. Examples of FDA recommended P450 probe substrates include repaglinide (CYP2C8), tolbutamide (CYP2C9) and midazolam (CYP3A4) (13).

New chemical entities are commonly screened both for their propensity to cause or be victims of DDIs and there is increasing interest in applying in vitro experiments analysing the interaction of new compounds with the UGT enzyme super-family (21, 58, 142). In order to ensure suitable experimental conditions and correct data interpretation tools are selected it is important to have a clear understanding of the enzyme kinetics involved in a reaction and establish accurate conditions for conducting inhibition studies in the in vitro system selected. Factors which may influence the in vitro metabolism, such as nonspecific substrate or inhibitor binding and pH, must be taken into account during experimental design (4, 84, 142).

Inhibition parameters include IC50 or Ki, reversible inhibition constant, values which are calculated from nonlinear regression of % control enzyme activity against a range of inhibitor concentrations (11, 143). IC50 analysis is used to investigate reversible enzyme inhibition but can also be used to assess time dependent inhibition (75, 82, 144, 145). Time-dependent inhibition can be investigated by performing IC50 experiments with and without pre-incubation of inhibitor with enzyme and suitable co-factors prior to co-incubation with the probe substrate. A pre-incubation of 30-minutes is considered sufficient for even weak inactivators of an 33

enzyme to be identified. The co-incubation assesses residual enzyme activity for the formation of probe metabolites. Time-dependent inhibition is assumed if a significantly lower IC50 is obtained following pre-incubation and the fold reduction in IC50 (IC50 shift) can be measured (75, 145).

Following this initial analysis of TDI, more complex and detailed experiments can be performed to generate kinact and KI data. kinact represents the maximal inhibition rate constant and KI the inhibitor concentration causing 50% kinact. These parameters are derived from inactivation data using a range of inhibitor concentrations following multiple pre-incubation time points. The ratio of kinact/KI is used as an indicator of the in vitro inhibitory potency of compounds or metabolites causing irreversible inhibition (11, 41, 144, 146).

1.4.2 In vitro systems for investigation of metabolic drug-drug interactions The in vitro experimental systems employed to investigate metabolic DDIs include human liver microsome (HLM) systems, S9 supernatant mixes consisting of microsomes and cell cytosol, and human hepatocyte cells (147, 148).

HLMs are a subcellular metabolising system which are readily available, cost-effective and have proved useful in the investigation of inhibitory DDIs as well as enzyme kinetic analysis (56, 76, 149). Advantages associated with this system include suitability to high throughput assays and that the enzymes and cofactors contributing to metabolism in the system can be precisely defined. As the contribution of a metabolic pathway to the overall clearance of a drug is an important factor influencing DDIs, the primary disadvantage of microsomal systems is that not all enzymes and cytosolic co-factors which are present in the physiological state are accounted for (76, 147, 150-152). Thus, a complete inhibition profile is not always obtained from experiments conducted in HLM systems. Accurate determination of DDIs is achievable but relies on the addition of the specific cofactors appropriate for the reaction under investigation, for instance UDPGA for glucuronidation reactions or combined reduced Nicotinamide Adenine Dinucleotide Phosphate (NADPH) and UDPGA cofactor conditions for the assessment of both P450 and UGT metabolism in a single system (59, 150, 153).

S9 mixes contain a wide range of microsomal and cytosolic enzymes, such as aldehyde dehydrogenase, and require the addition of suitable co-factors for metabolism to occur. Both phase I and II metabolism can be analysed and a fuller metabolism profile may be obtained than in HLMs depending on the contribution of cytosolic metabolism to the compound of interest (148, 152). The role of cytosolic enzymes has received increased interest as a result of differences between in vitro and in vivo metabolism attributed to the absence of these enzymes in the in vitro systems used (154, 155). For example, Sall et al,. (2012) (152) reported major differences in the metabolism of repaglinide between studies in HLM, S9 and human hepatocyte systems.

Active uptake and efflux of compounds by membrane transporter proteins is not accounted for in HLM or S9 systems. This may result in variation from physiological concentrations of inhibitor and substrate available to enzymes (150) and makes investigation of transporter contribution to drug metabolism and DDIs infeasible in these systems. A further difficulty encountered is that pooling of samples from a range of donors is required to account for interindividual variability in metabolising enzyme expression (11). In spite of these disadvantages, and as a result of their availability and established characterisation, human liver microsome, and more recently, S9 systems are successfully employed in vitro tools for the investigation of metabolic DDIs (152, 156, 157).

1.4.3 Investigation of inhibition of OATP1B1 in vitro The contribution of membrane transporter proteins to drug metabolism and DDIs can also be assessed in vitro (88). Experiments are conducted to quantify the contribution to hepatic uptake of a drug by measuring the uptake clearance of transporter substrates with time (158). Measurement of substrate concentration both extra- and intra-cellularly provides an indication of the extent of transporter uptake and can be assessed in the presence and absence of potential inhibitors (111). As for metabolic enzymes, the effect of pre-incubation on OATP1B1 inhibitory potential can be assessed by performing inhibition experiments with or without a pre-incubation with inhibitor. Pre-incubation lengths reported in the literature range from 30 to 60 minutes (Table 1.3). Following pre-incubation, the medium containing inhibitor is removed and replaced with fresh medium containing both inhibitor and probe substrate. The uptake of a probe substrate in the presence of multiple inhibitor concentrations is compared to uptake in the absence of inhibitor. Inhibitor concentrations and transporter expression and localization can be monitored in vitro to gain an insight into the mechanism of inhibition (125, 137).

1.4.4 In vitro systems for investigation of OATP1B1 inhibition A number of in vitro systems can be used to assess inhibition of OATP1B1 which range in their complexity from cell lines expressing a single transporter to hepatocyte systems, such as sandwich culture, suspension and 3D systems.

Human Embryonic Kidney 293 cells stably transfected with the OATP1B1 transporter are the most commonly used in vitro system for investigation of OATP1B1 inhibition. However, other cell lines such as MDCK and Chinese Hamster Ovary (CHO) cells can also be used (99, 159- 161). Cell lines have been developed which express OATP1B1 or other OATPs by cloning the human genes in stable human or animal cell lines, reducing the need for human tissue. Selection for a specific transporter is possible by co-transfecting genes with the transporter which convey resistance to a toxic agent (e.g., geneticin); this enables selection of only transfected cells expressing the transporter by adding the agent to the growth media during cell culture (129, 162). Although these systems are extremely useful for investigating the interactions of a drug with OATP1B1, any results from in vitro studies and comparisons between studies should be interpreted with care as they will be influenced by a number of factors. For instance, transporter expression can vary depending on transfection vector and 35

host cell line. In addition, they lack the multiple contributors to drug transport and metabolism present in vivo.

1.4.5 In vitro systems suitable for investigation of metabolising enzymes and OATP1B1 inhibition Co-expression of multiple transporters or transporters and enzymes such as OATP1B1- UGT1A1-MRP2 enables simultaneous investigation of interplay between specific metabolism and transport processes (163). However, primary human hepatocytes provide a whole cell system and a fuller physiological representation than subcellular systems as they contain a more complete range of enzymes, co-factors and transporters. Hepatocytes allow investigation of both phase I and phase II drug metabolism simultaneously as well as hepatic uptake by expression of specific membrane transporters (24, 56, 147, 157). Fresh liver slices, hepatocyte suspensions and isolated or cultured hepatocytes may be used if available. Although hepatocytes are considered to present the most accurate representation of the in vivo situation, their use is limited by the availability of freshly isolated hepatocytes and also inter-individual variability observed between donors (76). Cryopreserved human hepatocytes are often used to overcome the difficulties of obtaining freshly isolated cells. This method of storage ensures maintenance of important metabolising enzymes and transporter function during storage and that key functions are maintained on re-activation. The accumulation of inhibitor during inhibition studies may also be assessed in hepatocyte systems (24, 76, 164- 166). Samples from a range of donors must be pooled in order to overcome inter-individual variability but these systems are generally more expensive and difficult to obtain than subcellular systems.

1.4.6 Quantification of drug-drug interactions in vitro The formation of probe metabolite or the depletion of the parent as well the extent of substrate uptake over time can be quantified using LC-MS/MS which is rapid, highly selective and robust. LC-MS/MS enables qualitative analysis of the inhibitory characteristics of a compound and evaluation of enzyme kinetics (5, 56, 167). Either radio labelled or non-radio labelled compounds may be used (5). The transporter-mediated uptake of radiolabelled compounds over time can also be analysed by liquid scintillation counting (LSC) (168, 169). This method is highly sensitive to even small concentrations of radiolabelled compound. The inclusion of calibration and quality control samples in both LC-MS/MS and LSC analysis enables verification of the results obtained.

The analysis of in vitro enzyme and transporter inhibition data assumes standard Michaelis- Menten kinetics. Nonlinear regression of untransformed data using specialised data-fitting software is applied and kinetic parameters, such as IC50 or Ki, are generated (84, 170). It is possible for Ki values to be estimated from IC50 values, assuming that inhibition is competitive and that the substrate concentration used is equal to or less than its Km for the enzyme inhibited, by dividing the corresponding IC50 value by two (171). Correct use of in vitro inhibitory information may enable prediction of a compounds metabolism and in vivo 36

pharmacokinetics in the presence of an inhibitor and offers a quantitative approach to improving decision making in drug development and discovery (80, 172-175).

1.4.7 Prediction of drug-drug interactions The data and kinetic parameters generated from in vitro experiments can be used to perform in vitro-in vivo extrapolation (IVIVE) and predict the magnitude of DDIs (11, 24, 80, 84). Specialised models are required to predict the behaviour of a compound and a range of modelling options exist which perform static, dynamic and physiologically based pharmacokinetic modelling (PBPK), some of which are now implemented in commercially available software (7, 10, 176, 177). Using these tools during drug development enables quantification of DDI risk from in vitro data which can help guide decision making about the requirement for further in vitro or in vivo investigations (178, 179). Each of these modelling approaches has advantages and disadvantages. These include a risk of under prediction of DDI risk if using static models as they cannot always account for all components involved in a DDI and are based on the use of single inhibitor and substrate concentrations. However, the relatively small amount of input data required makes these models an accessible option in early drug development where limited information may be available for a victim or perpetrator drug (180). In contrast, far more drug and physiological parameters are required for PBPK models which challenge their application and verification (174, 180). However, PBPK models are increasingly applied during drug development and have been reported to accurately predict the extent of complex DDIs involving multiple inhibition mechanisms and inhibitors (124, 181, 182).

A clear understanding of the links between in vitro and in vivo experimental results is still developing. Basic models, such as that shown in Equation 1.2, are applied which predict the magnitude of DDIs by either metabolic enzymes or transporters, and can be used to rank inhibitory potency. These are qualitative models which make assumptions such as that the inhibited metabolic or transporter pathway is the only route of elimination, inhibition is competitive and the inhibitor does not affect the absorption or plasma protein binding of the substrate.

푨푼푪′ 푰 Equation 1.2 = ퟏ + 푨푼푪 푲풊

AUC’ and AUC represent the AUC of the victim drug in the presence and absence of perpetrator, respectively. Ki is the unbound reversible inhibition constant determined in vitro (5, 176). For enzyme inhibition [I] is the inhibitor concentration available to the enzyme, an estimated value approximated to the inhibitor plasma concentration in the systemic circulation, and a range of surrogates are used for modelling purposes (37, 80, 178, 179, 183). The inhibitor concentration used as a surrogate for [I] for predicting transporter DDIs depends on 37

the location of the transporter in question. The estimated hepatic input concentration is thought to be the most representative for hepatic uptake transporters, such as OATP1B1 (184). When the substrate concentration used in inhibition experiments is much lower than the Km value, the distinction between competitive and non-competitive inhibition mechanisms is not relevant and Equation 1.2 may be utilised. Time-dependent inhibition requires initial binding of an inhibitor to the active site of the target enzyme as a substrate and it is susceptible to competitive inhibition. Therefore, DDIs resulting from time-dependent enzyme inhibition can be predicted using inhibition data obtained following pre-incubation and an analogous model to that used for reversible enzyme inhibition provided that the substrate concentration is << Km and assuming that the time-dependent inhibitor behaves similarly to a competitive inhibitor (75, 81, 145).

Equation 1.2 can be extended to incorporate the contribution of clearance of unchanged drugs, parallel metabolic pathways and multiple mechanisms of interaction to DDIs (Equation 1.3). The contribution of intestinal interactions is also incorporated based on a hepatic enzyme interaction as the ratio of the intestinal wall availability in the presence (FG′) and absence (FG) of the inhibitor. Changes in the intrinsic clearance (CLint) of a victim drug in the presence of a perpetrator can be described by mechanistic equations depending on the actual mechanism of interaction and multiple mechanisms may be considered (11, 185).

푨푼푪′ 푭푮′ ퟏ Equation 1.3 = 풙 푨푼푪 푭 풏 풇풎푪풀푷풊 풏 푮 ∑풊 + (ퟏ− ∑풊 풇풎푪풀푷풊) 푪푳풊풏풕⁄푪푳풊풏풕′

Where the prime superscript indicates the parameter in the presence of an inhibitor. fmCYPi denotes the fraction of a victim drugs clearance accountable to the particular P450 enzyme subject to the inhibition effect, i indicates the existence of multiple enzymes (n), (1 - ∑fmCYPi) represents clearance by other P450 enzymes and/or renal clearance and the terms i and j indicate the potential to incorporate the existence of multiple enzymes and inhibitors. Assuming competitive inhibition this model can be adapted to predict OATP1B1 mediated DDIs provided the fraction of victim drug transported by OATP1B1 is known (186).

Complex mechanistic models may also be applied which incorporate equations such as those shown in Table 1.4. These models can account for the net effect of enzyme induction and multiple inhibitory mechanisms as well as multiple inhibitory species and provide quantitative estimates of DDIs (11, 41, 187, 188). Multiple inhibitory species, such as a parent drug and its metabolite, may alter the extent of a DDI by both inhibiting the same enzyme, inhibiting the same enzyme but by a different mechanism or by inhibiting different enzymes.

Mechanism Mechanistic Equation for CLint ratio Hepatic 푪푳풊풏풕 Intestinal 푪푳푮 푪푳풊풏풕′ 푪푳푮′ Reversible CL m CL m a int 1/IK int 1/IK inhibition   j ij  G j i j CLint ' j CLint ' j

Irreversible m m CL kIinact j   CL kIinact j G  inhibitionb int 1 int 1   CLint ' j kdeg  KIj  I  CLint ' j kdeg  KI j I G 

Inductionc CL 1 CL 1 int  int  CL ' m EI  CL ' m EI  int 1  max j int 1  max jG j I EC50 j j IGj EC50 a where [I] is the inhibitor concentration available to the enzyme and Ki is the inhibition constant, [IG] refers to concentration in the intestine during absorption phase. b where kinact is the maximal inactivation rate constant, KI the inhibitor concentration at 50% of kinact and kdeg the endogenous degradation rate constant of the enzyme. C where Emax is the maximal induction effect and EC50 the concentration at 50% of Emax. In each case the possibility of in inhibitory species is accounted for with the subscript j.

The quality of the predictions made by a model are dependent on the input data and it must be considered that there are factors, such as multiple inhibition mechanisms, contributing to in vivo metabolism that are not incorporated into the modelling situation due to a lack of suitable data. Problems encountered with IVIVE include false negative and false positive predictions (5, 11, 189). False negative predictions are of particular concern to decision making in drug development and therefore use of simple models which provide the most conservative estimate of DDI risk are the recommended first line analysis in regulatory guidelines (13, 14). Factors which can influence IVIVE include the impact of parallel pathways of elimination on the risk of DDIs, protein binding and multiple mechanisms of inhibition (11, 143). Development of more complex, physiologically based pharmacokinetic models which account for the interplay between and inhibition of multiple metabolising enzymes and transporters in vivo, and can incorporate the inhibitory properties of metabolites are leading to improved predictions of DDIs. The risk of DDIs can be investigated using models developed in house in softwares such as NONMEM and Matlab (124, 190) or in commercially available softwares containing suitable models, such as SimCYP (133, 182). However, in line with the increased complexity of these models, the input data required for inhibitors and victim drugs is far more extensive than that necessary for more basic models (182, 189-191).

1.5 The contribution of metabolites to metabolic drug-drug interactions Metabolites, like parent compounds, may interact with metabolising enzymes either reversibly or irreversibly and elicit a desirable or undesirable biological effect. Though the majority of drug metabolism results in the formation of stable metabolites the characterisation of a metabolites pharmacological properties is a key component in support of safety studies; metabolite identification is normally initiated during the compound selection phase of drug discovery (192, 193). The effects of a metabolite are related to its circulating concentration which is influenced by factors such as the stability of the metabolite in the plasma or liver and protein binding. Despite metabolism being a clearance route, in many cases metabolites circulate in plasma in abundance, for example hydroxynefazodone AUC values are 2-fold greater than those of its parent drug (194). Our understanding of how metabolites reach the circulation and why they are present at high concentrations is limited (17, 195).

The role of metabolites in inhibitory DDIs is not well characterised; however, as the majority of currently administered drugs have circulating metabolites, many of which have enzyme inhibitory properties, metabolite propensity to cause DDIs requires further investigation (23, 64, 196). Examples of metabolites with enzyme inhibitory properties that contribute to DDIs include hydroxyitraconazole which has CYP3A inhibitory effects and norfluoxetine which inhibits CYP2D6 (23, 180, 197). N-desmethyldiltiazem inhibits CYP3A4, contributing to in vivo DDIs, and fenfluramine was removed from the American market as a result of valvular heart disease associated with its metabolite norfenfluramine (19, 195). The FDAs metabolites in safety testing guidelines recommend investigation of a metabolites P450 interaction potential if present at > 25% of parent AUC following the strategy outlined in Figure 1.4 (13). The guideline also recommends that metabolites with potential to cause toxic DDIs, such as acyl glucuronides, should be subjected to safety studies (13, 198). In addition to this, the EMA recommends investigation of metabolites contribution to DDI if they account for > 10% of total drug exposure and the JMHLW recommend investigating enzyme inhibition effects of any phase I metabolite whose AUC accounts for at least 10% of the total AUC of drug-related substances and at least 25% of the AUC of the parent drug following a similar strategy to that shown in Figure 1.4 (14, 15).

Evaluation of metabolite contribution to DDIs requires identification of the enzymes responsible for formation of the metabolite, knowledge of circulating and intracellular concentrations of metabolites, the enzyme inhibitory potency of the metabolites and the impact of the metabolite on the kinetics of the parent compound (23, 33, 179, 186). The precise properties governing the propensity of a metabolite to cause DDIs and the molecular mechanisms involved are not always clear and there is a lack of information regarding metabolite disposition in the literature (199). Thus, the effects of active metabolites have not been greatly incorporated into model development assessing DDIs. Investigation of the physicochemical properties and enzyme and transporter inhibitory potency of metabolites is 40

necessary to improve our understanding of DDIs, to optimise the experimental analysis of metabolite induced DDIs and for the development of improved mathematical models which enable accurate prediction of metabolite DDI potential (124, 176, 199, 200).

1.6 The contribution of metabolites to transporter drug-drug interactions In general, metabolites have an increased propensity for transporter facilitated distribution in comparison to their parent drugs as a result of altered physicochemical properties, such as lipophilicity, which enhance their excretion (17, 25). This presents a potential site for DDIs which is increasingly recognised to be of clinical relevance (133, 201). Current guidelines recommend pre-clinical toxicology studies for metabolites accounting for 10% of a bioavailable dose in human excreta and exposure based investigations of enzyme inhibitory potential. However no specific investigation of metabolites inhibitory effects on transporters are suggested (13, 14, 201). Limited quantitative information is available regarding metabolite- transporter interactions and, unlike that reported for metabolic DDIs, there is currently no clear classification system describing the strength of transporter-based DDIs. Although metabolites such as ezetimibe glucuronide and cyclosporine AM1 have been reported to potently inhibit hepatic transporters in vitro (IC50 < 1 µM) (124, 202), understanding of the physicochemical factors governing metabolite-transporter interactions is incomplete and predictive modelling of transporter contribution to DDIs has been limited.

1.7 The contribution of glucuronide metabolites to drug-drug interactions As a result of their prevalence, potential to inhibit P450 enzymes and interact with hepatic transporters, glucuronide metabolites are of increasing importance in the area of metabolite DDIs (25, 203, 204). Many glucuronides are non-DDI inducing, easily-excreted substances, however others, such as morphine glucuronides, are pharmacologically active (205) while some cause adverse drug reactions. For example furosemide acyl glucuronide is associated with jaundice and hypersensitivity reactions and zomepirac, which also has an acyl glucuronide metabolite, was withdrawn from use as a consequence of its association with idiosyncratic toxicity related to the chemical instability of this metabolite under physiological conditions (198, 203, 206). The pharmacological or toxicological properties of these metabolites depend on their physicochemical characteristics. N-O-glucuronides and acylglucuronides are considered to be of most importance to toxicity though there is variation in the extent of reactivity between glucuronide conjugates within these groups (20, 207, 208).

Glucuronide conjugates may be eliminated by renal or biliary excretion facilitated in most cases by MRP2 transporters (17, 59). The latter can result in enterohepatic circulation; the process whereby drugs absorbed from the intestine to the systemic circulation via the portal vein can return to the intestine via the bile duct. An increasing number of glucuronides, for example troglitazone glucuronide, are reported to be substrates of basolateral MRP3 and 4 transporters which facilitate transport from hepatocytes directly into the blood (209). The OATP1B1 and OATP1B3 hepatic uptake transporters have also been reported to facilitate movement of glucuronides e.g., sorafenib glucuronide, across the basolateral membrane of

hepatocytes in the opposite direction to MRP3 and 4 transporters (169). The interplay between these efflux and uptake transporters has been reported to cause liver-to-blood shuttling, dubbed ‘hepatocyte hopping,’ of bilirubin and sorafenib glucuronides in mouse models (Figure 1.5). This is considered to be a detoxification process which prevents saturation of biliary excretion of glucuronides in upstream hepatocytes (204, 210, 211). Both hepatocyte hopping and the biliary excretion of glucuronides and parent drugs may be of relevance to glucuronide toxicity and DDIs in man (211, 212).

1.7.1 Inhibition of metabolising enzymes by glucuronides The enzyme inhibitory potency of glucuronide conjugates has not been fully established and the predisposition of parent compounds to form reactive metabolites requires investigation.

However, IC50 or Ki data have been reported in the literature for a total of 15 glucuronides against a range of metabolising enzymes, as summarised in Table 1.5 and Figure 1.6. Full details of study design and references are provided in Appendix Table 6.1. Parent drugs with glucuronide metabolites have been reported to inhibit an even wider range of metabolising enzymes, illustrated in Figures 1.6 and 1.7, against which glucuronide metabolites have not been explored. Therefore, systematic investigation of the inhibitory potential of glucuronides against these enzymes and analysis of the effect of additional glucuronide-parent pairs to enable comparison and identification of trends is warranted.

The majority of glucuronide P450 inhibition data have been reported for CYP2C8 (22/26 P450

IC50 or Ki data) (Figure 1.7), reflecting the increasing recognition of its potential role in DDIs.

Other P450 enzymes for which glucuronide mediated inhibition data have been reported include CYP2B6, CYP2D6, CYP2C9 and CYP3A4. Fewer glucuronides had been investigated against these enzymes and inhibition is generally less potent than that reported for CYP2C8 (Table 1.3, Figure 1.8). However, the P450 and UGT inhibitory potential of glucuronides has been reported to be similar to or more potent than the parent drugs where comparisons have been made (Figure 1.8). For example, the CYP3A4 inhibitory potency of clopidogrel glucuronide was ~ 6-fold greater than that of its parent drug (213). Further research with a larger dataset of glucuronides and a broader range of metabolising enzymes is required to explore any trends in glucuronide-parent enzyme inhibitory potential and the mechanisms involved.

Figure 1.6 Metabolising enzymes inhibited in vitro by glucuronides (A) and parent drugs (B).

A total of 40 and 382 IC50 or Ki values were reported for glucuronides and their parent drugs inhibiting metabolising enzymes, respectively. Full details of study design and references are provided in Appendix Table 6.1

A large range in glucuronide CYP2C8 inhibitory potency has been reported, for example gemfibrozil glucuronide caused > 100-fold more potent inhibition than clopidogrel glucuronide in HLM when montelukast and amodiaquine were used as probes, respectively (214, 215). However, probe substrate selection and experimental conditions clearly influenced the extent of CYP2C8 inhibition, for example clopidogrel glucuronide IC50 values of approximately 4 µM and 195 µM were reported using cerivastatin as a probe substrate in recombinant microsomes and using amodiaquine as a probe substrate in HLM, respectively (213, 216). These factors complicate comparisons of the CYP2C8 inhibitory properties of different glucuronide metabolites. Time-dependent inhibition of CYP2C8 has been reported for gemfibrozil and clopidogrel glucuronides, significantly enhancing their CYP2C8 inhibitory potential by up to 100- and 16-fold, respectively, in HLM (156, 216, 217). These glucuronides were reported to cause more potent CYP2C8 inhibition through a different inhibitory mechanism to their parent drugs when investigated in the same study. The greatest difference in glucuronide-parent inhibitory potency was reported for gemfibrozil glucuronide which caused up to 35-fold more potent CYP2C8 inhibition than gemfibrozil (214). TDI has not been reported for any other glucuronides or enzymes but was not explored in the majority of cases and therefore the effect of pre-incubation on glucuronide inhibitory potency in general is unknown.

A further potential mechanism by which glucuronide metabolites may contribute to enzymatic DDIs is by inhibition of UGT enzymes (Table 1.5). This may limit the glucuronidation of a victim drug and result in increased exposure to a parent compound. An example of this is gemfibrozil inhibiting the UGT1A1 metabolism of repaglinide which occurs in a time dependent manner in human liver microsome systems and is thought to contribute to the increased repaglinide exposure observed following co-administration of these compounds (218). Thus, UGT inhibition may be an important contributor to DDIs and may need to be accounted for in experimental analysis and any modelling of in vitro data. This emphasises that the synergistic effects of multiple interaction mechanisms need to be understood in order to improve our approach to the investigation of glucuronide associated DDIs (38, 203, 218).

Glucuronide IC Parent IC range Parent drug Enzyme 50 50 Probe substrates References range (µM) (µM) Epigallocatechin * Catechol-O-Methyltransferase 1.8 - 2.3 0.07 - 0.08 2-hydroxyestradiol, 4- (219) Epigallocatechin * Catechol-O-Methyltransferase 2.5 - 4 0.07 - 0.08 hydroxyestradiol Epigallocatechin * Catechol-O-Methyltransferase 0.6 - 0.8 0.07 - 0.08 Canagliflozin CYP2B6 55 16 hydroxybupropion (220) Canagliflozin CYP2C8 64 75 amodiaquine Atorvastatin CYP2C8 45 (35) 21.9 (22.5) amodiaquine (217) Clopidogrel CYP2C8 3.7 - 340 (12 - 280) 2.8 - 49 (24 - 117) amodiaquine, paclitaxel, (213, 215, 216, cerivastatin 221) CYP3A4 27 4.7 cerivastatin Carboxyl Esterase 1 (CES1) 24.8* NA 4-nitrophenol Diclofenac CYP2C8 14 (9.6) 54 (57) amodiaquine (217, 221) Carboxyl Esterase 1 (CES1) 4.32* NA 4-nitrophenol Gemfibrozil CYP2C8 3 - 67 (0.46 - 2.3) 36 - 120 (102 - 150) amodiaquine, (33, 156, 214, 215, cerivastatin, paclitaxel 217, 218) CYP3A4 267 372 cerivastatin UGT1A1 25% (69 - 130) 113 repaglinide CYP3A4 267 372 cerivastatin Indomethacin CYP2C8 26 (26) 88 (> 200) amodiaquine (217)

Glucuronide IC Parent IC range Parent drug Enzyme 50 50 Probe substrates References range (µM) (µM) Mefenamic acid CYP2C8 8.5 (8.4) 14.9 (22) amodiaquine (217) Simvastatin CYP2C8 3.8 (3.5) 8.3 (10.2) amodiaquine (217) Vidupiprant * CYP2C8 2.7 - 7.3 1.1 - 6 montelukast, paclitaxel, (222) rosiglitazone Troglitazone CYP2C8 25% 5 paclitaxel (223) Ketoprofen CYP2C8 26 (22) > 200 (> 200) amodiaquine (217) (S)-naproxen* Carboxyl Esterase 1 (CES1) 707 NA 4-nitrophenol (221) (R)-naproxen * Carboxyl Esterase 1 (CES1) 468 NA 4-nitrophenol Ibuprofen * Carboxyl Esterase 1 (CES1) 355 NA 4-nitrophenol

* Ki not IC50 data NA parent drug inhibitory potential not assessed in same study as glucuronides % Percent of enzyme mediated metabolism inhibited at the maximum inhibitor concentration relative to control metabolism in the absence of inhibitor

1.7.2 Glucuronides and parent drugs inhibiting the OATP1B1 transporter As a result of the role of OATP1B1 in the transport of many drugs its potential contribution to DDIs is increasingly recognised. Glucuronide metabolite interactions with the OATP1B1 transporter have not been well characterised to date, however, reports of inhibition do exist (Table 1.6). The most potent in vitro inhibition has been reported for ezetimibe glucuronide

(IC50 0.15 µM) (202), indicating that transporter function could be significantly altered following administration of the parent drug, potentially influencing the pharmacokinetics of co- administered drugs. Gemfibrozil glucuronide, its parent compound and ezetimibe are reported to be less potent inhibitors of OATP1B1 highlighting variation between glucuronides OATP1B1 inhibitory potency and also that parent and glucuronide metabolites inhibitory potential may not correlate; the greatest difference was for ezetimibe where the glucuronide caused 100- fold more potent inhibition than parent (202). In contrast, clopidogrel parent drug caused 2- fold more potent OATP1B1 inhibition than its glucuronide indicating that glucuronides are not universally more potent inhibitors than their parent drugs (224). OATP1B1 inhibition has been reported for additional parent drugs which have glucuronide metabolites e.g., diclofenac, repaglinide and telmisartan, where glucuronide inhibitory potential has not been explored (225, 226).

Limited information on substrate-dependent inhibition or the use of clinically relevant probes for investigation of OATP1B1 inhibition by glucuronides is available. However, variation in inhibitory potency has been reported for glucuronides and parent drugs where OATP1B1 inhibition has been explored with multiple probes in same study. For example, clopidogrel glucuronide IC50 values of 10.9 µM and 33.5 µM were reported with cerivastatin and estrone- sulphate, respectively, as probe substrates in HEK293 cells (224). To date, a number of different in vitro systems and different probe substrates have been used for investigation of glucuronide OATP1B1 inhibition making direct comparison of glucuronide inhibitory potential difficult. The effect of pre-incubation on OATP1B1 inhibitory potency has not been explored for glucuronide metabolites.

A number of parent drugs and glucuronides have been reported to be substrates of OATP1B1 though uptake data is scarce. Ezetimibe and mycophenolic acid glucuronides have been reported to be substrates of OATP1B1 as have sorafenib and its glucuronide (91, 169, 202). As very few compounds had been analysed as both inhibitors and substrates of OATP1B1 no clear conclusions regarding the mechanisms by which glucuronide metabolites may inhibit OATP1B1 can yet be made. Investigation of glucuronide metabolite OATP1B1 inhibition would therefore improve understanding of the potential role of this transporter in glucuronide mediated DDIs.

Inhibitor Inhibitor concentration Probe substrate Cell system IC50/Ki (µM) Reference

Clopidogrel acyl glucuronide 0.1-100 µM cerivastatin HEK293-transfected 10.9 (224)

Clopidogrel acyl glucuronide 0.1-100 µM estrone-sulphate HEK293-transfected 33.5 (224)

Clopidogrel 0.1-100 µM cerivastatin HEK293-transfected 3.95 (224)

Ezetimibe glucuronide 0.001-1 µM bromosulphopthalein HEK293-transfected 0.15 (202) Ezetimibe Not Available bromosulphopthalein HEK293-transfected 14.8

Gemfibrozil glucuronide 0-300 µM cerivastatin MDCK-transfected cells 24.3 (33)

Gemfibrozil 0-300 µM cerivastatin MDCK-transfected cells 72.4 (33)

Gemfibrozil glucuronide Not Available pitavastatin HEK293-transfected 22.6 (168)

Gemfibrozil Not Available pitavastatin HEK293-transfected 25.2 (168)

Gemfibrozil glucuronide 20 µM pravastatin Hepatocytes (cryopreserved) 9.3 (227)

Gemfibrozil 20 µM pravastatin Hepatocytes (cryopreserved) 35.8 (227)

Gemfibrozil glucuronide 20 µM pravastatin X. laevis oocytes-injected 7.9 (227)

Gemfibrozil 20 µM pravastatin X. laevis oocytes-injected 15.5 (227)

Inhibitor Inhibitor concentration Probe substrate Cell system IC50/Ki (µM) Reference

Raloxifene 4-beta-glucuronide 10 µM estrone-sulphate CHO-transfected 70% (228)

Raloxifene 10 µM estrone-sulphate CHO-transfected 40% (228)

Troglitazone glucuronide 10 µM estrone-sulphate X. laevis oocytes-injected 70% (229)

Troglitazone 10 µM estrone-sulphate X. laevis oocytes-injected 60% (229)

% Percent of transporter uptake activity inhibited at the maximum inhibitor concentration relative to control uptake in the absence of inhibitor

1.8 Project Aims The mechanisms underlying the involvement of metabolites in drug-drug interactions remain incompletely understood. Due to the prevalence of glucuronidation as a route of metabolism there is a need for improved understanding of the potential contribution of these metabolites to DDIs and of the mechanisms facilitating this process. The overall aim of this project is to evaluate the potential of glucuronide metabolites of a range of therapeutic compounds to interact with specific P450 and UGT enzymes and the uptake transporter OATP1B1. The contribution of the inhibition of uptake transporter and enzymes to the DDI potential of these metabolites will be assessed in conjunction with corresponding parent drugs.

In Chapter 2, kinetic parameters describing the extent of enzyme inhibition for 10 glucuronides were generated in HLM by performing IC50 experiments. Repaglinide was used as a marker of CYP3A4, CYP2C8 and UGT1A1, enabling simultaneous assessment of the effects of glucuronides on all three enzymes by monitoring the formation of three separate probe metabolites. Glucuronides of interest and their parent drug P450 inhibitory potential was further explored in IC50 experiments optimised for P450 metabolism to allow direct comparison of parent–metabolite inhibitory potency without glucuronidation of the parent drug. In addition, the effect of co-factor conditions on glucuronide P450 inhibitory potential was assessed. The TDI potential of all inhibitors was explored against all enzymes by inclusion of a 30-minute pre-incubation with inhibitor prior to addition of probe substrate. The effect of pre-incubation with inhibitor on glucuronide and parent drug enzyme inhibitory potential was compared.

The aim of Chapter 3 was to assess the inhibitory potential of 10 glucuronide metabolites against the OATP1B1 hepatic uptake transporter in stably transfected HEK293 cells using the prototypical probe substrate estradiol-17-β-glucuronide. The effect of a 30-minute pre- incubation on the OATP1B1 inhibitory potential of all glucuronides was investigated. Further to this, the OATP1B1 inhibitory potential of parent drugs of glucuronides of interest was investigated and glucuronide-parent inhibitory potential and time-dependent effects compared. In order to explore possible substrate-dependent inhibition of OATP1B1 by glucuronides and their parent drugs, additional experiments using the clinically relevant probe substrate pitavastatin were conducted for compounds of interest. Inhibition experiments using pitavastatin as a probe substrate were also conducted with and without a pre-incubation with inhibitor and substrate-dependent pre-incubation effects were evaluated. The OATP1B1 inhibitory potential of all compounds was compared to physicochemical properties previously reported to be associated with OATP1B1 inhibition.

The final aim, as described in chapter 4, was to assess the in vivo DDI potential of glucuronides using basic and static mechanistic models. In this chapter, reported clinical plasma data (AUC, Cmax) for glucuronide metabolites, parent drugs and metabolites of potent P450 inhibitors collated from the literature were evaluated with respect to the FDA 25% exposure limit. The clinical exposure data obtained, in combination with in vitro OATP1B1 and

52 enzyme inhibition parameters generated in Chapters 2 and 3, were used to predict DDIs using mechanistic static models. Glucuronide CYP2C8 DDI potential was predicted alone and in combination with parent drugs for a subset of compounds. Where possible, glucuronide and parent drug DDI potential were compared and the accuracy of predictions assessed by comparison to observed DDIs reported in the literature.

1.9 Compounds selected for investigation Following comprehensive analysis of literature data 38 glucuronides in total were identified with either clinical glucuronide metabolite exposure data or data indicating inhibition of either metabolising enzymes or transporters. Based on available glucuronide standards and the data collated, 10 glucuronides were selected for initial experimental investigation. It should be noted that compound selection for this thesis was conducted prior to several recent publications reporting the inhibitory effects of glucuronides on OATP1B1 or CYP2C8 (216, 217).

1.9.1 Gemfibrozil acyl-β-D-glucuronide Gemfibrozil is a lipid-regulating agent prescribed to lower cholesterol levels. Gemfibrozil glucuronide concentration-time profiles have been characterised in man and in vitro glucuronide formation data are also available (230, 231). Glucuronidation has been reported to account for up to 50% of the metabolism of the parent drug in vivo in rats (232). The glucuronide metabolite is predominantly formed by UGT2B7 in the liver and undergoes hydroxylation by CYP2C8 (156, 233). Gemfibrozil glucuronide has been reported to be a substrate of MRP2, 3 and 4 in vitro which facilitate its removal from hepatocytes into bile (MRP2) and blood (MRP3 and 4) (209). CYP2C8, CYP3A4 and OATP1B1 inhibition by gemfibrozil glucuronide has been reported in vitro with the CYP2C8 inhibition occurring in a time dependent manner involving formation of a hydroxyl-glucuronide metabolite (33, 156, 186). Both parent and glucuronide metabolites have been observed to cause UGT1A1 inhibition in vitro (218).

Figure 1.9 Chemical structure of gemfibrozil acyl-β-D-glucuronide

1.9.2 Clopidogrel acyl-β-D-glucuronide Formation of clopidogrel acyl-β-D glucuronide has been characterised in man and involves hydrolysis of the parent drug followed by conjugation of the hydrolysis product. However, the specific UGT enzymes responsible for formation of clopidogrel acyl-β-D glucuronide have not been not indicated (207, 216). Time-dependent inhibition of CYP2C8 has been reported for the glucuronide in vitro as has inhibition of CYP3A4. CYP2C8, CYP3A4, CYP2B6 and 2C19 inhibition by the parent compound has been observed in vitro. The parent has been reported to cause TDI of CYP3A4 and be more potent than the glucuronide. Both the glucuronide and the parent are reported to inhibit OATP1B1 in vitro (78, 216, 224, 234).

Figure 1.10 Chemical structure of clopidogrel acyl-β-D-glucuronide

1.9.3 Raloxifene-4’-glucuronide Raloxifene is a selective estrogen receptor modulator used to prevent osteoporosis. Raloxifene glucuronide formation has been observed in vitro and in man. The UGT1A1, 1A8, 1A9 and 1A10 enzymes have the highest activity for raloxifene glucuronidation; raloxifene-4-glucuronide, raloxifene-6-glucuronide, raloxifene-6,4'-di-glucuronide metabolites are produced (66, 235-237). In human studies raloxifene-4’-glucuronide has been shown to be the main metabolite in plasma and intestinal glucuronidation is an important contributor to pre-systemic clearance of raloxifene (237, 238). CYP3A4, 2B6 and 2C8 inhibition by the parent compound has been observed in vitro (239-241). Both the parent drug and glucuronide metabolite have been indicated as substrates of OATP and MRP2 transporters and inhibitors of OATP1B1, OATP1B3 and MRP2 transporters (228, 235, 242).

Figure 1.11 Chemical structure of raloxifene-4-glucuronide

1.9.4 Ezetimibe phenoxy-β-D-glucuronide Ezetimibe is an anti-hyperlipidemic medication prescribed to lower cholesterol levels. Formation of ezetimibe phenoxy-β-D-glucuronide, the main circulating metabolite of ezetimibe in human plasma, has been reported in vivo (243). In vitro, glucuronide formation is performed predominantly by UGT1A1, 1A3 and 2B15 enzymes; phenoxy-β-D-glucuronide and hydroxy- β-D-glucuronide metabolites are produced (244). CYP3A4 inhibition by the parent compound has been observed in vitro. The glucuronide is a potent inhibitor of OATP1B1 and OATP1B3 in vitro (IC50 < 1µM) and more potent than the parent compound. The glucuronide has also been reported to be a substrate of OATP1B1, OATP2B1 and MRP2 transporters (147, 163, 202).

Figure 1.12 Chemical structure of ezetimibe phenoxy-β-D-glucuronide

1.9.5 Mefenamic acid acyl-β-D-glucuronide Mefenamic acid is a widely prescribed non-steroidal anti-inflammatory drug associated with toxicities which may be a result of formation of glucuronide metabolites. No clinical exposure data for mefenamic acid acyl β-D-glucuronide were available in the literature; however, it has been reported to be the major metabolite of mefenamic acid and in rats with reported glucuronide: parent AUC ratios of 29% (245-247). Glucuronide formation has been observed in vitro though the specific UGTs responsible have not been identified. Inhibition of CYP2C8 has been reported for the glucuronide in vitro and inhibition of CYP2C8 and CYP1A2 by the parent drug has also been reported (206, 217, 248).

Figure 1.13 Chemical structure of mefenamic acid acyl- β-D-glucuronide

1.9.6 Mycophenolic acid β-D-glucuronide Mycophenolic acid is formed following hydrolysis of the pre-drug mycophenolic mofetil, an immunosuppressive agent. Mycophenolic acid has been reported to undergo glucuronidation in vivo and in vitro with UGT2B7, UGT1A10 and UGT1A9 identified as the major metabolising enzymes responsible (249-251). Acyl-β-D-glucuronide and β-D-glucuronide metabolites are produced in the liver; however, extra hepatic glucuronidation also occurs in the intestine and kidney (62, 252, 253). The β-D-glucuronide is the primary metabolite; and is a substrate of OATP1B1, OATP1B3, OAT3 and MRP2 transporters (91, 252, 254, 255).

Figure 1.14 Chemical structure of mycophenolic acid β-D-glucuronide

1.9.7 Repaglinide acyl-β-D-glucuronide Repaglinide is an oral prandial glucose regulator used in the treatment of diabetes for which glucuronidation has been as an important metabolic pathway in vivo. In vitro, the formation of repaglinide acyl-β-D-glucuronide is performed predominantly by UGT1A1 and UGT1A3 enzymes in the liver (152, 218).The parent drug is a recommended in vivo probe substrate of both CYP2C8 and OATP1B1 and inhibition of OATP1B1 by the parent compound has been observed in vitro (13, 226).

Figure 1.15 Chemical structure of repaglinide acyl-β-D-glucuronide

1.9.8 Diclofenac acyl-β-D-glucuronide Diclofenac is a non-steroidal anti-inflammatory agent for which glucuronidation contributes to approximately 20 % of total metabolic clearance (256).Glucuronidation of diclofenac has been observed in vitro, performed predominantly by UGT2B7, 1A3 and 1A9 (62, 257). Diclofenac β-D-glucuronide and acyl-β-D-glucuronide metabolites are produced. The acyl glucuronide is the dominant form in human excretions and is hydroxylated by the CYP2C8 enzyme (47, 257). Both the parent drug and acyl-β-D-glucuronide inhibit CYP2C8 in vitro (217). The parent compound is an inhibitor of OATP1B1, OATP1B3, UGT1A1 and 2B7 in vitro and a substrate of OATP1B3 (110, 258-260).

Figure 1.16 Chemical structure of diclofenac acyl-β-D-glucuronide

1.9.9 Telmisartan acyl-β-D-glucuronide Telmisartan acyl-β-D-glucuronide is the only metabolite of telmisartan, an angiotensin II receptor antagonist used in the management of hypertension, reported in humans. The concentration-time profile of this metabolite in man has been reported and UGT1A3, 1A1 and 1A9 identified as the metabolising enzymes responsible for its formation in the intestine and the liver (261-263). The glucuronide metabolite and parent are reported to be substrates of OATP1B3 but not OATP1B1 (111, 264). In vitro inhibition of OATP1B1, CYP2C9 and CYP2J2 has been reported for the parent drug (168, 265, 266).

Figure 1.17 Chemical structure of telmisartan acyl-β-D-glucuronide

1.9.10 Raltegravir β-D-glucuronide Raltegravir is an antiretroviral drug used to treat HIV infection. Circulating concentrations of raltegravir glucuronides have been quantified in man (267). In vitro, UGT1A1 is the predominant enzyme responsible for formation. The parent drug has been reported to inhibit

UGT1A1 by 20% in vivo and in vitro IC50 values have been characterised (268-270). The parent is a substrate of the OAT1 and MRP2 transporters and an inhibitor of OAT1, OAT3 and OATP1B3 (271-273).

Figure 1.18 Chemical structure of raltegravir β-D-glucuronide

Chapter 2 In vitro assessment of inhibition of metabolising enzymes by glucuronide metabolites

2.1 Introduction The inhibition of metabolising enzymes remains a cause of concern in drug development due to its recognised contribution to the magnitude of drug-drug interactions (DDI) (10, 11, 13, 179, 188, 274, 275). P450 enzymes and associated DDIs have been extensively studied in this area as a result of their major contribution to drug metabolism (30, 274, 276). However, other metabolising enzymes, such as the UGTs, are increasingly investigated due to recognition of their potential involvement in DDIs (38, 144).

Consideration of the contribution of multiple inhibitors (e.g., parent and metabolite as in the case of itraconazole and hydroxyitraconazole) and/ or multiple inhibition mechanisms (e.g., reversible and irreversible inhibition) is important in the assessment of DDIs (23, 41, 186, 197, 199, 200). The latest Food and Drug Administration (FDA) guidance recommends in vitro investigation of the effect of metabolites on P450 enzymes if present at ≥ 25% parent systemic exposure. In addition, the European Medicines Agency (EMA) recommends investigation of metabolites’ contribution to DDI if they account for > 10% of total drug exposure (13, 14). The potency and mechanism of inhibition by metabolites must be considered, together with any potential synergistic effects of the parent and metabolite in vivo (11, 18, 277).

Direct glucuronidation is a major conjugation pathway and represents the main metabolic pathway for 46% of the top 200 prescribed drugs (USA) eliminated by metabolism (278). In addition, available literature data indicate that in many cases glucuronides, for example zidovudine and morphine glucuronides (279, 280), exceed the FDA exposure limit requiring investigation of P450 inhibitory potential. Glucuronides have been reported to cause renal toxicity (e.g., diclofenac glucuronide) and to inhibit metabolic enzymes and certain transporters (e.g., gemfibrozil glucuronide inhibits CYP2C8 and OATP1B1) (21, 186, 281). Recently reported clinical DDIs involving glucuronides include interactions between gemfibrozil and clopidogrel glucuronides and repaglinide (216, 282). In both cases, the increase in AUC of repaglinide following co-administration with either gemfibrozil or clopidogrel has been attributed, in part, to mechanism-based inhibition of CYP2C8 by the glucuronide metabolites of these drugs (156, 216). This time-dependent inhibition of CYP2C8 has been confirmed in vitro, as a 30-minute pre-incubation of glucuronide with human liver microsomes increased the CYP2C8 inhibitory potency of gemfibrozil and clopidogrel glucuronides by 14 and 5-fold, respectively (156, 216).

Currently, data characterising the effect of glucuronides on metabolic enzymes and their clinical DDI potential are generally limited, as is information regarding the effect of pre- incubation on their enzyme inhibitory potential. In addition, it is unclear whether only

59 glucuronides of parent drugs which inhibit enzymes affect enzyme activity and to what extent the mechanism of inhibition by glucuronides and their potency are comparable to those of the parent drug.

2.2 Aims The objective of this chapter is to investigate the effect of glucuronides on a range of metabolic enzymes. Literature database collation, described in section 1.7, identified CY2C8, CYP3A4, CYP2C9, UGT1A1 and UGT2B7 as enzymes for which most inhibition data were reported by either glucuronides or parent drugs with these metabolites. The current study characterised the CYP2C8, CYP3A4 and UGT1A1 inhibitory potential of selected glucuronides and corresponding reference inhibitors for these enzymes. Different metabolic pathways of repaglinide were used as markers of enzyme activity. Experiments were conducted using pooled HLM in the presence of combined P450 and UGT co-factors to enable simultaneous assessment of all 3 enzymes of interest. The P450 inhibitory potential of glucuronides of interest and respective parent drugs was further assessed in the presence of P450 co-factors in isolation in order to investigate any effect of co-factor conditions on glucuronide inhibitory potency. Comparison of glucuronide and parent compound enzyme inhibitory potential against different metabolic enzymes was performed. The time-dependent inhibitory potential of all drugs on CYP2C8, CYP3A4 and UGT1A1 was investigated by inclusion of a 30-minute pre- incubation step with buffer or buffer containing inhibitor in all experiments.

2.3 Methods

2.3.1 Selection of repaglinide as a probe substrate Repaglinide is an FDA recommended probe substrate for clinical investigation of CYP2C8 inhibition (13). Clinical DDIs resulting in increased repaglinide AUC have been reported with a number of inhibitors as summarised by Sall et al., (2012). These range from weak interactions (AUC increase < 2-fold) following co-administration with trimethoprim (CYP2C8 inhibition) and ketoconazole (CYP3A4 inhibition) to strong DDIs with a 19-fold increase in AUC after combined administration of itraconazole and gemfibrozil (OATP1B1 and CYP2C8 inhibition) (282-284). Recently, the repaglinide M4 metabolic pathway has been proposed as a specific CYP2C8 probe for in vitro investigation of DDIs (152). Selection of repaglinide as a CYP2C8 probe substrate in the current study enabled simultaneous analysis of CYP3A4 and UGT1A1 inhibition (in addition to CYP2C8) by monitoring formation of repaglinide M1 and repaglinide glucuronide metabolites, respectively.

Repaglinide undergoes extensive metabolism in vivo with < 2% of the drug excreted unchanged following oral administration (285). The metabolism of repaglinide has been well characterised in vivo with 6 metabolic pathways reported (Figure 2.1) (152, 286). The main in

60 vivo metabolite of repaglinide is M2, reported to account for 66% of the excreted drug in urine and faeces following oral administration of repaglinide (285). M2 is also produced in vitro in S9 fractions and human hepatocytes and its formation has been attributed to P450 metabolism followed by aldehyde dehydrogenase (152). Repaglinide M1 and M4 are quoted as the main in vitro metabolites of repaglinide in HLM, which lack aldehyde dehydrogenase enzymes (152, 286). Experiments using specific monoclonal antibodies as inhibitors have reported the formation of M1 and M4 to be mainly due to CYP3A4 and CYP2C8, respectively, in HLM (286). The specificity of CYP2C8 and CYP3A4 to formation of repaglinide M4 and M1 metabolites, respectively, has also been demonstrated in recombinant enzymes where M4 formation was 35-fold higher in CYP2C8 than CYP3A4 (152). The opposite trend was reported for M1 with 32-fold greater production in CYP3A4 than CYP2C8 enzymes (152). In addition to oxidative metabolism, repaglinide undergoes glucuronidation and UGT1A1 was reported to be the major contributor to the formation of this metabolite in vitro (218, 285). These three metabolic pathways of repaglinide can be monitored simultaneously in HLM while excluding transporter interference which would be present in cell systems such as hepatocytes; consequently pooled HLM were chosen for use in enzyme inhibition studies. In this system the % contribution of each of these 3 pathways was reported to be 63%, 19% and 18% for M4, glucuronidation and M1, respectively (152). The Km for repaglinide M4, glucuronide and M1 were 9, 37 and 47 µM, respectively (152).

2.3.2 Reagents Gemfibrozil, mefenamic acid, ezetimibe, telmisartan, raltegravir, raloxifene 4’, mycophenolic acid, repaglinide and clopidogrel glucuronides, despiperidyl-2-amino repaglinide (M1) and 3’- Hydroxy repaglinide (M4) were purchased from Toronto Research Chemicals Inc., Canada. Diclofenac glucuronide was provided internally by AstraZeneca. Gemfibrozil, diclofenac, telmisartan, repaglinide, mefenamic acid, clopidogrel, ketoconazole, trimethoprim, rifamycin SV, indomethacin, tolbutamide, quinidine, NADP+, UDPGA, isocitric acid, isocitric dehydrogenase, EthyleneDiamineTetraacetic Acid (EDTA), Saccharic Acid Lactone (SAL) and alamethacin were obtained from Sigma-Aldrich Company Ltd, UK. Methanol and magnesium chloride were purchased from BDH Laboratory Supplies (VWR International, Lutterworth, Leicestershire, UK).

Pooled HLM (Lot No. 38289 and 38290) with equal gender mix from 150 donors were obtained from BD Gentest (Woburn, MA, USA). CYP3A4 catalysed testosterone 6-β-hydroxylation, CYP2C8 paclitaxel 6α-hydroxylation and UGT1A1 estradiol 3-glucuronidation activities reported in this HLM pool were 4600, 170 and 940 pmol/min/mg protein, respectively. Microsomes were stored at -80ºC prior to experiments. Donor demographics of the microsomal pools are displayed in Table 2.1.

Table 2.1 Demographics of human hepatic pooled microsomes used for CYP2C8, CYP3A4 and UGT1A1 IC50 assays.

Lot No. 38289 and 38290

Number of donors 150

Mean age (± SD) 53 (14)

Age range (years) 18 - 79

% Females 50

% Caucasian 100

2.3.3 Assessment of CYP2C8 and CYP3A4 inhibition by repaglinide glucuronide Repaglinide was selected as a probe substrate for investigating the CYP2C8, CYP3A4 and UGT1A1 inhibitory potential of a range of glucuronides. Considering that repaglinide glucuronide was selected as a marker of UGT1A1 inhibition, preliminary studies were performed to assess the effect of this glucuronide on CYP2C8 and CYP3A4 in pooled human liver microsomes using repaglinide as a probe substrate. Based on time and protein linearity studies performed previously in house, enzyme inhibition experiments using repaglinide as a

62 probe substrate were performed with a 10-minute incubation with 0.3 mg/mL protein. Repaglinide glucuronide concentrations ranged from 0 to 75 µM, the upper limit represents the highest achievable concentration with the glucuronide standard available. All inhibitor stock solutions were prepared in DMSO with a final concentration of organic solvent in experiments of < 1%.

Experiments were performed in duplicate on at least three separate occasions in 1 mL 96-well polypropylene reaction plates (Fisher Scientific, UK Ltd, Loughborough, UK) on a heating block at 37°C shaken at 900 rpm. The final incubation volume was 200 µL. Incubation mixtures contained pooled HLM (0.3 mg/mL) and an NADPH regenerating system (1 mM NADP+, 7.5 mM isocitric acid, 1 unit/mL, isocitric dehydrogenase and 10 mM magnesium chloride) in 0.1 M phosphate buffer (pH 7.4). HLM were incubated for 30-minutes with the NADPH regenerating system and buffer or buffer containing inhibitor prior to inhibition experiments in order to investigate potential time-dependent inhibition (75, 145). Control incubations were performed in each experiment without inhibitor but with equal solvent concentration to determine 100% metabolite formation.

The 30-minute pre-incubations were initiated by addition of HLM to the regenerating system and buffer with or without inhibitor. At 30-minutes, 50 µL of the incubation mixture was removed from pre-incubations containing inhibitor and quenched in ice-cold methanol containing an appropriate internal standard. Enzymatic inhibition reactions were then initiated immediately by the addition of repaglinide at a final concentration of 5 µM, a concentration below the Km for the formation of the metabolites by the enzymes of interest. The use of a low probe substrate concentration ([S] << Km) ensured unbiased parameter estimation regardless of the inhibition mechanism for M1 and repaglinide glucuronide (competitive or non- competitive inhibition of the enzyme), i.e., that IC50 = Ki. In the case of M4, the repaglinide concentration used was close to its reported Km in HLM (9 µM); however, use of repaglinide concentrations below 5 µM was analytically challenging and would not allow quantification of all metabolites of interest under inhibition conditions. The selected concentration of 5 µM was assumed to be low enough to enable unbiased parameter estimation regardless of the inhibition mechanism of CYP2C8. The reaction was continued for 10 minutes to measure residual P450 activity in comparison to the control and then terminated by addition of ice cold terminating solution containing internal standard. Specific internal standards used for each compound are listed in Appendix Table 6.5.

2.3.4 Assessment of CYP2C8, CYP3A4 and UGT1A1 inhibition by reference inhibitors To validate the use of repaglinide as a probe substrate for the enzymes of interest, inhibition experiments were also conducted using reference inhibitors for CYP2C8, CYP3A4 and UGT1A1. Ketoconazole is a potent, selective inhibitor of CYP3A4 at concentrations up to ~2 µM whereas it almost completely inhibits CYP2C8 at higher concentrations (≥10µM) (13, 287).

Ketoconazole was selected as a CYP3A4 reference inhibitor in the current study and the concentrations used ranged 0.001 – 1 µM. Gemfibrozil is listed as a potent CYP2C8 inhibitor in the FDA 2012 draft guidance but as a compound of interest in this study was not suitable to be used as a reference inhibitor. Therefore, trimethoprim (0.3 – 300 µM), was selected as an alternative but less potent inhibitor of CYP2C8 listed in the FDA guidance. Currently, no specific UGT1A1 inhibitor is recommended by regulatory bodies. Rifamycin SV has a reported

UGT1A1 IC50 value of 12 µM in HLM using estradiol-17-β-glucuronide as a probe substrate (288). No published inhibition data for CYP2C8 or CYP3A4 by rifamycin SV were available to our knowledge; therefore it was selected as a reference inhibitor of UGT1A1 using a concentration range of 0.3 – 300 µM.

Experiments were performed as described in section 2.3.3. To enable investigation of both P450 and UGT inhibition, HLM were activated by a 15-minute incubation with alamethacin (50 µg/mg protein) on ice, prior to the 30-minute pre-incubation with buffer or with buffer containing inhibitor. Incubation conditions were adapted from Kilford et al., (2009) and enabled UGT metabolism and assessment of its inhibition, as reported previously (153, 289). In addition, experiments were performed with combined P450 and UGT co-factors to allow investigation of inhibition of both enzyme families. Incubation mixtures contained alamethacin activated pooled HLM (0.3 mg/mL), increasing concentrations of inhibitor, a combined NADPH and UDP regenerating system (1 mM NADP+, 7.5 mM isocitric acid, 1.2 unit/ml isocitric dehydrogenase, 10 mM magnesium chloride and 5 mM UDPGA) and 1 M phosphate buffer (pH 7.1). The phosphate buffer used for combined co-factor experiments contained 3.45 mM magnesium chloride which is required for the UGT enzyme reaction, 1.15 mM EDTA which prevents UGT enzyme inhibition by Cu2+ present in the incubations and 115 μM SAL as it inhibits β- glucuronidase and thereby prevents any potential enzyme-catalysed hydrolysis of the formed glucuronide (290-292). A final repaglinide concentration of 8 µM, a concentration below the

Km for the formation of the metabolites of interest, was used.

Based on the initial results obtained in combined co-factor conditions, further experiments were performed for rifamycin SV using P450 co-factor conditions, as described in section 2.3.3. These conditions were selected to further explore the effect of rifamycin SV on P450 enzymes and examine the influence of co-factor conditions on P450 inhibition. Additional inhibition experiments were also conducted for rifamycin SV with UGT co-factors only using a repaglinide concentration of 8 µM, phosphate buffer pH 7.1 and a UDP regenerating system (7.5 mM isocitric acid, 1.2 unit/ml isocitric dehydrogenase, 10mM magnesium chloride hexahydrate and 5 mM UDPGA) to assess the effect of co-factor conditions on UGT1A1 inhibition.

2.3.5 Assessment of CYP2C8, CYP3A4 and UGT1A1 inhibition by glucuronides The CYP2C8, CYP3A4 and UGT1A1 inhibitory potential of 9 glucuronides was assessed in pooled human liver microsomes using repaglinide as a probe substrate. Experiments were

64 performed with combined co-factor conditions, as described in section 2.3.4. The glucuronide concentrations assessed ranged 0.1 – 100 µM (upper limit represents the highest achievable concentration with glucuronide standards available).

Following inhibition screening of all glucuronides, additional experiments with P450 co-factors were performed for the glucuronides which caused the most potent inhibition (> 50%) of CYP2C8 and CYP3A4 in combined co-factor experiments, namely; gemfibrozil, clopidogrel, mefenamic acid, telmisartan, and diclofenac glucuronides. In addition, the effect of co-factor condition on P450 inhibitory potential was assessed for ezetimibe glucuronide and mycophenolic acid glucuronide, which showed < 50% inhibition in combined co-factor experiments. These experiments were conducted using P450 co-factor conditions as described for repaglinide glucuronide in section 2.3.3. The extent of glucuronide mediated CYP2C8 and CYP3A4 inhibition between co-factor conditions was compared. The inhibitory potential of the parent drugs of the glucuronides which caused the most potent CYP2C8 inhibition were also assessed in these conditions to enable direct comparison of glucuronides and parent compound P450 inhibitory potential without loss of parent compound to the glucuronide.

2.3.6 Analysis of inhibitor concentrations and glucuronide stability

Concentrations of inhibitors (glucuronides and reference inhibitors) were monitored during IC50 experiments in pooled HLM in combined co-factor experiments. In addition, concentrations of parent drugs and repaglinide glucuronide were monitored in P450 co-factor experiments. Inhibitor concentrations were measured at the end of the 30-minute pre-incubation where this was done in the presence of inhibitor and also following the 10 minute co-incubation with repaglinide. Glucuronide concentrations were only monitored in combined co-factor experiments due to the limited availability of these compounds. Analytical issues did not allow monitoring of diclofenac glucuronide and mefenamic acid glucuronide by LC/MS-MS methods.

2.3.7 LC/MS-MS Analysis of samples Following termination of the reactions, samples with inhibitor concentrations > 10 µM were serially diluted to 10 µM with a solution containing phosphate buffer, DMSO and HLM at the same concentrations as in the incubations. Samples were centrifuged for 10 minutes at 2500 g and placed in a -20°C freezer for ~ 1hour. On removal from the freezer samples were re- centrifuged under the same conditions; ~120 µL of the supernatant was removed and used for sample analysis.

All samples were analysed on a Waters 2795 high-performance liquid chromatography system with a Micromass Quattro Ultima triple quadruple mass spectrometer using the methods described in Appendix Section 6.2.1. A standard curve for each of the probe metabolites (M1, M4, repaglinide glucuronide) and inhibitor where appropriate were prepared and analysed at the start and end of each analysis to verify satisfactory stability and lack of any potential

65 carryover. The standard curves included at least 10 different concentrations including a solvent blank and analyte at above and below the experimental concentrations. Calibration standards were prepared in a matrix identical to the sample extracts to compensate for matrix interference.

2.3.8 Data analysis

The mean repaglinide metabolite formation rate was assessed in control conditions across all experiments for M1, M4 and repaglinide glucuronide in both combined and P450 co-factor experiments. The mean % control enzyme activity of duplicate samples at each inhibitor concentration were analysed using GraFit™ v7 (Erithacus Software Ltd, Horley, UK) by fitting

Equation 2.1 to the data using a nonlinear least squares fitting routine to obtain IC50 estimates for each experiment. CYP2C8, CYP3A4 and UGT1A1 inhibition was determined as % of the repaglinide M4, M1 and glucuronide control response, respectively.

푹풂풏품풆 Equation 2.1 풚 = 풔 + 풃풂풄풌품풓풐풖풏풅 [풙] ퟏ+( ) 푰푪ퟓퟎ where y is the percent of control enzyme activity in the absence of inhibitor, x is the inhibitor concentration, Range is the fitted uninhibited value minus the background and s is the slope factor. The equation assumes that y falls with increasing x.

Criteria to include experiments for analysis were concentration-dependent inhibition of probe substrate exhibited and standard errors on test inhibitors’ IC50 of less than 40%. If test compound produced concentration-dependent inhibition, it was classed as an inhibitor of this enzyme. IC50 data in the subsequent sections are presented as the mean of values obtained in at least three separate experiments with the standard deviation. Mean IC50 values were compared using Student’s t-test to assess the significance of differences between inhibition observed under with and without pre-incubation conditions for each inhibitor. Differences were considered significant if p < 0.05.

2.3.9 Correction of IC50 data for nonspecific binding

The nonspecific binding of drugs to microsomes (fu,mic) was used to correct in vitro parameters. Preliminary experimental investigation of fu,mic was performed for glucuronides for which CYP2C8, CYP3A4 or UGT1A1 IC50 data were obtained, namely gemfibrozil, clopidogrel, mefenamic and telmisartan glucuronides. Diclofenac glucuronide was not investigated due to analytical issues. The fu,mic methodology was a high-throughput dialysis method using a reusable 96-well micro-equilibrium dialysis device HTD 96 (HTDialysis LLC, Gales Ferry, CT), as described by Gertz et al., (2008) (143). Dialysis membranes had a 12 to 14 kDa molecular mass cut-off and were purchased from HTDialysis, LLC (Gales Ferry, CT).

Phosphate buffer containing 5 µM or 50 µM of a glucuronide (to cover any range in IC50), was added to the acceptor chamber. Phosphate buffer containing pooled human liver microsomes at a microsomal protein concentration of 0.3 mg/mL, the same as that used in inhibition experiments, were added to the donor chamber. A Breathe Easy gas permeable membrane (Diversified Biotech, Boston, MA) was used to cover the dialysis plate to prevent evaporation from the plate and enable CO2 to reach the wells of the plate, maintaining a constant pH. The dialysis plate was left to equilibrate for 16 h on a plate shaker (450 rpm) at 37°C. At the end of the experiment, 100 µL samples were transferred to a 96-well plate well containing equal volume ice-cold methanol containing an appropriate internal standard and prepared for LC/MS-MS analysis (section 2.3.8). Although standard equilibrium dialysis methods were used, glucuronides could not be monitored at the end of the 16 hour incubation time required for equilibrium to be reached. This is most likely due to instability of glucuronides over the extended incubation period in comparison to the maximum incubation of 40 minutes used in inhibition experiments. Therefore, fu,mic values were predicted for all glucuronides to enable correction of in vitro inhibition data.

In general, two equations are commonly used to calculate fu,mic directly from drug lipophilicity data (293, 294). Limitations of these equations have been analysed (143) and on average the Hallifax and Houston (2006) equation has been shown to give the most accurate predictions of fu,mic. Therefore, fu,mic values were calculated for all inhibitors investigated in this study using the Hallifax and Houston equation (Equation 2.2).

Equation 2. 2

ퟏ 풇풖, 풎풊풄 = ퟐ ퟏ+푪 풙 ퟏퟎퟎ.ퟎퟕퟐ 풙 풍풐품푷/푫 + ퟎ.ퟎퟔퟕ 풙 풍풐품푷/푫 − ퟏ.ퟏퟐퟔ where C is the microsomal protein concentration and LogP/D is the distribution coefficient of all drug species between octanol and water at pH=7.4. The LogP was used for basic drugs, whereas the LogD was used for acidic and neutral drugs. All LogD and LogP values were predicted using ADMET Predictor (Simulation Plus, version 7).

2.4 Results

2.4.1 Inhibition of CYP2C8 and CYP3A4 by repaglinide glucuronide

Preliminary IC50 experiments were conducted for repaglinide glucuronide in HLM with P450 co-factor conditions to assess any inhibitory effect of this glucuronide on CYP2C8 or CYP3A4. Repaglinide M4 and M1 were used as markers of CYP2C8 and CYP3A4 activity, respectively. Experiments were performed to ensure that the P450 inhibitory effects observed for drugs of interest were not confounded by the effects of repaglinide glucuronide produced during experiments in combined co-factor conditions. Pre-incubation with buffer did not significantly influence the formation of either repaglinide M4 or M1.

The inhibitory effect of repaglinide glucuronide on CYP2C8 was marginal across the concentration range investigated regardless of the pre-incubation condition (< 50% inhibition); with the exception of the highest concentration where up to 40% inhibition was observed (Figure 2.2A). In the case of CYP3A4, the inhibitory effect of repaglinide glucuronide was also marginal across the concentration range and pre-incubation conditions investigated (< 25% inhibition of CYP3A4) (Figure 2.2B). Based on these findings and the fact that repaglinide glucuronide was formed in the nM range in control conditions, repaglinide glucuronide was not expected to cause significant P450 inhibition. Therefore, repaglinide was considered to be a suitable probe substrate for use in the current studies.

2.4.2 Inhibition studies in human liver microsomes – reference inhibitors All reference inhibitors were studied in combined co-factor conditions to verify the experimental design and the suitability of repaglinide as a probe substrate for CYP2C8, CYP3A4 and UGT1A1. Pre-incubation of enzymes with buffer did not significantly influence formation of repaglinide M4, M1 or glucuronide. CYP2C8, CYP3A4 and UGT1A1 inhibition was determined as % of the repaglinide M4, M1 or glucuronide control response, respectively.

The IC50 values obtained and corrected for fu,mic are presented in Tables 2.2 and 2.3. No pre-incubation effect on the inhibitory potency was observed for any of the reference inhibitors against CYP2C8, CYP3A4 or UGT1A1.

Ketoconazole was selected as a reference inhibitor of CYP3A4 (13) and its effects on CYP2C8 and UGT1A1 were also monitored (Figure 2.3). CYP3A4 IC50 values of 0.023 µM and 0.024 µM were obtained without and with pre-incubation with inhibitor, respectively. Up to 40% inhibition of CYP2C8 was observed at the highest ketoconazole concentrations whereas no inhibition of UGT1A1 by ketoconazole was observed under the conditions investigated.

Trimethoprim, a reference inhibitor for CYP2C8 (13), caused inhibition of CYP2C8 with an IC50 value of 112 µM. Pre-incubation did not affect its inhibitory effect on CYP2C8 with an estimated IC50 of 123 µM (Figure 2.4 A). No inhibition was observed for CYP3A4. For UGT1A1 an increase in repaglinide glucuronide formation of up to ~30% was observed with increasing trimethoprim concentrations, however this effect was variable and not statistically significant (Figure 2.4C).

Rifamycin SV was selected as the best available reference inhibitor for UGT1A1 based on literature analysis. There were no literature data indicating inhibition of CYP2C8 or CYP3A4 by rifamycin SV. The UGT1A1 inhibitory potential was first investigated in combined co-factor conditions and the effects of rifamycin SV on CYP2C8 and CYP3A4 were also monitored.

Rifamycin SV was found to inhibit all 3 enzyme pathways investigated, IC50 profiles are shown in Figure 2.5 A-C. The most potent inhibition was seen for CYP3A4 with IC50 values of ~2.5

µM regardless of pre-incubation condition (Table 2.2). CYP2C8 and UGT1A1 IC50 values of 26.8 and 11.7 µM were obtained, respectively, following pre-incubation with buffer. Pre- incubation with inhibitor had a marginal effect on the inhibition of these enzymes by rifamycin

SV with IC50 values of 29.3 and 10.4 µM obtained for CYP2C8 and UGT1A1, respectively.

The UGT1A1 inhibitory potential of rifamycin SV was further investigated in conditions optimised for UGT metabolism to enable comparison of IC50 estimates between co-factor conditions and specifically investigate the effect of rifamycin SV on UGT1A1. The formation of repaglinide glucuronide in control incubations was comparable between pre-incubation with buffer alone and pre-incubation with buffer containing < 1% DMSO. The UGT1A1 IC50 results obtained were comparable to those reported in combined co-factor conditions (~ 10 µM) with no pre-incubation effect observed on UGT1A1 inhibition (Figure 2.6). Additional inhibition experiments were performed with P450 co-factors to characterise the effect of rifamycin SV on CYP3A4 and CYP2C8 and investigate differences between co-factor conditions. Under these conditions, CYP2C8 IC50 values ranged from 18.6 to 25.8 µM without and with pre- incubation, respectively. In the case of CYP3A4, IC50 values of ~ 1.5 µM were obtained for both pre-incubation conditions (Figure 2.6). Rifamycin SV IC50 profiles in P450 and UGT co- factor conditions are provided in Appendix Figure 6.4.

Figure 2.6 Comparison of rifamycin SV CYP2C8, CYP3A4 and UGT1A1 IC50 values obtained using repaglinide as a probe substrate in HLM and P450, UGT or combined co-factors without (A) and with (B) pre-incubation of the inhibitor

2.4.3 CYP2C8, CYP3A4 and UGT1A1 inhibition by glucuronide metabolites The inhibitory effect of selected glucuronides on CYP2C8, CYP3A4 and UGT1A1 was assessed in human liver microsomes ± 30 minute pre-incubation. CYP2C8 inhibition was observed for 6 glucuronides investigated. All CYP2C8 IC50 plots are shown in Figure 2.7; corresponding figures for UGT1A1 and CYP3A4 are provided in Appendix Section 6.3. When pre-incubation with buffer was performed, CYP2C8 IC50 values were obtained for 4/10 glucuronides and ranged from 8.6 - 54.1 µM for telmisartan and diclofenac glucuronides, respectively (Table 2.2). Where IC50 was not characterised, the % inhibition caused at the highest inhibitor concentration was noted if the reduction in enzyme activity was statistically significant. A leftward shift in IC50 of approximately 10-fold following pre-incubation was seen in the case of gemfibrozil glucuronide. A pre-incubation effect was also observed for clopidogrel glucuronide with a maximum of 50% inhibition observed without pre-incubation and an IC50 value of 36.5 µM obtained following pre-incubation with inhibitor. Time-dependent inhibition of CYP2C8 was not observed for any other glucuronides.

Table 2.2 IC50 values for inhibition of repaglinide M4, M1 and glucuronide metabolism by CYP2C8, CYP3A4 and UGT1A1, respectively. Data represent mean

± sd of at least 3 separate experiments performed in pooled HLM with combined co-factor conditions without (IC50 (0)) and with (IC50 (30)) a pre-incubation with inhibitor. Values in brackets represent IC50 corrected for fu,mic

CYP2C8 CYP3A4 UGT1A1 a Inhibitor fu,mic IC50 (0) IC50 (30) IC50 (0) IC50 (30) IC50 (0) IC50 (30) (µM) (µM) (µM) (µM) (µM) (µM) Gemfibrozil glucuronide 0.98 38.4 ± 9.7 (38) 3.9 ± 2.5 (3.9) NI NI NI NI Telmisartan glucuronide 0.97 8.6 ± 3.5 (8.3) 7.3 ± 1.1 (7.1) 3.9 ± 1.2 (3.8) 4.8 ± 4.8 (4.6) 2.2 ± 1.4 (2.1) 2.9 ± 0.9 (2.9) Diclofenac glucuronide 0.98 54.1 ± 7.9 (53) 60.9 ± 4 (60) NI NI 50% 50% Mefenamic acid glucuronide 0.98 14.4 ± 5.6 (14) 14.9 ± 3 (15) 24.9 ± 2.5 (24) 27.3 ± 1.9 (27) 26.2 ± 1.4 (26) 23.7 ± 1.3 (23) Clopidogrel glucuronide 0.97 50% 36.5 ± 12.7 (36) NI NI NI NI Raloxifene 4'- glucuronide NI NI NI NI 40% 20% Raltegravir glucuronide NI NI 30% 30% 40% 40% Mycophenolic acid glucuronide NI NI NI NI 30% NI Ezetimibe glucuronide 0.98 45% 30% NI NI NI NI Rifamycin SV 0.94 26.8 ± 8.9 (25) 29.3 ± 3.9 (27) 2.5 ± 0.5 (2.3) 2.4 ± 0.5 (2.2) 11.7 ± 4.6 (11) 10.4 ± 3.4 (10) Trimethoprim 0.97 112 ± 28 (80) 123 ± 18 (88) NI NI NI NI Ketoconazole 0.023 ± 0.01 0.024 ± 0.01 0.71 40% 25% NI NI (0.016) (0.017) a Predicted using Hallifax and Houston equation (2006), protein concentration of 0.3 mg/mL

NI – no inhibition observed

% Percent of enzyme mediated metabolism inhibited at the maximum inhibitor concentration relative to control metabolism in the absence of inhibitor

In the case of CYP3A4 and UGT1A1, IC50 values were only characterised for telmisartan and mefenamic acid glucuronides (Figure 2.8 – 2.10). Of all glucuronides investigated these were the only examples which inhibited all 3 enzymes investigated. For telmisartan glucuronide

CYP3A4 IC50 values of 3.9 and 4.8 µM were obtained without and with pre-incubation with inhibitor, respectively. UGT1A1 was inhibited to a similar extent, IC50 values ranged from 2.2 to 2.9 µM between pre-incubation conditions. Mefenamic acid glucuronide caused less potent inhibition of both CYP3A4 and UGT1A1 than telmisartan glucuronide, IC50 values > 20 µM were obtained for both enzymes regardless of pre-incubation with inhibitor (Table 2.2).

Figure 2.8 Comparison of IC50 values obtained against CYP2C8, CYP3A4 and UGT1A1 without (A) and with (B) pre-incubation for telmisartan and mefenamic acid glucuronides. All

IC50 values were corrected for predicted fu,mic, as stated in Table 2.2

For the other 7 glucuronides investigated, some extent of inhibition of CYP3A4 and UGT1A1 was observed at the highest inhibitor concentrations but IC50 values could not be obtained. The reduction in CYP3A4 and UGT1A1 enzyme activity was not statistically significant for gemfibrozil, clopidogrel, mycophenolic acid or ezetimibe glucuronides. For raltegravir and raloxifene glucuronides UGT1A1 activity was inhibited by 40% at the highest inhibitor concentrations. CYP3A4 activity was also inhibited by up to 30% at the highest concentration of raltegravir glucuronide but no inhibition was observed in the presence of the highest concentration of raloxifene glucuronide. No time-dependent effects on inhibition of either CYP3A4 or UGT1A1 were observed for any of the glucuronides investigated.

2.4.4 CYP2C8 and CYP3A4 inhibition studies in human liver microsomes – P450 co- factors The CYP2C8 and CYP3A4 inhibitory potential of the most potent glucuronides identified in combined co-factor experiments was further explored using conditions optimised for P450 metabolism. These experiments were performed for the 5 glucuronide metabolites for which

CYP2C8 IC50 values had been characterised in combined co-factor experiments and their respective parent drugs; gemfibrozil, clopidogrel, telmisartan, diclofenac and mefenamic acid (Table 2.3). This enabled direct comparison of glucuronide and parent drug P450 inhibitory potential under the same experimental conditions and also comparison of any effect of co- factor condition on glucuronide P450 inhibitory potential. Experiments were also performed for ezetimibe and mycophenolic acid glucuronides (for which CYP2C8 IC50 values were not obtained in combined co-factor conditions), to explore whether co-factor conditions influenced the assessment of their inhibitory potential.

Pre-incubation with buffer resulted in CYP2C8 IC50 values of 11.1 to 48.4 µM for telmisartan and mefenamic acid glucuronides, respectively (Figure 2.11). An increase in CYP2C8 inhibitory potential following pre-incubation with inhibitor was observed for gemfibrozil glucuronide (8-fold) and clopidogrel glucuronide (2.5-fold). The opposite trend was seen for telmisartan glucuronide under these conditions with a 30% increase in the IC50 value; however, this change was not statistically significant (p = 0.19). As in combined-cofactor conditions, CYP2C8 IC50 values could not be characterised for either ezetimibe or mycophenolic acid glucuronides, as the maximum inhibition observed at the highest inhibitor concentrations was 40% for ezetimibe glucuronide and for mycophenolic acid glucuronide no inhibitory effect was observed. In the case of CYP3A4, inhibition was fully characterised only for telmisartan glucuronide in P450 co-factor conditions; IC50 values ranged from 5.7 - 6.6 µM depending on pre-incubation condition (Table 2.3). For the remaining glucuronides maximum inhibition ranged from 30 to 50% at the highest inhibitor concentrations for gemfibrozil and mefenamic glucuronides, respectively. Pre-incubation had no effect on CYP3A4 inhibition for all glucuronides investigated under these conditions; observations were consistent with combined co-factor experiments. IC50 profiles for inhibition of CYP3A4 by all glucuronides are provided in Appendix Figure 6.3.

CYP2C8 CYP3A4 a Inhibitor fu,mic IC50 (0) IC50 (30) IC50 (0) IC50 (30) (µM) (µM) (µM) (µM) Telmisartan 0.85 22.5 ± 3.7 (19) 17.9 ± 1.7 (15) 10.6 ± 9.4 (9) 9.7 ± 9.1 (8.2) Telmisartan glucuronide 0.97 11.1 ± 5.1 (11) 16.1 ± 1.8 (16) 5.7 ± 0.3 (5.6) 6.6 ± 0.1 (6.4) Gemfibrozil 0.96 49.4 ± 7.6 (47) 56.4 ± 8.5 (54) 50% 50% Gemfibrozil glucuronide 0.98 47.9 ± 8.9 (47) 6.1 ± 1.1 (5.9) 35% 30% Diclofenac 0.97 51.5 ± 11.5 (50) 64.7 ± 14.0 (63) 60% 60% Diclofenac glucuronide 0.98 44.8 ± 15 (44) 39.4 ± 2.7 (39) 50% 50% Mefenamic acid 0.96 16.8 ± 5.6 (16) 16.9 ± 8.2 (16) NI NI Mefenamic glucuronide 0.98 48.4 ± 9 (47) 60.5 ± 7.4 (59) 50% 50% Clopidogrel 0.82 21.6 ± 9.2 (18) 25.7 ± 3.3 (21) 14.1 ± 8.7 (12) 20.4 ± 9.3 (17) Clopidogrel glucuronide 0.97 15.3 ± 1.8 (15) 6.7 ± 2.2 (6.5) 20% 30% Rifamycin SV 0.94 18.7 ± 4.4 (17) 25.8 ± 11.6 (24) 1.5 ± 0.7 (1.4) 1.5 ± 0.3 (1.4) Ezetimibe glucuronide NC 40% 40% NI NI Mycophenolic acid glucuronide NC NI NI NI NI Repaglinide glucuronide NC 45% 40% NI NI a Predicted using Hallifax and Houston (2006) equation, protein concentration of 0.3 mg/mL NI – no inhibition observed NC – Not calculated

The CYP2C8 and CYP3A4 inhibitory potential of parent drugs of glucuronides of interest were investigated. CYP2C8 IC50 values were obtained for all 5 parent drugs investigated and ranged from 16.8 to 51.5 µM for mefenamic acid and diclofenac, respectively; the values refer to without pre-incubation condition (Table 2.3, Figure 2.12). Under the same conditions, CYP3A4

IC50 values were obtained for 2 compounds; telmisartan and clopidogrel, with IC50 values of 10.7 µM and 14.1 µM obtained without pre-incubation, respectively (Table 2.3, Figure 2.13). For gemfibrozil and diclofenac up to 60% CYP3A4 inhibition was observed at the highest concentration, whereas mefenamic acid showed no inhibition of CYP3A4. Pre-incubation with inhibitor did not influence the CYP3A4 or CYP2C8 inhibitory potency of any of the parent drugs investigated.

2.4.5 Comparison of the enzyme inhibitory potency between glucuronides and parent drugs Of the 5 glucuronide-parent pairs investigated, 4 glucuronides caused more potent inhibition of CYP2C8 than their parent drugs under P450 co-factor conditions, as shown in Table 2.3. The exception to this trend was mefenamic acid which was up to 3.6-fold more potent than its glucuronide, regardless of the pre-incubation condition used (Figure 2.14). The greatest difference in inhibition potency, ~ 9-fold, was observed between gemfibrozil and gemfibrozil glucuronide following pre-incubation, as a result of the time-dependent inhibition of CYP2C8 observed for this glucuronide (Figure 2.14). Following pre-incubation with buffer alone a comparable extent of CYP2C8 inhibition was observed for gemfibrozil glucuronide and its parent compound, with IC50 values of 49.4 µM and 47.9 µM, respectively. Similarly, clopidogrel glucuronide caused ~ 4-fold more potent CYP2C8 inhibition than its parent drug following pre- incubation with inhibitor as a result of its time dependent inhibitory effects on CYP2C8.

A similar, but less pronounced trend was observed for both diclofenac and its glucuronide, as the glucuronide and parent drugs elicited similar CYP2C8 inhibition following pre-incubation with buffer alone. Following pre-incubation with inhibitor, diclofenac glucuronide caused on average 1.6-fold more potent CYP2C8 inhibition than diclofenac (p = 0.037). Telmisartan glucuronide was more potent than its parent drug although the difference was not significant under either pre-incubation condition investigated (p= 0.035) (Figure 2.14).

In terms of CYP3A4 inhibition, IC50 values could only be characterised for clopidogrel and telmisartan parent drugs (Table 2.3, Figure 2.13). In the case of telmisartan, pre-incubation had no effect on IC50 values (10.7 µM vs 9.7 µM). Telmisartan glucuronide also inhibited CYP3A4 and was approximately 2-fold more potent than the parent, however, this was not statistically significant (p = 0.4). Contrastingly, clopidogrel glucuronide inhibited CYP3A4 activity by 30%; however the parent compound caused more potent inhibition with an IC50 value of 20.4 µM obtained following pre-incubation with inhibitor. No pre-incubation effect on the inhibition of CYP3A4 by clopidogrel was observed.

For the remaining 3 glucuronide-parent pairs the extent of CYP3A4 inhibition was marginal and did not exceed 50% at the inhibitor concentrations investigated. Mefenamic acid glucuronide caused up to 50% inhibition whereas no inhibition was observed for the parent compound. Gemfibrozil and diclofenac parent drugs caused up to 20% greater CYP3A4 inhibition than their glucuronides at the highest inhibitor concentrations.

2.4.6 Impact of pre-incubation on P450 and UGT inhibition The time-dependent inhibitory potential of all inhibitors was assessed against CYP2C8, CYP3A4 and UGT1A1 by inclusion of a 30-minute pre-incubation with either buffer alone or buffer containing inhibitor in all experiments. Comparison of IC50 values obtained under both pre-incubation conditions is shown in Figure 2.15. Gemfibrozil and clopidogrel glucuronides were the only glucuronides for which a pre-incubation effect on CYP2C8 inhibitory potency was observed. In combined co-factor conditions, a 10-fold increase in CYP2C8 inhibitory potential was observed for gemfibrozil glucuronide following pre-incubation (Figure 2.7A). In

P450 co-factor condition experiments a leftwards shift in CYP2C8 IC50 curves for gemfibrozil was consistent with data obtained with combined co-factors with an 8-fold increase in potency (Figure 2.6A). An increase in clopidogrel glucuronide potency following pre-incubation with inhibitor was evident and the extent of the effect was dependent on the co-factor conditions used. In combined co-factor conditions IC50 values could only be quantified following pre- incubation with inhibitor. In contrast, in P450 co-factor conditions IC50 values were quantified both with and without pre-incubation with inhibitor with a 2.5-fold increase in inhibitory potential observed following pre-incubation with inhibitor. For all other glucuronides and the parent compounds and reference inhibitors investigated, IC50 values were within 2-fold (p > 0.05) between pre-incubation conditions in all co-factor conditions investigated (Figure 2.15). In addition, no pre-incubation effects were observed for inhibition of CYP3A4 or UGT1A1 by any inhibitors included in this study.

2.4.7 Impact of co-factor selection on enzyme inhibitory potential

The CYP2C8 and CYP3A4 IC50 values obtained in experiments using either combined (UGT and P450) or P450 co-factors were compared, as shown in Figure 2.16. For 5/7 glucuronides investigated similar CYP2C8 inhibition was observed between co-factors; the values refer to pre-incubation with inhibitor. CYP2C8 IC50 values were within 30% of each other for gemfibrozil, telmisartan and diclofenac glucuronides without pre-incubation with inhibitor. Use of P450 co-factor had no effect on the less potent inhibitors, (e.g., mycophenolic acid glucuronide and ezetimibe) as the maximum inhibition observed at the highest inhibitor concentration, was similar to that observed in combined co-factor experiments without pre- incubation. In the case of gemfibrozil, ezetimibe and mycophenolic acid glucuronides, CYP2C8 inhibition data following pre-incubation with inhibitor were consistent between co- factor conditions. In contrast, following pre-incubation with inhibitor, telmisartan glucuronide was on average 2-fold more potent in combined co-factor than in P450 co-factor conditions (p = 0.002), whereas the opposite trend was seen for diclofenac glucuronide (40% more potent in P450 co-factor conditions, p = 0.002).

Clopidogrel glucuronide represents an interesting example as the choice of co-factors and pre-incubation conditions affected whether this metabolite would be classed as an inhibitor of CYP2C8 or not. In combined co-factors, following pre-incubation with buffer CYP2C8 activity was reduced by ~50% at the highest glucuronide concentration; however, IC50 could not be quantified (Figure 2.17). In contrast, in P450 co-factor experiments an IC50 value of 15.3 µM was obtained. Similarly, following pre-incubation with inhibitor clopidogrel glucuronide CYP2C8 inhibition was ~5-fold more potent in P450 than combined co-factor conditions. The co-factor effect on CYP3A4 inhibition by this glucuronide was far less evident. In contrast to clopidogrel glucuronide, mefenamic acid glucuronide was observed to cause up to 4-fold more potent inhibition of CYP2C8 in combined co-factor conditions than in P450 co-factor conditions; this trend was evident for both pre-incubation conditions (Figure 2.17).

Co-factor conditions did not influence the CYP2C8 time-dependent inhibitory potential of any of the drugs investigated; clopidogrel and gemfibrozil glucuronides were identified as time- dependent inhibitors of CYP2C8 in both P450 and combined co-factor conditions. The increase in CYP2C8 inhibitory potency following pre-incubation with inhibitor was comparable between co-factor conditions for gemfibrozil glucuronide. Similar analysis could not be performed for clopidogrel glucuronide as this metabolite was a non-inhibitor in combined co-

84 factor conditions and comparison of the –fold shift in IC50 between co-factor conditions was not possible.

CYP3A4 inhibition was similar between co-factor conditions for telmisartan, gemfibrozil, diclofenac, clopidogrel, mycophenolic acid and ezetimibe glucuronides under both pre- incubation conditions. However, IC50 values for CYP3A4 were only obtained in both co-factor conditions for telmisartan glucuronide, as illustrated in Figure 2.16. CYP3A4 inhibition by mefenamic acid glucuronide was more potent in combined than P450 co-factor conditions; only 50% inhibition was observed in P450 co-factor conditions whereas IC50 values of ~ 25 ̫µM were obtained in combined co-factor conditions, regardless of the pre-incubation condition. The P450 inhibitory potential of the UGT1A1 reference inhibitor rifamycin SV was also investigated in different co-factor conditions; differences in inhibition of CYP2C8 and CYP3A4 inhibition observed between co-factor conditions and under both pre-incubation conditions by this drug were not statistically significant.

2.4.8 Monitoring of inhibitor concentrations

Inhibitor concentrations were monitored during IC50 experiments in order to evaluate any potential loss during the pre-incubation step or co-incubation with repaglinide. Nominal vs. observed concentration profiles for representative glucuronides are shown in Figure 2.18. Corresponding profiles for parent drugs and reference inhibitors (ketoconazole and trimethoprim) are presented in Appendix Section 6.3. Diclofenac and mefenamic acid glucuronides and rifamycin SV concentrations were not monitored due to analytical issues. In total, 7 glucuronides, ketoconazole and trimethoprim were monitored in IC50 experiments in combined co-factor conditions. No significant loss of these inhibitors was observed and subsequently these inhibitors were not monitored in experiments with P450 co-factor conditions. Repaglinide glucuronide and parent compounds were monitored during P450 co- factor experiments, as IC50 experiments were only conducted in these conditions.

No significant decrease in inhibitor concentration was observed over the 30 minute pre- incubation with inhibitor or the 10-minute co-incubation with repaglinide for the inhibitors analysed (Figure 2.18). The single exception to this trend was clopidogrel parent drug for which a decrease in inhibitor concentration in comparison to stock solutions in buffer was observed following both the 30-minute pre-incubation and the 10 minute co-incubation with repaglinide (Figure 2.19). At the inhibitor concentrations of 0.3, 1 and 3 µM clopidogrel concentrations fell below the LLOQ (0.05 µM) following both the 30-minute pre-incubation and the 10-minute co-incubations. At the higher clopidogrel concentrations (10 – 200 µM) a decrease of up to 80% was observed in comparison to the nominal concentrations, following the 30-minute pre-incubation. Where IC50 experiments were performed following 30-minute pre-incubation with inhibitor, a concentration of 0.17 µM was obtained at the 10 µM nominal concentration following the 10-minute co-incubation with repaglinide. At the other clopidogrel concentrations, a 70 to 86% decrease in inhibitor concentration, in comparison to the nominal concentrations was observed. For the experiments conducted without pre-incubation with inhibitor a 60 to 75% reduction in inhibitor concentration in comparison to the nominal concentrations was observed during the 10-minute co-incubation with repaglinide. This reduction in inhibitor concentration during the incubation may result in an overestimation of the IC50 for clopidogrel based on nominal concentrations. It was not possible to determine

CYP2C8 or CYP3A4 IC50 values using measured clopidogrel concentration data as too few inhibitor concentration values were quantifiable. However, when inhibition data were plotted against measured clopidogrel concentrations obtained following pre- and co-incubation steps (Figure 2.20), inhibition of both CYP2C8 and CYP3A4 was ~ 50% at ≤10 µM of clopidogrel.

These data indicate that the actual clopidogrel concentrations required to cause IC50 are lower than the values of ~ 20 µM for CYP3A4 and CYP2C8 determined using nominal clopidogrel concentrations to plot IC50 profiles.

Figure 2.18 Nominal vs. measured inhibitor concentrations monitored during CYP2C8, CYP3A4 and UGT1A1 IC50 experiments in pooled HLM with combined co-factors. Inhibitor concentrations were monitored at the end of 30-minute pre-incubation with inhibitor (), 10-minute co-incubation with repaglinide following 30-minute pre-incubation with inhibitor () and 10-minute co-incubation with repaglinide without pre-incubation with inhibitor (). Data represent mean ± sd of at least 3 separate experiments. Inhibitor concentrations were monitored for gemfibrozil glucuronide (A), clopidogrel glucuronide (B), ezetimibe glucuronide (C), telmisartan glucuronide (D), raloxifene 4’-glucuronide (E), raltegravir glucuronide (F) and mycophenolic glucuronide (G) in combined P450 and UGT co-factor experiments. Repaglinide glucuronide concentrations (H) were monitored in P450 co-factor experiments

Figure 2.19 Nominal vs. measured clopidogrel parent drug concentrations monitored during

CYP2C8 and CYP3A4 IC50 experiments in pooled HLM with P450 co-factors. Inhibitor concentrations were monitored in the inhibitor stock solutions in PB (), at the end of 30- minute pre-incubation with inhibitor (), 10-minute co-incubation with repaglinide following 30-minute pre-incubation with inhibitor () and 10-minute co-incubation with repaglinide without pre-incubation with inhibitor (). Data represent mean ± standard deviation of at least 3 separate experiments

2.5 Discussion The current chapter provides the analysis of the CYP2C8, CYP3A4 and UGT1A1 inhibitory potential of a range of glucuronides in human liver microsomes using repaglinide as a probe substrate. Prior to testing potential inhibitors it was ensured that repaglinide glucuronide did not inhibit P450 enzymes at concentrations produced in combined co-factor experiments (Section 2.4.1). A 30-minute pre-incubation with buffer or buffer containing inhibitor was included in all experiments to investigate TDI. Where possible, inhibitor concentrations were monitored at the end of the pre-incubation and following the co-incubation with repaglinide. The results illustrate the importance of considering the contribution of metabolites to DDIs as well as that of parent drugs.

2.5.1 Inhibitory effects of reference inhibitors on CYP2C8, CYP3A4 and UGT1A1 Ketoconazole, trimethoprim and rifamycin SV were selected as reference inhibitors to explore the use of repaglinide as a probe substrate for investigation of CYP2C8, CYP3A4 and

UGT1A1 inhibition, respectively (Table 2.2). The CYP3A4 IC50 results obtained for ketoconazole, ~ 0.02 µM without and with pre-incubation, were in line with literature data available for this compound. In HLM ketoconazole CYP3A4 IC50 values have been reported to range 0.005 µM with nifedipine to 20.4 µM with ospemifene as probe substrates (295, 296). The reduction in CYP2C8 activity observed in this study at the highest ketoconazole concentrations were in agreement with literature data indicating higher concentrations are required to characterise CYP2C8 IC50 (297, 298). For trimethoprim, competitive inhibition of

CYP2C8 has been reported and literature IC50 data ranged from 7.6 to 52 µM with amodiaquine and rosiglitazone as probe substrates, respectively, in HLM (299, 300). The IC50 values obtained in this study, (> 100 µM), were higher than the reported literature data, potentially as a result of the different probe substrate used. However, the results obtained in this study were in line with trimethoprim causing weak to moderate inhibition of CYP2C8 (13). No significant inhibitor loss was observed during either incubation period for either inhibitor indicating that they were stable during inhibition studies and the IC50 values obtained were representative of the actual concentration causing inhibition.

Rifamycin SV UGT1A1 IC50 values (10.44 – 12.1 µM) obtained in this study using repaglinide as a probe substrate in both combined co-factor and additional UGT co-factor experiments were in agreement with literature data (IC50 12 µM) (288). In addition, CYP2C8 and CYP3A4 inhibition by rifamycin SV was characterised for the first time to our knowledge; the rank order of inhibition of the enzymes investigated by rifamycin SV was CYP3A4 > UGT1A1 > CYP2C8. As a result, the CYP2C8 and CYP3A4 inhibitory potential of rifamycin SV was further investigated in P450 co-factor conditions. The results obtained were consistent (within 2-fold) across both cofactor conditions investigated (Figure 2.16) and indicate that rifamycin SV may contribute to DDIs by inhibition of these metabolising enzymes.

The results obtained for the three reference inhibitors investigated support the use of different metabolic pathways of repaglinide as probe substrates for the investigation of CYP2C8, CYP3A4 and UGT1A1 inhibition in HLM. The M2 metabolite of repaglinide is not formed in HLM due to lack of the relevant cytosolic enzymes (152). This may influence comparisons of the CYP2C8, CYP3A4 and UGT1A1 inhibitory potential of the inhibitors investigated between in vitro systems, such as S9 fractions and hepatocytes, and the in vivo situation where M2 is the predominant metabolite of repaglinide (285).

2.5.2 Inhibition of CYP2C8, CYP3A4 and UGT1A1 by glucuronides – combined co- factor conditions The CYP2C8, CYP3A4 and UGT1A1 inhibitory potential of 9 glucuronides was assessed in alamethacin activated pooled human liver microsomes with combined P450 and UGT co- factors (Table 2.2). No significant inhibitor loss was observed during either pre-incubation or co-incubation with repaglinide for any glucuronides indicating that they were stable during inhibition studies.

Of all the enzymes investigated, inhibition of CYP2C8 by glucuronides was the most pronounced (Table 2.2). Telmisartan glucuronide was the most potent CYP2C8 inhibitor following pre-incubation with buffer alone with CYP2C8 IC50 values < 10 µM; no literature data were available for comparison and, to our knowledge, this is the first report of telmisartan glucuronide inhibiting this enzyme. For the remaining glucuronides IC50 values obtained following pre-incubation with buffer were > 10 µM or only % inhibition was observed at the highest inhibitor concentrations indicating a broad range in the CYP2C8 inhibitory potential of glucuronides. Gemfibrozil glucuronide caused the most potent inhibition of CYP2C8 following pre-incubation with inhibitor in this study (IC50 3.9 µM). IC50 values obtained both without (38.4

µM) and with pre-incubation with inhibitor were comparable to literature data; IC50 values ranged from 3 – 24.5 µM with monteleukast and paclitaxel as probes, respectively, without pre-incubation with inhibitor (156, 214) and were < 2 µM following pre-incubation with inhibitor using paclitaxel and amodiaquine as probe substrate in HLM (156, 217). Clopidogrel glucuronide literature IC50 values of 195 µM and 12 µM were reported without and with pre- incubation with inhibitor, respectively, using amodiaquine as a probe substrate in HLM with P450 co-factors (216). In comparison, in the study performed here, clopidogrel glucuronide caused less potent inhibition of CYP2C8 under both pre-incubation conditions investigated; potentially as a result of the different co-factor conditions and probe substrate used. When pre-incubation was performed with buffer alone, clopidogrel CYP2C8 IC50 was not characterised; potentially as a result of the lower maximum inhibitor concentration used. In addition, the IC50 value obtained following pre-incubation with inhibitor was 3-fold greater than that reported by Tornio et al., (2014) (216). Mefenamic acid glucuronide inhibited CYP2C8 in the study performed here to a similar extent as that reported by Jenkins et al., (2011) (217);

IC50 values were within 2-fold of the reported literature data (8 µM) obtained in HLM using amodiaquine as a probe substrate. Contrastingly, for diclofenac glucuronide the IC50 values

90 obtained in this study (> 50 µM) with and without pre-incubation with inhibitor were greater than that reported by Jenkins et al., (2011) (14 µM). In this case, the differences in IC50 values obtained may be a result of the different probe substrates used. However, for both mefenamic acid and diclofenac glucuronides pre-incubation with inhibitor did not influence CYP2C8 inhibitory potential; this observation was consistent between the study performed here and that reported by Jenkins et al., (2011).

For the remaining glucuronides investigated, inhibition of CYP2C8 was marginal and did not exceed 50% at the highest inhibitor concentrations. Although CYP2C8 inhibition by glucuronides was the most prominent of all enzymes, it is evident that not all glucuronides cause inhibition of this enzyme. In addition, the mechanism of CYP2C8 inhibition by glucuronides also appears to differ with TDI observed for clopidogrel and gemfibrozil glucuronides, whereas the remaining glucuronides caused reversible inhibition. Hydroxylation of glucuronides by CYP2C8 has been reported, for example desloratedine and diclofenac glucuronides, potentially providing a mechanism for the reversible CYP2C8 inhibition observed (47, 156, 301). However, further exploration of P450 metabolism of glucuronides is required to explore this.

Telmisartan and mefenamic glucuronides were the only metabolites found to inhibit all 3 enzymes investigated in this study (Table 2.2, Figure 2.8). For telmisartan glucuronide UGT1A1 inhibition was most potent followed by CYP3A4 then CYP2C8. For mefenamic acid glucuronide enzyme inhibitory potential was in the rank order CYP2C8 > CYP3A4 = UGT1A1. No literature data were available for comparison for either glucuronides on either CYP3A4 or UGT1A1. The mechanism of CYP3A4 and UGT1A1 inhibition by telmisartan and mefenamic acid glucuronides is currently unclear though could potentially results from competitive inhibition. The remaining glucuronides inhibited CYP3A4 and UGT1A1 no more than 50% at the highest inhibitor concentrations; limited literature data were available for comparison. Gemfibrozil glucuronide did not significantly reduce the activity of either CYP3A4 or UGT1A1 at the highest inhibitor concentration. Sall et al., (2012) (152) reported 30% inhibition in recombinant CYP3A4 with a gemfibrozil glucuronide concentration of 75 µM using repaglinide as a probe substrate (0.5 – 150 µM). Gan et al., (2010) (218) reported gemfibrozil glucuronide

UGT1A1 IC50 values of 130 µM and 69 µM without and with pre-incubation with inhibitor, respectively, in pooled HLM with UGT co-factors using repaglinide (0.2 µM) as a probe substrate. The less potent inhibition observed in the studies performed here may be a result of the different inhibitor concentrations and co-factor conditions used. Clopidogrel glucuronide was the only other inhibitor for which literature CYP3A4 IC50 data were available for comparison. In this study no significant reduction in CYP3A4 activity was observed at the highest clopidogrel glucuronide concentration (75 µM). Tornio et al., (2014) (216) reported CYP3A4 inhibition of 45% in HLM using midazolam as a probe with a maximum inhibitor

91 concentration of 500 µM. The use of different inhibitor concentrations likely explains the differences in CYP3A4 inhibition observed.

2.5.3 IC50 experiments using P450 only co-factors – impact of co-factor conditions on the assessment of the inhibitory potential of glucuronides

The CYP2C8 and CYP3A4 inhibitory potential of the 5 glucuronides for which CYP2C8 IC50 values were obtained in combined co-factor experiments was further investigated in P450 co- factor conditions to enable direct comparison to parent drugs in the same conditions. It was also explored for the less potent inhibitors mycophenolic acid and ezetimibe glucuronides. The P450 inhibitory potential of glucuronides between co-factor conditions was compared as illustrated in Figure 2.16.

For gemfibrozil, ezetimibe and mycophenolic acid glucuronides, similar CYP2C8 and CYP3A4 inhibition was observed between co-factor conditions. In addition, the TDI of CYP2C8 by gemfibrozil glucuronide was consistent. In the case of mefenamic acid glucuronide, up to 4- fold more potent CYP2C8 and CYP3A4 inhibition was observed in combined than in P450 co- factor experiments under both pre-incubation conditions investigated (Figure 2.13). In combined co-factor experiments, mefenamic acid glucuronide concentrations were monitored and did not decrease over the pre-incubation period or the inhibition experiments. In both P450 and combined co-factor experiments the presence of SAL in the incubation mixtures ensured that the glucuronide was not back-converted to the parent compound by β- glucuronidase (302, 303). This ensured that the inhibition observed was solely due to the glucuronide and not the parent drug. However, it is not currently known if mefenamic acid glucuronide itself is subject to P450 metabolism as has been reported for diclofenac and desloratidine glucuronides (47, 301). The decreased CYP2C8 and CYP3A4 inhibitory potential observed in P450 co-factor conditions may be a result of loss of mefenamic acid glucuronide due to metabolism by P450 enzymes. In combined co-factor experiments, regeneration of the glucuronide may have been possible due to the activation of UGT enzymes. However, Jenkins et al., (2011) (217) explored the CYP2C8 inhibitory potential of mefenamic glucuronide in P450 co-factor experiments using amodiaquine as a probe substrate in HLM and reported CYP2C8 IC50 values similar to those obtained in this study, performed in combined co-factor conditions. Further investigation and characterisation of mefenamic acid glucuronide metabolism may help to explain the difference between co-factor conditions observed in this study. Telmisartan glucuronide was also observed to cause more potent CYP2C8 inhibition after pre-incubation in combined co-factor experiments than in P450 co-factor experiments. This may also be due to a reduction in glucuronide concentration in P450 co-factor conditions over the 30-minute pre-incubation with inhibitor. Further investigation with monitoring of telmisartan glucuronide concentrations would be useful.

In contrast to telmisartan and mefenamic glucuronides, clopidogrel and diclofenac glucuronides caused more potent inhibition of CYP2C8 following pre-incubation with inhibitor in P450 than combined co-factor experiments. For diclofenac glucuronide, which is

92 hydroxylated by CYP2C8 (47), it is possible that in the absence of UGT co-factors higher concentrations of the hydroxyl glucuronide could be formed following pre-incubation with the glucuronide, resulting in increased inhibition of CYP2C8. Notably, for clopidogrel glucuronide the choice of co-factors influenced whether it was classed as an inhibitor (P450 co-factor conditions) or not (combined co-factors), following pre-incubation with buffer. The time- dependent inhibition of CYP2C8 by clopidogrel glucuronide observed here in both co-factor conditions is in line with previous reports (216); the mechanism responsible for the enhanced effect in P450 co-factor conditions may be similar to that hypothesised for diclofenac glucuronide.

2.5.4 Comparison of CYP2C8 and CYP3A4 inhibitory potential between glucuronides and parent drugs The CYP2C8 and CYP3A4 inhibitory potential of gemfibrozil, mefenamic acid, telmisartan, clopidogrel and diclofenac was investigated in HLM with P450 co-factor conditions. These compounds were selected based on their glucuronide CYP2C8 inhibitory potential and investigation enabled direct comparison to the effects of glucuronides. With the exception of clopidogrel, no significant loss of inhibitor was observed indicating that the remaining inhibitors were stable during inhibition studies.

All 5 parent drugs investigated inhibited CYP2C8 whereas CYP3A4 inhibition was observed for 2 parent drugs (clopidogrel and telmisartan) (Table 2.4). No time-dependent inhibition was observed for either CYP2C8 or CYP3A4 by the parent drugs investigated. Mefenamic acid caused the most potent inhibition of CYP2C8; IC50 values ~ 16 µM. This was similar to the reported literature data obtained in HLM using amodiaquine as a probe substrate (IC50 14.9 µM) and no TDI was observed (217). Currently, the mechanism of CYP2C8 inhibition by mefenamic acid is unknown. However, the metabolism of this compound has been attributed to unspecified P450 and UGT enzymes in hepatocytes (304); therefore, CYP2C8 inhibition may be competitive.

Telmisartan and clopidogrel were the next most potent inhibitors of CYP2C8, with IC50 values of 17 and 26 µM, respectively, following pre-incubation with inhibitor. Telmisartan also inhibited CYP3A4; inhibition was more potent than that of CYP2C8 with IC50 values ~ 10 µM. No literature data were available for comparison of the effects of telmisartan on CYP2C8 or

CYP3A4. For clopidogrel, the CYP2C8 IC50 data obtained in this study were within the range reported in the literature; 2.8 to 49.3 µM using cerivastatin in recombinant enzymes (213) and amodiaquine in HLM (216), respectively. Similarly, the inhibition data obtained for CYP3A4 were also within the range reported in the literature, 5 – 165 µM, following pre-incubation with buffer (213, 216). However, no significant reduction in the CYP2C8 inhibitory potential of clopidogrel was observed in this study following a 30-minute pre-incubation with inhibitor, in contrast to the 50% decrease reported by Tornio et al., (2014) (216). In addition, no pre-

93 incubation effects on clopidogrel CYP3A4 inhibitory potential were observed in this study, in contrast to the 6-fold increase in inhibitory potency following a 30-minute pre-incubation reported by Tornio et al., (2014). The incubation conditions and in vitro system used were similar between this study and that conducted by Tornio et al., (2014), however, different probe substrates were used which may contribute to the differences in CYP2C8 inhibition observed. Clopidogrel concentrations were noted to decrease over both the pre-incubation and co- incubation periods in the IC50 experiments performed in this chapter (Figure 2.19). This is in line with the reduced CYP2C8 inhibition following pre-incubation with inhibitor (due to loss of inhibitor) and also with the increased CYP3A4 inhibition (production of a time dependent inhibitor of CYP3A4) reported by Tornio et al., (2014). However, these results indicate that the

IC50 values obtained using nominal inhibitor concentration data in these studies may in fact be higher than the actual concentrations of clopidogrel causing inhibition.

In this study CYP2C8 IC50 values > 50 µM were obtained for both diclofenac and gemfibrozil. In the case of diclofenac a similar extent of CYP2C8 inhibition was reported by Jenkins et al.,

(2011) (217) using amodiaquine as a probe substrate in HLM (IC50 54 µM). The CYP3A4 inhibition observed in this study was 60% at the highest diclofenac concentration (100 µM), which is more potent than the reported literature value where an IC50 of 311 µM was obtained for diclofenac in purified enzyme using erythromycin as a probe (305). This may be due to differences in the probe substrate and in vitro system used. The results obtained in this study were in agreement with the CYP2C8 inhibition data reported in the literature; diclofenac

CYP2C8 IC50 values ranged 48 µM to 120 µM with paclitaxel as a probe without pre-incubation in HLM (156, 306). Similarly, pre-incubation did not increase the CYP2C8 inhibitory potential in this study or that reported by Ogilvie et al., (2006) (156) where an IC50 of 150 µM was obtained in HLM using paclitaxel as a probe substrate. Although no CYP2C8 IC50 data using repaglinide as a probe substrate in HLM were available for gemfibrozil for direct comparison, a Ki of 9.3 µM was reported (307). In addition, at a concentration of 100 µM, gemfibrozil was reported to inhibit CYP2C8 by 78% in human hepatocytes when repaglinide was used as a probe substrate (218), in agreement with the current data. In the case of CYP3A4, literature gemfibrozil IC50 data ranged from 184 µM with testosterone to 357 µM with pitavastatin as a probe substrate in HLM (306, 308). No CYP3A4 inhibition data for gemfibrozil using repaglinide as a probe were available for comparison; differences in the extent of CYP3A4 inhibition observed between studies may be due to different probe substrates used.

In conclusion, the parent drugs investigated in this study were shown to inhibit CYP2C8 and CYP3A4 in agreement with previously reported literature data.

2.6.5 Comparison of glucuronide and parent compound P450 inhibitory potency The CYP2C8 and CYP3A4 inhibitory potential of glucuronide metabolites and their respective parent drugs was investigated in HLM with P450 co-factors. HLM were not activated with alamethacin and only P450 co-factors were used in these experiments to ensure that glucuronides could not be formed from their parent drugs during the incubation. Glucuronides

94 caused 1.6 to 9-fold more potent inhibition of CYP2C8 than their parent drugs for 4/5 pairs. This trend is comparable to previous reports for gemfibrozil, clopidogrel and diclofenac glucuronides causing as or more potent inhibition of CYP2C8 than their parent drugs (216, 217). Both gemfibrozil and clopidogrel glucuronides caused time-dependent inhibition of CYP2C8, a mechanism different to that of the parent drug. For the remaining glucuronide- parent pairs no difference in CYP2C8 inhibition mechanism was observed. Mefenamic acid was an exception with the parent causing up to 3.6-fold more potent inhibition of CYP2C8 than the glucuronide in P450 co-factor experiments. However, in combined co-factor experiments the glucuronide inhibited CYP2C8 to a similar extent as the parent in P450 co-factor experiments. The reduced glucuronide inhibitory potency in P450 co-factors may potentially be due to loss of glucuronide; therefore mefenamic acid glucuronide may cause as potent inhibition of CYP2C8 as its parent compound in vivo where UGT enzymes and co-factors are present. However, Jenkins et al., (2011) (217) reported similar CYP2C8 inhibitory potency for mefenamic acid glucuronide and its parent in P450 co-factor conditions using amodiaquine as a probe substrate. The reasons for the disparity between studies are unknown.

In terms of CYP3A4 inhibition, IC50 values were characterised for telmisartan and clopidogrel parent drugs. Telmisartan glucuronide inhibited CYP3A4 to a similar extent as the parent; however, clopidogrel glucuronide did not with only 30% inhibition observed at the highest inhibitor concentration. These results indicate that the effects of glucuronide-parent pairs on P450 enzymes require individual characterisation.

In the studies presented in this chapter, glucuronides caused as or more potent CYP2C8 inhibition than their parent drugs in the majority of cases. However, for CYP3A4, IC50 values were characterised for fewer glucuronide-parent pairs and no trend in glucuronide-parent inhibitory potency was determined. Based on the limited dataset generated in these studies the CYP2C8 inhibitory potential of glucuronide metabolites of parent drugs which inhibit this enzyme should be investigated in vitro to assess their DDI potential.

2.5.6 Investigation of time-dependent enzyme inhibition A 30-minute pre-incubation with buffer or buffer containing inhibitor was included in all experiments to investigate potential time dependent inhibition of CYP2C8, CYP3A4 or UGT1A1. No pre-incubation effect was observed on the CYP3A4 or UGT1A1 inhibitory potential of any inhibitors investigated. CYP2C8 IC50 values were characterised for 12/18 of the compounds investigated, however, inhibition was not subject to a pre-incubation effect in either P450 or combined co-factor experiments except for by gemfibrozil and clopidogrel glucuronides. Both gemfibrozil and clopidogrel glucuronides have been previously identified as mechanism based inhibitors of CYP2C8 (156, 216). The 8- to 10-fold increase in inhibitory potency following pre-incubation with gemfibrozil glucuronide observed in this study were similar to those reported in the literature. Following pre-incubation with inhibitor, a 14- and 15- fold increase in inhibitory potency were reported in HLM using paclitaxel and amodiaquine as probes, respectively (156, 217). In the case of clopidogrel glucuronide, a 16-fold increase in

CYP2C8 inhibitory potential was reported following pre-incubation with inhibitor using amodiaquine as a CYP2C8 probe substrate in HLM with P450 co-factors (216). In this study, the shift in clopidogrel glucuronide IC50 was not quantifiable in combined co-factor experiments as only % inhibition was observed in experiments performed with pre-incubation with buffer.

In P450 co-factor experiments, a 2-fold shift in IC50 was observed following pre-incubation with inhibitor. The smaller shift in IC50 in this study in comparison to that of Tornio et al., (2014)

(216) may be a factor of the different probe substrates used and the lower IC50 values obtained without pre-incubation in P450 co-factor experiments in this study.

2.5.7 Conclusion In conclusion, repaglinide was shown to be a suitable probe substrate for investigation of CYP2C8, CYP3A4 and UGT1A1 inhibition in vitro with all reference inhibitors investigated. The use of combined co-factors enabled simultaneous assessment of inhibition of both P450 and UGT metabolism. However, TDI of CYP2C8 was better characterised in incubation conditions optimised for the assessment of P450 metabolism, which should be used if investigation of TDI of this enzyme is of interest.

The studies performed in this chapter indicate that of the 3 enzymes investigated CYP2C8 is most susceptible to inhibition by glucuronides. It is clear that not all glucuronides display the same CYP2C8 inhibitory potential or the same mechanism of inhibition. Inclusion of pre- incubation of glucuronide with NADPH enables assessment of potential TDI which, if positive, should be followed by detailed characterisation to obtain kinact and KI parameters. In comparison to parent drugs, glucuronides caused as or more potent inhibition of CYP2C8 and for gemfibrozil and clopidogrel glucuronides a different inhibition mechanism was employed to that of the parent drug. Therefore, glucuronide CYP2C8 inhibitory potential and the mechanism of inhibition should be considered for compounds which inhibit this enzyme and are metabolised by UGTs.

Chapter 3 In vitro investigation of OATP1B1 inhibition by glucuronides

The experiments in this chapter were carried out at AstraZeneca, Alderley Park, Cheshire, UK in the period of August 2013 – August 2014.

3.1 Introduction The OATP1B1 transporter mediates the hepatic uptake of many therapeutically used drugs and therefore its inhibition can have serious clinical implications (88). Metabolites, in general, have increased polarity and decreased lipopholicity compared to parent drugs resulting in a predisposition to transporter facilitated distribution and excretion (201). This predisposition can result in a risk of DDIs if metabolites affect the transport of other drugs which are substrates of the same transporter (201). There is increasing evidence that metabolites can inhibit transporters in vitro, as reported for cyclosporine AM1 with OATP1B1 and OATP1B3 (124), which may contribute to in vivo DDIs.

Glucuronide metabolites are of interest in the area of DDIs due to the large number of drugs for which glucuronidation is a dominant metabolic pathway (38, 55). In addition, there are reports of some glucuronides being involved in clinical DDIs e.g., the interaction reported between clopidogrel and repaglinide is attributed, in part, to inhibition of CYP2C8 and OATP1B1 by clopidogrel glucuronide (216). Currently, the clinical DDI potential of glucuronides has not been thoroughly characterised and limited data are available for their effects on OATP1B1 in vitro. Where reports do exist, IC50 values are in the µM range as summarised in Table 1.6 and glucuronides have been observed to cause more potent OATP1B1 inhibition than their parent drugs in a number of cases e.g., ezetimibe glucuronide caused ~100-fold more potent OATP1B1 inhibition than its parent compound (202). More data are available for OATP1B1 inhibitors which have glucuronide metabolites, for example diclofenac and telmisartan (225, 309), than for glucuronides themselves. However, as the OATP1B1 inhibitory potential of glucuronides was not investigated in these studies, comparison of parent and glucuronide inhibitory potencies were not possible. The potential synergistic effects of parent drugs and glucuronides OATP1B1 inhibition in vitro and in vivo are unknown. It is also unclear if only glucuronides of parent drugs which inhibit OATP1B1 can influence the uptake activity of this transporter. In addition to inhibition, increasing numbers of glucuronides and parent drugs with these metabolites are reported to be substrates of OATP1B1 e.g., sorafenib and ezetimibe glucuronides (169, 202). The inhibitory potential of these substrates and understanding of their intracellular concentrations merits consideration, especially as it may provide an insight into the underlying mechanisms of OATP1B1 inhibition.

In vitro estimation of OATP1B1 inhibition is influenced by the assay conditions employed. Pre- incubation with inhibitor prior to co-incubation with a probe substrate has been reported to

97 enhance OATP1B1 inhibitory potency. Initially, this effect was reported for cyclosporine in HEK293-OATP1B1 cells (141) followed by a number of studies discussed in Section 1.3. The relevance of this effect with respect to glucuronides has yet to be explored. In addition, substrate-specific differences in OATP1B1 inhibition in vitro have also been reported (127, 128, 130, 131). The mechanisms underlying substrate-dependent inhibition are currently unknown; however, careful selection of appropriate probe substrates is necessary in order to properly characterise inhibition and obtain suitable data for use in prediction of OATP1B1 inhibition in vivo.

3.2 Aims The aim of this chapter was to assess the OATP1B1 inhibitory potential of 10 glucuronides in stably transfected HEK293 cells. Suitable probe substrates were selected based on available literature evidence collated using the University of Washington Drug Interaction Database; E17βG was used as an initial transporter probe. The OATP1B1 inhibitory potential of selected parent drugs and reference inhibitors was also assessed to allow comparison to glucuronide OATP1B1 inhibitory potency. Further investigation of OATP1B1 inhibition was performed for inhibitors of interest using pitavastatin as a clinically relevant probe substrate and results compared to those obtained using E17βG. The effect of pre-incubation on OATP1B1 inhibitory potency was explored in all experiments by inclusion of a 30-minute pre-incubation step with buffer alone or containing inhibitor. In addition, the physicochemical properties of the drugs investigated were analysed in relation to transporter inhibitory potency for any potential trends.

3.3 Methods

3.3.1 Selection of OATP1B1 probe substrates The University of Washington Drug Interaction Database (UW DIDB http://www.druginteractioninfo.org, licence provided by AstraZeneca) was used to collate in vitro OATP1B1 inhibition data reported in the literature. Criteria for inclusion of reported

OATP1B1 inhibition in the database were reported OATP1B1 IC50 or Ki values for named inhibitors with a specified probe substrate, in vitro system and pre-incubation condition. Probe substrate and inhibitor concentrations were reported in the majority of cases but not all. Where probe substrate or inhibitor concentrations were not provided, IC50 and Ki data were retained as part of the database and the absence of this information noted. The full database collated is provided in Appendix Table 6.2. Selection of a prototypical probe substrate for use in initial OATP1B1 inhibition studies was based on reported sensitivity to inhibition.

A clinically relevant probe substrate was also selected to further explore OATP1B1 inhibition by selected glucuronides. Probe substrates recommended by the transporter consortium as suitable for use in clinical studies of OATP1B1 inhibition include atorvastatin, pravastatin, pitavastatin and rosuvastatin (88). Most in vitro OATP1B1 inhibition data were reported using pitavastatin as a probe substrate. The in vivo contribution of OATP1B1 to pitavastatin uptake

98 was estimated using data from studies performed in subjects with different allelic variants of OATP1B1 (Equation 3.1) and clinical DDI studies (Equation 3.2), as done previously for repaglinide (124). Pitavastatin exposure data were collated from clinical OATP1B1 polymorphism studies and from DDI studies in the presence and absence of inhibitor. Details of ethnicity, gender and numbers of subjects included in the study were noted. The polymorphism approach is analogous to previous work estimating fm,CYPD6 (310) and assumes complete knock out of the transporter in the CC phenotype relative to the wild type (521TT). The DDI approach also assumes complete inhibition of OATP1B1 and no uptake of the victim drug by other transporters.

푨푼푪푻푻 Equation 3.1 풇푻,푶푨푻푷ퟏ푩ퟏ = ퟏ − 푨푼푪푪푪

Where AUCTT and AUCCC represent the area under the curve values of pitavastatin for the

SLCO1B1 521TT and 521CC polymorphic groups, respectively.

푨푼푪 Equation 3.2 풇 = ퟏ − 푻,푶푨푻푷ퟏ푩ퟏ 푨푼푪′

Where AUC and AUC’ represent the area under the curve values of pitavastatin in the absence and presence of OATP1B1 inhibitor, respectively.

3.3.2 Reagents Gemfibrozil, mefenamic acid, ezetimibe, telmisartan, raltegravir, repaglinide, raloxifene 4’, mycophenolic acid and clopidogrel glucuronides were obtained from Toronto Research Chemicals Inc, Canada. Diclofenac glucuronide was provided internally by AstraZeneca. [3H]- estradiol-17-β-glucuronide and Ultima Gold scintillation fluid were purchased from Perkin Elmer, US. The human embryonic kidney 293 cell line expressing human OATP1B1 cDNA were grown at the AstraZeneca Tissue Culture Unit (Alderley Park, UK). Dulbecco’s Modified Eagle’s Medium with glutamax and geneticin (G418) were purchased from Invitrogen, Life technologies, UK. Hank’s balanced salt solution (HBSS) and N-2-hydroxyethyl-piperazine-N- 2-ethanersulfonic acid (HEPES) were obtained from Gibco, Life technologies, UK and Accutase® was obtained from GE Healthcare Life Sciences, UK.

All other compounds and reagents were purchased from Sigma-Aldrich Company Ltd, UK.

3.3.3 Source and preparation on HEK293-OATP1B1 cells Human Embryonic Kidney cells expressing human OATP1B1 cDNA (HEK293-OATP1B1) were developed and characterized at AstraZeneca, Charnwood (129) and subsequently cultured under suitable conditions in 175 cm2 cell culturing flasks in the Tissue Culture Unit at AstraZeneca, Alderley Park. Routine passaging involved cell culture in medium containing Dulbecco’s Modified Eagle’s Medium with glutamax (i.e. supplemented with L-glutamine

4mM), foetal bovine serum (5%), geneticin (1mg/mL) and incubation in a humidified incubator at ~37°C in ~95% air: 5% carbon dioxide until 70-90% confluent. For inhibition studies culture medium was removed from flask, cells were washed 3 times with transport buffer made up of HBSS and HEPES and pH adjusted to 7.4. 7 mL Accutase was added to flasks and left to incubate for 5 to10 minutes to detach cells. Fresh culture medium was prepared and cells resuspended before cell viability was assessed by diluting 400 µL cells suspension with 400 µL trypan blue. Cells were counted using a hemocytometer and used if cell viability exceeded 95%. HEK293-OATP1B1 cells were resuspended in fresh culture medium and seeded into cell culture 24-well multiwell plates coated with poly-D-lysine at a density of 0.25 x106 cells/well. The 24-well assay plates coated with poly-D-lysine were purchased from BD Biosciences, UK. Cells were grown to ~90% confluency in culture medium in a humidified incubator at ~37°C in ~95% air: 5% carbon dioxide for 2-3 days prior to use.

OATP1B1 cell transport and DDI assays using both E17βG (129) and pitavastatin were validated at AstraZeneca, Alderley Park. Time linearity and Km determination studies were performed with E17βG and pitavastatin; both of which were reported to demonstrate

Michaelis-Menten kinetics with Km values of 5.4±1.3 µM (129) and 2.0 µM (internal value provided by AstraZeneca), respectively.

3.3.4 Assessment of OATP1B1 inhibitory potential and effect of pre-incubation using E17βG as a prototypical probe substrate The inhibitory properties of 19 compounds including 10 glucuronide metabolites, selected parent drugs and reference inhibitors (cyclosporine, rifampicin, rifamycin SV and erythromycin) were assessed in HEK293 cells expressing human OATP1B1 between passages 3 and 23. The glucuronide concentrations assessed ranged 0.1 – 100 µM (upper limit represents highest achievable concentration with glucuronide available); parent drugs concentrations ranged 1 - 1000 µM for gemfibrozil, ezetimibe and diclofenac and 0.1 - 100 µM for repaglinide and telmisartan. Reference inhibitor concentrations ranged 0.01 – 6 µM for cyclosporine, 0.01 – 10 µM for rifampicin and 1 – 1000 µM for erythromycin. Reference inhibitor concentration ranges were selected based on reported literature IC50 data (details are presented in Appendix Table 6.2). All inhibitor stock solutions were prepared in DMSO with a final concentration of organic solvent in experiments of < 1%. Experiments were performed in triplicate on at least three separate occasions. Inhibitor potency was assessed by investigating the cellular uptake rates of the probe substrate, E17βG (0.02μM), over two minutes in the presence of increasing concentrations of inhibitor. The impact of pre-incubation on inhibitor potency was assessed by performing all experiments with a pre-incubation step of 30 minutes with buffer alone or containing inhibitor at 37°C, as reported previously (124). Control incubations were performed in each experiment without inhibitor but with an equal solvent concentration to pre-incubations with inhibitor to determine 100% uptake. Experiments using rifamycin SV (0.001-100 µM) as a reference inhibitor were conducted in conjunction with all inhibition experiments as a positive control.

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Culture medium was removed from the required number of 24-multiwell plate wells containing HEK293-OATP1B1 cells which were washed 3 times with warm transport buffer to remove traces of culture medium. Pre-incubation solutions were added containing transport buffer or transport buffer plus inhibitor. At 30 minutes the pre-incubation solution was removed and fresh medium containing inhibitor and probe added. The use of a low probe substrate concentration ([S] << Km) ensured unbiased parameter estimation regardless of the transporter inhibition mechanism (competitive or non-competitive inhibition of the transporter), i.e., that IC50=Ki. After the 2-minute incubation, solutions were removed from each well and the experiment stopped by washing three times with 400 μL ice cold transport buffer prior to solubilisation with 500 μL of 1% (v/v) Triton X-100. Cells were left to lyse for at least 30 minutes and aliquots of 400 μL of each well transferred to a scintillation vial containing ~5 mL liquid scintilant (Ultima Gold, Perkin Elmer, UK). Samples were mixed using a bench-top vortex and analysed for total radioactivity using a Packard Tri-Carb A2100 TR Liquid Scintillation Analyser (Perkin Elmer, UK). Inhibitor concentrations were not monitored during inhibition experiments due to the use of a radiolabelled probe substrate and a lack of suitable LC/MS-MS facilities.

3.3.5 Assessment of OATP1B1 inhibitory potential and effect of pre-incubation using pitavastatin as a probe substrate

IC50 experiments using pitavastatin as a clinically relevant probe substrate were performed in a similar manner as described for E17βG. A total of 14 inhibitors were analysed including reference inhibitors, the 4 glucuronides that caused the most potent OATP1B1 inhibition with E17βG as a probe and their respective parent drugs. Gemfibrozil glucuronide and its parent compound were also investigated on account of the clinical DDI which has been attributed in part to this transporter and previous in vitro reports (186, 282). Experiments were conducted with a 1-minute incubation with solutions containing a 1 µM pitavastatin concentration ([S] <

Km) to enable monitoring of uptake. Cells were lysed using 50:50 acetonitrile and water containing 40 nM rosuvastatin as internal standard and placed in a -20°C freezer for at least 30 minutes. 300 µL samples were then diluted with equal volume water and centrifuged at 3000rpm, 4°C for 15 minutes. Sub-aliquots of 150 µL were transferred for LC-MS/MS analysis.

3.3.6 LC-MS/MS For analysis of pitavastatin uptake, samples were placed in LC autosampler vials, sample aliquots (10 µl) were injected into an LC-MS/MS system and analysed using the methods described in Appendix Section 6.2.2. For each assay, calibration standards which included analyte at above and below the experimental concentrations and a solvent blank were prepared in a matrix identical to the sample extracts to compensate for matrix interference. Calibration standards were analysed at the start and end of each analysis to verify satisfactory stability and lack of any potential carryover. Standard curves for each assay included at least 9 different concentrations of analytes and an appropriate internal standard.

3.3.7 Data analysis

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The E17βG or pitavastatin uptake in control incubations performed in each experiment without inhibitor but with equal solvent concentration was assessed to determine 100% uptake. The mean % control OATP1B1 activity of triplicate samples at each inhibitor concentration were analysed using GraFit™ v7 (Erithacus Software Ltd, Horley, UK) by fitting Equation 3.3 to the data using a nonlinear least squares fitting routine to obtain IC50 estimates for each experiment.

푹풂풏품풆 Equation 3.3 풚 = 풔 + 풃풂풄풌품풓풐풖풏풅 [풙] ퟏ+( ) 푰푪ퟓퟎ where y is the percent of control, x is the inhibitor concentration, Range is the fitted uninhibited value minus the background and s is the slope factor. The equation assumes that y falls with increasing x.

The mean E17βG and pitavastatin uptake in the control condition was assessed across all experiments to verify the quality of the cellular system and demonstrate that changes in IC50 between probes and pre-incubation conditions were not influenced by the control data. Criteria to include experiments for analysis were > 80% inhibition of uptake of probe substrate into HEK293-OATP1B1 cells at the highest rifamycin SV concentration with concentration- dependent inhibition of probe substrate exhibited and standard errors on rifamycin SV IC50 values of less than 40%. If test compound produced concentration-dependent inhibition of

OATP1B1 it was classed as an inhibitor of this transporter. IC50 data are presented as the mean of values obtained in at least three experiments with the standard deviation. Mean IC50 values were compared using Student’s unpaired t-test to assess the significance of differences between pre-incubation conditions and probe substrates for each inhibitor. Differences were considered significant if p < 0.05. IC50 values obtained with both probes were compared by calculating the geometric mean fold error (gmfe) (Equation 3.4). The gmfe indicates an absolute deviation from the line of unity (185).

ퟏ 푰푪 풑풊풕풂풗풂풔풕풂풕풊풏 ∑|풍풐품 ퟓퟎ | Equation 3.4 품풎풇풆 = ퟏퟎ푵 푰푪ퟓퟎ푬ퟏퟕ휷푮

where N is the number of observations.

Mean IC50 values were converted to Ki using equation 3.5 to correct for the differences in probe substrate concentrations used.

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푰푪ퟓퟎ Equation 3.5 푲풊 = 푺 ퟏ+ 푲풎

Where S is the probe substrate concentration used in the assay, Km is the Michaelis-Menten constant for the substrate in the system used and inhibition is assumed to be competitive. Ki values calculated for both probes were compared in the same manner as described above for

IC50 values.

3.3.8 Prediction of physicochemical properties of OATP1B1 inhibitors and correlation with inhibitory potency cLogP, LogD, pKa, topological polar surface area and counted hydrogen bond donor protons and acceptors have been reported as key descriptors of OATP1B1 inhibition potency (225, 309). ADMET Predictor (Simulation Plus, version 7) software was used to predict each physicochemical descriptor for the glucuronides, parent drugs and reference inhibitors assessed in this study. Net charge was determined from pKa values obtained using ADMET Predictor and calculated using the Hendersson-Hasselbalch equation. These parameters were explored in relation to OATP1B1 inhibition data obtained in the current study using both E17βG and pitavastatin as probes. Differences between parent compound and glucuronide inhibitory potency in conjunction with physicochemical properties were compared. Correlation was also performed in relation to enzyme inhibitory potential reported for the same set of glucuronides in Chapter 2.

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3.4 Results

3.4.1 Selection of a prototypical OATP1B1 probe substrate A total of 461 inhibition data values (IC50 or Ki) for OATP1B1 were collated from 126 references. Table 3.1 lists references for studies with each probe substrate and the concentration ranges used in OATP1B1 inhibition experiments. E17βG was the most commonly used probe substrate (143 IC50 or Ki values) followed by statins (119 IC50 or Ki values). HEK293-cells transfected with the OATP1B1 transporter was the most commonly used cell system, utilised in 63% of cases (Figure 3.1). Other cell systems in decreasing order of use included Chinese Hamster Ovary cells > Madin-Darby Canine Kidney cells > xenopous leavis oocytes > human hepatocytes (cryopreserved) > HeLa cells > Sandwich cultured human hepatocytes > fresh human hepatocytes.

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Probe substrate Number of IC and Ki Probe substrate 50 concentration ranges used References values reported (µM)

(15R)-11C-TIC (PET probe) 1 0.1 (311)

8-fluorescein-cAMP 15 2.5 (312-315)

Atorvastatin 26 0.5 - 3 (130, 225, 316-321)

Atorvastatin acid 4 0.5 - 1 (119, 141)

Bosentan 4 10 (160, 322)

Bosentan hydroxy metabolite (Ro 48-5033) 1 10 (160)

Bromosulfophthalein (BSP) 37 0.05 – 5 (127, 226, 316, 323-330)

Cerivastatin 15 0.005 (33, 224, 331)

Cholyl-glycylamido-fluorescein (CGamF) 10 1 (332)

Dehydroepiandrosterone sulphate (DHEAS) 1 1 (321)

Eltrombopag 4 10 (333)

(111, 124, 127, 129, 130, 158, 186, 288, Estradiol (17-beta-) 143 0.002 – 2 334-358)

(117, 118, 125, 127, 130, 158, 224, 352, Estrone-sulphate 62 20 – 330* 359-369)

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Number of IC and Ki Probe substrate concentration Probe substrate 50 References values reported ranges used (µM)

Fimasartan 2 2 – 50 (370)

Fluorecein-3 3 2 (371, 372)

Fluorescein sodium 31 5 – 10 (309, 373)

Fluorescein-methotrexate 12 5 (374)

Bromosulfophthalein 2 0.05 (202, 350)

Lithocholyl-lysine (LCA-NBD) 4 0.50 (375)

Mesalamine (5-ASA) 4 7.5 - 100 (376)

Olmesartan 2 0.5 – 10 (362)

Phalloidine 4 1 (377)

Pitavastatin 43 0.1 - 2.5 (130, 168, 318, 352, 378-380)

Pravastatin 11 5 – 200 (118, 130, 227, 260, 381-383)

(130, 333, 356, 357, 379, 384-388) Rosuvastatin 19 0.1 - 10

Simvastatin 1 1 (131)

*Units are shown in nM

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A range of IC50 values was reported for the same inhibitor depending on the cell system, pre- incubation conditions and probe substrate used. The biggest range in IC50 (> 100-fold) was seen for paclitaxel with an IC50 of 50 µM using bromosulphopthalein as a probe in Madin- Darby Canine Kidney cells and 0.03 µM using E17βG in Chinese Hamster Ovary transfected cells. Differences in IC50 values were also evident in cases where an inhibitor had been investigated in the same cell system and pre-incubation conditions with multiple probe substrates e.g., a 24-fold range in the IC50 values reported for cyclosporine using E17βG and 8-fluorescein-cAMP as probes (Figure 3.2). This substrate-dependent inhibition was specifically investigated by Izumi et al., (2013) (127) for 13 OATP1B1 inhibitors with the probe substrates E17βG, bromosulphopthalein and estrone-3-sulphate. E17βG was the most sensitive probe substrate to inhibition of OATP1B1 uptake activity with the lowest IC50 values reported for all 13 inhibitors studied. The greatest difference in IC50 was reported for ritonavir with a 117-fold range in IC50 between estrone-3-sulphate and E17βG (127). Where multiple studies (3 or more IC50 values) were available using E17βG with the same inhibitor, cell system and pre-incubation conditions, inter-study variability in IC50 data was investigated. The most pronounced differences in IC50 were seen in the case of cyclosporine with a 12-fold range in IC50 data using E17βG as a probe. For the remaining inhibitors, inter-study variability in IC50 data was 6-fold or less using E17βG as probe. Based on its sensitivity to OATP1B1 inhibition and the availability of literature data for comparison, E17βG was selected as the initial probe substrate for the studies performed here.

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3.4.2 Selection of a clinically relevant OATP1B1 probe substrate – contribution of OATP1B1 to pitavastatin uptake

Similar ranges in IC50 values were reported in studies using E17βG as a probe and those using statins; the second most commonly used in vitro OATP1B1 probe substrates (Table 3.1). Representative data for rifampicin, a potent OATP1B1 inhibitor recommended for use in clinical trials (88), obtained without a pre-incubation step in HEK293-OATP1B1 cells are shown in Figure 3.3. Some substrate dependent inhibition was observed even between statin probes, with rifampicin IC50 ranging from 1.1 - 5 µM with rosuvastatin and atorvastatin as probe substrates, respectively. This may in part have been due to the 10-fold higher atorvastatin concentration (1 µM) than rosuvastatin (0.1 µM) used though both probe concentrations are

< Km indicating that unbiased parameter estimates should have been obtained regardless of the mechanism of inhibition e.g., competitive or non-competitive. In addition, where cerivastatin was used as a probe at a concentration of 5 nM an even higher IC50 was obtained (5.65 µM) indicating that differences in OATP1B1 inhibition between statin probes may be substrate specific and not purely a result of experimental design. In other instances more similar estimates of OATP1B1 inhibition have been reported for certain statins e.g., for pitavastatin and atorvastatin acid at 1 µM, IC50 values of 2.2 and 3.25 µM, were obtained, respectively.

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Of the statins used as probes for in vitro OATP1B1 inhibition, pitavastatin was the most commonly used in the database collated here (43/199 cases). In vitro, OATP1B1 has been reported to mediate ~85% of pitavastatin active uptake in human hepatocytes with minimal contribution of the other hepatic OATP transporters, namely OATP1B3 and OATP2B1 (~14% and ~0.25%, respectively) (159, 168). The in vivo fraction transported by individual OATPs was estimated by comparing either transporter expression or activity data in HEK293 cells relative to hepatocytes. The % contribution of pitavastatin in 3 different human hepatocyte donors ranged from 87.7 to 93.5% (determined using specific transporter probes) and 75.1 to 85.7% (using the relative expression factor method) (159). However, it should be considered that as assessment was performed for a limited number of hepatocyte donors these results are unlikely to account for OATP1B1 variability. In HEK293 cells, pitavastatin is significantly taken up by OATP1B1 (75% or more) and has been reported to be sensitive to inhibition by a range of compounds (159, 168).

In vivo, pitavastatin pharmacokinetics have been most extensively studied in patients with the 521T > C OATP1B1 variation, which results in elevated circulating concentrations of the drug due to reduced uptake by the transporter (389-391). The in vivo contribution of OATP1B1 to pitavastatin uptake was estimated using data from studies performed in subjects with different allelic variants of OATP1B1, as shown in Table 3.2, and calculated using Equation 3.1. Based on these studies, the estimated fraction of pitavastatin transported by OATP1B1 (fT,OATP1B1) was on average 68% (range 61 – 74%). Based on pitavastatin exposure data reported in clinical DDI studies with cyclosporine and rifampicin, as shown in Table 3.3, the in vivo contribution of OATP1B1 to pitavastatin uptake estimated using Equation 3.2 was 81% (range 78 – 85%). In addition, pitavastatin has recently been reported as a clinically sensitive and

109 selective probe substrate for OATP1B1 inhibition and demonstrated greater sensitivity to changes in OATP1B1 activity than rosuvastatin when concomitantly administered to healthy subjects with rifampicin PO or IV (379). Considering combined evidence from in vitro, OATP1B1 polymorphic and DDI clinical studies, pitavastatin was selected as a suitable, clinically relevant probe substrate for OATP1B1 in vitro inhibition studies in the current project.

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Table 3.2 Summary details for the SLCO1B1 polymorphism studies used for the assessment of pitavastatin fT,OATP1B1

Dose AUC521TT AUC521CC nTT nCC Ethnicity Gender fT, OATP1B1 Reference (mg) (h.ng/mL) (h.ng/mL)

4 281 5 737 6 Korean Male 0.62 (389)

2 81.1 11 250 3 Japanese Male 0.68 (391)

6 Male 1 (dose normalised) 44.0 21 170 3 Chinese 0.74 (390) 6 Female

Table 3.3 Summary details for clinical DDI studies used for the assessment of pitavastatin fT,OATP1B1

AUC AUC + Pitavastatin control inhibitor

Inhibitor Inhibitor dose Route of administration dose (mg) (h.ng/mL) (h.ng/mL) ftOATP1B1 Reference

Cyclosporine 2 mg/kg Oral 2 77 347 0.78 (392)

Rifampicin 600 mg Oral 4 377 1990 0.81 (393)

Rifampicin 600 mg Oral 1 29 193 0.85 (379)

Rifampicin 600 mg iv 1 29 157 0.82 (379)

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3.4.3 Effect of glucuronides on OATP1B1 using E17βG as a probe The inhibitory effect of 10 glucuronide metabolites on OATP1B1 was assessed in stably transfected HEK293 cells using radiolabelled E17βG as a probe substrate (Table 3.4, Figure

3.4). OATP1B1 inhibition was determined as % of the control response and IC50 values ranged from 1.8 to 55.1 µM for telmisartan and mefenamic acid glucuronides, respectively, following pre-incubation with buffer alone. Of the glucuronides investigated, telmisartan and repaglinide glucuronides were the most potent inhibitors of OATP1B1, with IC50 < 5 µM under these conditions. IC50 values were not characterised for glucuronides of mycophenolic acid and raltegravir; for mycophenolic acid glucuronide maximal 20% inhibition was observed at the highest inhibitor concentrations whereas no significant reduction in OATP1B1 activity was observed for raltegravir glucuronide (Figure 3.4). The effect of a 30-minute pre-incubation with inhibitor on the OATP1B1 inhibitory potency of glucuronides was also investigated. The IC50 values obtained following pre-incubation with inhibitor ranged from 1.2 – 24.7 µM for telmisartan and gemfibrozil glucuronides, respectively. An increase in inhibitory potency following 30-minute pre-incubation with glucuronide in buffer was observed for 3/10 glucuronides, namely, clopidogrel (2-fold), ezetimibe (3-fold) and mefenamic acid (3-fold) glucuronides.

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Table 3.4 IC50 values for OATP1B1-mediated uptake of E17βG in stably transfected HEK293- OATP1B1 cells. Data represent mean ± SD of at least 3 separate experiments. Data were obtained without (0) or with (30) a 30-minute pre-incubation with inhibitor

Inhibitor Mean IC50 (0) Mean IC50 (30)

(µM) (µM)

Telmisartan 0.91 ± 0.4 0.73 ± 0.04

Telmisartan glucuronide 1.8 ± 0.2 1.2 ± 0.7

Repaglinide 2.6 ± 0.4 0.94 ± 0.4

Repaglinide glucuronide 3.1 ± 1 2.1 ± 0.7

Gemfibrozil 62.8 ± 13.2 24.5 ± 2.3

Gemfibrozil glucuronide 33.5* ± 9.1 24.7 ± 9.3

Diclofenac 20.7 ± 7.3 21.1 ± 2.2

Diclofenac glucuronide 16.5 ± 5.7 15.9 ± 3.8

Ezetimibe 48.9 ± 26.3 47.8 ± 11.5

Ezetimibe glucuronide 27.2 ± 8.9 11.9* ± 1.7

Mefenamic acid glucuronide 55.1 ± 11.91 19.3 ± 9.3

Raloxifene 4'-glucuronide 42.7 ± 1.1 30.7 ± 5.4

Clopidogrel glucuronide 46.8 ± 11.3 24.7 ± 7.7

Cyclosporine 0.39 ± 0.03 0.15 ± 0.01

Rifampicin 0.67 ± 0.3 0.27 ± 0.03

Erythromycin 14.8 ± 5.7 16.6 ± 10

Rifamycin SV 0.20 ± 0.02 0.087 ± 0.006

Ki values were calculated from mean IC50 values using Equation 3.3 and were equal to the IC50 values presented and therefore are not presented * Glucuronide caused statistically significantly (p > 0.05) more potent inhibition than the respective parent drug  Statistically significant (p > 0.05) increase in inhibitory potency following pre-incubation with inhibitor

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3.4.4 Effect of parent drugs on OATP1B1 using E17βG as a probe The parent drugs of the 4 glucuronides that showed potent OATP1B1 inhibition (telmisartan, repaglinide, diclofenac and ezetimibe) were selected for investigation and their inhibitory potential using E17βG as a probe was assessed in the same way as for glucuronide metabolites (Table 3.4, Figure 3.5). Gemfibrozil was also included in this dataset due to reported clinical DDIs attributed in part to inhibition of this transporter (282). Inhibition of OATP1B1 by parent drugs was in the same rank order as observed for glucuronide metabolites. When pre-incubation with buffer was performed, IC50 values ranged 0.9 – 62.8 µM with telmisartan being the most and gemfibrozil being the least potent inhibitor of OATP1B1. An increase in OATP1B1 inhibitory potency was observed following pre-incubation for repaglinide (3-fold) and gemfibrozil (3-fold), resulting in IC50 values of 0.94 and 24.5 µM, respectively.

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Figure 3.5 Inhibitory effects of 5 selected parent drugs on OATP1B1-mediated uptake of E17βG in stably transfected HEK293-OATP1B1 cells. Data represent mean ± SD of at least 3 separate experiments without () and with () pre-incubation with inhibitor. E17βG uptake was investigated in the presence of increasing concentrations of gemfibrozil (A), telmisartan (B), repaglinide (C), diclofenac (D) and ezetimibe (E)

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3.4.5 Effect of glucuronides and parent drugs on OATP1B1 using pitavastatin as a probe The inhibitory effect of 5 selected glucuronides and their parent drugs on OATP1B1 was assessed in stably transfected HEK293 cells using pitavastatin as a clinically relevant probe substrate (Table 3.5, Figures 3.6 and 3.7). Following pre-incubation with buffer, telmisartan and repaglinide glucuronides IC50 was < 10 µM, with telmisartan glucuronide being the most potent. Ezetimibe, diclofenac and gemfibrozil glucuronides caused less potent inhibition with

IC50 values ranging 17.3 – 28.3 µM for diclofenac and gemfibrozil glucuronides, respectively. No time-dependent effect on OATP1B1 inhibition was observed for any of the glucuronides investigated. Analogous to their glucuronides, IC50 for telmisartan and repaglinide parent drugs were also < 10 µM with repaglinide causing the most potent OATP1B1 inhibition (Table

3.5). Gemfibrozil, ezetimibe and diclofenac IC50 values were >100 µM without pre-incubation. An increase in inhibitory potency following pre-incubation was observed for ezetimibe (10-fold) and gemfibrozil (2-fold) resulting in IC50 values of 51.2 and 136 µM, respectively.

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Inhibitor Mean IC50 (0) Ki (0) Mean IC50 (30) Ki (30)

(µM) (µM) (µM) (µM)

Telmisartan 4.04 ± 2 2.7 4.4 ± 2.8 2.9

Telmisartan glucuronide 1.9 ± 0.6 1.3 2.8 ± 0.8 1.9

Repaglinide 2.9# ± 1.1 1.9 2.7# ± 0.6 1.8

Repaglinide glucuronide 8.6 ± 2.9 5.7 7.5 ± 3.6 5.

Gemfibrozil 238 ± 46 159 136 ± 23 91.2

Gemfibrozil glucuronide 28.3* ± 4.6 18.9 24.4* ± 8.1 16.3

Diclofenac 171 ± 44 114 251 ± 28 168

Diclofenac glucuronide 17.3* ± 3.3 11.6 18.1* ± 2.3 12.1

Ezetimibe 496 ± 8 331 51.2 ± 3.5 34.3

Ezetimibe glucuronide 24.9* ± 9.4 16.6 23.9* ± 8.2 16

Cyclosporine 0.34 ± 0.06 0.23 0.13 ± 0.02 0.09

Rifampicin 0.69 ± 0.3 0.46 0.43 ± 0.2 0.29

Erythromycin 58 ± 2.8 38.8 70.6 ± 5.3 47.2

Rifamycin SV 0.3 ± 0.3 0.2 0.039 ± 0.003 0.03

* Glucuronide caused statistically significant (p > 0.05) more potent inhibition than its respective parent drug # Parent drug caused statistically significant (p > 0.05) more potent inhibition than its glucuronide  Statistically significant (p > 0.05) increase in inhibitory potency following pre-incubation with inhibitor

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3.4.6 Comparison of glucuronide and parent drugs OATP1B1 inhibitory potency The OATP1B1 inhibitory potency of selected glucuronides and parent drugs was assessed using both E17βG and pitavastatin as probe substrates in separate experiments (Table 3.4). When E17βG was used as the probe substrate the glucuronide and parent drugs OATP1B1 inhibitory potency was not statistically significantly different in the case of repaglinide or diclofenac under either pre-incubation conditions investigated (p > 0.05). Ezetimibe and its glucuronide also demonstrated similar OATP1B1 inhibitory potential following pre-incubation with buffer alone; however, following pre-incubation with inhibitor the glucuronide caused ~4- fold more potent OATP1B1 inhibition than its parent drug (Figure 3.8 A and B). The opposite trend was seen for gemfibrozil with the glucuronide causing 2-fold more potent inhibition than its parent following pre-incubation with buffer; the extent of inhibition was comparable between the parent and glucuronide following pre-incubation with inhibitor (Figure 3.8 C and D). In the case of telmisartan the parent drug caused 2-fold more potent inhibition of OATP1B1 than its glucuronide (pre-incubation with buffer). In contrast, IC50 values obtained for glucuronide and parent following pre-incubation with inhibitor were not statistically different (p < 0.05) (Figure 3.8 E and F).

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When pitavastatin was used as probe substrate, gemfibrozil and diclofenac glucuronides caused > 8-fold more potent OATP1B1 inhibition than parent drugs regardless of the pre- incubation conditions as illustrated for diclofenac in Figure 3.9 (A, B) (Table 3.5). Ezetimibe glucuronide also caused more potent OATP1B1 inhibition than the parent with differences in potency more pronounced where inhibition data were generated without pre-incubation with inhibitor. Following pre-incubation with inhibitor the inhibitory potency of ezetimibe increased, resulting in IC50 values approximately 2-fold greater than that of the glucuronide (Figure 3.9 C, D). In the case of repaglinide, the parent drug OATP1B1 inhibition was up to 4-fold more potent than its glucuronide; this trend was consistent across pre-incubation conditions (Figure 3.9 E, F). Telmisartan glucuronide and its parent drug, the most potent glucuronide-parent pair (IC50 < 2 µM), inhibited OATP1B1 to similar extents with IC50 values within 2-fold for both pre-incubation conditions.

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3.4.7 OATP1B1 inhibition by reference inhibitors Inhibition of OATP1B1 was observed for all 4 reference inhibitors using E17βG as a probe with IC50 values ranging from 0.20 to 14.8 µM for rifamycin SV and erythromycin, respectively, following pre-incubation with buffer (Table 3.4, Figure 3.10 A-D). Pre-incubation with inhibitor resulted in a 3-fold increase in inhibitory potency for cyclosporine, whereas no time-dependent effect on the potency of erythromycin was observed relative to pre-incubation performed with buffer. A 2.5-fold increase in OATP1B1 inhibitory potency was observed for rifampicin following pre-incubation with inhibitor, however this was not statistically significant (p = 0.08). The extent of OATP1B1 inhibition by rifamycin SV, the control inhibitor used in each experiment was > 90% at the highest rifamycin SV concentration (100 µM) with concentration dependent inhibition of probe substrate observed in each experiment. Pre-incubation with rifamycin SV resulted in an IC50 value of 0.087 µM (n=1) and a 2-fold increase in inhibitory potency compared to pre-incubation with buffer alone (IC50 0.20 µM).

Inhibition of OATP1B1 was also observed in the presence of the 4 reference inhibitors when pitavastatin was used as a probe (Table 3.5, Figure 3.10 E-H). Analogous to data with E17βG,

IC50 values were < 1 µM for cyclosporine, rifamycin SV, and rifampicin, with rifamycin SV causing the most potent inhibition (0.3 µM). Erythromycin IC50 values were > 50 µM. Pre- incubation with inhibitor resulted in an increase in OATP1B1 inhibitory potency for cyclosporine resulting in an IC50 value of 0.13 µM. An 8-fold increase in inhibitory potency was observed for rifamycin SV however this was not determined to be statistically significant due to the variability in IC50 values (p = 0.17). The average maximum inhibition caused by this control inhibitor across experiments was ~80%. Analogous to E17βG data, no time-dependent effect was observed for rifampicin or erythromycin.

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Figure 3.10 Inhibitory effects of 4 reference inhibitors on OATP1B1-mediated uptake of E17βG (A-D) and pitavastatin (E-H) in stably transfected HEK293- OATP1B1 cells. Data represent mean ± SD of at least 3 separate experiments without () and with () pre-incubation with inhibitor. Probe substrate uptake was investigated in the presence of cyclosporine (A, E), rifampicin (B, F), erythromycin (C, G) and rifamycin SV (D, H)

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3.4.8 Effect of pre-incubation with inhibitor on inhibition of OATP1B1 The effect of a 30-minute pre-incubation step with inhibitor prior to co-incubation of inhibitor and probe substrate on OATP1B1 inhibitory potential was investigated for 19 drugs with E17βG and 14 drugs with pitavastatin as probe substrates, respectively (Table 3.4 and 3.5, Figures 3.5 – 3.7 and 3.11). On average, the –fold difference in inhibitory potency between pre-incubation conditions was 1.7-fold using E17βG and 2.3-fold using pitavastatin as an OATP1B1 probe substrate. Statistically significant (p < 0.05) pre-incubation effects were observed for 7 drugs using E17βG as a probe and 3 drugs using pitavastatin; cyclosporine and gemfibrozil OATP1B1 inhibitory potential increased following pre-incubation with both probes by up to 3-fold. For the remaining inhibitors for which a pre-incubation effect was observed, the increase in OATP1B1 inhibitory potential varied between probes; statistically significant increases in OATP1B1 inhibitory potency of 2 to 3-fold were observed for ezetimibe, clopidogrel and mefenamic acid glucuronides as well as repaglinide parent drug and rifamycin SV using E17βG. However, with pitavastatin as a probe substrate this trend was not observed. In the case of ezetimibe, a 10-fold increase in inhibitory potency following pre-incubation was observed with pitavastatin as a probe, but not with E17βG.

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Figure 3.11 Comparison of OATP1B1 IC50 data obtained without and with a 30-minute pre-incubation with inhibitor using E17βG (A) or pitavastatin (B) as a probe substrate. The dashed line represents the line of unity. Data were available for rifamycin SV (1), cyclosporine (2), rifampicin (3), telmisartan (4), telmisartan glucuronide (5), repaglinide (6), repaglinide glucuronide (7), erythromycin (8), diclofenac glucuronide (9), diclofenac (10), ezetimibe glucuronide (11), gemfibrozil glucuronide (12), raloxifene glucuronide (13), clopidogrel glucuronide (14), mefenamic acid glucuronide (15), ezetimibe (16) and gemfibrozil (17)

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3.4.9 Comparison of E17βG and pitavastatin OATP1B1 inhibition data

Comparison of IC50 values for 14 drugs obtained using E17βG and pitavastatin as probe substrates is shown in Figure 3.12. In 8/14 cases without and 11/14 cases with pre-incubation with inhibitor, E17βG was more sensitive than pitavastatin to OATP1B1 inhibition. For the data obtained following pre-incubation with buffer an average departure from line of unity (gmfe) of

2.14-fold was obtained and IC50 values were within 2-fold between probes in 57% of cases. Similar trends were seen for data obtained following pre-incubation with inhibitor with a gmfe of 2.33-fold and IC50 values within 2-fold between probes in 50% of cases.

The closest agreement in IC50 values between probes was obtained for diclofenac glucuronide with the IC50 values within 15% between probes and pre-incubation conditions (Figure 3.13A).

Contrastingly, the greatest difference in IC50 values between the probes used was seen for the parent drug, diclofenac, following pre-incubation with inhibitor (12-fold) (Figure 3.13B). The

IC50 values for the 5 glucuronides investigated with both pitavastatin and E17βG were within 2-fold under both pre-incubation conditions with the exception of repaglinide glucuronide. The repaglinide glucuronide IC50 values obtained using pitavastatin were up to 2.8-fold greater than those obtained using E17βG (Figure 3.13C). The impact of probe substrate selection on the extent of OATP1B1 inhibition varied between glucuronides and their parent drugs for all 5 pairs investigated. Following pre-incubation with buffer alone, 2-10-fold lower IC50 values were obtained when E17βG was used as a probe substrate than pitavastatin in the case of gemfibrozil, ezetimibe and diclofenac. Repaglinide and telmisartan IC50 values were not statistically different between probe substrates (p < 0.05). Following pre-incubation with inhibitor IC50 values were not statistically different between probe substrates for telmisartan and ezetimibe. However, OATP1B1 inhibition was 3-12-fold greater when E17βG and not pitavastatin was used as a probe substrate for repaglinide, gemfibrozil and diclofenac.

The four reference inhibitors investigated in this study were also investigated with both E17βG and pitavastatin as probe substrates (Figure 3.12). In the case of cyclosporine and rifampicin,

IC50 values were within 2-fold between probe substrates under both pre-incubation conditions.

Similarly, rifamycin SV IC50 values were within 2-fold following pre-incubation with buffer alone.

However, following pre-incubation with inhibitor, the rifamycin SV IC50 value obtained using pitavastatin as a probe was ~ 50% (p < 0.05) of that obtained using E17βG as a probe.

Erythromycin IC50 values were 4-fold lower using E17βG than pitavastatin as a probe substrate under both pre-incubation conditions investigated.

In order to account for differences in the concentrations of the two probe substrates used in these experiments IC50 values were converted to Ki values (Tables 3.4 and 3.5) under the assumption of competitive inhibition of OATP1B1. Comparison of Ki values calculated for 14 compounds investigated using E17βG and pitavastatin as probes is shown in Appendix Figure 6.6. The Ki values obtained with both probes were compared and improved agreement between probes with a gmfe of 1.53- and 1.31-fold obtained for inhibition experiments without and with pre-incubation with inhibitor, respectively. The Ki values of each inhibitor obtained

129 against the two probe substrates were within 3-fold in 75% of cases and 2-fold in 61% of cases. Despite the improved correlation between the sensitivity of E17βG and pitavastatin to

OATP1B1 inhibition following conversion of IC50 values to Ki values, the assumption of competitive inhibition following pre-incubation may not be correct, in particular following pre- incubation with the inhibitor. Therefore, without clear understanding of the inhibition mechanism, IC50 values were considered as more representative for use in comparison of different OATP1B1 inhibitors, probe substrates and pre-incubation conditions, though in many cases differences between IC50 and Ki values was marginal.

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3.4.10 Investigation of the correlation of physicochemical properties and CYP2C8 and OATP1B1 inhibitory potential Physicochemical properties of glucuronides, parent drugs of interest and reference inhibitors were predicted using ADMET Predictor (Simulation Plus, version 7) and are reported in Appendix Table 6.10. Glucuronides had greater molecular weight, PSA, number of hydrogen bond donors and acceptors and lower LogD7.4 and LogP compared to parent drugs. These properties were analysed against inhibition data generated for OATP1B1 and CYP2C8. No trend in CYP2C8 inhibitory potency was seen with any of the physicochemical properties examined and the IC50 dataset obtained against CYP3A4 and UGT1A1 (Chapter 2) was too scarce to determine any trends with physicochemical properties.

No trend in OATP1B1 inhibitory potency was seen with hydrogen bond donor or acceptor number or net charge for the compounds in this study. There was a general tendency, though no direct correlation was observed, for increased inhibitory potency with increasing molecular weight, topological surface area, LogP or LogD7.4, which was clearer when analysing glucuronides alone than in conjunction with parent drugs and reference inhibitors (Figure 3.14). More inhibitors were investigated using E17βG as a probe substrate, however, comparable trends in OATP1B1 inhibitory potential with physicochemical properties were observed between probe substrates. The glucuronide LogD7.4 values ranged from -1.53 to 1.12 for clopidogrel and telmisartan glucuronide, respectively, with the latter characterised as the most potent OATP1B1 inhibitor of the glucuronides studied. Raloxifene glucuronide was an exception to this trend as its LogD7.4 value (2.30) exceeded that of telmisartan glucuronide but had an OATP1B1 IC50 > 30 µM with and without pre-incubation. In general, LogD7.4 values of glucuronides were lower than those of the potent reference inhibitors cyclosporine, rifampicin and rifamycin SV. Similar trends were observed for increasing OATP1B1 inhibitory potency with increasing molecular weight, LogP and polar surface area.

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3.5 Discussion The contribution of inhibition of OATP1B1 to DDIs has been increasingly studied and investigation of its inhibition is recommended as part of a novel drugs development (13, 14, 87, 394). In contrast to metabolic enzymes, the effect of drug metabolites on OATP1B1 has been less studied, however, there is increasing evidence that both P450 metabolites (e.g., cyclosporine AM1) and glucuronides (e.g., gemfibrozil glucuronide) are inhibitors of OATP1B1 (124, 186). The current chapter provides an analysis of the inhibitory potential of a range of glucuronides, selected parent drugs and reference inhibitors on this hepatic uptake transporter in stably transfected HEK293 cells.

3.5.1 Inhibitory effects of glucuronides on OATP1B1 Following critical analysis of reported OATP1B1 inhibition data, initial experiments were performed in HEK293 cells using the prototypical probe substrate E17βG (Table 3.4). For glucuronides of interest, secondary experiments using pitavastatin were conducted in order to explore any potential substrate-dependent inhibition and to assess inhibition using a clinically relevant probe (Table 3.5). All experiments were performed with and without a 30-minute pre- incubation step in order to assess for any time-dependent effect on inhibitory potency.

OATP1B1 IC50 values were determined for 8/10 glucuronides investigated using E17βG as a probe substrate. IC50 values were determined in the majority of cases. However, the extent of inhibition varied with IC50 values below < 10 µM observed for the two most potent glucuronides, telmisartan and repaglinide glucuronide. In contrast, only marginal inhibition of OATP1B1 was observed at the highest inhibitor concentration of other glucuronides, for example mycophenolic acid glucuronide reduced uptake of E17βG by only 20% at the highest inhibitor concentration (100 µM) (Figure 3.4). For the 5 glucuronides selected for further investigation with pitavastatin, similar effects on OATP1B1 were observed as when using E17βG as a probe substrate. Telmisartan glucuronide was the most potent inhibitor of

OATP1B1 regardless of the probe substrate used (IC50 1.22 – 2.22 µM). This glucuronide has been reported to be a substrate of OATP1B3 but not OATP1B1 (264), illustrating that glucuronides do not have to be substrates of OATP1B1 to cause inhibition. Conversely, mycophenolic acid glucuronide did not significantly inhibit OATP1B1 activity despite being reported to be a substrate of this transporter. IC50 values were obtained for ezetimibe and repaglinide glucuronides, both substrates of OATP1B1 (202, 395), with both probe substrates investigated here supporting the hypothesis of competitive inhibition. These contrasting results indicate that OATP1B1 inhibitory potential and the underlying mechanism of inhibition varies between glucuronides. Limited literature IC50 data were available for comparison of the effects of glucuronides on OATP1B1; however, the gemfibrozil glucuronide IC50 results obtained in this study were in agreement with previously reported studies using cerivastatin and pitavastatin as probe substrates (33, 168). In the case of ezetimibe glucuronide the

OATP1B1 IC50 values obtained in this study were ~200-fold higher than those reported previously in HEK293 cells where bromosulphopthalein was used as a probe substrate (202),

135 potentially as a result of substrate-dependent inhibition and differences in the substrate concentration used.

The OATP1B1 inhibitory potential of glucuronides was associated with increasing molecular weight, LogD7.4, Log P and polar surface area (Figure 3.14). This was in agreement with general trends seen for OATP1B1 inhibitors (225, 309) highlighting the importance of these physicochemical properties as indicators of potential OATP1B1 inhibition. However, no correlation between glucuronide OATP1B1 inhibitory potency and the other physicochemical properties investigated was observed. Further investigation of a wider range of glucuronides is required in order to assess which physicochemical properties may provide an indication of their OATP1B1 inhibitory potential.

3.5.2 Inhibitory effects of parent drugs on OATP1B1 The OATP1B1 inhibitory potential of 5 parent drugs selected based on their glucuronides inhibitory potency was investigated using both E17βG and pitavastatin as probe substrates.

IC50 values were determined in all cases (Tables 3.4 and 3.5). Telmisartan and repaglinide caused the most potent OATP1B1 inhibition in agreement with available literature data (168,

186). In the case of telmisartan the IC50 values obtained in this study using E17βG as a probe substrate, 0.73 and 0.91 µM with and without pre-incubation with inhibitor, respectively, were within the range reported in the literature (0.44 – 1.1 µM) although no studies using this probe were available for direct comparison. The telmisartan IC50 values obtained here using pitavastatin as a probe substrate, 4.4 and 4.0 µM with and without pre-incubation with inhibitor, respectively, were ~10-fold higher than the value reported by Hirano et al., (2006)(168) using the same probe in the same in vitro system. The reason for the disparity between studies is unclear. More potent inhibition of OATP1B1 by telmisartan was also reported by de Bruyn et al., (2013) (309) using fluorescein sodium as a probe substrate in HEK293 cells. In this instance the variation in telmisartan OATP1B1 inhibitory potency between studies may be a result of substrate-dependent inhibition. Like its glucuronide, telmisartan has been reported to be a substrate of OATP1B3 but not OATP1B1 (111, 264), further illustrating that inhibition can be caused by non-substrates of OATP1B1 and that inhibition by mechanisms other than direct competition merit consideration. The literature OATP1B1 IC50 data collated for repaglinide ranged from 0.32 to 3.4 µM with E17βG and eltrombopag as probe substrates, respectively.

The repaglinide OATP1B1 IC50 data generated in the current study using both pitavastatin and E17βG as probes were within the reported literature range across both pre-incubation conditions investigated (Tables 3.4 and 3.5). The mechanism of inhibition of OATP1B1 by repaglinide is likely of a competitive nature due to its role as a substrate, for which it is employed as a clinical probe substrate for this transporter (13).

Diclofenac and ezetimibe caused less potent OATP1B1 inhibition (IC50 > 20 µM) than telmisartan and repaglinide with both pitavastatin and E17βG as probes. In the case of diclofenac no literature data were available for comparison. The mechanism of OATP1B1

136 inhibition by diclofenac requires further investigation, for instance, exploration of its potential as a probe substrate of this transporter to assess if inhibition is competitive. As observed for its glucuronide, ezetimibe IC50 values obtained with both probes used in this study were higher than those reported by Oswald et al., (2008) (202), potentially as a result of the use of different probe substrates. The mechanism of OATP1B1 inhibition by ezetimibe requires consideration as it was reported not to be significantly transported into OATP1B1 expressing HEK293 cells in comparison to vector control cells (202).

Gemfibrozil was the least potent OATP1B1 inhibitor of the 5 parent drugs studied. Reported literature OATP1B1 IC50 values for gemfibrozil ranged 7 - 42 µM and 38 - 100 µM where E17βG and pitavastatin were used as probe substrates, respectively (Appendix Table 6.2).

The gemfibrozil IC50 data generated in this study using E17βG as a probe were within the reported literature range. However, when pitavastatin was used as a probe substrate the IC50 value obtained without pre-incubation (238 µM) was 2-fold greater than that reported by Sharma et al., (2012) (130) (100 µM) using the same cell system and a probe substrate concentration of 1.25 µM. In comparison to the value reported by Soars et al., (2012) (352) obtained in HEK293 cells at a probe substrate concentration of 1 µM, there was a 6-fold difference between the reported IC50 value and that obtained in the current study. Both the literature studies and that conducted here used an incubation time of 1 minute for the analysis of OATP1B1 inhibition by gemfibrozil. No clear explanation for the difference in gemfibrozil OATP1B1 inhibitory potential between studies is apparent based on the reported experimental protocols.

Though more OATP1B1 inhibition data were available in the literature for comparison for parent drugs than the glucuronides studied it is noteworthy that direct comparisons are still limited due to paucity of data with the same probes, pre-incubation conditions and cell systems used in this study. The inter-study variability noted here is not dissimilar to that observed between other studies in the literature database and may be influenced by factors such as experimental design and potential differences in transporter expression level between in vitro systems.

3.5.3 Comparison of OATP1B1 inhibitory potential between glucuronides and parent drugs

For the 5 glucuronide-parent pairs investigated, glucuronides generally proved to be either equal or more potent inhibitors of OATP1B1 than their parent drugs. These findings are in agreement with previously reported data for gemfibrozil and ezetimibe (33, 202) and have been investigated for the first time for repaglinide, telmisartan and diclofenac glucuronide- parent pairs. These results are also in line with reports of other types of metabolites having similar or greater OATP1B1 inhibitory potential as their parent drugs, as observed for cyclosporine AM1 (124). In the case of gemfibrozil, the glucuronide caused 2-fold more potent

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OATP1B1 inhibition than the parent drug following pre-incubation with buffer with both probe substrates investigated. However, following pre-incubation with inhibitor both the metabolite and parent demonstrated statistically similar OATP1B1 inhibitory potential when E17βG was used as a probe substrate in agreement with reports by Hirano et al., (2006) (168) using pitavastatin as a probe. Interestingly, in these studies gemfibrozil glucuronide caused up to 8- fold more potent OATP1B1 inhibition than its parent when pitavastatin was used as a probe, potentially as a result of substrate dependent inhibition by the parent drug. This difference in gemfibrozil glucuronide and its parents inhibitory potency has been reported in several other studies e.g., glucuronide was ~3-fold more potent than its parent when cerivastatin was used as a probe substrate (33).

Ezetimibe glucuronide was reported to cause ~100-fold more potent OATP1B1 inhibition than its parent drug by Oswald et al., (2008) (202). The current study confirmed that ezetimibe glucuronide was a more potent inhibitor of OATP1B1 than the parent drug. However, this trend was less evident than that observed by Oswald et al., (2008); the glucuronide caused up to 20-fold more potent OATP1B1 inhibition than the parent depending on pre-incubation conditions and probe substrate used. The more potent inhibition of OATP1B1 by ezetimibe glucuronide than its parent drug may be a result of its higher affinity as a substrate of OATP1B1 (202).

Repaglinide and diclofenac glucuronides demonstrated similar or greater inhibitory potential than parent drugs when E17βG and pitavastatin were used as probes. This trend was evident regardless of the pre-incubation conditions used. Telmisartan caused ~2-fold more potent OATP1B1 inhibition than its glucuronide following pre-incubation with buffer with E17βG as a probe substrate. Contrastingly, following pre-incubation with inhibitor when E17βG was used as a probe substrate and under both pre-incubation conditions investigated with pitavastatin as a probe substrate, OATP1B1 mediated uptake was inhibited to a similar extent by both telmisartan glucuronide and its parent drug (Table 3.4).

Parent drug and glucuronide inhibitory potency rank order differed between probe substrates and no trends in the relationship between glucuronides and parent OATP1B1 inhibitory potency were determined with this dataset. Further investigation into the mechanism of OATP1B1 inhibition by these glucuronide-parent pairs is required as is consideration of whether the same mechanism is employed by both the parent and glucuronide in all cases with all probe substrates.

3.5.4 Inhibitory effects of reference compounds on OATP1B1 The reference inhibitors selected for investigation included cyclosporine, rifampicin, rifamycin

SV and erythromycin. IC50 values were obtained for all four compounds with both E17βG and pitavastatin as probe substrates and were within 2-fold between the probe substrates for all reference inhibitors investigated. Rifamycin SV, rifampicin and cyclosporine had IC50 values <

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1 µM with both probe substrates; rifamycin SV, which was used as a control inhibitor in all IC50 experiments, caused the most potent OATP1B1 inhibition. Erythromycin was the least potent reference OATP1B1 inhibitor with IC50 > 10 µM. In all cases the IC50 values obtained in this study were within the inhibitor concentration range used and within the range of reported literature IC50 data (Appendix Table 6.2). In relation to these reference inhibitors, neither glucuronides nor parent drugs demonstrated as potent OATP1B1 inhibition as cyclosporine, rifamycin SV or rifampicin. However, telmisartan and repaglinide glucuronides and parent drugs caused more potent OATP1B1 inhibition than erythromycin with both probes used.

3.5.5 The effect of pre-incubation with inhibitor on OATP1B1 inhibition This study has confirmed the increase in inhibitory potency following pre-incubation with inhibitor previously reported for cyclosporine and its metabolite (Table 1.3) (124, 128, 141).

The OATP1B1 IC50 values obtained for cyclosporine in this study, though within the range of reported literature data, were 2 to 8-fold higher than those reported by Gertz et al., (2013) (124) and Izumi et al., (2015) (128), respectively, without pre-incubation and using E17βG as a probe substrate. This trend remained following pre-incubation with inhibitor; the IC50 values obtained in this study were up to 10-fold greater than those obtained Gertz et al., (2013) and Izumi et al., (2015). Similarly, the results obtained in this study using pitavastatin as a probe substrate were 4- to 5-fold greater than those reported by Izumi et al, (2015), without and with pre-incubation, respectively. The observed range in IC50 values may be a result of differences in probe substrate concentration or differences between transporter expression systems. However, the extent of the increase in inhibitory potency in this study as a result of a 30- minute pre-incubation with inhibitor (3-fold) was similar to previously reported values (4-10 – fold with E17βG, 4-fold with pitavastatin) (Figure 3.15) with the exception of data reported from Amunsden et al., (2010) (141).

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In comparison to the other probe substrates for which the pre-incubation effect on OATP1B1 inhibition had been explored, the cyclosporine IC50 values obtained in this study with both E17βG and pitavastatin as probes without pre-incubation were similar to that reported by Amundsen et al., (2010) (141) using atorvastatin (0.47 µM). Following pre-incubation, the OATP1B1 inhibition reported by Amundsen et al., (2010) was ~7-fold more potent than in the studies conducted in this chapter. In comparison to the IC50 values obtained by Izumi et al., (2015) (128) using atorvastatin, bromosulphopthalein and estrone-sulphate as probe substrates, the IC50 values obtained in this study were generally higher (4-6 fold across probes and pre-incubation conditions). This range in inhibition potencies may be a factor of substrate- dependent inhibition and the experimental conditions employed e.g., length of pre-incubation (Table 1.3). The increase in the OATP1B1 inhibitory potency of cyclosporine observed in this study was in agreement with that reported in the literature, supporting the use of E17βG and pitavastatin for investigation of the effects of pre-incubation on inhibition of OATP1B1.

A pre-incubation effect was observed for a number of additional inhibitors, however, this response was not detected for all inhibitors, neither was it consistent for the same inhibitors between probe substrates. This supports the hypothesis that different inhibitors and probe substrates may interact with OATP1B1 differently, potentially as a result of multiple binding sites (131, 132). However, the reasons for differences in pre-incubation effects between inhibitor-substrate combinations requires further investigation as does the potential for down regulation of OATP1B1 or trans-inhibition potentially contributing to pre-incubation effects, as reported previously for a range of other inhibitors and OATP transporters (126, 135, 137). For

140 most drugs investigated pre-incubation effects had not been previously explored and literature data were not available for comparison. An increase in inhibitory potency was observed for the first time to our knowledge for rifamycin SV, clopidogrel, ezetimibe and mefenamic acid glucuronides and gemfibrozil and repaglinide using E17βG as a probe substrate. Similarly, a previously uncharacterised pre-incubation effect was observed for ezetimibe when pitavastatin was used as a probe. No clear trend in pre-incubation effects on OATP1B1 inhibition were observed for the glucuronide-parent pairs analysed, indicating that their potential pre-incubation effects require separate investigation. The pre-incubation effects reported in this study were not associated with inhibitory potency and it remains unclear why only certain drugs exhibit increased inhibitory potential following pre-incubation. Monitoring of inhibitor concentrations, intra- and extra-cellularly, as well as transporter expression level and localisation during inhibition experiments, as in Shitara et al., (2012) (135), is required to further assess the mechanism of pre-incubation effects on OATP1B1 inhibition.

3.5.6 Comparison of E17βG and pitavastatin sensitivity to OATP1B1 inhibition

Pitavastatin was the clinical OATP1B1 probe substrate for which most uptake data (fT,OATP1B1) were available in the literature and was also the most used statin probe substrate for investigating inhibition of OATP1B1 in vitro. All methods used to estimate the fT,OATP1B1 indicated that the contribution of OATP1B1 to pitavastatin uptake in vivo is > 60% OATP1B1 which, in conjunction with the reported sensitivity of pitavastatin to inhibition of the transporter in vitro, supports its use as a suitable clinically relevant probe for assessing OATP1B1 inhibition. The OATP1B1 inhibitory potential of a total of 14 compounds was investigated using pitavastatin and E17βG as probe substrates. Both probes exhibited similar sensitivity to

OATP1B1 inhibition for the majority of compounds investigated when IC50 data were examined and also when these data were corrected for differences in probe substrate concentration by conversion to Ki. The greatest exceptions to this were gemfibrozil, ezetimibe and diclofenac parent drugs which caused up to 12-fold more potent OATP1B1 inhibition when E17βG was used as a probe compared to pitavastatin (Figure 3.12).

An increase in OATP1B1 inhibitory potency following a 30-minute pre-incubation with inhibitor was observed for a greater number of drugs with E17βG (7) than with pitavastatin (3). However, this effect was observed for different inhibitors with each of the probe substrates e.g., an increase in OATP1B1 inhibitory potency following pre-incubation with inhibitor was observed for ezetimibe with pitavastatin, but not E17βG. The exceptions to this were cyclosporine and gemfibrozil, for which pre-incubation effects on OATP1B1 inhibitory potency were consistent with both probe substrates. From these results it is apparent that the inhibitor- substrate combination selected for in vitro investigation of OATP1B1 inhibition is important. This may be a factor of differential OATP1B1 binding of the probes and inhibitors, potentially involving multiple OATP1B1 binding sites (131, 132), however, further investigation is required.

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3.5.7 Conclusions and future directions

To summarise, the current study indicates that glucuronides inhibit OATP1B1 with a range of potencies. Therefore, glucuronide OATP1B1 inhibitory potential should be considered for drugs metabolised by UGTs. This is especially recommended if the parent drug demonstrates OATP1B1 inhibition as for the drugs investigated in this study all glucuronide-parent pair’s inhibited OATP1B1 uptake activity. The location of glucuronide formation and risk of enterohepatic re-circulation should be taken into account in order to consider the mechanism and site of OATP1B1 inhibition and the in vivo concentrations i.e., circulating or intracellular, that are of most relevance.

In comparison to parent drugs, glucuronides generally caused equal or more potent inhibition of OATP1B1. The synergistic effects of parents and glucuronides in vitro requires investigation in order evaluate the risk of DDI posed by these drugs in vivo. Investigation of OATP1B1 inhibition for a larger number of drugs and their glucuronide metabolites is required to validate if all glucuronides of OATP1B1 inhibitors demonstrate inhibitory properties themselves.

Pitavastatin and E17βG probe substrates showed comparable sensitivity to OATP1B1 inhibition by the inhibitors investigated here with the exception of gemfibrozil, diclofenac and ezetimibe parent drugs for which substrate-dependent inhibition was observed. Pre-incubation with inhibitor was shown to increase the OATP1B1 inhibitory potential of a number of drugs with pitavastatin and E17βG. However, this effect was not consistent across probe substrates for the same inhibitors. Inclusion of a pre-incubation step is recommended for in vitro OATP1B1 inhibition studies to obtain the most conservative estimate of DDI potential and examine the potential mechanism of inhibition. Exploration of the effect of pre-incubation with a wider range of probe substrates is required to investigate substrate dependent pre- incubation effects. In addition, investigation of the impact of a pre-incubation on OATP1B1 inhibitory potency in a variety of in vitro systems with a range of pre-incubation lengths would be useful to study the consistency and extent of this effect on this transporters uptake activity. However, differences in the expression level of the transporter of interest and the presence of other transporters and metabolising enzymes on OATP1B1 inhibition would also require consideration.

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Chapter 4 Collation of metabolite clinical exposure data and prediction of the clinical risk of drug-drug interactions

4.1 Introduction The latest FDA guidance recommends that pharmacokinetic interactions between an investigational new drug and drug likely to be co-administered should be defined during drug development (13). To explore this, quantitative assessment of potential DDIs using a variety of models ranging from mechanistic static to full physiologically-based pharmacokinetic (PBPK) models are considered, depending on the stage of drug development (13). In vitro enzyme and transporter inhibition data, in conjunction with clinical exposure data, are used to predict the magnitude of in vivo DDIs. The in vitro-in vivo extrapolation (IVIVE) approach is now well established to help guide the design of further studies (11, 178, 396, 397).

In spite of developments in modelling approaches for prediction of DDIs, the prediction of metabolite DDI risk remains challenging due to the limited availability of clinical exposure data and in vitro enzyme and transporter inhibition data. However, as a result of reports of metabolites inhibiting either metabolic enzymes or transporters, the importance of incorporating metabolite DDI potential is increasingly recognised. Models which include the enzyme or transporter inhibitory effects of metabolites have been developed as reported for metabolites of diltiazem and cyclosporine. Accounting for the inhibitory effects of metabolites, in combination with inhibition by the parent drug, resulted in an increase in the predicted extent of DDI for these inhibitors (124, 200). Regulatory approaches in the evaluation of metabolite DDI potential differ slightly in their specifications. However, both FDA and EMA guidelines recommend consideration of metabolite in vitro inhibitory potential against P450 enzymes, when metabolite exposure is ≥25% of parent systemic exposure (FDA) or > 10% of total drug exposure (EMA) (13, 14). In order to assess the ability of a metabolite to contribute to DDIs, the mechanism of inhibition and the relevant metabolite concentration (i.e., intracellular or systemic) need to be considered in conjunction with the potency of inhibition. Multiple and extensive analyses of the contribution of metabolites to DDIs have recently been performed for inhibitors of P450 enzymes (18, 23, 277) which demonstrate that in some instances the inhibitory effects of the parent compound alone do not explain the DDI observed in vivo. For instance, N,N-didesethylamiodarone inhibits CYP2C9 more potently than its parent drug in vitro and has been predicted to be a major contributor to the in vivo DDI between warfarin and amiodarone (398). Recently, predictive models ranging from static to PBPK models have been developed describing DDIs for clopidogrel and gemfibrozil with repaglinide which incorporate the contribution of glucuronide metabolites to the DDI (182, 216). As a result of their prevalence, potential to inhibit P450 enzymes and interact with membrane transporter proteins, glucuronide metabolites are of increasing interest in the area of metabolite mediated

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DDIs (17, 25, 203). However, the in vivo DDI risk posed by inhibition of metabolising enzymes or OATP1B1 by glucuronides has not been extensively explored so far. Information on the clinical exposure and DDI potential of glucuronides with respect to current FDA guidelines is also currently unknown and information regarding the synergistic effects of parents and glucuronides in vivo is sparse.

4.2 Aims The aim of this chapter was to investigate the potential clinical DDI risk posed by glucuronides based on in vitro enzyme and transporter inhibition data generated in Chapters 2 and 3. Clinical exposure data for glucuronides and parent drugs were collated from the literature and the predicted DDI potential of glucuronides and parent drugs on their own and combined was assessed. Clinical exposure data of potent P450 and OATP1B1 inhibitors listed in the FDA guidance and their metabolites were also collated. Metabolite : parent exposure ratios were determined for glucuronides and metabolites of potent P450 and OATP1B1 inhibitors and the relevance to FDA exposure limits for investigation of metabolite P450 inhibitory potential assessed.

4.3 Methods

4.3.1 Collation of clinical exposure data for P450 and glucuronide metabolites Metabolite and parent compound systemic exposure data, in the form of AUC and maximum plasma concentration (Cmax) data, were collated from the literature and using the University of Washington Drug Interaction Database (UW DIDB) (www.druginteractioninfo.org). This analysis was done for all strong in vivo P450 and OATP1B1 inhibitors listed in the FDA 2012 guidelines (13), as summarised in Tables 4.1. A strong P450 inhibitor is defined as an inhibitor that increases the AUC of a substrate for that P450 enzyme by equal to or more than 5-fold or causes a greater than 80% decrease in its CL. From the list of in vivo inhibitors collated from FDA guidelines, food products (grapefruit juice) and combination therapies (oral contraceptive and ritonavir-boosted formulations) were excluded from further analysis. Clinical glucuronide metabolite and parent compound systemic exposure data were also collated where available. Plasma concentration – time data were retrieved using GetDataBack V.2.24 software when only concentration-time profiles were reported and used to determine Cmax and AUC. Inclusion criteria for all metabolites were quantifiable metabolite and parent compound exposure data (Cmax or AUC) reported in the same study for the parent and metabolite alone without co-administration with other drugs. Metabolite and parent drug exposure data were obtained from studies in healthy subjects where possible, following single and multiple dosing and at different doses of the parent drug. Ratios of total metabolite exposure (AUC) and Cmax relative to parent compound were calculated in order to evaluate

144 if the metabolites complied with the FDA recommendations for investigation of P450 inhibitory potential.

P450 Enzyme/ Transporter Strong Inhibitors inhibited

CYP1A2 Ciprofloxacin, Enoxacin, Fluvoxamine

CYP2C8 Gemfibrozil

CYP2C19 Fluconazole, Fluvoxamine, Ticlopidine

CYP3A Boceprevir, Clarithromycin, Conivaptan, Indinavir, Itraconazole, Ketoconazole, Mibefradil, Nefazodone, Nelfinavir, Posaconazole, Ritonavir, Saquinavir, Telaprevir, Telithromycin, Voriconazole

CYP2D6 Bupropion, Fluoxetine, Paroxetine, Quinidine

OATP1B1 inhibitor Atazanavir, Cyclosporine, Eltrombopag, Gemfibrozil, Lopinavir, Rifampicin, Ritonavir, Saquinavir, Tipranavir

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4.3.2 Assessment of the clinical relevance of in vitro enzyme inhibition by glucuronides Initial analysis was performed by predicting the AUC ratio using the basic model shown in Equation 4.1.

푪풎풂풙 Equation 4.1 푹 = ퟏ + 푲풊

R represents the predicted AUC ratio of the victim drug with and without the perpetrator drug and Cmax is the maximal total systemic inhibitor concentration. The cutoff for R is 1.1, indicating the investgational drug to be an enzyme inhibitor requiring further analysis using static mechanistic models. Glucuronide, parent compound and reference inhibitor Cmax data were collated from the literature, as described in section 4.3.1. Glucuronide and parent compound Cmax data were collated from the same study where possible. In the case of gemfibrozil glucuronide, which has been reported to accumulate in isolated perfused rat liver by 35- to 42- fold by a carrier mediated process (399), the estimated unbound concentration in human liver, assuming similar accumulation as in rats, has been reported to range from 81 to 97 µM (33). Therefore, the DDI risk of gemfibrozil glucuronide was also predicted using the mean estimated liver concentration (89µM). For the reference inhibitors and parent compounds where glucuronide exposure data were not available, exposure data were collated for the standard dose of each drug and the highest exposure reported in DDI studies recorded. Where metabolite Cmax data were available from multiple studies, the highest reported Cmax value was used in the prediction of DDI potential to provide the most conservative estimate of DDI risk. If metabolite exposure data were available over a range of parent drug doses the effect of increasing dose on metabolite DDI potential was also explored. Inhibition data were used, in conjunction with collated clinical exposure data (Section 4.3.1), to calculate the DDI potential using equation 4.1.

AUC ratios (R values) were predicted for glucuronides, parent drugs and reference inhibitors against CYP2C8, CYP3A4 and UGT1A1 using inhibition data generated in human liver microsomes using repaglinide as a probe substrate (Chapter 2). IC50 values obtained without a pre-incubation step and corrected for nonspecific binding were converted to Ki using Equation 4.2.

푰푪ퟓퟎ Equation 4.2 푲풊 = 푺 ퟏ+ 푲풎

Where S is the probe substrate concentration used in the assay, Km is the Michaelis-Menten constant for the substrate in the system used under the assumption that inhibition is

146 competitive. The exceptions to this were clopidogrel and gemfibrozil glucuronides, which both caused a time-dependent shift in CYP2C8 IC50. For these compounds the AUC ratio was predicted using CYP2C8 IC50 data obtained following pre-incubation with inhibitor, as reported previously (75, 145). The R values obtained in this analysis were compared to reported changes in repaglinide AUC where clinical DDI and inhibitor exposure data were reported in the same study, as summarized in Table 4.2. Predictions were classed as successful if within two-fold of the observed value. Predicted AUC ratios < 2 for DDIs observed to be > 2 were classed as false negative and predicted AUC ratios > 2 for observed ratios < 2 were classed as false positive interactions, on the basis of previous consensus reports (4, 41, 179).

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Inhibitor Inhibitor dose (mg) Repaglinide dose (mg) No. Subjects Mean fold change in Reference repaglinide AUC

Clopidogrel 75 (3 days) (300 on first day) 0.25 9 3.90 (216)

Clopidogrel 300 (single dose) 0.25 9 5.10 (216)

Gemfibrozil 600 (2.5 days) administered with 0.25 10 6.98 (231) repaglinide

Gemfibrozil 30 (single dose) 0.25 10 1.77 (230)

Gemfibrozil 100 (single dose) 0.25 10 4.48 (230)

Gemfibrozil 300 (single dose) 0.25 10 6.70 (230)

Gemfibrozil 900 (single dose) 0.25 10 8.26 (230)

Trimethoprim 160 (3 days) 0.25 9 1.63 (284)

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The above analysis assumes that the victim drug is cleared exclusively by the liver by way of a single metabolic pathway. In addition, it is also assumed that the total Cmax is the concentration of inhibitor available to the enzyme. Following the initial analysis, mechanistic static models (Equations 4.3 and 4.4), were applied for inhibitors which exceeded the R cut- off of 1.1. In contrast to the Cmax/Ki approach, the mechanistic static model incorporated the fraction of victim drug, in this case repaglinide, metabolised by a specific P450 enzyme (fmCYP). Where possible, predicted AUC ratios were compared to observed changes in repaglinide AUC in the presence of inhibitor in vivo. Equations 4.3 and 4.4 can be used in the prediction of in vivo DDIs for competitive reversible and time-dependent inhibitors, respectively, when fmCYP values are known and the other P450 pathways involved in the metabolism of the substrate are not subject to inhibition (41, 75, 145, 310).

푨푼푪′ ퟏ Equation 4.3 = 푨푼푪 풇풎푪풀푷ퟐ푪ퟖ 푰 + (ퟏ− 풇풎푪풀푷ퟐ푪ퟖ) ퟏ+ 풖 푲풊

푨푼푪′ ퟏ Equation 4.4 = 푨푼푪 풇풎푪풀푷ퟐ푪ퟖ 푰 + (ퟏ− 풇풎푪풀푷ퟐ푪ퟖ) ퟏ+ 풖 ퟎ.ퟓ푰푪ퟓퟎ

where AUC’ and AUC are the AUC of the victim drug in the presence and absence of the inhibitor, respectively, fmCYP2C8 represents the fraction of a substrate drug metabolized by the inhibited pathway via CYP2C8, 1 – fmCYP2C8 represents clearance by other elimination pathways (i.e., renal or other P450 enzymes). Iu is the unbound inhibitor concentration in the liver; for parent drugs and reference inhibitors I,in,max values, the maximum hepatic inlet concentration, were calculated and for glucuronides Cmax,u values were used as inhibitor concentration input data. Ki is reversible inhibition of CYP2C8 and IC50 obtained following a 30-minute pre-incubation with inhibitor.

A range of values for repaglinide fmCYP2C8, from 0.49 to 0.92, have been reported in the literature (152, 230, 282, 400). A value of 0.49, reported by Sall et al., (2012) (152) following in vitro analysis in human hepatocytes, was used as an initial input value for the investigation of the DDI potential of inhibitors investigated in this study. Predicted DDI potential was also explored using the maximum reported fmCYP2C8 value of 0.92, estimated from in vivo data obtained in a DDI study between gemfibrozil and repaglinide using nonlinear regression analysis (230). It should be considered that use of the fmCYP2C8 input value reported by Honkalammi et al., (2011) (230) (0.92) does not account for the contribution of hepatic uptake

149 of repaglinide and therefore may be larger than the actual amount of repaglinide metabolized by this enzyme, leading to an overestimation of the DDI.

The accurate prediction of an in vivo DDI is also influenced by the inhibitor concentration used in Equations 4.3 and 4.4. It is not possible to measure the inhibitor concentration available to the enzyme within the human liver and therefore surrogate concentrations are used. Currently, there is no definitive consensus on which inhibitor concentration is best to use for prediction purposes; those which can be used include systemic or portal vein concentration and total or unbound plasma concentration. The FDA guidance recommends using the maximal total inhibitor concentration (free and bound) in the basic model (Equation 4.1) and I,in,max as the inhibitor concertation in the mechanistic static models (Equations 4.3 and 4.4). Calculation of I,in,max (Equation 4.5) relies on information on hepatic blood flow (Qh), inhibitor dose (D), fraction of the dose of inhibitor which is absorbed (Fa), the fraction of absorbed inhibitor dose which escapes gut wall extraction (FG) and the absorption rate constant (ka) to provide an absorption term and the maximum systemic plasma concentration of the inhibitor (Cmax). The FDA recommended Qh input value is 1500 mL/min.

풌풂 푫 푭풂 푭푮 Equation 4.5 푰, 풊풏, 퐦퐚퐱 = 푪풎풂풙 + 푸풉

Refinement of ka has been shown to improve DDI prediction accuracy (80); for inhibitors where ka values were reported in the literature, these were used to calculate I,in,max for use in DDI prediction. Unfortunately, ka values are not consistently reported in in vivo clinical studies; therefore, in order to avoid false-negative prediction and obtain the largest I,in,max, a theoretically maximal value for ka of 0.1 min-1 was used, assuming the gastric emptying is the rate limiting step for absorption (13, 37), if inhibitor specific ka values were not reported in the literature. However, a number of studies suggest use of a ka of 0.03 min-1 as a more realistic value (401). In this study, if inhibitor FaFG values could not be obtained from the literature, a value of 1 was used as theoretically maximal values to avoid false negative predictions in line with FDA guidelines (13, 37, 350). For glucuronide metabolites, and metabolites in general, I,in,max is not an appropriate inhibitor concentration for use in prediction of DDIs as the input parameters required for its calculation, such as dose, do not apply or are complicated by factors such as the location of metabolite formation and enterohepatic recirculation. Consequently, I,in,max values were substituted with Cmax data were corrected for plasma protein binding values predicted or collated from the literature. The fup values for all inhibitors were predicted using SimCYP version 14, or collated from the literature where experimental values were reported. The fup values could not be determined experimentally due to instability of glucuronides reported in Chapter 2 section 2.3.9.

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The synergistic effect of CYP2C8 inhibition by glucuronides and their parent drugs on predicted DDI potential with repaglinide as a victim drug was further evaluated using Equation 4.6. The effect of multiple inhibitors, parent and glucuronide, acting via different inhibition mechanisms, reversible and time-dependent inhibition of CYP2C8, respectively are incorporated into this model (186). The predicted DDI potential was compared to observed data where possible.

푨푼푪′ ퟏ Equation 4.6 = 푨푼푪 풇풎푪풀푷ퟐ푪ퟖ 푰 푰 + (ퟏ− 풇풎푪풀푷ퟐ푪ퟖ) (ퟏ+ ퟏ ) 풙 (ퟏ+ ퟐ ) 푲풊ퟏ ퟎ.ퟓ푰푪ퟓퟎ

AUC’, AUC, fmCYP2C8 and 1 – fmCYP2C8 are as defined in Equation 4.3. I,in,max,u was used as the inhibitor concentration in the case of the parent, whereas Cmax,u was employed in the case of glucuronides. Ki1 is the reversible inhibition constant for CYP2C8 by parent compound and IC50 is obtained following 30-minute pre-incubation of CYP2C8 with the glucuronide. I1 is the unbound inhibitor concentration of parent in the liver (I,in,max,u), I2 is the unbound inhibitor concentration of glucuronide in the liver (Cmax,u).

4.3.3 Prediction of clinical relevance of in vitro OATP1B1 inhibition The clinical relevance of in vitro OATP1B1 inhibition was also investigated. Initially, the ratio of inhibitor Cmax/IC50 was determined using the reported maximum systemic concentration data collated from the literature and OATP1B1 IC50 values reported in Chapter 3. Inhibitor Cmax data and details of the clinical studies collated are as described in section 4.3.1. Further analysis of OATP1B1 DDI potential was performed by calculating R, as defined in Equation 4.7.

풇풖풑 푰,풊풏,풎풂풙 Equation 4.7 푹 = ퟏ + 푲풊 where R represents the AUC ratio of the victim drug in the absence and presence of perpetrator, fup is fraction unbound in plasma and I,in,max is the estimated maximum inhibitor concentration at the inlet to the liver calculated using Equation 4.5 (13).

As described for evaluation of metabolic DDIs above (Section 4.3.3), the FDA recommendation is that inhibitor specific parameters are used for calculation of I,in,max where possible and surrogate values to estimate the most conservative risk of DDI where inhibitor specific values are not available (13). For glucuronide metabolites I,in,max values were substituted with Cmax corrected for fup due to lack of input parameters for calculation of

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I,in,max. All R values were calculated substituting Ki for IC50 values obtained in Chapter 3.

Conversion from IC50 to Ki assumes competitive inhibition and the variable effects of a 30- minute pre-incubation with inhibitor on OATP1B1 inhibitory potential indicated that this may not be the case.

The predictive equation above does not account for the victim drug properties and the contribution of OATP1B1 to its hepatic uptake. Therefore, the OATP1B1 DDI risk for the inhibitors investigated was also predicted using Equation 4.8.

푨푼푪′ ퟏ Equation 4.8 = 푨푼푪 풇푻, 푶푨푻푷ퟏ푩ퟏ 푰 + (ퟏ− 풇푻,푶푨푻푷ퟏ푩ퟏ) ퟏ+ 풖 푰푪ퟓퟎ

Where Iu is the Cmax,u for glucuronides and I,in,max,u for parent drugs and reference inhibitors. IC50 is the inhibition data obtained following pre-incubation with inhibitor.

In this static mechanistic model the fraction of substrate transported by OATP1B1 (fT,OATP1B1), as well as the fraction transported by unspecified routes (1 – fT,OATP1B1) are incorporated (186).

Pitavastatin fT,OATP1B1 was calculated using three different approaches as summarised in Table

4.3. The estimated pitavastatin fT,OATP1B1 from in vitro data in human hepatocytes was comparable to reported in vivo. A low-high range of fT,OATP1B1 values (0.68 – 0.86) were used to predict the pitavastatin AUC ratio in the absence and presence of inhibitors. Predicted pitavastatin AUC ratios were compared to observed data (Table 4.4) where possible and the accuracy of the predictions explored. Predicted AUC ratios < 2 for DDIs observed to be > 2 were classed as false negative and predicted AUC ratios > 2 for observed ratios < 2 were classed as false positive interactions, on the basis of previous consensus reports (4). Predictions were classed as successful if within 2-fold of the observed value. In addition, based on the predicted DDI risk with pitavastatin, the risk of a DDI with repaglinide was also predicted for compounds of interest using Equation 4.8 and OATP1B1 inhibition data obtained in Chapter 3 using a repaglinide fT,OATP1B1 value of 0.5 reported by Gertz et al., (2014) (396) based on OATP1B1 polymorphic studies.

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Table 4.3 Summary of in vitro and in vivo assessment of the contribution of OATP1B1 to the hepatic uptake of pitavastatin. Full study details are provided in Section 3.4.2

Study Mean pitavastatin fTOATP1B1 (%) Range pitavastatin fTOATP1B1 (%) Reference

In vitro studies 86 75.1 - 95.1 (159)

SLCO1B1 polymorphism studies 68 61 - 74 (389-391)

Clinical DDI studies 81 78 - 85 (379, 392, 393)

No. Mean fold change Precipitant Inhibitor dose (mg) Pitavastatin dose (mg) Reference subjects in pitavastatin AUC

Gemfibrozil 600 (7 days) 4 (6 days alone, 7 days with gemfibrozil) 10 1.25 (392)

Cyclosporine 2 mg/kg 2 (6 days) 10 4.51 (392)

Rifampicin 600 (15 days) 4 (5 days) 10 1.35 (392)

Rifampicin 600 4 12 5.28 (393)

Rifampicin 600 1 8 5.41 (379)

Erythromycin 500 (6 days) 4 10 2.79 (392)

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4.3.4 Prediction of Gemfibrozil and gemfibrozil glucuronide DDI with repaglinide using SimCYP Based on the DDI potential of gemfibrozil glucuronide the DDI of gemfibrozil with repaglinide was further explored using the population-based absorption, distribution, metabolism, and excretion simulator in SimCYP (version 14; SimCYP Ltd., Sheffield, United Kingdom). This enabled prediction of the inhibitory effects of gemfibrozil and its glucuronide on both CYP2C8 and OATP1B1. The study design was of the standard oral dose of both victim and perpetrator drugs; 0.25 mg of repaglinide co-administered with 600 mg of gemfibrozil similar to that reported in the clinical DDI study by Tornio et al., (2008) (231). The default model input parameters utilised in the simulations of repaglinide, gemfibrozil and gemfibrozil glucuronide concentration – time profiles are shown in Table 4.5. The DDI was initially assessed using SimCYP input inhibition data, presented in Table 4.6, reported to have been obtained by optimization of OATP1B1 and CYP2C8 Ki values which allow recovery of the observed DDI. The potential DDI risk of gemfibrozil and its glucuronide with repaglinide was then predicted using the in vitro CYP2C8 and OATP1B1 inhibition data generated in Chapters 2 and 3. The results obtained were compared to those generated using the standard SimCYP input data in the same model.

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Table 4.5 Summary of drug input parameters for PBPK modelling and simulations

Gemfibrozil Parameter Repaglinide Gemfibrozil glucuronide Physicochemical properties Molecular weight 452.6 250.33 426.46 LogP 3.98 4.29 2.40 pKa 4.16 4.70 3.10

fup 0.023 0.01 0.03 Blood/plasma ratio 0.62 0.75 0.75 Absorption Absorption type 1st order 1st order Fraction absorbed 0.98 1 Caco-2 permeability (x10-6 cm/s) 24.1 Absorption scalar 1.35

fuG 1 1 1 Distribution Full PBPK Minimal PBPK Distribution model Full PBPK Model Model Model Vss (L/kg) 0.24 0.08 0.08 Elimination CYP2C8 Vmax 300.8

CYP2C8 Km 2.3

fmCYP2C8 64.25

CYP3A4 Vmax 958.2 CYP3A4 Km 13.2 fmCYP3A4 35.66

UGT2B7 Vmax 353

UGT2B7 Km 2.13 Hepatobiliary transport Passive diffusion (µL/min/106 cells) 0.089 CLint,active (OATP1B1) (µL/min) 246

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SimCYP input In vitro inhibition data Gemfibrozil glucuronide parameters (µM) Cmax plasma (μM) 75.86 Unbound Cmax plasma (μM) 2.03 Estimated Cmax liver (μM) 7.46 Estimated unbound Cmax liver (μM) 0.21 Ki against CYP2C8 (μM) 0.8 5.9 Ki against OATP1B1 (μM) 0.01 24.7 (16.3) Gemfibrozil Cmax plasma (μM) 110.37 Unbound Cmax plasma (μM) 0.84 Estimated Cmax liver (μM) 11.22 Estimated unbound Cmax liver (μM) 0.09 Ki against CYP2C8 (μM) 9.00 47 Ki against OATP1B1 (μM) 0.01 24.5 (91.2)

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4.4 Results

4.4.1 Metabolite exposure of potent P450 inhibitors A database of clinical metabolite exposure data was collated for strong in vivo P450 inhibitors listed in the FDA 2012 guidance. The compounds included in this list were from 12 therapeutic classes and 4 different clinical indications (Figure 4.1).

Figure 4.1 Overview of the therapeutic indications of 25 potent P450 inhibitors listed in the FDA 2012 guidance

In vivo AUC and Cmax metabolite exposure data which met the inclusion criteria were available for 13/25 strong P450 inhibitors. In addition, metabolites were reported for saquinavir and ritonavir; however, in vivo exposure data for the metabolites were not available (402, 403). A total of 84 AUC values were obtained for 25 different metabolites with data collated from multiple studies or across a range of doses. For example, exposure data were reported for hydroxyclarithromycin following administration of 250 to 500 mg of the parent drug (404). A summary of the results obtained is shown in Table 4.7, full details are provided in Appendix Table 6.3. The AUC ratios of metabolite to parent compound ranged from < 1% for O- desmethylquinidine to > 1000% for hydroxybupropion. In total, 14/25 metabolites exceeded the FDA 25% exposure limit (61/84 values). The AUC metabolite : parent exposure ratio exceeded 25% for 20/84 values (10 metabolites) and exceeded 100% for 41/84 values (10 metabolites). The Cmax ratios of metabolite to parent ranged from 0.44% for meta-chlorophenylpiperazine (mCPP) to 803% for hydroxybupropion. A total of 86 metabolite : parent Cmax ratios were calculated with 32/86 >25% (8 metabolites) and 31/86 > 100% (8 metabolites).

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AUC Metabolite : Parent (%) 0.8 - 1407 (O-desmethylquinidine - hydroxybupropion)

Cmax Metabolite : Parent (%) 0.44 – 803 (meta-chlorophenylpiperazine - hydroxybupropion)

AUC Metabolite : Parent < 25% o-desmethylquinidine, desethyleneciprofloxacin, o- desmethylquinidine, meta-chlorophenylpiperazine, keto-itraconazole, N-desalkylitraconazole, hydroxy- voriconazole, o-desmethylquinidine, RU 76363, dihydroxy-voriconazole, fluvoxamino acid, N- desalkylitraconazole, 3-hydroxy-quinidine, desethylhydroxynefazodone, boceprevir diastereoisomer SCH 534129, 14- hydroxyclarithromycin

AUC Metabolite : Parent > 25% 14-hydroxyclarithromycin, 3-hydroxy-quinidine, gemfibrozil glucuronide, hydroxynefazodone, nelfinavir hydroxy-t-butylamide metabolite (M8), norfluoxetine, boceprevir active diastereoisomer (SCH534128), erythrohydrobupropion and threohydrobupropion, voriconazole N-oxide

AUC Metabolite : Parent > 100% 14-hydroxyclarithromycin, norfluoxetine, hydroxyitraconazole, gemfibrozil glucuronide, quinidine N-oxide, voriconazole N-oxide, hydroxynefazodone, 7-hydroxyitraconazole, boceprevir keto-reduced metabolites (SCH629144), hydroxybupropion

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Data were reported for metabolites and parent compounds across multiple doses in a single study for gemfibrozil, quinidine, clarithromycin, telithromycin and itraconazole. Increasing AUC and Cmax values were observed with increasing dose for all compounds. AUC ratios for gemfibrozil glucuronide, 14-hydroxyclarithromycin and hydroxyitraconazole exceeded 25% across all doses investigated, details are provided in Appendix Table 6.3. Of these, the highest metabolite AUC was reported for hydroxyitraconazole which consistently exceeded 200% of parent compound exposure across a 50 to 200 mg dose range (Figure 4.2). In contrast, for RU 76363, a metabolite of telithromycin, the metabolite: parent exposure ratio was < 25 % over a 400 – 1600 mg dose range (Figure 4.2). These data indicate that metabolite exposure can exceed 25% of that of parents even at lower doses; however, not all metabolites necessarily exceed the exposure limit.

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For quinidine, a strong CYP2D6 inhibitor, data were reported for 3 different metabolites; quinidine N-oxide, 3-OH quinidine and O-desmethylquinidine (Figure 4.3), but only quinidine N-oxide exceeded the FDA exposure limit across all doses (1 – 100 mg). In contrast, the 3- OH quinidine and O-desmethylquinidine metabolite exposure ratios were consistently 10% or 20% of the parent compound, respectively, across the dose range. Exposure data were also available for multiple metabolites of bupropione, boceprevir, itraconazole, voriconazole and nefazodone (Table 4.7). All bupropione metabolites exceeded the FDA 25% exposure limit. However, for the remaining inhibitors for which exposure data were reported for multiple metabolites, some metabolites exceeded 25% of their parent drugs systemic exposure while others did not, highlighting that the assessment of P450 inhibitory potential requires consideration of each metabolite individually.

For the remaining analyses, exposure ratios were based on data obtained for a single dose of the parent drug. Metabolite exposure data were available for itraconazole and its metabolites following single and repeated dosing up to 7 days. Parent compound and hydroxyitraconazole AUC increased by ~2-fold between days 1 and 7 resulting in a 35% decrease in metabolite: parent AUC ratios for keto-itraconazole and N-desalkylitraconazole, but no alteration in the AUC ratio of itraconazole: hydroxyitraconazole (Figure 4.4A). In the case of voriconazole,

160 metabolite and parent exposure following oral and iv administration had been explored in a single study. Route of administration did not influence the exposure ratios of voriconazole metabolites (Figure 4.4B), most likely due to its high oral bioavailability (82.6%) (408). For compounds with substantial intestinal metabolism, for example clarithromycin which has an AUC following oral administration ~50% of that reported following iv administration (409), the route of administration of the parent drug may influence the metabolite: parent AUC and Cmax ratios.

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4.4.2 Metabolite exposure data of potent OATP1B1 inhibitors The FDA 2012 guidance lists 9 drugs as in vivo inhibitors of OATP1B1. Of these gemfibrozil, ritonavir and saquinavir were listed as inhibitors of both P450 and OATP1B1 and therefore their metabolite exposure was discussed in section 4.4.1. Of the remaining OATP1B1 inhibitors, metabolite exposure data were only available for cyclosporine. Two formulations of cyclosporine are available; Sandimmune® and the newer Neoral® which has decreased interindividual variability and increased AUC and Cmax of the parent drug in comparison to the original formulation (410). The dataset of cyclosporine metabolites collated was rich and included exposure data for 5 metabolites following single and multiple doses and both iv and oral administration (Figure 4.5). Most data were available for the AM1 metabolite which exceeded parent compound systemic exposure across all doses and studies for which data were available (411-413). The highest AM1: cyclosporine AUC ratio of 359% was reported for following a 600 mg oral dose of Sandimmune® (413). Similarly, AM17, for which a single report was available, exceeded the FDA 25% exposure ratio limit with a value > 300% (413). The AM4N, AM9, AM1c and AM17 metabolites exceeded the recommended AUC ratio limit only at the 500 and 600 mg dose of the parent compound. AM19, for which a single AUC value was collated, did not exceed the FDA exposure limit (414). The exposure of cyclosporine and its metabolites AM1, AM1c and AM9 were investigated following both oral and iv administration of the parent drug (415). In all cases, lower Cmax and AUC values for the parent and the metabolites were obtained following iv administration in comparison to oral administration. Metabolite : parent AUC and Cmax ratios calculated using data following oral administration were up to 90% greater than those obtained following iv administration (415), most likely due to intestinal metabolism of cyclosporine.

Figure 4.5 AUC and Cmax exposure ratios (%) for cyclosporine and its metabolites in vivo. References for in vivo exposure data are presented in Appendix Table 6.3

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4.4.3 Collation of glucuronide metabolite exposure data Glucuronides were reported for 38 drugs in total based on in vivo exposure data, in vitro formation data or in vitro enzyme/ transporter inhibition data. Of these only gemfibrozil was listed as a potent P450 inhibitor and an in vivo inhibitor of OATP1B1 in the FDA 2012 guidance. The broad range of therapeutic indications of the parent compounds for which glucuronides were identified are shown in Figure 4.6.

Figure 4.6 Therapeutic indications of 38 compounds reported to have glucuronide metabolites

Clinical glucuronide exposure data were reported for 16 drugs. AUC and Cmax ratios of glucuronide to parent drug are shown in Figure 4.7. Clinical study design and glucuronide exposure data per individual study are shown in Appendix Table 6.4. The AUC ratio of glucuronide to parent compound ranged from < 10% for furosemide glucuronide to > 1000 % for mycophenolic acid, codeine, ezetimibe, morphine and clopidogrel glucuronides. For 15/16 of the glucuronides for which exposure data were available, glucuronide metabolite plasma concentrations exceeded 25% of the parent compounds systemic exposure. Furosemide glucuronide was the only example which did not exceed the FDA exposure limit with parent drug concentrations ~10 fold greater than the glucuronides. The Cmax ratio of glucuronide to parent ranged from 11% for furosemide glucuronide to > 1000% for codeine, clopidogrel, ezetimibe and morphine glucuronides. Glucuronide: parent AUC and Cmax ratios were influenced by study and study design e.g., dose and route of administration in some, but not all cases. For example, the AUC ratio of oxazepam glucuronide was similar between doses of 15 mg and 30 mg, whereas at a dose of 60 mg of the parent drug, morphine glucuronide had an AUC ratio ~ 20-fold greater than that observed following a 20 mg dose of the parent drug.

For the glucuronides for which fup values were collated for DDI prediction purposes (Table 4.8), parent drug and glucuronide Cmax data were corrected for plasma protein binding resulting in reduced Cmax values. When glucuronide : parent Cmax ratios were calculated using Cmax,u data, similar ratios were obtained for ezetimibe glucuronide. For telmisartan,

163 clopidogrel and gemfibrozil glucuronides an increase in the glucuronide : parent Cmax ratio was observed ranging from 3 to 18-fold for telmisartan and gemfibrozil, respectively.

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Table 4.8 Clinical data collated for glucuronides, parent drugs and reference inhibitors and used for prediction of DDI risk

Dose parent Cmax Cmax, u Inhibitor fu ka (min-1) F F I,in,max,u (µM) References drug (mg) (µM) p (µM) a G

Telmisartan 80 1.5 0.0046 0.0067 - - 0.055 (262, 416)

Telmisartan glucuronide 80 0.04 0.014* 0.00056

Clopidogrel 300 0.009 0.02 0.0002 0.023 0.75a 0.21 (216, 417)

Clopidogrel glucuronide 300 4 0.1 0.4

Clopidogrel 75 0.002 0.02 0.00004 0.023 0.75a 0.054

Clopidogrel glucuronide 75 1.5 0.1 0.15

Cyclosporine 200 1.1 0.03 0.033 0.03 0.39b 0.073 (124, 418)

Gemfibrozil 30 5.6 0.0065 0.036 0.015 0.98c 0.044 (33, 186, 230, 419) Gemfibrozil glucuronide 30 1.2 0.12 0.14

Gemfibrozil 100 20 0.0065 0.13 0.015 0.98 c 0.16

Gemfibrozil glucuronide 100 6 0.12 0.69

Gemfibrozil 300 60 0.0065 0.39 0.015 0.98 c 0.47

Gemfibrozil glucuronide 300 18 0.12 2.07

Gemfibrozil 900 190 0.0065 1.23 0.015 0.98 c 1.46

Gemfibrozil glucuronide 900 50 0.12 5.75

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Dose parent Cmax Cmax, u Inhibitor fu ka (min-1) FaF I,in,max,u (µM) References drug (mg) (µM) p (µM) G

Gemfibrozil 600 170 0.0065 1.1 0.015 0.98 c 1.26 (33, 186, 231, 419) Gemfibrozil glucuronide 600 68 0.12 7.82

Gemfibrozil 600 99 0.0065 0.64 0.015 0.98 c 0.80 (33, 186, 214) Gemfibrozil glucuronide 600 73.6 0.12 8.46

Gemfibrozil 600 90 0.0065 0.58 0.015 0.98 c 0.74 (33, 186, 419, 420) Gemfibrozil glucuronide 600 44 0.12 5.06

Repaglinide 4 0.118 0.015 0.0018 0.032 0.89d 0.0043 (396, 421- 423)

Diclofenac 75 7.8 0.005 0.039 - - 0.12 (424-426)

Rifampicin 600 25.8 0.15 3.9 0.05 - 7.52 (427-429)

Rifamycin SV 250 51.7 0.081* 4.2 - - 6.12 (430, 431)

Erythromycin 500 2.6 0.64* 1.6 - - 30.73 (431, 432)

Trimethoprim 160 9.99 0.48 4.8 - 22.43 (284, 433)

*fup predicted using SimCYP version 14 -1 - Inhibitor parameter not reported in the literature, FDA recommended value used to estimate I,in,max,u, i.e., ka of 0.1 min and FaFG of 1 were used. A Qh of 1500 mL/min was used for all I,in,max,u calculations For glucuronides, I,in,max,u could not be calculated due to lack of dose data – Cmax, u values were used for DDI predictions a b c d Individual Fa or FG data were reported in the literature and FaFG was calculated; Fa 0.75 (417), Fa 0.9, FG 0.44 (124), Fa 0.98 (419), FG 0.89 (396)

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4.4.4 Investigation of drug-drug interaction risk associated with inhibition of metabolising enzymes by glucuronide metabolites in vitro

4.4.4.1 Investigation of glucuronide DDI risk using a basic model In order to evaluate the clinical relevance of enzyme inhibition by the compounds investigated in vitro in Chapter 2, R = 1 + Cmax/Ki values were calculated for compounds where CYP2C8,

CYP3A4 or UGT1A IC50 values had been characterised in HLM using inhibitor Cmax data collated from the literature (Table 4.8). Predictions of DDI potential were conducted for gemfibrozil, telmisartan and clopidogrel glucuronides and their parent compounds as well as diclofenac and the 3 reference inhibitors ketoconazole, rifamycin SV and trimethoprim (Table 4.9). The 1 + Cmax/Ki values calculated using inhibition data from P450, UGT and combined co-factor experiments were found to be within 2-fold in all cases. These results indicate that for the inhibitors investigated in multiple co-factor conditions, the predicted DDI potential against CYP2C8, CYP3A4 or UGT1A1 was not affected by the co-factor conditions used in vitro.

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Predicted repaglinide AUC’/AUC = 1 + Cmax/Ki Observed repaglinide AUC’/AUC Inhibitor Dose parent drug (mg) Cmax (µM) CYP2C8 P450 + UGT CYP2C8 P450

Telmisartan 80 1.46a NE 1.1 NA

Telmisartan glucuronide 80 0.04 a 1.0 1.0 NA

Clopidogrel 300 0.009b NE 1.0 5.10

Clopidogrel glucuronide* 300 4.044 b 1.1 1.6 5.10

Clopidogrel 75 0.002 b NE 1.0 3.90

Clopidogrel glucuronide* 75 1.5 b 1.0 1.2 3.90

Gemfibrozil 600 170c NE 5.9 6.98

Gemfibrozil glucuronide* 600 68 c 18.6 12.4 6.98

Gemfibrozil 30 5.6d NE 1.2 1.77

Gemfibrozil glucuronide* 30 1.2 d 1.3 1.2 1.77

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Predicted repaglinide AUC’/AUC = 1 + Cmax/Ki Observed repaglinide AUC’/AUC Inhibitor Dose parent drug (mg) Cmax (µM) CYP2C8 P450 + UGT CYP2C8 P450

Gemfibrozil 100 20 d NE 1.6 4.48

Gemfibrozil glucuronide* 100 6 d 2.6 2.0 4.48

Gemfibrozil 300 60 d NE 2.7 6.70

Gemfibrozil glucuronide* 300 18 d 5.7 4.0 6.70

Gemfibrozil 900 190 d NE 6.5 8.26

Gemfibrozil glucuronide* 900 50 d 14.0 9.4 8.26

Diclofenac 75 7.68e NE 1.2 NA

Ketoconazole 200 11.84f NI NE NA

Rifamycin SV 250 51.66g 4.6 4.3 NA

Trimethoprim 160 9.99h 1.2 NE 1.63 a (262), b (216), c (231), d (230), e (424), f (434), g (435), h (284) *IC50 data obtained following pre-incubation used instead of Ki data as a result of time-dependent inhibition NA no data available NE No experiment performed in these co-factor conditions NI No IC50 value characterised in these co-factor conditions Telmisartan and telmisartan glucuronide R values of 1 calculated for CYP3A4 Ketoconazole R value of 624 was calculated using CYP3A4 inhibition data Rifamycin SV CYP3A4 R values of 28.1 and 41.6 were calculated using inhibition data from combined and P450 co-factor experiments, respectively. UGT1A1 R values of 7.4 were calculated using inhibition data obtained in both P450 and UGT co-factor experiment

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Most exposure data were available for gemfibrozil glucuronide and its parent drug with Cmax reported for both the parent and glucuronide in the same study over a 30 – 900 mg dose range in DDI studies with repaglinide. The gemfibrozil glucuronide R values calculated using IC50 data generated in P450 co-factor experiments were within 40% of those calculated using IC50 values from combined co-factor experiments (Figure 4.8). Gemfibrozil glucuronide was predicted to cause a DDI based on CYP2C8 inhibition data at all doses and across both co- factor conditions in which CYP2C8 inhibition was explored (R > 1.1) (Table 4.9). A 10-fold increase in the predicted CYP2C8 DDI potential of gemfibrozil glucuronide was observed across a 30 to 900 mg dose range; R ranged from 1.3 to 14 using inhibition data from combined co-factor experiments. These results indicate a linear relationship in the predicted DDI risk and the dose of the inhibitor. The highest gemfibrozil glucuronide CYP2C8 R value of 18.6 was obtained using exposure data reported following repeated administration of 600 mg of gemfibrozil (2.5 days) as the Cmax of the glucuronide increased. When the DDI risk of gemfibrozil glucuronide was calculated using the mean estimated liver concentration (89 µM) (33), a 23-fold increase in repaglinide AUC was predicted. These data indicate the importance of understanding the relevant inhibitor concentration available to the enzyme.

For the parent drug, the predicted R values based on CYP2C8 inhibition ranged from 1.7 to 7.2 over the 30 to 900 mg gemfibrozil dose range. Gemfibrozil CYP2C8 R values were up to 50% lower than those calculated for the glucuronide using inhibition data obtained in P450 co- factor experiments. The CYP2C8 R values obtained following multiple dosing of 600 mg of gemfibrozil for 2.5 days was 10% below that obtained at the single 900 mg dose.

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Figure 4.8 CYP2C8 R values (1+ Cmax/Ki) calculated for gemfibrozil and its glucuronide using

Ki and IC50 values, respectively, obtained following pre-incubation with inhibitor in HLM with either combined or P450 co-factors. The dashed line represents the FDA limit of 1.1 after which further investigation of enzyme inhibitory potential is recommended. Inhibitor exposure values used for prediction of DDI potential at the 600 mg dose were obtained following multiple administrations of gemfibrozil (2.5 days) before co-administration with repaglinide. All other values were predicted using inhibitor exposure data obtained following a single dose of gemfibrozil, details are listed in Table 4.8

The R values (1 + Cmax/Ki) calculated for the effect of the remaining inhibitors on CYP2C8, CYP3A4 and UGT1A1 are presented in Table 4.9. The UGT1A1 inhibitory potential of parent drugs was not explored as in vitro inhibition of this enzyme by parent drugs was not assessed; therefore, comparison to glucuronide DDI potential for this enzyme was not possible. Parent and glucuronide exposure data were available in a repaglinide DDI study for clopidogrel at two separate doses. At the 75 mg dose, clopidogrel glucuronide was predicted to cause a DDI (R 1.2) using inhibition data obtained in P450 co-factor experiments but not combined co-factor conditions. A clinical DDI (R>1.1) was indicated for clopidogrel glucuronide using Cmax data obtained following the 300 mg dose using inhibition data from both P450 and combined co- factor experiments. At the 300 mg dose of clopidogrel, a 10-25% increase in the predicted DDI risk of clopidogrel glucuronide against CYP2C8 was observed in comparison to the 75 mg dose as a result of the increased Cmax. No DDI was predicted for the parent drug clopidogrel and the CYP2C8 DDI potential of the parent drug was lower than that of its glucuronide regardless of the dose of parent drug (R < 1.1). Similarly, although inhibition of CYP3A4 by clopidogrel was characterised in vitro, no DDI was predicted.

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No DDI studies with repaglinide were reported from telmisartan glucuronide, telmisartan or diclofenac. Therefore, inhibitor exposure data were collated from other DDI studies or clinical pharmacokinetic studies at the standard dose of the parent drug. A single Cmax value was available for telmisartan glucuronide which was used to predict the DDI risk as a result of CYP2C8, CYP3A4 and UGT1A1 separately. No clinical DDI was indicated for telmisartan glucuronide against any of the enzymes investigated (Table 4.9). For the parent drug, telmisartan, a DDI was predicted against CYP2C8 (R = 1.1) but not CYP3A4 (R = 1). Diclofenac was the only other investigational drug for which DDI potential could be calculated for CYP2C8. At the highest exposure value obtained at the standard dose of diclofenac, a risk of clinical DDI was predicted (R 1.2).

For the reference inhibitors investigated in this study, the highest exposure data reported at the standard dose was used to estimate the most conservative risk of DDI (Table 4.9). The highest R values, ~600, were obtained for ketoconazole for inhibition of CYP3A4. Inhibition of CYP2C8 and UGT1A1 by their respective reference inhibitors trimethoprim and rifamycin SV was also predicted to pose a DDI risk. In addition to inhibition of UGT1A1, rifamycin SV also inhibited CYP2C8 and CYP3A4 in experiments with combined and P450 co-factor conditions in HLM. A risk of DDI was predicted and R values ranged from 4.3 to 42 for CYP2C8 and CYP3A4, respectively and were within 2-fold between combined and P450 co-factor experiments. These results were in agreement with literature reports of clinical DDIs for trimethoprim with repaglinide and ketoconazole with other CYP3A4 substrates e.g., simvastatin (284, 436). No data for clinical DDIs caused by rifamycin SV were reported in the literature and its estimated clinically relevant inhibition of UGT1A1 in addition to both CYP2C8 and CYP3A4, is highlighted for the first time to our knowledge.

4.4.4.2 Investigation of the DDI risk of glucuronides with CYP2C8 using mechanistic models CYP2C8 was the enzyme for which most inhibition data were obtained for the glucuronides explored in vitro in Chapter 2 and the only enzyme for which a clinical DDI was indicated using the basic model (R = 1 + Cmax/Ki). Therefore, further assessment of the glucuronide mediated DDI risk as a result of CYP2C8 inhibition was performed incorporating the fraction of repaglinide metabolised by CYP2C8 using a mechanistic static model (Equations 4.2 and 4.3).

The analysis was performed using two different repaglinide fmCYP2C8 values (0.49 and 0.92

(152, 230)) for 3 glucuronides, 4 parent drugs and 2 reference inhibitors for which IC50 could be characterised against CYP2C8 and clinical inhibitor exposure data were available. Inhibitor concentrations used in the model are provided in Table 4.8, results are shown in Table 4.10.

Gemfibrozil glucuronide was the only metabolite for which a risk of clinical DDI was predicted; repaglinide AUC ratios of 1.25 and above were obtained at gemfibrozil doses over 100 mg

(Table 4.10) regardless of the repaglinide fmCYP2C8 value used. However, predicted AUC ratios obtained using fmCYP2C8 of 0.92 were on average 1.6- (combined) and 1.4- (P450) fold greater than those obtained using fmCYP2C8 of 0.49. The predicted DDI risk of gemfibrozil glucuronide

173 was the same as or greater than that of its parent drug; using an fmCYP2C8 value of 0.49 the glucuronide DDI risk was up to 30% greater than that of the parent drug and using an fmCYP2C8 value of 0.92 the glucuronide DDI risk was up to 60% greater than that of the parent drug, over the 30 – 900 mg dose range. The only other compound for which a clinical DDI was indicated was trimethoprim. A repaglinide AUC ratio of 1.5 was obtained using fmCYP2C8 of 0.92 indicating a clinical DDI; however, at the lower fmCYP2C8 of 0.49 no DDI was predicted. Although neither clopidogrel glucuronide nor its parent drug were predicted to cause a clinical DDI with repaglinide, it is noteworthy that the glucuronides predicted DDI potential exceeded that of the parent drug using both fmCYP2C8 values.

174

Predicted repaglinide AUC’/AUC

Compound Dose parent drug (mg) fmCYP2C8 0.49 fmCYP2C8 0.92 Observed repaglinide AUC’/AUC

P450 + UGT P450 P450 + UGT P450

Telmisartan 80 a 1.00 1.00 NA

Telmisartan glucuronide 80 a 1.00 1.00 1.00 1.00 NA

Clopidogrel 300 b 1.01 1.02 5.10

Clopidogrel glucuronide 300 b 1.01 1.06 1.02 1.11 5.10

Clopidogrel 75 b 1 1 3.90

Clopidogrel glucuronide 75 b 1.00 1.02 1.01 1.04 3.90

Gemfibrozil 600 c 1.02 1.04 6.98

Gemfibrozil glucuronide 600 c 1.65 1.54 3.79 2.96 6.98

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Predicted repaglinide AUC’/AUC

Compound Dose parent drug (mg) fmCYP2C8 0.49 fmCYP2C8 0.92 Observed repaglinide AUC’/AUC

P450 + UGT P450 P450 + UGT P450

Gemfibrozil 30 d 1 1 1.77

Gemfibrozil glucuronide 30 d 1.03 1.02 1.06 1.04 1.77

Gemfibrozil 100 d 1 1 4.48

Gemfibrozil glucuronide 100 d 1.15 1.10 1.30 1.20 4.48

Gemfibrozil 300 d 1.01 1.01 6.7

Gemfibrozil glucuronide 300 d 1.34 1.25 1.90 1.59 6.7

Gemfibrozil 900 d 1.02 1.04 8.26

Gemfibrozil glucuronide 900 d 1.58 1.47 3.20 2.51 8.26

Diclofenac 75 e 1.02 1.04 NA

Rifamycin SV 250 f 1.03 1.03 1.06 1.05 NA

Trimethoprim 160 g 1.2 1.47 1.63 a (262), b (216), c (231), d (230), e (424), f (435),g (284) NA – no data available

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Comparison of the DDI potential of glucuronide-parent pairs was limited, however, the results obtained indicate that in vivo glucuronides may pose a similar or greater risk of DDI than parent drugs in some cases. The inhibitory effects of clopidogrel, telmisartan and gemfibrozil glucuronides in combination with their respective parent drugs was explored (Figure 4.9, Table 4.11). The predicted risk of DDI for the glucuronide alone was within 5% of that predicted for glucuronide and parent drug in combination. However, for clopidogrel and gemfibrozil parent drugs, an increase in the extent of the predicted DDI of up to 60% was observed for glucuronide and parent drug in combination in comparison to the parent alone.

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Predicted repaglinide AUC'/AUC Observed Compound Dose parent drug (mg) repaglinide fmCYP2C8 0.49 fmCYP2C8 0.92 AUC'/AUC

Clopidogrel 300a 1.06 1.13 5.1

Clopidogrel 75a 1.02 1.05 3.9

Gemfibrozil 600b 1.02 1.04 6.98

Gemfibrozil 30c 1.1 1.21 1.77

Gemfibrozil 100c 1.25 1.61 4.48

Gemfibrozil 300c 1.56 3.05 6.7

Gemfibrozil 900c 1.49 2.60 8.26

Telmisartan 80d 1.00 1.01 1.63 a(216), b(231), c(230), d(262)

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4.4.4.3 Comparison of predicted to observed repaglinide DDIs with glucuronides Predicted changes in the AUC of repaglinide due to gemfibrozil glucuronide, clopidogrel glucuronide, parent drugs and trimethoprim were compared to observed values reported in the literature in DDI studies where inhibitor exposure data were reported (Table 4.12). In total, 22 comparisons were made between observed changes in repaglinide AUC in the presence of inhibitor and values predicted using the basic model (Equation 4.1) analysing glucuronides and parent drugs DDI risk separately. Inhibitor Cmax data were used in this model to predict the DDI risk and 10/22 of the predicted increases in repaglinide AUC in the presence of inhibitor were within 2-fold of the observed values collated from the literature. A risk of DDI was correctly predicted to be within 2-fold of the observed in 3 cases; however, the extent of the change in repaglinide AUC was under or over estimated. For the remaining 9 predictions, false negative predictions of the DDI risk were obtained.

A total of 29 repaglinide AUC ratio values predicted using the mechanistic static model incorporating the CYP2C8 inhibitory effects of parent drugs and glucuronides separately as well as in combination, were compared to observed data. For the AUC’/AUC ratios calculated using an fmCYP2C8 of 0.49, 5/29 of the predicted values were within 2-fold of those observed in vivo; however all remaining predictions gave false negative results. When an fmCYP2C8 of 0.92 was used, the same 5 repaglinide AUC ratios were predicted within 2-fold of observed. Of the remaining datasets 19 predicted AUC ratios gave a false negative indication of the extent of DDI and 5 were > 2, indicating a risk of clinical DDI, but the extent of DDI was underpredicted by more than 2-fold.

Most clinical DDI data were available for comparison to predicted DDI potential for gemfibrozil glucuronide and its parent drug (Figure 4.12). The predicted repaglinide AUC ratios were compared to those reported in vivo at 5 gemfibrozil doses (30 – 900 mg). The DDI potential of gemfibrozil glucuronide was predicted using inhibition data from either P450 or combined co- factor experiments; 10 comparisons in total. Using the basic model (1 + Cmax/Ki) the predicted changes in repaglinide AUC in the presence of gemfibrozil glucuronide were within 2-fold of the observed in 6/10 cases. At the 600 mg dose using combined co-factor inhibition data the predicted AUC ratio was 3-fold greater than the observed and therefore considered an overestimation of the DDI risk. Contrastingly, at the 100 mg dose using combined-cofactor inhibition data, a false negative prediction of the DDI was obtained. Using CYP2C8 inhibition data obtained in P450 co-factor experiments, at the 100 and 300 mg doses a clinical DDI was indicated (AUC’/AUC ratio > 2), however, predictions were less than half of the AUC ratios reported in vivo. For the parent drug, predicted changes in repaglinide AUC were within 2-fold of the observed using inhibitor exposure data reported following 30, 600 and 900 mg doses. At the 100 and 300 mg doses, false negative predictions of the extent of DDI with repaglinide were obtained. Using the mechanistic static model incorporating fmCYP2C8, false negative or under predictions of the extent of gemfibrozil DDI were obtained for both gemfibrozil glucuronide and the parent drug at all doses except 30 mg, regardless of the fmCYP2C8 input

179 value used. For clopidogrel and its glucuronide, the DDI risk was underpredicted using both the basic and mechanistic static model and both fmCYP2C8 input values. For both clopidogrel and gemfibrozil, inclusion of the CYP2C8 inhibitory effects of glucuronide and parent drug did not improve prediction accuracy. For trimethoprim, the reference CYP2C8 inhibitor used in these studies, the predicted change in repaglinide AUC was within 2-fold of the observed values using both the basic and the mechanistic static models and both fmCYP2C8 input values.

n > 2 but n of n within n false n > 2 but over under predictions 2-fold negative predicted predicted

R = 1 + Cmax/Ki 22 10 9 1 2

AUC'/AUC 29 5 24 0 0 fmCYP2C8 0.49

AUC'/AUC 29 5 19 5 0 fmCYP2C8 0.92

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4.4.5 Investigation drug-drug interaction risk associated with in vitro inhibition of OATP1B1

4.4.5.1 Determination of Cmax/IC50 ratios for OATP1B1 inhibitors The clinical relevance of in vitro OATP1B1 inhibition was assessed by calculating the ratio of

Cmax/IC50 for compounds investigated with either E17βG or E17βG and pitavastatin as probe substrates (Table 4.13). Clinical exposure data were collated and Cmax/IC50 ratios calculated for gemfibrozil, ezetimibe and telmisartan glucuronides and corresponding parent drugs as well as clopidogrel and raloxifene glucuronides, repaglinide and diclofenac parent drugs and reference OATP1B1 inhibitors. Use of IC50 data obtained following pre-incubation resulted in

Cmax/IC50 ratios that were equal to or up to 3-fold greater than those obtained using IC50 values generated without pre-incubation with both probes investigated (Figure 4.11). The exceptions to this were ezetimibe and rifamycin SV where pre-incubation with inhibitor resulted in a more pronounced increase in the predicted DDI risk; Cmax/IC50 ratios were up to 14-fold greater when IC50 values generated following pre-incubation with inhibitor were used in comparison to IC50 values obtained following pre-incubation with buffer alone, when pitavastatin was used as a probe substrate. The gmfe of predicted Cmax/IC50 ratios obtained with and without pre-incubation were 1.67 and 2.33 when E17βG and pitavastatin were used as OATP1B1 probes, respectively. As a result of the higher Cmax/IC50 pre-incubation ratios obtained, the IC50 values obtained following pre-incubation with inhibitor were used for all analysis of OATP1B1 DDI potential to provide the most conservative estimate of DDI risk.

Figure 4.11 Comparison of Cmax/ OATP1B1 IC50 ratios calculated using E17βG (A) or pitavastatin (B) as a probe substrate using IC50 data obtained without or with a 30-minute pre- incubation with inhibitor. Cmax data were collated from the literature and references are provided in Table 4.8. The dashed line represents the line of unity

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The FDA recommended limit of 0.1 indicating a risk of clinical DDI was exceeded for 10/14 inhibitors with E17βG as a probe and 6/14 inhibitors with pitavastatin, following a pre- incubation step (Table 4.13). Gemfibrozil and clopidogrel glucuronide were the only metabolites which exceeded the 0.1 threshold alongside the reference inhibitors and telmisartan, repaglinide, diclofenac and gemfibrozil parent drugs. A Cmax/IC50 < 0.1 was obtained for telmisartan glucuronide, raloxifene glucuronide, ezetimibe and ezetimibe glucuronide. The analysis was not performed for repaglinide or diclofenac glucuronides due to lack of Cmax data.

For gemfibrozil glucuronide, Cmax/IC50 ratios were calculated using exposure data obtained following administration of a range of doses of the parent drug. The FDA threshold of 0.1 was exceeded with both probes except for at the lowest dose for which exposure data was available (30 mg). The Cmax/IC50 ratio ranged from 0.05 to 2.99, using E17βG as a probe substrate and 0.05 to 3.02 when pitavastatin was used. For the parent drug gemfibrozil, the 0.1 limit was exceeded in all cases where Cmax data was available when inhibition data obtained using E17βG as a probe substrate was used. When pitavastatin was used as a probe substrate the 0.1 cut off was exceeded in all cases except at the lowest dose (30 mg) of gemfibrozil. The predicted DDI risk increased with increasing dose of gemfibrozil with maximum Cmax/IC50 ratios of 7.8 and 1.4 obtained at the highest dose (900 mg) with E17βG and pitavastatin, respectively. Gemfibrozil Cmax/IC50 ratios calculated using E17βG as a probe substrate were up to 5-fold greater than those of the glucuronide. The opposite trend was observed when pitavastatin was used as a probe substrate, glucuronide R values were up to 4-fold greater than those of the parent drug, due to the substrate-dependent inhibition of OATP1B1 by gemfibrozil.

The OATP1B1 inhibitory potential of clopidogrel glucuronide was only investigated using

E17βG as a probe and the FDA Cmax/IC50 ratio limit of 0.1 was not exceeded using exposure data available at a 75 mg dose. Where Cmax data was available following a higher dose of

300 mg, a Cmax/IC50 ratio value of 0.16 was obtained. Telmisartan glucuronide Cmax/IC50 ratios did not exceed the FDA cut off of 0.1 with either probe susbtrate investigated.

Contrastingly, the parent telmisartan exceeded the limit with Cmax/IC50 ratios > 60-fold greater than those of the glucuronide when E17βG used as a probe substrate and ~ 20-fold greater when pitavastatin inhibition data were used. For repaglinide and diclofenac parent drugs, the

Cmax/IC50 ratio limit was exceeded only when IC50 data obtained following pre-incubation with inhibitor and using E17βG as a probe was used.

4.4.5.2 Determination of R values for OATP1B1 inhibitors When the OATP1B1 DDI potential of glucuronides investigated in this study was predicted using a basic model (Equation 4.6, R = 1 + I,in,max,u/IC50) only gemfibrozil glucuronide was predicted to cause a DDI and only following 600 mg doses of the parent drug (Table 4.13). This finding was consistent using inhibition data obtained using either pitavastatin or E17βG as a probe substrate. No parent drugs were predicted to cause a DDI using inhibition data

183 obtained with either probe substrate investigated in vitro (R < 1.25). Of the reference inhibitors for which OATP1B1 DDI potential were explored, cyclosporine, rifamycin SV and rifampicin were predicted to cause a DDI; predicted R values ranged from 1.49 to 49.9 with E17βG as a probe substrate and 1.57 to 110 with pitavastatin as a probe substrate for cyclosporine and rifamycin SV, respectively. Erythromycin was also predicted to cause a clinical DDI risk when E17βG but not pitavastatin was used as a probe substrate; with predicted R values of 1.66 and 1.15, respectively.

184

Table 4.13 OATP1B1 Cmax/IC50 ratios and R values calculated using Equation 4.6 and inhibition data obtained in HEK293 cells following a 30-minute (30) pre- incubation with inhibitor using E17βG or pitavastatin as a probe substrate. The FDA cut off indicating further investigation of a drugs OATP1B1 DDI potential for Cmax/IC50 ratios is 0.1 and for R values is 1.25. Cmax/IC50 ratios for ezetimibe and its glucuronide were < 0.001 in all cases, results are provided in Appendix Table 6.11. Details of DDI studies for the observed pitavastatin AUC ratios are provided in Table 4.4

Predicted AUC’/AUC Observed Dose parent Cmax E17βG Pitavastatin pitavastatin drug (mg) (µM) Inhibitor AUC’/AUC IC50 (30) Cmax/IC50 R IC50 (30) Cmax/IC50 R

Telmisartan 80 1.46 a 0.7 2.0 1.0 2.9 0.5 1.0 NA

Telmisartan glucuronide 80 0.04 a 1.2 0.03 1.0 1.9 0.02 1.0 NA

Clopidogrel glucuronide 300 4.04 b 24.7 0.2 1.0 NE NE NE NA

Clopidogrel glucuronide 75 1.5 b 24.7 0.1 1.0 NE NE NE NA

Gemfibrozil 600 170 c 24.5 6.9 1.1 136 1.3 1.0 1.25

Gemfibrozil glucuronide 600 68 c 24.7 2.8 1.3 24.4 2.8 1.3 1.25

Gemfibrozil 600 99.9 d 24.5 4.1 1.0 136 0.7 1.0 1.25

Gemfibrozil glucuronide 600 73.6 d 24.7 2.9 1.3 24.4 3.0 1.4 1.25

Gemfibrozil 600 90 e 24.5 3.7 1.0 136 0.7 1.0 1.25

Gemfibrozil glucuronide 600 44 e 24.7 1.8 1.2 24.4 1.8 1.2 1.25

Gemfibrozil 30 5.6 f 24.5 0.2 1.0 136 0.04 1.0 NA

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Predicted AUC’/AUC Observed Dose parent Cmax E17βG Pitavastatin pitavastatin drug (mg) (µM) Inhibitor AUC’/AUC IC50 (30) Cmax/IC50 R IC50 (30) Cmax/IC50 R

Gemfibrozil glucuronide 30 1.2 f 24.7 0.1 1.0 24.4 0.1 1.0 NA

Gemfibrozil 100 20 f 24.5 0.8 1.0 136 0.2 1.0 NA

Gemfibrozil glucuronide 100 6 f 24.7 0.2 1.0 24.4 0.3 1.0 NA

Gemfibrozil 300 60 f 24.5 2.5 1.0 136 0.4 1.0 NA

Gemfibrozil glucuronide 300 18 f 24.7 0.7 1.1 24.4 0.7 1.1 NA

Gemfibrozil 900 190 f 24.5 7.8 1.04 136 1.4 1.0 NA

Gemfibrozil glucuronide 900 50 f 24.65 2.0 1.2 24.4 2.1 1.2 NA

Repaglinide 4 0.118g 0.94 0.1 1.0 2.7 0.04 1.0 NA

Diclofenac 75 7.67 h 21.13 0.4 1.0 251 0.03 1.0 NA

Cyclosporine 200 1.10i 0.15 7.3 1.48 0.13 8.5 1.5 4.51

Rifamycin SV 250 51.66 j 0.09 594 49.9 0.04 1325 110 NA

Rifampicin 600 25.75k 0.27 95 28.8 0.43 60 18.5 1.35 – 5.41

Raloxifene glucuronide 60 0.31l 30.71 0.01 1.0 NE NE NE NA

Erythromycin 500 2.56j 16.56 0.2 1.66 70.6 0.04 1.15 2.79 a (262), b (216), c (231), d (214), e (420), f (230), g (422), h (424), i (437), j(431), k (428), l (228) NE No experiment performed NA no data available

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4.4.5.3 Prediction of the OATP1B1 DDI risk of glucuronides incorporating fT,OATP1B1 The OATP1B1 DDI risk of the inhibitors investigated in this study with pitavastatin was further explored using a static mechanistic equation incorporating the fraction of pitavastatin transported by OATP1B1 (Equation 4.8). Use of inhibition data obtained with pitavastatin or E17βG gave a similar indication of DDI risk. The estimated contribution of OATP1B1 to pitavastatin uptake ranged from 68% (polymorphic data) to 86% (in vitro data) (Table 4.3).

Both fT,OATP1B1 input values were explored and the effect on the predicted clinical DDI risk with pitavastatin noted.

Of the glucuronides investigated, only gemfibrozil glucuronide was predicted to cause an increase in the AUC of pitavastatin posing a risk of clinical DDI (Table 4.14). This was only observed using glucuronide exposure data obtained following repeat administration of 600 mg of the parent drug and the higher fT,OATP1B1 input value of 0.86. At the other doses for which gemfibrozil glucuronide exposure data were available the predicted -fold change in pitavastatin AUC was < 1.25 regardless of the fT,OATP1B1 input value or probe substrate inhibition data used. As a result of the predicted DDI risk with pitavastatin the DDI risk of gemfibrozil glucuronide with repaglinide was also predicted using reported repaglinide fTOATP1B1 data and gemfibrozil glucuronide exposure data from the study with 600 mg repeat administration of the parent drug (214). An AUC ratio of 1.14 was predicted which was < 20% of the repaglinide AUC ratio reported for the in vivo DDI at this dose (6.98) (231).

For the other glucuronides for which OATP1B1 IC50 data and metabolite exposure data were available, no clinically relevant change in the AUC of pitavastatin was predicted. Similarly, no clinical DDI with pitavastatin was predicted for the parent drugs investigated, namely, repaglinide, diclofenac, telmisartan and ezetimibe (R < 1.25). In contrast to the glucuronides and their parent drugs, all 4 reference inhibitors investigated were predicted to cause a clinical DDI with pitavastatin using inhibition data obtained using both pitavastatin and E17βG as probe substrates and both fT,OATP1B1 input values investigated. The predicted AUC’/AUC ratio of pitavastatin ranged from 1.29 to 3.03 for cyclosporine and rifamycin SV, respectively when

E17βG was used as probe substrate and a fT,OATP1B1 of 0.68 were used. When pitavastatin was used as a probe substrate the AUC ratio of the victim drug ranged from 1.32 to 3.08 when fT,OATP1B1 0.68 were used. The use of fT,OATP1B1 0.86 resulted in an average 1.6-fold increase in the predicted AUC ratio of pitavastatin in comparison to fT,OATP1B1 0.68.

187

Table 4.14 Predicted pitavastatin AUC ratios calculated for gemfibrozil glucuronide, gemfibrozil and reference inhibitors calculated using a mechanistic static model. In vitro OATP1B1 inhibition data were generated in OATP1B1 expressing HEK293 cells using pitavastatin or E17βG as a probe substrate following 30- minute pre-incubation with inhibitor. For all other inhibitors pitavastatin AUC ratios were < 1.25 (Appendix Table 6.12). I,in,max,u values and Cmax u values were used as inhibitor concentration inputs for parent drugs and glucuronides, respectively (Table 4.8). Details of DDI studies and references for the observed pitavastatin AUC ratios are provided in Table 4.4

Predicted pitavastatin AUC’/AUC Dose parent Observed pitavastatin Inhibitor E17βG IC data Pitavastatin IC data drug (mg) 50 50 AUC’/AUC

fTOATP1B1 0.68 fTOATP1B1 0.86 fTOATP1B1 0.68 fTOATP1B1 0.86

Gemfibrozil 600a 1.03 1.04 1.01 1.01 1.25

Gemfibrozil glucuronide 600a 1.20 1.26 1.20 1.26 1.25

Gemfibrozil 600b 1.02 1.03 1.00 1.01 1.25

Gemfibrozil glucuronide 600b 1.21 1.28 1.21 1.28 1.25

Gemfibrozil 600c 1.02 1.03 1.00 1.00 1.25

Gemfibrozil glucuronide 600c 1.13 1.17 1.13 1.17 1.25

Gemfibrozil 30d 1.00 1.00 1.00 1.00 NA

Gemfibrozil glucuronide 30d 1.00 1.00 1.00 1.00 NA

Gemfibrozil 100d 1.00 1.01 1.00 1.00 NA

Gemfibrozil glucuronide 100d 1.02 1.02 1.02 1.02 NA

188

Predicted pitavastatin AUC’/AUC

Dose parent Observed pitavastatin Inhibitor drug (mg) E17βG IC50 data Pitavastatin IC50 data AUC’/AUC

fTOATP1B1 0.68 fTOATP1B1 0.86 fTOATP1B1 0.68 fTOATP1B1 0.86

Gemfibrozil 300d 1.01 1.02 1.00 1.00 NA

Gemfibrozil glucuronide 300d 1.06 1.07 1.06 1.07 NA

Gemfibrozil 900d 1.04 1.05 1.01 1.01 NA

Gemfibrozil glucuronide 900d 1.15 1.19 1.15 1.20 NA

Cyclosporine 200e 1.29 1.39 1.32 1.45 4.51

Rifampicin 600f 2.91 5.89 2.80 5.36 1.35 – 5.41

Erythromycin 500g 1.79 2.27 1.26 1.35 2.79

Rifamycin SV 250h 3.03 6.58 3.08 6.88 NA a(231), b (214), c (420), d (230), e (437), f (428), g(243), h(431) NA no data availalbe

189

4.4.5.4 Comparison of predicted and observed changes in pitavastatin AUC in the presence of OATP1B1 inhibitors The predicted change in pitavastatin AUC in the presence of an inhibitor was compared to observed clinical DDI data for gemfibrozil and its glucuronide, cyclosporine, rifampicin and erythromycin (Table 4.15, Figure 4.16). The DDI was predicted incorporating the fraction of pitavastatin transported by OATP1B1 into the mechanistic static model and using inhibition data obtained in HEK293 cells following a 30-minute pre-incubation with inhibitor. Pitavastatin inhibition and inhibitor exposure data were not reported in the same studies. Therefore, predicted changes in pitavastatin AUC were compared to observed values using inhibitor exposure data from the same dose as that applied in the DDI study. A total of 11 predictions could be compared to observed changes in pitavastatin AUC in the presence of inhibitor for each of the models and probe substrates used.

Use of the Cmax/IC50 approach and inhibition data obtained with E17βG as a probe substrate in vitro, only the predicted pitavastatin interaction with cyclosporine was within 2-fold of the observed value (Tables 4.13 and 4.15). For the remaining inhibitors, the extent of DDI was overestimated with the exception of erythromycin for which a false negative prediction was obtained. Using in vitro OATP1B1 inhibition data obtained using pitavastatin as a probe substrate, the predicted changes in pitavastatin AUC were within 2-fold of observed for gemfibrozil and cyclosporine (Tables 4.13 and 4.15). For gemfibrozil glucuronide, predicted changes in AUC were up to 2.4-fold greater than the observed values. For rifampicin, predicted changes in pitavastatin AUC were also over-predicted by 11 to 40 –fold depending on the observed data used for comparison. However, the 5-fold variation in reported pitavastatin AUC ratios in the presence of rifampicin makes 2-fold prediction accuracy difficult. For erythromycin, a false negative prediction of the extent of the DDI with pitavastatin was obtained; similar to that observed using inhibition data obtained using E17βG as a probe substrate.

When the change in pitavastatin AUC in the presence of inhibitor was predicted using

Equation 4.7 (1 + I,in,max,u/IC50), predicted AUC ratios were within 2-fold of those observed in vivo AUC ratio for gemfibrozil and gemfibrozil glucuronide using inhibition data obtained with both pitavastatin and E17βG as in vitro probe substrates (Table 4.15, Figure 4.12 A, B). An under prediction by ~ 60% was obtained for cyclosporine using in vitro inhibition data obtained with both pitavastatin and E17βG, whereas the predicted DDI for rifampicin was 5- to 20-fold greater than that observed in vivo. For erythromycin, the DDI prediction was within 2-fold of the observed when inhibition data obtained using E17βG was used. However, when

IC50 data generated using pitavastatin as an in vitro probe substrate was used, a false negative prediction of the extent of DDI was obtained.

An increase in the number of predicted pitavastatin AUC ratios within 2-fold of the observed changes were observed using the static mechanistic model incorporating fT,OATP1B1 (Figure

4.12 B, D). Regardless of the fT,OATP1B1 input value or the probe substrate inhibition data used,

190 predictions were within 2-fold of observed values for gemfibrozil and gemfibrozil glucuronide. Similarly, predicted pitavastatin AUC ratios were within 2-fold for erythromycin as a perpetrator using the fT,OATP1B1 value of 0.68 with inhibition data from both E17βG and pitavastatin. When the fT,OATP1B1 value of 0.86 was used the DDI risk of erythromycin was correctly predicted using E17βG but underestimated using pitavastatin inhibition data. The predicted change in pitavastatin AUC in the presence of rifampicin was also within 2-fold of the reported values in the literature but was overestimated in comparison to the observed change in pitavastatin AUC reported in the FDA pitavastatin approval package data (392). Contrastingly, for cyclosporine a 3-fold under prediction of the extent of DDI was obtained.

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Predicted –fold increase in pitavastatin exposure

Cmax/IC50 R AUC’/AUC

E17βG Pitavastatin E17βG Pitavastatin E17βG Pitavastatin fTOATP1B1 0.68 fTOATP1B1 0.86 fTOATP1B1 0.68 fTOATP1B1 0.86

within 2-fold 1 5 7 6 9 9 9 9

false negative 1 1 0 0 0 0 0 0

false positive 0 0 0 0 0 0 0 0

n > 2 fold but under predicted 0 0 1 1 1 1 1 1

n > fold 2 but over predicted 9 5 3 3 1 1 1 1

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4.4.6 Prediction of the drug-drug interaction between gemfibrozil and repaglinide using SimCYP The DDI between repaglinide and gemfibrozil was investigated using full PBPK models and a minimal PBPK model for gemfibrozil glucuronide in SimCYP. An increase in the AUC of repaglinide following co-administration with 600 mg of gemfibrozil of 6.83-fold was predicted using the repaglinide PBPK model in SimCYP and the CYP2C8 and OATP1B1 inhibition data provided by the software. This value was almost equal to the AUC ratio of 6.98 reported by Tornio et al., (2008) (231) in a clinical DDI study following co-administration of 0.25 mg

193 repaglinide with 600 mg of gemfibrozil and is not surprising as the inhibitor model was optimised using this DDI. In contrast, use of the SimCYP dynamic model predicted a 1.3-fold change in repaglinide AUC for the same DDI when in vitro CYP2C8 and OATP1B1 inhibition data obtained in Chapters 2 and 3 were used, resulting in an under prediction of the extent of DDI.

4.5 Discussion The contribution of metabolites and multiple inhibition mechanisms to drug-drug interactions is of increasing concern as pharmacotherapy using multiple drugs increases (3, 17). Glucuronide metabolites are of particular interest in this area due to the increasing importance of glucuronidation as a clearance pathway and high total circulating metabolite concentrations. Reports of inhibition of metabolising enzymes and transporters (OATP1B1) by these metabolites also contribute to the interest in characterisation of glucuronide DDI properties (186, 203, 394). In this chapter, the clinical relevance of in vitro inhibition of metabolising enzymes and OATP1B1 by glucuronides, parent compounds and reference inhibitors of interest was evaluated following FDA guidelines. In addition, the relevance of glucuronides and metabolites of potent inhibitors of P450 enzymes with respect to current FDA guidelines on metabolite exposure were assessed.

4.5.1 Clinical exposure of metabolites The collated literature database of clinical exposure data of metabolites of potent P450 inhibitors, in vivo inhibitors of OATP1B1 and glucuronide metabolites showed that the majority of metabolites were present at > 25% of the parent compounds systemic exposure (Tables 4.5 and 4.6, and Figure 4.7). In a number of cases, for example clopidogrel glucuronide, metabolites were present at plasma concentrations exceeding those of the parent drug. The relevance of the contribution of P450 inhibition by metabolites of P450 inhibitors to DDI risk has been thoroughly assessed by a number of groups (18, 19, 23, 277). The risk of in vivo DDIs as a result of metabolite P450 inhibition alone was generally considered to be low and less than might be expected based on the abundance of some metabolites (19, 277). However, it was recognised that metabolites of P450 inhibitors should be investigated in vitro in addition to the parent drug and that an accurate DDI prediction model should incorporate all inhibitory species.

The paucity of available clinical exposure data for many glucuronide metabolites limits conclusions as to whether all glucuronide metabolites circulate at > 25%. The reported plasma concentrations may have been influenced by the instability of glucuronide metabolites at physiological pH, resulting in reported plasma concentrations being lower than the actual value. However, considering the importance of glucuronidation as a clearance pathway for many drugs and the high exposure ratios observed in the available data indicate that

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glucuronide metabolites are likely to be candidates for P450 inhibition screening in line with the latest FDA guidelines (13). The mechanisms underlying the high circulating concentrations of some glucuronide metabolites are currently unclear and require further investigation. There is increasing evidence indicating glucuronides to be substrates of efflux transporters on both basolateral and canalicular membranes of hepatocytes. Ezetimibe, mycophenolic acid, gemfibrozil and raloxifene glucuronides have all been reported to be substrates of the MRP2 efflux transporter located on the canalicular membrane of hepatocytes contributing to their efflux into bile (163, 242, 438). Gemfibrozil glucuronide has also been reported to be a substrate for MRP3 and 4 transporters, located on the basolateral membrane of hepatocytes, associated with efflux of glucuronide back into blood (209, 439). Other glucuronides, such as troglitazone glucuronide, 7-hydroxycoumarin glucuronide and edaravone glucuronide, have also been reported to be substrates of MRP3 or 4 in in vitro transporter expressing systems or knock out mice (209, 440, 441). Therefore, it is possible that efflux of glucuronides from hepatocytes into blood via MRP3 and 4 may be a contributing mechanism to the high plasma concentrations observed in vivo. However, the factors governing the affinity of glucuronide for specific efflux transporters determining excretion into bile or the blood are currently unknown.

Although the reported plasma concentrations may not accurately reflect the metabolite concentrations responsible for enzyme or for uptake inhibition, plasma metabolite concentrations are currently the best surrogates for those at the enzyme or transporter active site available. However, the potential of glucuronide metabolites to accumulate in the liver must also be considered, as in that case the concentration at the site of inhibition in the liver may differ from systemic exposure (33, 186, 399). Existence of both clinical exposure data and corresponding in vitro enzyme and transporter inhibitory potency is limited for glucuronides (and other metabolites in general), which restricts analysis of the relationship between systemic metabolite exposure and inhibitory potency. The AUC of gemfibrozil glucuronide, the most potent CYP2C8 inhibitor of the glucuronides for which inhibition data were reported in the literature, exceeded > 25% of gemfibrozil AUC at all doses for which exposure data were available. However, imidafenacin glucuronide, which was present at greater concentrations than the parent compound, was reported to display weak inhibition of both CYP2C9 and CYP3A4. When glucuronide and parent drug Cmax data were corrected for plasma protein binding, the glucuronide : parent Cmax ratios increased by up to 20-fold for drugs investigated here (Appendix Table 6.4). As for metabolites of P450 inhibitors, the best model for predicting a novel drug with a glucuronide metabolites DDI potential will incorporate all inhibitory species. Therefore, the exposure and in vitro inhibitory potential of glucuronides of parent drugs found to inhibit P450 enzymes in vitro should be assessed as part of a novel compounds development.

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4.5.2 Evaluation of the clinical DDI risk of in vitro inhibition of metabolising enzymes by glucuronides The clinical relevance of in vitro inhibition of CYP2C8, CYP3A4 and UGT1A1 by glucuronides was initially explored using a basic model assuming a single pathway of metabolism for the victim drug (Equation 4.1, Table 4.9). Cmax was used as the inhibitor concentration available to the enzyme being inhibited. The clinical relevance of in vitro inhibition of CYP2C8 by parent drugs of glucuronides which inhibited this enzyme was also assessed. Where time-dependent inhibition of CYP2C8 was observed, inhibition data obtained following a 30-minute pre- incubation with the glucuronide was used to predict the risk of DDI. This was only the case for gemfibrozil and clopidogrel glucuronides against CYP2C8, as no increase in in vitro enzyme inhibitory potential following pre-incubation with inhibitor was observed for the other inhibitors or enzymes investigated. Analyses were performed for a reduced dataset of glucuronides (3/10) in comparison to the number for which CYP2C8 inhibition was characterised in vitro due to a lack of clinical exposure data. The inhibition of CYP3A4 and UGT1A1 by glucuronides was not predicted to be of clinical significance for any of the glucuronides investigated.

CYP2C8 was the enzyme for which most inhibition data were obtained in vitro; this was predicted to translate to a risk of clinical DDI (1+Cmax/Ki > 1.1) in vivo for clopidogrel and gemfibrozil glucuronides but not telmisartan glucuronide. These results indicated a risk of clinical DDI and requirement for further investigation using mechanistic static models for some but not all glucuronides. This is in line with reports of clinical DDIs associated with gemfibrozil and clopidogrel glucuronides with repaglinide (216, 282). However, no data for DDIs between telmisartan or its glucuronide and repaglinide were reported in the literature, potentially due to no DDI study having been performed as yet.

The dose of parent drug from which clinical glucuronide exposure data was obtained clearly influenced the predicted DDI risk of both gemfibrozil and clopidogrel glucuronides; DDI potential increased with increasing dose of parent drug and Cmax of glucuronide. Exposure data for telmisartan glucuronide were available only for the standard maximal clinical dose of the parent drug (80 mg). Analysis of the DDI risk of telmisartan glucuronide using exposure data obtained following administration of a higher dose of the parent drug may be useful in order to explore any potential increase in CYP2C8 DDI risk; however, it is unlikely that higher doses would be routinely prescribed. In vitro CYP2C8 inhibition potency did not correlate with predicted DDI risk e.g., telmisartan glucuronide caused more potent inhibition of CYP2C8 in vitro than clopidogrel glucuronide using inhibition data obtained in combined co-factor conditions, however, the predicted DDI risk of clopidogrel glucuronide was greater than that of telmisartan glucuronide. Glucuronides were predicted to cause comparable or more potent DDIs than their parent drugs in all cases where comparisons of glucuronide-parent pairs were possible indicating that glucuronide and parent DDI risk requires separate consideration as 196

well as in combination. Gemfibrozil, diclofenac and telmisartan parent drugs were predicted to cause a DDI whereas clopidogrel was not; indicating that not all parent drugs with glucuronide metabolites would be highlighted as a potential DDI risk based on assessment of the parent drug alone. When the predicted DDI potential of glucuronides and parent drugs obtained using the basic model were compared to those observed in vivo, almost half were under predicted (Tables 4.7 and 4.10, Figure 4.10). This indicates that following the FDA guidelines initial method of analysis of glucuronide DDI potential, a false negative prediction could be obtained.

The DDI risk associated with inhibition of CYP2C8 was further assessed using a mechanistic static model incorporating the fraction of repaglinide metabolised by CYP2C8. A DDI was predicted for gemfibrozil and clopidogrel glucuronides, but not telmisartan glucuronide or any of the parent drugs investigated. Fewer than 20% of the predictions were within 2-fold of observed changes in repaglinide AUC and the remainder were false negatives (Tables 4.8 and 4.10). Incorporation of glucuronide and parent combined reversible and time-dependent inhibition of CYP2C8 did not result in improved prediction accuracy for either gemfibrozil or clopidogrel in agreement with a previous report by Hinton et al., (2008) (186). IC50 determination is used as an initial strategy to investigate time-dependent inhibition in which a single substrate concentration method is the first in a hierarchy of experiments eventually leading to characterisation of kinact and KI. However, in addition to the simplicity of the static models used to predict the risk of DDI resulting from inhibition of CYP2C8 only, the use of IC50 data may have contributed to the underestimations obtained. Full characterisation of CYP2C8 inhibition by glucuronides and parent drugs of interest by determination kinact and KI and incorporation into the static equations used may result in improved prediction accuracy.

Further investigation of the CYP2C8 mediated DDI potential of glucuronides using more complex models accounting for the effects of metabolic and uptake processes on both the inhibitor and victim drug are required. Such models have been developed for repaglinide and its DDIs resulting from inhibition of OATP1B1 and CYP2C8 have been accurately predicted retrospectively. These studies have highlighted uncertainties in victim and perpetrator drug input parameters (182, 190). Development of such models requires significant amounts of data for both victim drug and the perpetrator, which restricted their development for most of the glucuronides investigated in this study. The approach becomes even more complex in the case when inhibitor includes not just the parent drug, but also its glucuronide where data are limited for adequate model development. However, development of such complex drug- metabolite PBPK models would potentially enable a fuller assessment of trends in the clinical relevance of glucuronide CYP2C8 inhibition. In addition, although plasma exposure data are currently the best surrogate concentration for use in analysis of enzyme mediated DDI risk of glucuronides, consideration of efflux via MRP transporters on both basolateral and canalicular 197

membranes (163, 204, 209) and the potential contributing role of OATP1B1 to accumulation of these metabolites in hepatocytes (439), are all important factors to consider when assessing glucuronide concentration at the site of enzyme inhibition, as extra and intracellular concentrations may be very different, influencing the analysis of DDI risk. Investigation of the intracellular concentrations of glucuronides would provide more relevant concentrations for prediction of the relevance of in vivo inhibition of metabolising enzymes by glucuronides. However, detailed information on the affinity of some of these metabolites for both efflux and uptake transporters is still unknown and cannot be implemented in PBPK models in a true mechanistic manner.

4.5.3 Evaluation of the clinical drug-drug interaction risk of in vitro inhibition of OATP1B1 by glucuronides

To evaluate the clinical significance of in vitro OATP1B1 inhibition Cmax/IC50 ratios were calculated. Cmax/IC50 ratios calculated using inhibition data obtained following pre-incubation with inhibitor predicted a greater risk of DDI than when using data without pre-incubation with both E17βG and pitavastatin as probe substrates (Figure 4.13, Table 4.13). Though the clinical significance of this is currently unknown, inclusion of a pre-incubation step is recommended in order to obtain OATP1B1 inhibition data which provides the most conservative estimate of the risk of DDI. A clinical DDI was predicted using in vitro inhibition data for gemfibrozil and clopidogrel glucuronides but not for telmisartan, raloxifene or ezetimibe glucuronides. The

Cmax/IC50 ratios of all parent drugs, except ezetimibe, and the reference inhibitors investigated with E17βG as an OATP1B1 probe substrate indicated a DDI. When pitavastatin was used as a probe substrate a clinical DDI was predicted only for telmisartan, gemfibrozil, reference inhibitors (except erythromycin) and gemfibrozil glucuronide. These results highlight that following this first line analysis, some but not all glucuronides pose a risk of clinical DDI.

Following on from the preliminary analysis, the potential OATP1B1 mediated DDI risk was predicted using a mechanistic static model (R = 1 + I,in,max,u/IC50) and a mechanistic static model incorporating the fraction of pitavastatin transported by OATP1B1 (fT,OATP1B1). Only gemfibrozil glucuronide, following multiple dosing of a 600 mg dose of parent drug, and the reference inhibitors were predicted to cause a DDI using this approach (change in victim drug AUC > 1.25) (Tables 4.13 and 4.14). This is in agreement with previous reports for clopidogrel glucuronides inhibition of OATP1B1 not significantly contributing to DDIs reported in vivo (216, 224, 442) and also with the fact that no pitavastatin DDIs are reported for raloxifene, ezetimibe or telmisartan glucuronides or their parent drugs. The transporter DDI prediction model incorporating fT,OATP1B1 has been reported to be extremely sensitive to the magnitude of the fT term analogous to that of the DDI metabolic models to the fmCYP term (80, 186). It should be considered that both pitavastatin fT,OATP1B1 values used may be overestimated due to lack of

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complete inhibition/ loss of functionality of the OATP1B1 transporter and potential contribution of other transporters to pitavastatin uptake, resulting in an overestimation of the DDI risk.

However, use of the model incorporating fT,OATP1B1 resulted in a greater number of predicted pitavastatin AUC ratios within 2-fold of those observed in vivo in comparison to the more basic models. An average increase of ~ 1.6-fold in the predicted OATP1B1 DDI risk was obtained using a high (fT,OATP1B1 0.86) in comparison to a low (fT,OATP1B1 0.68) input value. However, this did not influence whether glucuronides exceeded the AUC ratio threshold of 1.25, indicating a clinically relevant DDI. The gemfibrozil-repaglinide DDI was under predicted using a mechanistic static model incorporating the fraction of repaglinide transported by OATP1B1 and inhibition data obtained using both E17βG and pitavastatin as probe substrates. These results indicate that for victim drugs with complex pharmacokinetics, the use of even the most conservative inhibition data obtained using highly sensitive OATP1B1 probe substrates does not capture the extent of DDI and consideration of OATP1B1 inhibition alone does not account for the complexity of the in vivo situation.

Based on these results, the use of the more complex static mechanistic model provides the best strategy for assessing the OATP1B1 DDI potential of novel glucuronides and determining if further studies are required. However, limited conclusions about the clinical significance of glucuronide OATP1B1 inhibition in general can be drawn due to the reduced dataset for which analysis was possible. This is especially the case as only a single pathway i.e., inhibition of OATP1B1, has been considered and as the combined effects of glucuronides and parents have not been explored due to the simplicity of the static predictive models used. However, from the analyses presented here, it is evident that the inhibition of OATP1B1 by glucuronides is predicted to pose a greater risk of DDIs than the inhibition of metabolising enzymes. Further investigation of a wider range of glucuronides is required to be able to predict the clinical relevance of OATP1B1 inhibition by these metabolites. As described above for inhibition of metabolising enzymes, use of more dynamic models could potentially improve the prediction accuracy of glucuronide DDI potential and more completely account for the complex in vivo situation. However, victim and perpetrator drug input parameters require optimisation and verification due to their effect on the success of predictions (396) and validation of PBPK models for both victim and perpetrator drugs is essential. This currently limits the use of these models for predicting glucuronide DDI potential due to the incomplete understanding of their complex pharmacokinetics.

4.5.4 Investigation of the gemfibrozil – repaglinide drug-drug interaction using dynamic modelling approach An attempt was made to investigate the DDI between gemfibrozil and repaglinide incorporating dynamic inhibitor concentrations and the inhibitory effects (IC50 data) of both gemfibrozil and its glucuronide on OATP1B1 and CYP2C8 using PBPK models for both victim

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and perpetrator drugs developed in SimCYP (Tables 4.5 and 4.6). The use of a dynamic model for both repaglinide and gemfibrozil in conjunction with in vitro data generated in the current study provided no clear advantage to the accurate prediction of the extent of DDI in comparison to the static mechanistic approach. The finding is confirmed in the similar values predicted for the repaglinide AUC ratio (1.13) relative to the static models based on OATP1B1 inhibition (1.14) and CYP2C8 inhibition (3.05) in isolation. It is important to note that the dynamic model for gemfibrozil glucuronide is based on the clinical concentration – time profile (in contrast to Cmax used in the static model) and therefore is not a true bottom up approach, as it does not consider the effects of biliary clearance or enterohepatic recycling on hepatocyte glucuronide concentration. These results highlight the necessity for PBPK models for both victim and perpetrator drugs in cases where each of those show complex PK and interactions at multiple mechanisms. However, for more accurate models for gemfibrozil glucuronide to be developed, thorough characterisation of its disposition in hepatocytes and incorporation of kinact and KI inhibition parameters, not generated in this study due the resource intensive nature of in vitro experiments, is required. For novel drugs with glucuronide metabolites, prediction of plasma and hepatocyte concentrations of the glucuronide is challenging due to limited in vitro information on their disposition (e.g., subsequent metabolism) and interactions with hepatic and renal efflux and uptake transporters, which complicate the use of PBPK models at this stage. Although a predicted increase in repaglinide exposure more similar to that observed in vivo was obtained using SimCYP library OATP1B1 and CYP2C8 inhibition data for both gemfibrozil and its glucuronide, it is noteworthy that the input data in the SimCYP simulator have been optimised to recover the magnitude of this observed DDI. The in vitro CYP2C8 inhibition data generated in Chapter 2 for gemfibrozil and its glucuronide were up to 7-fold greater than the SimCYP optimised values. In the case of OATP1B1, gemfibrozil and its glucuronide in vitro inhibition data (Chapter 3) were up to 9000-fold greater (less potent) than the SimCYP simulator inhibition data and similar trends was seen in comparison to reported literature inhibition data (Appendix Tables 6.1 and 6.2). This highlights a need for appropriate PBPK model validation for the parent-metabolite models to be used in a prospective manner.

4.5.5 Conclusions As for metabolites of potent P450 inhibitors, glucuronides exceeded the FDA exposure limit in the majority of cases indicating that the enzyme inhibitory potential of glucuronides would require incorporation into a novel drugs development for optimal assessment of DDI potential. Although limited clinical exposure data are currently available for glucuronide metabolites, where in vivo DDI risk could be predicted based on in vitro inhibition of CYP2C8 or OATP1B1, a potential risk of clinically significant DDI were indicated only for gemfibrozil and clopidogrel glucuronides. The risk of a DDI as a result of inhibition of CYP2C8 was the most pronounced of all enzymes investigated with glucuronides, which posed a similar or greater risk of DDI 200

than their parent compounds where comparison was possible. The results obtained indicate that potent in vitro inhibition of CYP2C8 by glucuronides does not necessarily translate to a predicted clinical DDI risk using the basic models investigated here. However, the accuracy of the models employed in comparison to observed changes in repaglinide exposure in the presence of inhibitor was limited and assessment of glucuronide DDI potential was performed only for a reduced dataset of compounds, restricting conclusions as to the clinical risk of glucuronides in metabolic DDIs. The clinical DDI risk of glucuronides was similar to or greater than that of parent compounds when OATP1B1 inhibition was investigated. The use of inhibition data obtained following pre-incubation with inhibitor is recommended for investigation of OATP1B1 DDI potential in order to obtain the most conservative estimates of DDI risk. Pitavastatin and E17βG demonstrated similar sensitivity to prediction of OATP1B1 mediated DDIs. The most accurate predictions of glucuronide OATP1B1 DDI potential in comparison to observed clinical DDIs were obtained using static mechanistic models incorporating the fT,OATP1B1 of the victim drug. It is suggested that, if possible, the DDI potential of novel glucuronides be predicted using models which incorporate the inhibitory effects of glucuronides and parent drugs on CYP2C8 and OATP1B1, if both are inhibited, to obtain the most conservative estimate of DDI risk and avoid false negative predictions. The expansion of the static models used in this study was limited by a lack of input data for glucuronides and clinical DDI data for model validation purposes, resulting in an underestimation of DDI prediction. Development of dynamic models incorporating the effects of biliary clearance and enterohepatic recycling on hepatocyte glucuronide concentration offers a potential area for further research and improvement of glucuronide DDI prediction accuracy. However, this requires improved characterisation and understanding of hepatic disposition of glucuronides, as discussed above.

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Chapter 5 Final discussion The potential contribution of circulating metabolites of perpetrator drugs to complex DDIs is increasingly recognised. This trend is also evident for glucuronides mainly due to the increasing prevalence of glucuronidation as a route of metabolism for clinically used drugs and the high circulating concentrations of these metabolites. In addition, some glucuronides have been reported to inhibit metabolising enzymes and interact with hepatic transporters such as OATP1B1 (186, 216), though information as to the prevalence and extent of inhibition is scarce. Similarly, the clinical DDI potential of glucuronides is not well characterised and their synergistic effects in combination with parent drugs requires consideration.

The overall aim of the work presented in this Thesis was to evaluate the DDI potential of glucuronide metabolites selected based on clinical exposure and enzyme and transporter inhibition data reported in the literature. CYP2C8, CYP3A4 and UGT1A1 enzymes as well as the OATP1B1 transporter have all been implicated in DDIs and reports of inhibition by glucuronides exist (Table 1.3 and 1.4). Therefore, following on from selection of glucuronides of interest, in vitro experiments were performed to obtain relevant inhibition parameters towards CYP2C8, CYP3A4, UGT1A1 and OATP1B1. The effect of pre-incubation on both enzyme and transporter inhibitory potential was explored based on reports of increased inhibitory potency (141, 156) and in order to investigate potential mechanisms of inhibition. In addition, OATP1B1 inhibition in vitro is complicated by reports of substrate-dependent inhibition; therefore, this was explored for glucuronides of interest using prototypical and clinically relevant probe substrates. As the enzyme and OATP1B1 inhibitory potential of glucuronides relative to parent drugs is generally unknown, this aspect was investigated for glucuronide-parent pairs of interest. Where possible, the clinical relevance of in vitro inhibition of metabolising enzymes and OATP1B1 by glucuronides was assessed using predictive models.

5.1 Inhibition of metabolising enzymes by glucuronides in vitro

Analysis of literature data revealed a lack of information on the inhibitory potential of glucuronides against P450 and UGT enzymes. In addition, time-dependent inhibition of CYP2C8 by gemfibrozil glucuronide had been reported but the TDI potential of a broader range of glucuronides and against other enzymes had not been studied when this project began. The inhibitory potential of glucuronides, parent drugs and reference inhibitors against

CYP2C8, CYP3A4 and UGT1A1 enzymes were investigated here using conventional IC50 shift experiments deemed suitable for providing an initial assessment of inhibitory potential and investigation of time-dependent inhibition. Methods were adapted from previous studies (75, 145, 153) to allow analysis of glucuronides inhibition of both P450 and UGT enzymes. Following on, glucuronide and parent CYP2C8 and CYP3A4 inhibitory potential were 202

assessed separately in experiments optimised for P450 metabolism to avoid glucuronidation of parent drug and enable direct comparison of glucuronide-parent pairs in the same incubation conditions.

In general, the IC50 values obtained in this study in HLM for the parent drugs and glucuronides investigated were in agreement with those reported in the literature (Table 1.3). Inhibition of CYP2C8 by glucuronides was the most pronounced of all the metabolic enzymes investigated. Inhibition of CYP2C8 by glucuronides was equally or more potent than by their parent drugs for the metabolites investigated in this study (e.g., telmisartan) (Figure 5.1). These findings are in agreement with the limited literature data available, for example, simvastatin glucuronide was reported to cause more potent inhibition of CYP2C8 (IC50 3.8 µM) than its parent drug (IC50 8.3 µM) (217) and similar inhibitory potency was reported for canagliflozin glucuronide (IC50 64 µM) and its parent drug (IC50 75 µM) (220). The mechanism of this inhibition of CYP2C8 varied between glucuronides with a time-dependent increase in inhibitory potency observed for clopidogrel and gemfibrozil glucuronides, in line with previous reports (156, 216). For gemfibrozil glucuronide, the time-dependent effect has been reported to be a consequence of hydroxylation of the glucuronide by CYP2C8 (156). Hydroxylation of these metabolites by CYP2C8 has also been reported previously for diclofenac and desloratedine glucuronides (47, 301). No pre-incubation effect on CYP2C8 inhibitory potency was observed for the other 8 glucuronides or any of the parent drugs investigated. Further investigation of glucuronides with similar structure to clopidogrel and gemfibrozil glucuronides is required to enable prediction of which glucuronides may cause time-dependent inhibition. The differential inhibition mechanisms between glucuronide-parent pairs in conjunction with glucuronides causing similar or more potent inhibition of CYP2C8 than parent drugs highlight that the inhibitory potential and mechanism of inhibition by these metabolites need to be investigated separately and in addition to the effects of parent drugs. Pre-incubation effects on inhibitory potential should be assessed to explore the mechanism of inhibition and also to obtain the most conservative inhibition data for use in prediction where there is TDI.

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Figure 5.1 Comparison of glucuronide and parent drug enzyme inhibition data collated from the literature (red) or obtained in this study (purple) without pre-incubation for CYP2C8 () and CYP3A4 (X). The solid line represents the line of unity and the dashed lines represent 2- fold difference between glucuronide and parent inhibition data

The use of combined NADPH and UDPGA co-factors enabled simultaneous investigation of both P450 and UGT enzyme inhibition in HLM, extending experimental output. Combined co- factor conditions (153) also provided a more similar scenario to the in vivo situation and whole cell systems such as hepatocytes. However, it restricted investigation of parent compound and glucuronide inhibitory potency in the same incubation conditions as the parent drug would be glucuronidated. Furthermore, the choice of co-factor conditions had an impact on the CYP2C8 inhibitory potential of clopidogrel and mefenamic acid glucuronides. Clopidogrel glucuronide caused potent, time-dependent inhibition of CYP2C8 in P450 co-factor experiments and was 5-fold more potent than in combined co-factor experiments. The opposite was observed for mefenamic acid glucuronide indicating that adaptation of incubation conditions may have contrasting effects on the CYP2C8 inhibitory potential of different glucuronides. As a result of these complexities, the enzyme and inhibitor of interest as well as the metabolism of the inhibitor ought to be considered carefully when selecting co-factor conditions for investigation of enzyme inhibition by glucuronides and their parent drugs. Monitoring of inhibitor concentrations during inhibition experiments and investigation of metabolism of glucuronides by CYP2C8 would provide a useful insight into the differences observed, as highlighted in the case of clopidogrel.

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HLMs are the generic in vitro system used for metabolism studies, both in the presence and absence of inhibitors, partially due to convenience and their practicality (75, 153, 423). However, utilising HLMs can bias investigation of DDIs due to the simplicity of this system in comparison to the in vivo situation and whole cell systems e.g., hepatocytes, which have a full complement of enzymes, cofactors and transporter proteins present (147, 152, 443). The interplay of metabolism and transport processes in cellular systems would influence not only the concentration of substrate available to the enzyme but also that of the inhibitor. Inhibition experiments in human hepatocytes are warranted to better replicate the in vivo situation for glucuronides which inhibited CYP2C8. This is especially the case due to reports of accumulation of gemfibrozil glucuronide in hepatocytes (399, 439) and proposed liver-to-blood shuttling of sorafenib glucuronide (204), indicating that glucuronide concentrations at the site of enzyme inhibition may be very different to the concentration outside the cell. However, experiments in whole cell systems need careful planning to allow separate assessment of the inhibitory effects of glucuronides and parent drugs. A further difficulty is selection of an adequate hepatocyte format to capture both the uptake and efflux of glucuronides. For example, plated and hepatocytes in suspension, often used for assessing hepatic uptake, are limited by loss of cell polarisation compared to the in vivo situation and internalisation of efflux transporters (444). In contrast, sandwich cultured hepatocytes develop gap junctions and functional bile canalicular networks whilst in culture enabling investigation of drug efflux (445, 446), but lose uptake transporter activity over time (447). In addition, although assessment of the formation of the repaglinide M4 metabolite is suitable for specifically investigating inhibition of CYP2C8 (152), the use of repaglinide as a probe substrate in whole cell systems and in vivo would be complicated by its uptake by OATP1B1 (443, 448). Therefore, results of inhibition experiments with glucuronides in whole cell systems would need to be interpreted with an appreciation of the complexity of the disposition of the victim drug, in addition to the perpetrator.

5.2 Investigation of the OATP1B1 inhibitory potential of glucuronides in vitro This is the first study to investigate in a systematic manner the inhibitory effects of glucuronides and parent drugs on OATP1B1. IC50 values were obtained for 8/10 glucuronides investigated in this study and the parent drugs and reference inhibitors examined. Inhibition data obtained in OATP1B1-expressing HEK293 cells in this study using E17βG and pitavastatin as probe substrates (Table 3.4 and 3.5) generally agreed well with the limited literature data available for comparison. Increasing molecular weight, topological surface area,

LogP and LogD7.4 were associated with a general increase in OATP1B1 inhibitory potency in line with previous reports (225, 309). Glucuronides inhibited OATP1B1 to the same extent or more potently than parent drugs, indicating that inhibition of this transporter should be

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considered as a potential contributing mechanism to DDIs. An increase in OATP1B1 inhibitory potency following pre-incubation with inhibitor was observed for some, but not all drugs demonstrating that pre-incubation effects are not a universal characteristic of OATP1B1 inhibitors. The occurrence of pre-incubation effects was not associated with the inhibitory potency of the drugs investigated. For example, no pre-incubation effects were observed for telmisartan glucuronide, the most potent of the glucuronides investigated (IC50 ≤ 2 µM), with either E17βG or pitavastatin as probe substrate whereas pre-incubation increased the inhibitory potential of gemfibrozil (IC50 > 20 µM) by up to 3-fold, with both probes investigated.

Pitavastatin exhibited similar sensitivity to OATP1B1 inhibition as E17βG for the majority of inhibitors investigated (Figure 3.12), indicating that pitavastatin offers a suitable, clinically relevant alternative to the prototypical probe substrate E17βG for in vitro investigations of OATP1B1 inhibition. However, substrate-dependent inhibition was observed for the parent drugs gemfibrozil, ezetimibe and diclofenac where up to 12-fold more potent inhibition was observed using E17βG in comparison with pitavastatin as a probe substrate. The underlying mechanism of this effect is unclear but may be a result of differential OATP1B1 binding of the two probes, as the existence of multiple OATP1B1 binding sites has been suggested (131, 132). Further investigation of the substrate-dependent inhibition phenomenon is required to elucidate the mechanism involved. However, for the majority of inhibitors pitavastatin represents a useful, clinically relevant probe substrate to assess OATP1B1-mediated DDI risk considering its selectivity for this transporter as substantiated in its high contribution to pitavastatin uptake.

The inhibitory potency of glucuronides against OATP1B1 varied with IC50 values ≤ 2 µM for the most potent inhibitor telmisartan glucuronide and only marginal inhibition observed at the highest inhibitor concentrations of other glucuronides such as mycophenolic acid glucuronide, where IC50 values could not be quantified (Figures 3.4 and 3.6). Analysis of the mechanism of OATP1B1 inhibition by glucuronides is currently limited and should be interpreted with caution until further investigations are performed. The results obtained in this study indicated that different glucuronides may inhibit OATP1B1 by different mechanisms. For instance, telmisartan glucuronide, which is not a substrate of OATP1B1 (264), potently inhibited the transporter whereas less potent inhibition was observed for mycophenolic acid and ezetimibe glucuronides which are substrates of this transporter (91, 202). The inhibition observed may be competitive for glucuronides which are substrates; however, an alternative mechanism may underlie the effect of telmisartan. The lack of a consistent pre-incubation effect on OATP1B1 inhibitory potential indicates that down regulation of the transporter, which has been reported for OATP1B3, OATP1A2 and OATP2B1 (137-139), may not be occurring as this would be expected to be consistent independent of the probe substrate used. Shitara et al., (2009) (136) reported that inhibition of OATP1B1 by cyclosporine was due to reduced transporter activity 206

and not reduced expression, but recent studies propose down regulation via PKC activation (449). A recent study by Furihata et al., (2014) (134) suggests that different cellular mechanisms may underlie pre-incubation effects on inhibition of OATP1B1 and OATP1B3 transporters based on the differential changes in inhibitory potency following pre-incubation which were observed with direct-acting anti-hepatitis C virus agents. However, pre-incubation effects on OATP1B1 inhibition vary between inhibitors and it is possible that other inhibitors may cause more pronounced pre-incubation effects than observed in this study for glucuronide metabolites. The possibility of trans-inhibition of OATP1B1, as suggested for cyclosporine (135), may explain the pre-incubation effects observed for some inhibitors; however, the differences in pre-incubation effects between probe substrates indicate that multiple binding sites may also be involved in this aspect of OATP1B1 inhibition.

The results obtained here indicate that glucuronides do inhibit OATP1B1 and that this should be considered during drug development as it may have consequences for detoxification processes and DDIs (204). It is noteworthy that inhibition data were obtained for a greater number of glucuronides against OATP1B1 than metabolising enzymes, highlighting that transporter interactions may be of greater concern to DDIs. The underlying mechanisms of OATP1B1 inhibition by glucuronides have not been explained by the approaches used in this study. Investigations of whether the glucuronides investigated are substrates of OATP1B1 and monitoring of inhibitor concentrations both intra and extracellularly during inhibition experiments are necessary to improve understanding of the location and mechanism of inhibition. Investigation of a range of pre-incubation times would also be helpful to explore any effects on inhibitory potential and the mechanism involved. Careful consideration of analytical challenges resulting from glucuronide stability and potential conversion to the parent drug is also required to properly explore any time-dependent increase in OATP1B1 inhibition by glucuronides. Investigation of pre-incubation effects with additional probe substrates is also required to assess the substrate-dependency of this phenomenon. In addition, the long-lasting inhibitory potential of glucuronides and any changes in transporter localisation and expression should be investigated in HEK293 cells in order to delineate the underlying mechanisms and aid design of appropriate in vitro experiments. Evaluation of the inhibitory potential of glucuronides against other transporters involved in their disposition, e.g., MRP2, MRP3, MRP4 and OATP1B3 (209, 264), would also be useful to better establish their transporter mediated DDI potential in vivo. The transporter DDI risk of glucuronides needs to be considered in relation to more complex in vitro systems such as hepatocytes to establish if the extent of OATP1B1 inhibition and pre-incubation effects are sensitive to the in vitro system used. This approach would also enable assessment of the effect of interplay of multiple transport and metabolism processes on glucuronide DDI risk. However, experiments would require careful design and selection of suitable probe substrates in order to establish specific effects on OATP1B1. In addition, comparison of the results obtained between different in vitro 207

systems would require consideration of the differences between systems, such as the expression level of the transporter of interest.

5.3 Clinical exposure of glucuronides Current regulatory guidelines recommend assessment of a drug metabolites safety and DDI potential based on systemic exposure relative to that of the parent compound (13-15). As pertaining to inhibition of P450 enzymes, the FDA recommends investigation of a metabolites P450 inhibitory potential if present at > 25% parent drug systemic exposure. The basis of this exposure cut-off is unknown and its relevance to metabolite enzyme inhibitory potential and DDI risk alone in vivo is questionable (19, 23, 277). For the glucuronides, metabolite : parent exposure ratios could only be determined for 15 compounds based on reported literature data. For 14/15, glucuronides exceeded 25% of their parent drugs systemic exposure and glucuronide : parent AUC ratios exceeded 100% for a number of compounds including clopidogrel and ezetimibe. For the compounds for which glucuronide and parent drug Cmax data were corrected for plasma protein binding, the glucuronide : parent exposure ratio increased by up to 20-fold (gemfibrozil). The high circulating concentrations of glucuronides are in line with reports of the basolateral efflux of some glucuronides back into blood (204, 211). However, the properties governing whether a glucuronide is transported into bile, directly into the blood and then renally excreted or both are currently unknown and require further investigation. Current analyses highlighted greater availability of clinical exposure data for metabolites of strong P450 inhibitors than for glucuronides. Over half of the metabolites of potent P450 inhibitors exceeded the FDA 25% exposure limit and metabolite : parent exposure ratios were within the range of that reported for glucuronides. This analysis indicates that, based on reports of clinical exposure data, the P450 inhibitory potential of glucuronides, as well as metabolites of potent P450 inhibitors, would require assessment following current regulatory guidelines. Assessment of the DDI risk of glucuronide metabolites should be conducted in conjunction with assessment of both exposure and the inhibitory potential of the parent drug.

5.4 Prediction of the risk of glucuronide mediated drug-drug interactions In vitro data generated in Chapters 2 and 3 were used in conjunction with clinical exposure data of glucuronides, parent drugs and reference inhibitors collated from the literature to predict the risk of DDIs. Assessment of glucuronide DDI potential was limited by the paucity of clinical exposure data for the glucuronides for which in vitro inhibition data were obtained. The risk of DDI as a result of inhibition of CYP2C8 was assessed for a total of 3 glucuronides. A basic model (1 + Cmax/Ki) was employed for initial analysis in line with FDA guidelines and

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only gemfibrozil glucuronide and clopidogrel glucuronide were identified to cause a DDI. The predicted DDI risk of both gemfibrozil and clopidogrel glucuronides was greater than that of their parent drugs, highlighting that these glucuronides may contribute to interactions with victim drugs in vivo. However, use of the basic I/Ki model resulted in only half of the predicted AUC ratios being within 2-fold of those reported in vivo. The basic DDI prediction model is based only on inhibitor-related information and does not contain any substrate-specific parameters. Its ability to quantitatively predict DDIs is limited by this lack of substrate-related information, particularly a parameter to account for the reliance of the substrate on the enzyme in question for its metabolism. In order to address the importance of the enzyme of interest to the metabolism of the victim drug the fraction of repaglinide metabolised by CYP2C8 (fmCYP2C8) was incorporated into the prediction equations (Equations 4.3 and 4.4). Incorporation of the fmCYP parameter allows for multiple enzyme involvement in the hepatic metabolism of a drug and prevents the over-prediction of DDIs involving drugs that are substrates for this enzyme. When applying the static mechanistic inhibitor model, the magnitude of increase in repaglinide AUC for gemfibrozil and clopidogrel glucuronides and their parent drugs was generally under predicted. Combining the inhibitory effects of parents and glucuronides in the model resulted in an increase in the predicted DDI risk in comparison to parents on their own for both gemfibrozil and clopidogrel glucuronides. However, this approach did not improve prediction accuracy of repaglinide DDIs as the predicted magnitude of DDI was either non-significant or weak (< 2-fold change in AUC), supporting the existence of multiple elimination pathways. If a glucuronide metabolite of a novel compounds potential DDI risk were to be assessed in the manner described here, it is possible that a false negative prediction would be obtained. The under prediction of metabolic DDIs with glucuronides indicates that the inhibitor input concentration used is most likely not representative of that at the site of enzyme inhibition, highlighting the need for improved understanding of intracellular glucuronide concentrations.

As for CYP2C8, the DDI risk of glucuronides based on in vitro inhibition of OATP1B1 could only be assessed for a limited number of the glucuronides assessed in vitro, namely, gemfibrozil, clopidogrel, ezetimibe, telmisartan and raloxifene glucuronides, due to a lack of glucuronide clinical exposure data. For the glucuronides, parent drugs and reference inhibitors assessed, the extent of DDI risk predicted was similar using in vitro inhibition data obtained using E17βG and pitavastatin as probe substrates, highlighting the suitability of pitavastatin as a clinically relevant in vitro probe substrate. In general, the predicted DDI risk using inhibition data obtained following pre-incubation with inhibitor was greater than that without and should be used to obtain the most conservative estimate of a DDI.

Of the 5 glucuronides for which the DDI potential was assessed using the basic approach of calculating Cmax/IC50, only clopidogrel and gemfibrozil glucuronides were predicted to cause a clinical DDI. When glucuronide concentration was adjusted for plasma protein binding (R =

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1 + Cmax,u/IC50) only gemfibrozil glucuronide was predicted to cause a DDI following multiple doses of 600 mg of the parent drug. As for the metabolism model, the basic transporter DDI prediction model is based only on inhibitor-related information and consequently does not contain any substrate-specific parameters. Using a static mechanistic model incorporating the fraction of pitavastatin transported by OATP1B1, again, of the glucuronides investigated only gemfibrozil glucuronide was predicted to cause a DDI. Based on these analyses, not all glucuronides were predicted to pose a risk of DDI though they were predicted to pose a greater risk than parent drugs indicating that they should be investigated if the parent drug inhibits OATP1B1 and the potential contribution to DDIs explored. The use of static mechanistic models and incorporation of fT,OATP1B1 resulted in an increased number of predicted pitavastatin

AUC ratios within 2-fold of the observed data in comparison to Cmax/IC50 or R calculations

(Figure 4.12). However, use of fT and either OATP1B1 inhibition data obtained with E17βG and pitavastatin underpredicted the extent of the DDI between gemfibrozil glucuronide and repaglinide.

Use of the basic models as a first line analysis for the glucuronide metabolite of a novel compound would provide the most conservative estimate of the potential risk of DDI resulting from inhibition of OATP1B1. If a DDI were indicated assessment of the clinical relevance of the DDI potential of glucuronides using static mechanistic models is suggested to provide a more accurate indication of DDI risk, in line with current regulatory guidelines (13). The refined transporter model should be tested with a larger dataset including a wide range of OATP1B1 substrates and inhibitors with glucuronide metabolites. Data for other hepatic uptake transporters should also be applied and further refinement of this model is required before the DDI potential of glucuronides with transporters can be well understood. In addition, the mechanism of inhibition of OATP1B1 would require incorporation into the model, potentially including estimates of degradation rate constants for the transporter, if inhibition is not of a straightforward competitive nature.

Combined enzyme and transporter inhibition effects have been shown to result in unexpectedly potent DDIs in vivo, illustrated in the 19-fold increase in repaglinide AUC in the presence of itraconazole and gemfibrozil (282). However, transport and metabolism events cannot be combined into the static models used in the current analysis as a result of the sequential nature of these processes in vivo. Development of a dynamic model incorporating the effects of parent drugs and glucuronides on the OATP1B1 transport and CYP2C8 metabolism of a victim drug is required to more accurately replicate the in vivo situation, as have been reported by Gertz et al., (2014) (396) and Varma et al, (2015) (182). Accurate prediction of DDIs is influenced by input parameters, e.g., metabolic clearance, of both victim and perpetrator drugs and inhibition parameters (396). The development of PBPK models for

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glucuronides which capture organs that are of relevance for the assessment of DDI in a mechanistic way, rather than relying on plasma concentrations as surrogates is restricted by the requirement of a comprehensive amount of data. Although this is a desirable aim for optimal prediction of glucuronide DDI potential, its use would be limited to prediction of DDIs where large amounts of perpetrator and victim drug data are available and models for both victim and perpetrator drugs can be verified.

5.5 Concluding remarks The current study has systematically evaluated the inhibitory effects of 10 glucuronides in vitro on CYP2C8, CYP3A4, UGT1A1 and the OATP1B1 transporter for the first time. Based on clinical exposure data collated from the literature, glucuronides were found to exceed parent drug exposure in the majority of cases. However, exposure to transporters and metabolising enzymes is a complex process resulting from interplay between hepatic active uptake and efflux, passive diffusion, intracellular binding and metabolism. Consideration of these processes is necessary to properly understand glucuronide exposure and DDI potential.

CYP2C8 was the enzyme most sensitive to inhibition by glucuronides; IC50 values were obtained for 5/10 glucuronides investigated. The mechanisms and potency of inhibition varied and the TDI potential of glucuronides on CYP2C8 should be considered for glucuronide metabolites of novel drugs which inhibit this enzyme. Glucuronides caused comparable or more potent CYP2C8 inhibition than their parent drugs indicating that their inhibitory potential should be examined separately and their potential contribution to DDIs needs to be considered. IC50 values were obtained for a greater number of glucuronides against OATP1B1 than CYP2C8; inhibition potency varied between inhibitors and no trend in CYP2C8-OATP1B1 inhibition was observed. Glucuronides were found to inhibit OATP1B1 to a similar or greater extent than their parent drugs and in vitro assessment of glucuronide inhibitory potential is recommended if the parent drug inhibits this transporter. Pitavastatin and E17βG demonstrated similar sensitivity to inhibition of OATP1B1 however differential pre-incubation effects were observed between inhibitors and different substrates. Inclusion of a pre- incubation step in OATP1B1 inhibition experiments is recommended in order to assess the mechanism of inhibition and obtain the most conservative estimate of inhibitory potential. For the most part, in vitro inhibition of CYP2C8 and OATP1B1 by glucuronides was not predicted to result in clinical DDIs. However, prediction of DDI risk was limited by the models used not accounting for the complexity of the in vivo situation involving multiple inhibitors and multiple inhibition mechanisms. Efforts should be directed towards improving our understanding of the disposition of glucuronides by hepatic uptake and efflux transporters to refine the ability to predict DDI risk resulting from inhibition of multiple elimination mechanisms (CYP2C8 and OATP1B1) by glucuronides and their parent drugs.

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Chapter 6 Appendices 6.1 Literature database collation

All tables are provided on Disc 1

Table 6.1 Inhibition data collated from the literature for glucuronide metabolites and parent drugs with glucuronide metabolites against metabolising enzymes in vitro

Table 6.2 Database collation of in vitro OATP1B1 inhibition data

Table 6.3 Clinical exposure data collated for metabolites of potent P450 and OATP1B1 inhibitors and calculation of metabolite : parent exposure ratios

Table 6.4 Clinical exposure data collated for glucuronide metabolites and calculation of metabolite : parent exposure ratios

6.2 Methods

6.2.1 LC-MS/MS analysis of repaglinide metabolites and compounds for which enzyme inhibition was assessed: Sample aliquots (10 µl) were injected into an LC-MS/MS system and all compounds were analysed on a Luna C18 column (3 µm, 4.6 x 50 mm; Phenomenex, Macclesfield, UK) or a Luna Phenyl Hexyl column (3u 50x4.6mm column; Phenomenex, Macclesfield, UK) using an Alliance 2795 liquid chromatograph (Waters, Watford, UK). The mobile phases used were: Solvent A 90% water, 10% methanol and 0.05% formic acid; Solvent B 10% water, 90% methanol and 0.05% formic acid; Solvent C 90% water, 10% methanol and 1mM ammonium acetate; Solvent D 10% water, 90% methanol and 1 mM ammonium acetate. The flow rate was set at 1 ml/min and in all cases split eluent (0.2 ml/min) was analysed by electrospray atmospheric pressure ionization combined with multiple reaction monitoring of manually optimized product ions using a Micromass Quattro Ultima tandem mass spectrometer (Waters, Watford, UK). Capillary voltage was 3.5 kV; desolvation and source temperatures were 350 and 125°C, respectively and desolvation gas flow rate was 600 L/h. Tables 6.5 – 6.8 (located on Disc 1) describe the specific analytical parameters for each analyte investigated in this study. Ion chromatograms were integrated and quantified by quadratic regression of standard curves using MassLynx 4.1 (Waters, Watford, UK).

6.2.2 LC-MS/MS analysis of pitavastatin Sample aliquots (10 µl) were injected into an LC- MS/MS system and all compounds were analysed on an ACE x C18-A12 column (3µM, 50 x 2.1 mm; Hichrom, Berkshire, UK) connected to a Thermo Scientific Surveyor pump and Thermo PAL autosampler (Thermo Fisher Scientific, USA). The mobile phases used were: Solvent A water, 0.1% formic acid and Solvent B methanol, acetonitrile, 0.1% formic acid. Test sample was analysed by electrospray atmospheric pressure ionization combined with multiple 245

reaction monitoring of product ions using a Thermo-Finnigan TSQ Quantum Ultra mass spectrometer (Thermo Fisher Scientific, USA). Capillary voltage was 3.5 kV; desolvation and capillary temperatures were 350 and 250°C, respectively; sheath gas and auxiliary gas pressures were 80 and 40 L/h, respectively and column temperature was 60°C. Monitoring conditions for individual analytes are given in Table 6.9 (located on Disc 1). Data were collected and reported using XcaliburTM 2.1 software (Thermo Fisher Scientific, USA).

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6.3 Results

Figure 6.1 IC50 profiles for glucuronides against CYP3A4 in combined co-factor conditions in HLM. Repaglinide M1 formation was investigated in the presence of mefenamic acid glucuronide (A), telmisartan glucuronide (B), clopidogrel glucuronide (C), diclofenac glucuronide (D), ezetimibe glucuronide (E), mycophenolic acid glucuronide (F), raltegravir glucuronide (G), gemfibrozil glucuronide (H) and raloxifene 4’ – glucuronide (I). Data represent mean ± sd of at least 3 separate experiments performed without () and with () pre-incubation with inhibitor

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Figure 6.2 IC50 profiles for glucuronides against UGT1A1 in combined co-factor conditions in HLM. Repaglinide M1 formation was investigated in the presence of telmisartan glucuronide (A), mefenamic acid glucuronide (B), diclofenac glucuronide (C), gemfibrozil glucuronide (D), clopidogrel glucuronide (E), mycophenolic acid glucuronide (F), ezetimibe glucuronide (G), raltegravir glucuronide (H) and raloxifene 4’ – glucuronide (I). Data represent mean ± sd of at least 3 separate experiments performed without () and with () pre-incubation with inhibitor

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Figure 6.3 IC50 profiles for glucuronides against CYP3A4 in P450 co-factor conditions in HLM. Repaglinide M1 formation was investigated in the presence of telmisartan glucuronide (A), mefenamic acid glucuronide (B), gemfibrozil glucuronide (C), clopidogrel glucuronide (D) and diclofenac glucuronide (E). Data represent mean ± sd of at least 3 separate experiments performed without () and with () pre-incubation with inhibitor

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Figure 6.4 IC50 profiles for rifamycin SV against formation of repaglinide M4 (A), repaglinide M1 (B) and repaglinide glucuronide (C) by CYP2C8, CYP3A4 and UGT1A1, respectively. Data represent mean ± sd of at least 3 separate experiments conducted in pooled HLM with P450 co-factors (A, B) or UGT co-factors (C) without () and with () pre-incubation with inhibitor

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Figure 6.5 Nominal vs. measured inhibitor concentrations monitored during CYP2C8, CYP3A4 and UGT1A1 IC50 experiments in pooled HLM with combined co-factors for reference inhibitors or P450 co-factors for parent drugs. Inhibitor concentrations were monitored at the end of 30-minute pre-incubation with inhibitor (), 10-minute co-incubation with repaglinide following 30-minute pre-incubation with inhibitor () and 10-minute co-incubation with repaglinide without pre-incubation with inhibitor (). Data represent mean ± sd of at least 3 separate experiments. Inhibitor concentrations were monitored for telmisartan (A), mefenamic acid (B), gemfibrozil (C), diclofenac (D), ketoconazole (E), trimethoprim (F) 251

Figure 6.6 Comparison of E17βG and pitavastatin OATP1B1 Ki data without (A) and with (B) a 30-minute pre-incubation. The dashed line represents the line of unity. Rifamycin SV (1), cyclosporine (2), rifampicin (3), telmisartan (4), telmisartan glucuronide (5), repaglinide (6), repaglinide glucuronide (7), erythromycin (8), diclofenac glucuronide (9), diclofenac (10), ezetimibe glucuronide (11), gemfibrozil glucuronide (12), ezetimibe (13), gemfibrozil (14)

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Table 6.10 Physicochemical properties of OATP1B1 inhibitors predicted using ADMET Predictor (Simulation Plus, version 7) software

polar surface hydrogen bond hydrogen bond net molecular Inhibitor LogP LogD pKa area donors acceptors charge weight Telmisartan 5.80 3.12 4.03 72.94 1 4 -1.00 514.63 Telmisartan glucuronide 3.93 1.12 3.26 169.16 2 10 -1.00 690.76 Repaglinide 4.89 2.97 4.50 78.87 4 6 -0.98 452.60 Repaglinide glucuronide 1.77 -0.11 3.77 165.86 5 11 -0.98 584.67 Gemfibrozil 4.00 1.55 4.92 46.53 1 3 -1.00 250.34 Gemfibrozil glucuronide 1.76 -1.23 3.85 142.75 4 9 -1.00 426.47 Diclofenac 4.43 1.17 3.87 49.33 2 3 -1.00 296.15 Diclofenac glucuronide 2.16 -0.44 3.51 145.55 5 9 -1.00 472.28 Ezetimibe 4.26 4.25 9.81 60.77 2 4 0.00 409.44 Ezetimibe glucuronide 2.39 -0.61 3.67 156.99 5 10 -1.00 585.56 Mefenamic acid 3.80 glucuronide 2.01 -0.99 145.55 5 9 -1.00 417.42 Raloxifene 4'- 3.59 glucuronide 2.40 2.30 166.22 5 11 -0.21 649.72 Clopidogrel glucuronide 0.03 -1.53 3.32 136.76 4 9 -1.00 483.93 Cyclosporine A 2.79 2.79 278.80 5 23 0.00 1202.64 Rifampicin 2.54 2.42 6.33 216.66 6 16 -0.50 822.96 Erythromycin 1.72 0.39 8.72 193.91 5 14 0.85 733.94 Rifamycin SV 2.42 2.15 7.45 201.31 6 13 -0.46 697.78

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Table 6.11 OATP1B1 Cmax/IC50 ratios and R values calculated using a basic model (Equation 4.6) and inhibition data obtained in HEK293 cells following a 30- minute (30) pre-incubation with inhibitor using E17βG or pitavastatin as a probe substrate. The FDA cut off indicating further investigation of a drugs OATP1B1

DDI potential for Cmax/IC50 ratios is 0.1 and for R (1 +I,in,max,u or Cmax u/IC50) values is 1.25

Predicted AUC’/AUC Dose parent Cmax E17βG Pitavastatin -1 Drug fup ka (min ) I,in,max,u References drug (mg) (µM) Cmax/ Cmax/ IC50 (30) R IC50 (30) R IC50 IC50 Ezetimibe 20 0.013 0.061 0.014 0.028 47.83 0.00027 1.0 34.27 0.00038 1.0 (243, 450, 451) Ezetimibe 20 0.105 0.014 0.0015 11.95 0.0088 1.0 16.02 0.0066 1.0 (243) glucuronide Ezetimibe 20 0.012 0.061 0.014 0.028 47.83 0.00025 1.0 34.27 0.00035 1.0 (202, 450, 451) Ezetimibe 20 0.13 0.014 0.0018 11.95 0.011 1.0 16.02 0.0081 1.0 (202) glucuronide Ezetimibe 10 0.024 0.061 0.014 0.029 47.83 0.00050 1.0 34.27 0.00070 1.0 (450-452) Ezetimibe 10 0.22 0.014 0.0031 11.95 0.018 1.0 16.02 0.014 1.0 (452) glucuronide Ezetimibe 10 0.017 0.061 0.014 0.015 47.83 0.00036 1.0 34.27 0.00050 1.0 (450, 451, 453) Ezetimibe 10 0.12 0.014 0.0017 11.95 0.010 1.0 16.02 0.0075 1.0 (453) glucuronide Ezetimibe 10 0.013 0.061 0.014 0.0144 47.83 0.00027 1.0 34.27 0.00038 1.0 (450, 451, 454) Ezetimibe 10 0.19 0.014 0.0026 11.95 0.016 1.0 16.02 0.012 1.0 (454) glucuronide Ezetimibe 10 0.013 0.061 0.014 0.014 47.83 0.00027 1.0 34.27 0.00038 1.0 (450, 451, 455)

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I,in,max,u Predicted AUC’/AUC Dose parent Cmax E17βG Pitavastatin -1 Drug fup ka (min ) References drug (mg) (µM) Cmax/ Cmax/ IC50 (30) R IC50 (30) R IC50 IC50 Ezetimibe 10 0.24 0.014 0.0033 11.95 0.020 1.0 16.02 0.015 1.0 (455) glucuronide Ezetimibe 10 0.006 0.061 0.014 0.014 47.83 0.00013 1.0 34.27 0.00018 1.0 (328, 450, 451) Ezetimibe 10 0.06 0.014 0.00084 11.95 0.0050 1.0 16.02 0.0037 1.0 (328) glucuronide

Ezetimibe glucuronide fup extimated using SimCYP version 14 -1 FDA recommended value used to estimate I,in,max,u, i.e., ka of 0.1 min and FaFG of 1 were used. A Qh of 1500 mL/min was used for all I,in,max,u calculations . For ezetimibe glucuronide, I,in,max,u could not be calculated due to lack of dose data – Cmax, u values were used for DDI predictions

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Table 6.12 Predicted pitavastatin AUC’/AUC ratios using a mechanistic static model. In vitro OATP1B1 inhibition data were generated in HEK293 cells expressing the transporter using either pitavastatin or E17βG as a probe substrate following 30-minute pre-incubation with inhibitor I,in,max,u values and Cmax,u values were used as inhibitor concentration inputs for parent drugs and glucuronides, respectively (Table 4.8, Appendix Table 6.11)

Dose AUC’/AUC Inhibitor parent drug E17βG Pitavastatin

(mg) fTOATP1B1 0.68 fTOATP1B1 0.86 fTOATP1B1 0.68 fTOATP1B1 0.86 Telmisartana 80 1.1 1.1 1.0 1.0 Telmisartan glucuronidea 80 1.0 1.0 1.0 1.0 Clopidogrel glucuronideb 300 1.0 1.0 NE NE Clopidogrel glucuronideb 75 1.0 1.0 NE NE Repaglinidec 4 1.0 1.0 1.0 1.0 Diclofenacd 75 1.0 1.0 1.0 1.0 Ezetimibee 20 1.0 1.0 1.0 1.0 Ezetimibe glucuronidee 20 1.0 1.0 1.0 1.0 Ezetimibef 20 1.0 1.0 1.0 1.0 Ezetimibe glucuronidef 20 1.0 1.0 1.0 1.0 Ezetimibeg 10 1.0 1.0 1.0 1.0 Ezetimibe glucuronideg 10 1.0 1.0 1.0 1.0 Ezetimibeh 10 1.0 1.0 1.0 1.0 Ezetimibe glucuronideh 10 1.0 1.0 1.0 1.0 Ezetimibei 10 1.0 1.0 1.0 1.0 Ezetimibe glucuronidei 10 1.0 1.0 1.0 1.0 Ezetimibej 10 1.0 1.0 1.0 1.0

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Dose AUC’/AUC Inhibitor parent drug E17βG Pitavastatin

(mg) fTOATP1B1 0.68 fTOATP1B1 0.86 fTOATP1B1 0.68 fTOATP1B1 0.86 Ezetimibe glucuronidej 10 1.0 1.0 1.0 1.0 Ezetimibek 10 1.0 1.0 1.0 1.0 Ezetimibe glucuronidek 10 1.0 1.0 1.0 1.0 a(262), b(216), c(422), d(424), e(243), f(202), g(452), h(453), I (454), j (455), k(328)

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