Comprehensive PBPK Modeling of Rifampicin for Predicting

Complex Drug-drug Interactions Considering Various Enzyme

Inductions and OATP inhibition/induction effects

.

Yuichi Sugiyama

Head of Sugiyama Laboratory, RIKEN Baton Zone Program, RIKEN Cluster for Science, Technology and Innovation Hub,

RIKEN, Yokohama

March 19, 2019Delaware Valley Drug Metabolism Discussion Group (DVDMDG) “Transporter and ADMET/DDI" one-day symposium”

Background and Purpose

• Rifampicin is a well-known inducer and inhibitor of drug transporters and metabolic enzymes, and clinically relevant drug-drug interactions (DDIs) associated with rifampicin have been reported.

• Prediction of the multi-mechanism DDIs has been a challenge because the timing, duration, and route of rifampicin dosing can affect the magnitude of DDIs.

• In this study, we aimed to construct a comprehensive PBPK model of rifampicin that can predict CYP- and OATP1B-mediated DDIs. In particular, multiple aspects of rifampicin pharmacokinetics (saturable hepatic uptake, auto-induction, induction effects for CYP3A/CYP2C9/CYP2C8/OATP1B, and inhibition effects for OATP1B/MRP2) were incorporated into an unified PBPK model of rifampicin.

1

Key parameters for quantitative DDI prediction

2

1 +[I]Ki

Emax・[I]EC50 + [I]

Kinact・[I]Kiapp + [I]

Reversible inhibition

IrreversibleInhibition (MBI etc)

Induction

Enzyme

biosynthesis

degradation

Perpetrator drug* DDI parameters* Inducer/inhibitor

conc.time profile

Liver（CLh/Fh），Kidney（CLr），GI （Fg and/or Fa）

Metabolism（fm）

Hepatic transporter(active transport/passive diffusion）

Victim drug

* fm, ftransporter

[I]: dynamically changed with time

An unified PBPK model which considers the time dependent change in rifampicin as an inhibitor/inducer and substrates as victim will be established.

Based on thus established model, the prediction of complex DDIs in which induction and inhibition take places simultaneously will be attempted under different dose and dosing schedules.

Glibenclamide(OATP1B/CYP2C9/CYP3A)

Repaglinide(OATP1B/CYP2C8/CYP3A)

Coproporphyrin I(OATP1B/MRP2)

Rifampicin (Perpetrator)

Non-linearity Auto-induction

3

OATP1B

CYP3A

CYP2C9

CYP2C8

Midazolam

Tolbutamide

Pioglitazone

Pravastatin

InhibitionInduction

Estimation for non-linearity and auto-induction1 Estimation for

DDI parameters2 Predictions for complex DDIs3

Overview: PBPK model of rifampicin to predict complex DDIs

MRP2 14C-TIC

Non-linearity and auto-induction profiles of rifampicin

4

J Pharmacol Exp Ther. 2003; 304(1):223-8

Urine Feces RecoveryRifampicin 67 mg 20 mg 19%

Desacetyl rifampicin 34 mg 25 mg 13%Water-soluble metabolites

(Glucuronides) 32 mg 216 mg 55%

Recovery 30% 58% 88%

Kekkaku, 1981 56(12), p577-586fmUGT was assumed to be 0.76 (= (32+216) / 327).

Non-linearity

Auto-induction

Uptake study using OATP1B1 expressing cells

Excretion study and UGT metabolism

Dose (mg)

Cmax (uM)

Unbound Cmax (uM)

600 17 1.2

450 8.9 0.62

300 6.6 0.46

150 3.0 0.22

A Symposium on Rimactane. Nov 1st, 1968. by W. Riess

Clin. Pharmacokinet. 3, 108–127 (1978).

Km: 1.5±0.6 μM

Model structure and parameters of rifampicin

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Physiological parameters such as tissue volume or blood flow were used for previously reported values.PBPK analyses were performed using Napp (Numeric Analysis Program for Pharmacokinetics) version 2.31.

Hepatic disposition of rifampicin and rate-limiting step of elimination

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𝐂𝐂𝐂𝐂𝐢𝐢𝐢𝐢𝐢𝐢,𝐚𝐚𝐚𝐚𝐚𝐚 = 𝐏𝐏𝐏𝐏𝐚𝐚𝐚𝐚𝐢𝐢,𝐢𝐢𝐢𝐢𝐢𝐢 + 𝐏𝐏𝐏𝐏𝐝𝐝𝐢𝐢𝐢𝐢,𝐢𝐢𝐢𝐢𝐢𝐢 ×𝐂𝐂𝐂𝐂𝐢𝐢𝐢𝐢𝐢𝐢,𝐦𝐦𝐦𝐦𝐢𝐢

𝐏𝐏𝐏𝐏𝐝𝐝𝐢𝐢𝐢𝐢,𝐦𝐦𝐢𝐢𝐢𝐢 + 𝐂𝐂𝐂𝐂𝐢𝐢𝐢𝐢𝐢𝐢,𝐦𝐦𝐦𝐦𝐢𝐢

PSinflux

Extended clearance conceptFractional elimination from the inside of hepatocytes

fmUGT: 0.76fmOthers: 0.24

OATPs

fHPSefflux1 : 1.3

fHCLint,metabolism

β : 1-β (β: 0.2, 0.5 or 0.8)

Qhepatic

Hepatocytes

Hepatic extracellular space

fBPSinflux~8 : 1

β

Metabolism limited

Uptake limited

Case 1 (metabol limited)PSdif,eff >> CLint,met

Case 2 (uptake limited)PSdif,eff << CLint,met

0 10.50.2 0.8

CLint,metPSdif,eff

CLint,metCLint,met

β value

Uptake limited

Sensitivity analysisfor three values (0.2, 0.5 and 0.8)

Parameter estimation of saturable hepatic uptake of rifampicin

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Km value for hepatic uptake was determined to be 0.18 μmol/L (ca. 8 fold lower than in vitro Km value)

1) Calculated or estimated from PK profile at 150mg dose, 2) JPET 304:223–228, 2003.

Figure shown using β of 0.2

Dose (mg)AUC (ug*h/mL)

Observed Optimized

600 56 60450 44 40300 23 22150 8.7 8.6

A Symposium on Rimactane. Nov 1st, 1968. by W. Riess

Rifampicin Initial Fitted (Mean±SD)β＝0.2 β＝0.5 β＝0.8

ka (/h) 3.3 1) 38±18 894±376651 978±473726Lag time (h) 0.45 1) 0.46±0.01 0.46±0.46 0.47±0.49

fbCLint,all (L/h/kg) 0.20 1) 0.30±0.03 0.31±0.03 0.31±0.03Unbound Km

(ug/mL)1.2 2)

(1.5 µM)0.15±0.05(0.18 µM)

0.15±0.05(0.18 µM)

0.15±0.05(0.19 µM)

PSdif,ent (L/h/kg) 0.14 2.3±2.3 0.067±0.681 0.066±0.689

Estimation of auto-induction parameters of rifampicin

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kdeg values of UGT in liver and intestine are assumed to be equal to that of CYP3A4.

Fitted (Mean±SD)β＝0.2

Lag time (h) 0.25±0.00fbCLint,all (L/h/kg) 0.25±0.03

Emax for UGT 1.3±0.5PSdif,ent (L/h/kg) 0.16±0.08

Observed data: Chemotherapy. 16: 356-370 (1971)

EC50 value (~50 ng/mL) for CYP3A induction by rifampicin was assumed to be equal to that for auto-induced UGT.

Emax and EC50 values for auto-induction were determined to be 1.3 and 64 nmol/L.

1st step: EC50 estimation

2nd step: Emax estimation

Fitted (Mean±SD)

EC50 for CYP3A 53±8 ng/mL(64 nmol/L)

Emax for CYP3A 4.3±2.1

DDI data Midazolam (p.o., 3 mg) Rifampicin (p.o., 5 days, 0-75 mg/day)

Auto-induction data p.o., 14 days, 300-900 mg/dose

Observed data: Clin. Pharmacol. Ther. 90, 100–108 (2011)

Estimated unbound concentration-time profiles of rifampicin after the last dose of oral repeated rifampicin dosing (600 mg)

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Concentrative hepatic uptake of rifampicin and its saturation were adequately incorporated into the rifampicin model.

CPT Pharmacometrics Syst. Pharmacol. 7, 186-196 (2018).

Blood

Hepatocytes

Enterocytes

Km for hepatic uptake: 0.15 μg/mL

EC50 for auto-induction: 0.053 μg/mL

Comparison of predicted and observed blood concentration-time profiles of rifampicin after single oral or intravenous dosing

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CPT Pharmacometrics Syst. Pharmacol. 7, 186-196 (2018).

Oral dose

Infusion dose

100 mg 250 mg 300 mg150 mg

750 mg 900 mg600 mg

300 mg 600 mg

450 mg 600 mg300 mg

450 mg

150 mg

● Furesz et al. (1967).● Acocella. (1978).● RIFADIN® (Japanese IF).● Acocella et al. (1971).● Lai et al. (2016).● Prueksaritanont et al. (2014).● Peloquin et al. (1997).● Kohno et al. (1982).● Riess. (1968).

■ Acocella et al. (1977).■ RIFADIN® (prescribing information).■ Prueksaritanont et al. (2014).■ Lau et al. (2007).

Predicted profiles of rifampicin reasonably captured the observed ones.

Glibenclamide(OATP1B/CYP2C9/CYP3A)

Repaglinide(OATP1B/CYP2C8/CYP3A)

Coproporphyrin I(OATP1B/MRP2)

Rifampicin (Perpetrator)

Non-linearity Auto-induction

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OATP1B

CYP3A

CYP2C9

CYP2C8

Midazolam

Tolbutamide

Pioglitazone

Pravastatin

InhibitionInduction

Estimation for non-linearity and auto-induction1 Estimation for

DDI parameters2 Predictions for complex DDIs3

MRP2 14C-TIC

Estimated Emax:4.6

Estimation of CYP3A induction parameter of rifampicin

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Observed data: Clin Pharmacol Ther. 2003; 74, 275-287

AUCR: 0.45(midazolam: infusion)

AUCR: 0.07(midazolam: oral)

After RIF 600 mg (6 days)Midazolam (control)

CYP3A fm: 0.93Others fm: 0.07

fBCLint

Liver

Fg was described by Qgut model.

EC50 value: 53 ng/mL (64 nM) CYP3A4 kdeg,liver: 0.0158 /h

Mol Pharmacol 41:1047-55,1992 CYP3A4 kdeg,enterocyte: 0.0288 /h

DMD,37:1658-1666,2009

RIF

Induction

Midazolam(Fa = 1, Fg = 0.47, Fh = 0.68)

fBCLint = 0.53 L/h/kg

Estimation of CYP2C9 and CYP2C8 induction parameters of rifampicin

CYP2C9 induction

Observed data: Europ. J. Clin. Pharmacol. 9: 219-227 (1975)

CYP2C8 induction

Observed data: Br J Clin Pharmacol. 61: 70-78 (2005)

CYP2C9 fm: 0.98Others fm: 0.02

Liver

Pioglitazone(FaFg = 0.85, Fh = 0.97) fBCLint = 0.050 L/h/kg

Tolbutamide(Fh = 0.99)

fBCLint = 0.015 L/h/kg

fBCLint

Tolbutamide (infusion, control)

After RIF 600 mg (5 days)Pioglitazon (p.o., control)

AUCR: 0.45

AUCR: 0.46

Estimated Emax:2.4

Estimated Emax:2.6

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CYP2C8 fm: 0.84CYP3A fm: 0.16

Liver

fBCLint

RIFInduction

After RIF 1200 mg (8 days)

RIFInduction

Changes of CYP and transporter activities under rifampicin treatment

Rifampicin (600 mg) PO dosing

CYP3A (enterocytes), kdeg (0.0288 /h)Emax (4.57)

CYP2C9 (liver), kdeg (0.00666 /h)Emax (2.41)

CYP2C8 (liver), kdeg (0.0301 /h)Emax (2.55)

CYP3A (liver), kdeg (0.0158 /h)Emax (4.57)

Common EC50 value (64 nM) for OATP1B and CYP isoforms was used.15

OATP1B (liver), kdeg (0.0158 /h)Emax (2.3), Ki (0.19 μM)1)

MRP2 (liver) Ki (0.87 μM) 2)

CYP activities

Transporter activities

1) Yoshikado and Yoshida et al. CPT. 100, 513–523 (2016).2) Yoshikado et al. CPT Pharmacometrics Syst Pharmacol.

7, 739-747 (2018).

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The induction and inhibition parameters of rifampicin were obtained by fitting to clinical DDI data with each probe substrate for CYP3A (midazolam), CYP2C9 (tolbutamide), CYP2C8 (pioglitazone), OATP1B (pravastatin), and MRP2 (11C-TIC-Me).

Thereafter, complex DDIs with glibenclamide (a substrate for CYP2C9, CYP3A and OATP1B) and repaglinide (a substrate for CYP2C8, CYP3A and OATP1B) were finally predicted and compared with the observed data to verify the established PBPK model of rifampicin.

Predicted DDIs with glibenclamideMet limited: Noβ value: 0 (Low) 1 (High)

Predicted, not optimized!

Yes

Overall, when glibenclamide β value was 0.2-0.5, rifampicin PBPK model quantitatively predicted DDIs with glibenclamide in each dosing condition.

Observed data: Int. J. Clin. Pharmacol. Ther. Toxicol. 23, 453–460 (1985).

CYP2C9 fm: >0.85CYP3A fm: < 0.15

OATP1B

1 : 4

fHCLmet

Hepatocytes

β : 1-β (β: 0.2, 0.5 or 0.8)

Glib

encl

amid

e A

UC

R

18

RIF

Induction

Glibenclamide(Fa = 1, Fg = 0.85, Fh = 0.89)

fBCLint,all = 0.12 L/h/kg

4 : 1 β0.2 0.5 0.8

OATP1BHepatocytes

Predicted DDIs with repaglinide

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Met limited: Noβ value: 0 (Low) 1 (High)

Yes

Predicted, not optimized!

Predicted AUCRs of repaglinide with the β value of 0.2-0.5 were mostly in the range of the observed AUCRs in all the dosing conditions of rifampicin.

Rep

aglin

ide

AU

CR

Kim, S. et al., J Pharm Sci. 106: 2715-2726 (2017).

Repaglinide(FaFg = 1, Fh = ~0.5)

fBCLint,all = 1.03 L/h/kg

1 : 1.72.6 : 1

1 : 2

CYP2C8 fm: ~0.8 CYP3A fm: ~0.2

fHCLmet+bile(fbile = 0.21)

RIF

Induction

β0.2 0.5 0.8

Structure of PBPK models for CP-I and rifampicin

CP-I Rifampicin

vsyn

The basic model structure for OATP1Bs substrates was reported previously (Yoshikado et al., Clin Pharmacol Ther 100:513-523, 2016).

The biosynthesis rate (vsyn) of CP-I is incorporated. Yoshikado et al. (2018) CPT-PSP

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Asaumi et al., CPT-PSP 7:186-196, 2018

Prediction of OATP-mediated DDIs using endogenous substrates of OATP1Bs; the study has been already published by Takehara I et al. (Pharm Res., 35:138, 2018) and the PBPK model based analyses

Yoshikado et al., CPT Pharmacometrics Syst Pharmacol. 7:739-747 (2018)

Biomarker for OATP1B, coproporphiline (CP-I)

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19

β = 0.8

β = 0.2

Parameters optimization by nonlinear least-squares fitting of CP-I bloodconcentration (OATP1Bs and MRP2 inhibitions were taken into account)

Three different preset β β = CLint/(PSeff+CLint)

Optimized parameters: CLint,all, FaFg (vsyn) and Ki,u,OATP1Bs

+RIF (600 mg)+RIF (300 mg)Control

Parameter Unit Value

β - 0.8 0.5 0.2

Rdif - 0.035

γ - 0.020

fbile - 0.84

ktransit h-1 5.2

ka h-1 3.0

CLint,all L/h/kg

39.6 42.9 47.7

vsynnmol/h/kg 0.45 0.27 0.22

FaFg - 0.29 0.35 0.36

Ki,u,OATP1Bs μM 0.085 0.100 0.111

Ki,u,MRP2 μM 0.87

Observations: Takehara I et al., Pharm Res., 35:138 (2018) β = 0.5

Using Napp ver. 2.31

Initial in vivo Ki,u,OATP1Bs: 0.23 μM (for pitavastatin)

Step 1 phase I trial of New Chemical Entity

(NCE)

CP-I levels

time

conc doses

of NCE

Obtain in vivo Ki,OATP1Bs (CP-I) using

CP-I (an endogenous probe)

Step 2

in vitro transport study to obtain inhibitory potency of NCE

using CP-I and a probe drug

PBPK modeling-based simulations

in vitro Ki,OATP1Bs (Drug)

in vitro Ki,OATP1Bs (CP-I)

in vivo Ki,OATP1Bs (CP-I)

Xin vivo Ki,OATP1Bs (Drug)

in vitro Ki,OATP1Bs (CP-I)

in vitro Ki,OATP1Bs (Drug) =

Step 3

Obtain

Quantitative prediction of the impact of NCE on the pharmacokinetics of a probe drug (concentration-time profiles, AUC, Cmax)

Strategy to predict DDI for a probe substrate using CP-I as an endogenous biomarker

Yoshikado T, Toshimoto K, Maeda K, Kusuhara H, Kimoto E, Rodrigues AD, Chiba K,SugiyamaY. PBPK Modeling of Coproporphyrin I as an Endogenous Biomarker for Drug Interactions Involving Inhibition of Hepatic OATP1B1 and OATP1B3.

CPT Pharmacometrics Syst Pharmacol. 7:739-747 (2018)

Flowchart: Thanks to Dr. Wooin Lee

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Pitavastatin (Ki,u = 0.22 μM)

21

Rosuvastatin (Ki,u = 0.14 μM)

Atorvastatin (Ki,u = 0.28 μM) Fluvastatin (Ki,u = 0.35 μM)

+RIF (600 mg)+RIF (300 mg)Control

Prediction of the effect of RIF on blood concentration-time profiles of statins(Correction of in vivo Ki,uOATP1Bs based on substrate-dependent difference of in vitro Ki,u)

Observations: Takehara I et al., Pharm Res., 35:138 (2018)

Predicted and observed AUC and Cmax of statins in the absence and presence of RIF using our PBPK models

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Taking substrate-dependent Ki,u,OATP1Bs into consideration

Observations: Takehara I et al., Pharm Res., 35:138 (2018)

Summary

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Our established PBPK model demonstrate the robustness and utility to quantitatively predict transporter- and metabolic enzyme-mediated DDIs with other victim drugs.

Glibenclamide(OATP1B/CYP2C9/CYP3A)

Repaglinide(OATP1B/CYP2C8/CYP3A)

Coproporphyrin I(OATP1B/MRP2)

Rifampicin (Perpetrator)

Non-linearity Auto-induction OATP1B

CYP3A

CYP2C9

CYP2C8

Midazolam

Tolbutamide

Pioglitazone

Pravastatin

InhibitionInduction

MRP2 14C-TIC

Other mechanism DDIs with cyclosporin A and gemfibrozil

CYP2C9 polymorphism

The PBPK model established based on many clinical studies for probe

substrates (CYP3A4, CYP2C9, CYP2C8, OATP1B1, MRP2) was capable of

accurately predicting complex rifampicin-induced alterations in the profiles

of diverse victim drugs and endogenous biomarkers handled by multiple

metabolizing enzymes and transporters such as glibenclamide, repaglinide,

and coproporphyrin I (an endogenous biomarker of OATP1B activities) with

various dosing regimens. In particular, the incorporation of OATP1B

induction may change the current practice of assessing DDI risk for

OATP1B substrates.

Our robust rifampicin PBPK model may enable quantitative prediction of

DDIs across diverse potential victim drugs and endogenous biomarkers

handled by multiple metabolizing enzymes and transporters.

Conclusion

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Acknowledgements

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Ryuta Asaumi (Ono Pharmaceutical Co., Ltd.)

Kota Toshimoto (RIKEN)

Wooin Lee（Seoul National University）

Karsten Menzel (Merck & Co., Inc)

Hiroyuki Kusuhara (Univ of Tokyo)

Yoshifusa Tobe, Ken-ichi Nunoya, Haruo Imawaka (Ono Pharmaceutical Co., Ltd.)