Characterization of UGT Inhibition as a

Necessary and Important Strategy in

Drug Development

Theunis C. Goosen DDI-2017

20th Anniversary of the International Conference on

Drug-Drug Interactions

Seattle, Washington

21 June, 2017

2

Why Pursue Glucuronidation?

NNH

O

O

F

OH

O

NNH

O

OH

F

OH O-Gluc

O

NNH

O

OH

F

OH OH

O

Atorvastatin acid (AVA) Atorvastatin lactone (AVL)

AVA Glucuronide (G2)UGTs

1A1, 1A3

IC50

Gemfibrozil (GFZ): 316 M

Fenofibrate (FENO): NS

Lactonization

Prueksaritanont et al., 2002, DMD; 30:505; Goosen et al., 2007, DMD; 35:1315.

• Clinical DDIs were observed with most statins when co-administered with gemfibrozil,

but not with fenofibrate

AUC change from baseline following co-administration with gemfibrozil:

1. Cerivastatin (5.6-fold),

2. Simvastatin (2.9-fold), Lovastatin (2.8-fold), Pravastatin (2.4-fold), Rosuvastatin (1.9-fold)

3. Pitivastatin (1.3-fold)

4. Fluvastatin (NS)

3

Clinical Outcome with Atorvastatin

Goosen et al., 2007, DMD; 35:1315.

4

Concerned about UGT-Mediated DDI?

5

Study Description FDA Guidance EMA Guidance

Victim DDI

In vitro or clinical studies Determine if UGTs contribute to ≥25% of systemic clearance.

Determine if non-CYP enzymes (eg, UGTs) contribute to ≥25% of elimination.

Reaction Phenotyping UGTs 1A1, 1A3, 1A4, 1A6, 1A9, 2B7, 2B15.

Non-CYP phenotyping is recommended, specific UGTs are not mentioned.

Clinical impact If UGT-mediated systemic clearance ≥25% or unknown, consider in vivo studies with inhibitors/inducers as appropriate or genotyped pharmacokinetic studies, particularly for UGT1A1 substrates. PBPK modelling of clinical impact could be considered.

CYP and non-CYP enzymes (eg, UGTs) involved in ≥25% of drug elimination should be identified.

Perpetrator of DDI

Inhibition In vitro or in vivo UGT inhibition studies are not explicitly mentioned.

In vitro inhibition studies with UGTs involved in DDI (e.g., UGT1A1 and UGT2B7), if the NCE is primarily metabolized by those enzymes.

Induction Not mentioned Not mentioned

Regulatory Expectations

6

Regulatory Questions on DDI (2015)

• ~30 Regulatory questions on DDI over 24 month period from

~10 regulatory agencies

• Dominated by CYP inhibition, UGT inhibition / assay methodology,

induction eg CYP2B6 and transporter DDI (~50% of questions)

7

Enzyme Ramelteon Hepatic fm

Fluvoxamine Ki (µM)

CYP1A2 0.49 0.156

CYP2C19 0.42 0.24

CYP3A4 0.086 18

DDI Resulting from Inhibition – CYP vs UGT

Effect of Fluvoxamine (Perpetrator) on Ramelteon (Victim) Exposure

128-fold for AUC0-inf

70-fold for Cmax

Effect of Valproic Acid (Perpetrator) on Lamotrigine (Victim) Exposure

Enzyme Valproic Acid

Ki (µM)

UGT1A4 NA

UGT2B7 387

2.6-fold AUC0-inf

53% CL

Rowland A, et al. Drug Metab Dispos, 2006, 34:1055.

8

Can We Predict UGT Interactions?

SimCYP

PBPK models

Clearance fm CYP vs UGT – HLM, Hhep, and relay

Reaction Phenotyping

• Identification of isoform-selective inhibitors

• Incorporating absolute enzyme quantification and RAF/ISEF in rhUGT phenotyping

• Utilizing genotyped liver microsomes

Substrate-Specific Assays

Define NME inhibition potential

9

Development of UGT-Selective Probe Assays

• Developed LC/MS/MS

Assays for 5 major hepatic

UGTs

• Utilized authentic

glucuronide standards

• Characterized enzyme

kinetics at low protein

concentrations (0.025

mg/mL)

• Determined structure of

5HTOL-glucuronide by NMR

• Optimized incubation

conditions for buffer,

albumin, alamethicin,

saccharolactone

Assay Utility

• High-throughput screening

of compounds as UGT

inhibitors (UGT1A3 (CDCA,

2B15 - Oxazepam)

• Regulatory Evaluation of

NCE as UGT inhibitor

Walsky RL, et al. Drug Metab Dispos, 2012, 40:1051

10

Absolute Quantification of UGTs in HLM

Fallon et al.; J Prot Res, 2013, 12:4402.

“Major” hepatic UGTs in terms of drug metabolism are UGTs

1A1, 1A3, 1A4, 1A6, 1A9, 2B7, 2B15

11

Absolute Quantification of UGTs in HIM & HKM

Fallon et al.; Drug Metab Dispos, 2013, 41:2076.

12

Raloxifene Subject to Significant Intestinal Extraction

Gut wall

To Faeces

Portal vein Liver

100%

Gut lumen Fa = 0.63

Fgut = 0.054 UGT1A1, 1A8, 1A9 & 1A10

Fh = 0.59 UGT1A1 & 1A9

Raloxifene Metabolism (Label): Raloxifene undergoes extensive first-pass metabolism to the glucuronide conjugates. No other metabolites have been detected, providing strong evidence that raloxifene is not metabolized by cytochrome P450 pathways. Unconjugated raloxifene comprises less than 1% of the total radiolabeled material in plasma.

Fabs x Fgut x Fhep = Foral

0.63 x 0.054 x 0.59 = 0.02

Mizuma; Int J Pharm, 2009, 13:378.

What is the DDI potential if raloxifene is co-administered with a potent UGT1A1 inhibitor (eg atazanavir,

sorafenib, zafirlukast)

13

Intestinal UGTs Metabolizing Raloxifene

Fallon et al.; Drug Metab Dispos, 2013, 41:2076.

14

Fg for Drug Predominantly Cleared by UGT

No obvious drugs with Fg < 0.78 and UGT metabolism mainly by UGT1A1 or UGT2B7

15

UGT1A1 Genotype Impacting PK (Raloxifene)

UGT1A1 Genotype AUC Ratio (fold)

Fiboflapon 4.5

Belinostat 2.1

Ezetimibe 1.8

Febuxostat 1.3

Deferasirox 3.9 (Cmin)

Raltegravir 2.5 (Cmin)

16

Dapaglifozin PBPK Model

• Dapagliflozin (Farxiga® - BMS/AZ) is a selective sodium-glucose

cotransporter 2 (SGLT2) inhibitor indicated for type 2 diabetes mellitus.

• Dapagliflozin is primarily metabolized by UGT1A9 to its major

inactive metabolite, dapagliflozin 3-O-glucuronide(D3OG).

• Minor clearance routes: UGT2B7 (dapagliflozin 2-O-glucuronide),

cytochrome P450, renal.

• Mefenamic acid (UGT1A9 inhibitor) DDI data were available and the

primary goals were to investigate UGT1A9 IVIVE and prediction of

mefenamic acid DDI.

17

Clinical Data on Dapagliflozin

• Intravenous clearance data for dapagliflozin (14.1 L/h) following 14C

microdose resulted in absolute bioavailability (F=78%) and accordingly

Fa of dapagliflozin is >90%.

• Fractional metabolism by UGT1A9 is high – Fm UGT1A9 = 82%.

• Assumed 12% unchanged parent in feces from human mass balance

studies was absorbed and metabolized to D3OG, excreted into bile and

then converted back into the unconjugated dapaglifozin in intestine.

• Oxidative metabolism is low (9% if feces).

Boulton et al., Br J Clin Pharmacol 2013 75:763.

18

Recombinant UGT Phenotyping and Chemical Inhibitors in HLM and HKM

HLM Control Digoxin

(10 µM) Tranilast

(10 µM)

CLint (µL/min/mg) 3.48 0.37 0.22

% Inhibition or Fm 89 94

Incubate 1000 µM Dapagliflozin with 13 recombinant UGTs

UGT1A9 is Primary Isoform

HKM Control Digoxin (10 µM)

Tranilast (10 µM)

CLint (µL/min/mg) 10.4 0.75 0.52

% Inhibition or Fm 93 95

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In Vitro Enzyme Kinetic Parameters

In vitro assay, 2% albumin was used to limit impact of inhibitory fatty acids on CLint

ENZYME Km Km,u Vmax CLint,u

µM µM pmol/min/mg µL/min/mg

HLM 84.8 12 563 47 HKM 132 18 1717 93

HIM 85.2 12 8.5 0.7 rUGT1A9 180 25 1228 49

With 2% BSA

ENZYME Km Km,u Vmax CLint,u

µM µM pmol/min/mg µL/min/mg

HLM 387 337 1710 5 HKM 173 151 2479 16

HIM 201 175 6.1 0.035

rUGT1A9 83 72 1213 17

Without 2% BSA

Inclusion of BSA increased in vitro estimates of unbound CLint and

accordingly improved IVIVE

20

Dapagliflozin Simcyp Input Parameters for Clearance

Elimination

Parameters values source

HLM (UGT1A9) µL/min/mg

36 ~80% D3OG Human PK best fit;

HLM + rUGT data

HLM (UGT2B7) µL/min/mg

4 ~10% D2OG Human PK best fit;

HLM + rUGT data

Additional HLM µL/min/mg

4 ~10% oxidative metabolism from

human mass balance

CLR (L/h) 0.2 Clinical PK

Retrograde CLint from in vivo PK CLint from HLM and rUGT

Dose CLp

(mL/min/kg) CLb

(mL/min/kg) CLint

(µL/min/mg)

PO (10 mg) 4.4 5.0 44

in vitro system CLint

(µL/min/mg)

HLM 47.4

rUGT1A9 RAF: 0.4 19.4

RAF: 0.8 39.4

Fa*Fg=0.9

HLM Enzyme Kinetics with Appropriate fm UGT1A9 and UGT2B7

21

Concordance Between Predicted and Observed PK

Tmax (h) Cmax (ng/mL) AUC (ng/mL.h)

Pred 0.61 131 529

Obs 0.75 147 539

UGT1A9 Liver

UGT1A9 Kidney

UGT2B7 Liver

UGT2B7 Kidney

Additional HLM

Renal

Mean 68.6 12.7 8.1 0.44 8.4 1.8

Median 70.5 10.8 7.2 0.32 8.0 1.6

Geometric Mean

67.3 10.2 7.3 0.34 7.7 1.6

Median % fm and fe in absence of inhibitor(s)

UGT1A9 Liver

UGT1A9 Kidney

UGT2B7 Liver

UGT2B7 Kidney

Additional HLM

Renal

Based on the current Simcyp model predictions

UGT1A9 contributes 78% of total clearance

22

Mefenamic Acid 500 mg PO Predicted and Observed Pharmacokinetics

• Mefenamic acid, 500 mg (PO) then 250 mg (PO) every 6 h (15 doses) for 4 days • Day 2, dapagliflozin 10 mg (PO) • 10 trial x 10 subjects

Clinical Trial Design

23

Mefenamic Acid - In Vitro and In Vivo Ki for UGT1A9

Input Parameter: Ki (UGT1A9) = 0.3 uM

Assume Competitive Inhibition Ki = 5.5 µM

Fu (mefanamic acid) with

2% BSA = 0.038

Free Ki = 0.21 µM (in-vitro)

In-vivo Ki estimated using sensitivity

analysis Mefenamic Acid IC50 = 11 µM

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Dapagliflozin Co-Adminstration with Mefenamic Acid Predicted and Observed DDI

Predicted DDI

Observed DDI

Geometric mean 90% CI

CMax ratio 1.13 1.12 1.14

AUC ratio 1.56 1.58 1.54

Geometric mean 90% CI

CMax ratio 1.13 1.03 1.24

AUC ratio 1.51 1.44 1.58

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Clinical Induction of UGTs by Rifampin

Drug UGTs % Change in AUC

Morphine 2B7, 2B4 - 27

Codeine 2B7 = 2B4 - 81

Zidovudine (AZT) 2B7 - 50

Lorazepam 2B7 = 2B15 55 ( CL)

Mycophenolic Acid 1A9 - 27

26

In Vitro Induction - TaqMan® Low Density Arrays

• Rifampin (0.04 – 10 µM) induced UGTs in

cryopreserved HHEP in a sandwich culture system

Little effect on UGT1A9 mRNA; need to study catalytic activity

27

Rifampin Induction of UGT2B4 fm CYP UGTs Clinical DDI w/ Rifampin

Parent AUC

Metabolite M/P AUCR

-Rif +Rif

Dapa 0.11 1A9, 2B7 0.78 D3OG (1A9) 0.8 0.9

Cana 0.13 1A9, 2B4 0.49 M7 (1A9) 0.69 0.95

M5 (2B4) 0.73 1.58

- Canagliflozin exhibited 2-fold increase in M5 metabolite/parent ratio - M5 is formed by UGT2B4, indicative of UGT induction

- Dapagliflozin mainly matabolized by UGT1A9 with no

significant UGT2B4 contribution

- CYP contribution (~10%) could result in clinical induction (eg linezolid)

28

Conclusions

• Clinical studies are required to identify sensitive

UGT substrates and inhibitors

• PBPK modeling is useful to understand UGT-

mediated disposition – Models need to consider entero-hepatic recirculation of aglycone

• In vitro tools require further development (eg in

vitro induction and evaluation of catalytic activity)

• Important to measure drug metabolites (eg CYP

vs UGT) in clinical induction studies to increase

mechanistic understanding of DDI

29

Acknowledgements

Pfizer

Kim Lapham

Jon Bauman

Bob Walsky*

Alyssa Dantonio

Ernesto Callegari

Mark Niosi

Jian Lin

Christine Orozco

Karine Bourcier*

Georgina Giddens*

Ruth Hyland*

Louis Leung

Scott Obach

Larry Tremaine

University of North Carolina

Phil Smith

John Fallon

Thank you for your kind

attention!

SimCYP

Helen Humphries

Kim Crewe

Karen Rowland-Yeo

31

Characterization of UGT Inhibition as a Necessary and Important Strategy in Drug Development (Theunis C. Goosen, Pfizer Inc.; Groton, CT) Assessment of human UDP-glucuronosyltransferase (UGT) drug-drug interaction (DDI) liability, both as perpetrator and victim, is highlighted in regulatory agency DDI guidance for pharmaceutical industry. This emerging guidance reflects the scientific advances in in vitro assays developed to characterize UGT inhibition. Nevertheless, there are potential limitations in our ability to assess the contribution of UGTs to overall metabolic drug clearance and eventual clinical DDI interaction risk. Since an increasing number of new chemical entities are primarily cleared through UGT-mediated metabolism, establishing the clinical relevance of UGT-mediated DDI is becoming significant through all phases of drug development. Reagents and protocols need to be established and a validation set of compounds with clearance or DDI perpetration analyzed for in vitro-in vivo extrapolation (IVIVE). In addition, the utility of modeling and simulation approaches, including physiologically-based pharmacokinetic (PBPK) models are increasingly evident in the prediction of the DDI risk profile of drugs cleared by UGTs. Application of PBPK modeling concepts will be illustrated using drug development examples to demonstrate the diversity in scientific and regulatory points of view in modeling and simulation to address UGT-mediated DDI in new drug development.

Abstract