Citation: CPT Pharmacometrics Syst. Pharmacol. (2018) 7, 186–196; doi:10.1002/psp4.12275
All rights reserved

C 2018 ASCPTV

ARTICLE

Comprehensive PBPK Model of Rifampicin for
Quantitative Prediction of Complex Drug-Drug
Interactions: CYP3A/2C9 Induction and OATP
Inhibition Effects
Ryuta Asaumi1, Kota Toshimoto2, Yoshifusa Tobe1, Kenta Hashizume3, Ken-ichi Nunoya1, Haruo Imawaka1,
Wooin Lee4 and Yuichi Sugiyama2*

This study aimed to construct a physiologically based pharmacokinetic (PBPK) model of rifampicin that can accurately andquantitatively predict complex drug-drug interactions (DDIs) involving its saturable hepatic uptake and auto-induction. Usingin silico and in vitro parameters, and reported clinical pharmacokinetic data, rifampicin PBPK model was built and relevant
parameters for saturable hepatic uptake and UDP-glucuronosyltransferase (UGT) auto-induction were optimized by fitting. The
parameters for cytochrome P450 (CYP) 3A and CYP2C9 induction by rifampicin were similarly optimized using clinical DDI
data with midazolam and tolbutamide as probe substrates, respectively. For validation, our current PBPK model was applied
to simulate complex DDIs with glibenclamide (a substrate of CYP3A/2C9 and hepatic organic anion transporting polypeptides(OATPs)). Simulated results were in quite good accordance with the observed data. Altogether, our constructed PBPK modelof rifampicin demonstrates the robustness and utility in quantitatively predicting CYP3A/2C9 induction-mediated and/or OATP
inhibition-mediated DDIs with victim drugs.
CPT Pharmacometrics Syst. Pharmacol. (2018) 7, 186–196; doi:10.1002/psp4.12275; published online 25 January 2018.
Study Highlights
WHAT IS THE CURRENT KNOWLEDGE ON THE
TOPIC?
þ Rifampicin, an inducer of CYP3A/2C9 and an inhibitor of
OATPs, is associated with many DDIs. A well-constructed
PBPK model will allow for quantitative prediction of DDIs.
WHAT QUESTION DID THIS STUDY ADDRESS?
þ This study constructed a PBPK model of rifampicin
incorporating its saturable hepatic uptake and autoinduction to predict CYP3A/2C9 induction-mediated and
OATP inhibition-mediated DDIs. Relevant parameters ofrifampicin were obtained via stepwise optimization using
the nonlinear and auto-induction pharmacokinetic data and
DDI data with midazolam and tolbutamide as probe substrates. The obtained parameters were applied to simulate

complex DDIs with glibenclamide (a substrate for bothCYP3A/2C9 and OATPs).
WHAT DOES THIS STUDY ADD TO OURKNOWLEDGE?
þ The constructed PBPK model adequately captured
in vivo CYP3A/2C9 induction effects in the liver andsmall intestine, and successfully predicted the CYP3A/
2C9 induction-mediated and/or OATP inhibitionmediated DDIs with glibenclamide.
HOW MIGHT THIS CHANGE DRUG DISCOVERY,
DEVELOPMENT, AND/OR THERAPEUTICS?
þ This rifampicin PBPK model presents advancement
toward accurate prediction of DDIs with potential victim
drugs that are in clinical use or under development.

Rifampicin has been an essential part of tuberculosis therapy
for decades. The pharmacokinetics of rifampicin has long
been of interest due to its nonlinear and auto-induction behaviors.1,2 In addition, rifampicin acts as a perpetrator drug formany co-administered drugs, causing clinically relevant drugdrug interactions (DDIs) via induction of metabolic enzymes(e.g., cytochrome P450 (CYP), UDP-glucuronosyltransferases(UGTs)), and/or via inhibition of hepatic organic anion transporting polypeptides (OATPs).3,4 For instance, rifampicin induces CYP3A and CYP2C9, both of which are main CYP
isoforms handling oxidative metabolism for the majority of drug
molecules.3,5 With co-administration of repeated rifampicin

doses, drugs that are metabolized by CYP3A and/or
CYP2C9 displayed decreases in their systemic exposure andefficacy.3 On the other hand, statins (substrates of OATPs),
upon co-administration with rifampicin, were shown to have an
increased systemic exposure (mostly via OATP inhibition),6,7
thereby increasing the risk for serious adverse events, suchas rhabdomyolysis. Cases of complex DDIs have also beenreported between rifampicin and co-administered drugs,
which are substrates for both metabolic enzymes and
OATPs.8,9 Given that the magnitude of DDIs may welldepend on the route, duration, and timing of rifampicin
administration, there has been an increasing need for1
Pharmacokinetic Research Laboratories, Ono Pharmaceutical Co., Ltd., Tsukuba, Ibaraki, Japan; 2Sugiyama Laboratory, RIKEN Innovation Center, RIKEN, Yokohama,
Kanagawa, Japan; 3Drug Development Solutions Division, Sekisui Medical Co., Ltd., Ibaraki, Japan; 4College of Pharmacy and Research Institute of Pharmaceutical
Sciences, Seoul National University, Seoul, Korea. *Correspondence: Yuichi Sugiyama (ychi.sugiyama@riken.jp)
Received 2 October 2017; accepted 28 December 2017; published online on 25 January 2018. doi:10.1002/psp4.12275

Rifampicin PBPK Model to Predict Complex DDIs
Asaumi et al.
187

correctly and quantitatively predicting DDIs incurred byrifampicin administered in various dosing regimens.
Because DDI prediction using static models utilizes theoretical maximum concentrations of a perpetrator drug at the sitesof DDI evaluation, the risk for DDIs tends to be overestimated.
Thus, the use of physiologically based pharmacokinetic(PBPK) models has been recommended as a more quantitative approach.10,11 By considering the concentration-time profiles of both a perpetrator and a victim drug at the sites of DDI
evaluation, the PBPK models may offer mechanism-based
DDI predictions under various circumstances.
Construction of a PBPK model for DDI predictions involving
rifampicin requires two sets of parameters; one for predicting
concentration-time profiles of rifampicin in blood and tissues,and the other for predicting DDIs on pharmacokinetics of victim drugs. To properly set these parameters, the following key
information was gleaned from the literature.
Rifampicin displays a high bioavailability of 90% after asingle oral dose and it is primarily eliminated in the liver,12likely involving OATP-mediated uptake and UGT-mediatedmetabolism. Rifampicin is a substrate of OATP1B1 (withthe reported Michaelis-Menten constant (Km) value of
1.2 lg/mL) and OATP1B3.13 After single oral doses of 450
and 600 mg rifampicin, its maximum plasma concentrations(Cmax) were reported to be 7.3 and 14.4 lg/mL, respectively (the corresponding unbound Cmax of 0.51 and 1.0 lg/mL,
considering its unbound fraction in plasma of 0.07).14,15
Given that these unbound Cmax values are quite close to theKm value for OATP1B1, the saturation of hepatic uptake may
well contribute to nonlinear rifampicin pharmacokinetics.
After single oral dosing, rifampicin was excreted mainly aswater-soluble metabolites (probably, rifampicin glucuronides)
in feces or urine.16 The fractional metabolism (fm) value ofrifampicin for UGT was calculated to be 0.759 from the literature,16 suggesting that the UGT-mediated metabolism isinvolved in its auto-induction. Based on these data, we reasoned that the following parameters need to be incorporated
into our PBPK model of rifampicin: (i) parameters related tothe saturable hepatic uptake and UGT auto-induction; (ii)parameters to estimate unbound rifampicin concentrationtime profiles at the DDI sites (e.g., unbound fraction in hepatocytes and enterocytes); (iii) parameters related to the
induction of metabolic enzymes (the maximum induction
effect (Emax) and the concentration for the half maximum
induction effect (EC50)) and the inhibition of OATPs (the inhibition constant (Ki)).
For construction of PBPK models for victim drugs, it isimportant to adequately capture their elimination mechanisms. For a victim drug whose elimination involves both
hepatic uptake and metabolism, it is necessary to estimate
the fractional influx of an affected active uptake pathway as
well as the fractional metabolism of an affected metabolic
pathway. For example, the antidiabetic agent glibenclamide
is primarily eliminated from the liver via OATP-mediateduptake and subsequent metabolism by CYP3A andCYP2C9. Zheng et al.8 reported the case of complex DDIs
between glibenclamide and rifampicin, depending on the
duration and timing of rifampicin co-administration (involving
OATP inhibition and/or CYP3A/2C9 induction by rifampicin).
For the construction of a PBPK model of glibenclamide, theaccurate estimation for the contribution of hepatic active
uptake and passive diffusion, and CYP3A and CYP2C9
metabolism will be necessary.
In setting various parameters of perpetrator and victim
drugs, Yoshikado et al.15 proposed a global in vitro-in vivo
extrapolation method that combines the use of in vitro parameters and optimization of in vivo parameters, and successfully
implemented the proposed method for the analyses oftransporter-mediated DDIs. In the present study, we aimed toconstruct a comprehensive PBPK model of rifampicin that can
predict DDIs mediated by CYP3A/2C9 induction and/or OATP
inhibition using the global in vitro-in vivo extrapolation method.
Specifically, the rifampicin PBPK model was constructedbased on the in silico, in vitro, and in vivo data. Relevant
parameters of rifampicin related to the saturable hepatic
uptake and UGT auto-induction were optimized. Thereafter,parameters for CYP3A and CYP2C9 induction by rifampicin
were obtained by fitting to clinical DDI data with a probe substrate for CYP3A (midazolam) and CYP2C9 (tolbutamide),
respectively. Incorporating these induction parameters as wellas our previously reported OATP inhibition parameter of rifampicin,15 complex DDIs between rifampicin and glibenclamide
were finally simulated and compared with the observed data tovalidate the constructed PBPK model of rifampicin.
METHODS
Construction of PBPK models
The PBPK models were constructed and optimized using
the methods described in the previous report15 andNumeric Analysis Program for Pharmacokinetics, Napp(version 2.31).17 The nonlinear least-squares fitting wasused in the optimization processes with the weight for the
calculation set as none or the square root of the value. All
nomenclature and differential equations are shown in Supplementary Text.
The PBPK models of rifampicin, midazolam, tolbutamide,
and glibenclamide were constructed based on their in silico,in vitro, and clinical pharmacokinetic data available from theliterature (Figure 1). Each model included the central(blood) compartment, three large-volume tissues (muscle,skin, and adipose), and the liver, which was divided into
five compartments tandemly connected by hepatic bloodflow (5-liver model) to mimic the dispersion model. For
rifampicin and glibenclamide, each liver compartment was
further subdivided into extrahepatic and hepatocellular compartments to incorporate the hepatic uptake process of thedrugs. For rifampicin, absorption was described by a segregated flow model to accurately capture its unboundconcentration-time profile in enterocytes related to its metabolic enzyme induction.18 For midazolam and glibenclamide, absorption was described by Qgut model to take theirintestinal metabolism into consideration.19 Enterohepatic
circulation was not incorporated in any of the constructed
models, given that its contribution was negligible for rifampicin and not indicated for the other drugs.20
Determination of PBPK model parameters
Basic physiological and pharmacokinetic parameters wereobtained either from the previous reports or by calculations
www.psp-journal.com

Rifampicin PBPK Model to Predict Complex DDIs
Asaumi et al.
188

Figure 1 Structures of physiologically based pharmacokinetic (PBPK) models of rifampicin (a), midazolam (b), tolbutamide (b), and glibenclamide (c). CLint,E, intrinsic clearance of enterocyte metabolism; CLint,met, intrinsic clearance of hepatic metabolism; CLrenal, renal
clearance; Fa, fraction of dose absorbed from the gut lumen; fB, unbound fraction in blood; fE, unbound fraction in enterocytes; Fg, fraction available after intestinal metabolism; fH, unbound fraction in hepatocytes; HC, hepatocytes; HE, hepatic extracellular space; ka,absorption rate constant; PSact,inf, active uptake intrinsic clearance on sinusoidal membrane; PSdif,inf, influx intrinsic clearance by passive diffusion through sinusoidal membrane; PSdif,E, intrinsic clearance by passive diffusion through basolateral membrane of enterocytes; PSdif,eff, efflux intrinsic clearance by passive diffusion through sinusoidal membrane; Qtissue, blood flow rate in tissue; Tlag, lagtime in intestinal absorption. The rate constant for drug excretion from gut lumen to feces is represented as ka(1/Fa-1).

(see Supplementary Text, Supplementary Tables S1and S2). The b value is a hybrid parameter reflective of the
major rate-limiting steps of overall hepatic intrinsic clearance (CLint,all), as described in Eqs. 1 and 2 (the contribution of CLint,bile was deemed minimal for rifampicin andglibenclamide20). Due to the difficulties in estimating theparameter b from in vitro experiments, sensitivity analyses
were performed using three b values (0.2, 0.5, and 0.8) forthe models of rifampicin and glibenclamide.
CLint;all 5 PSact;inf 1PSdif;inf 3
b5

CLint;met
PSdif;eff 1CLint;met

CLint;met
PSdif;eff 1CLint;met

(1)
(2)

When CLint,met is much greater than PSdif,eff (high b), CLint,all
can be approximated as PSinf (equal to the sum of PSact,inf
CPT: Pharmacometrics & Systems Pharmacology

and PSdif,inf), and the rate-limiting step for CLint,all becomes
the hepatic uptake. In contrast, when CLint,met is negligible
compared to PSdif,eff (low b), CLint,all can be approximated as
PSinf 3 CLint,met/PSdif,eff and CLint,all is determined not only byhepatic uptake but also by metabolism. To reflect these different conditions, three b values were selected in this study. For
all analyses, dosing regimens were kept similar to the original
study designs reported.
The stepwise optimization of the necessary parameters
was carried out. Specifically, rifampicin parameters were
determined as follows. The distribution volume of rifampicin
was adjusted by applying a common scaling factor to in silico
Kp values of each tissue (SFKp) by simultaneous fitting to thereported blood rifampicin concentration-time profiles after
single intravenous doses of 300 and 600 mg rifampicin.21
The unbound Km (Km,u) value of rifampicin for hepatic uptake

Rifampicin PBPK Model to Predict Complex DDIs
Asaumi et al.
189
Table 1 Optimized parameters of rifampicin, midazolam, tolbutamide, and glibenclamide
Glibenclamide

Parameters

Rifampicinb 5 0.2

Midazolamka (/h)37.6 6 17.6

1.29 6 0.21

Tlag (h)0.255 6 0.000

0.031 6 0.008-

0.773 6 0.079

0.773 6 0.079

0.773 6 0.079

SFKpb

6.65 6 0.98

0.201 6 0.035

0.500 6 0.073

0.568 6 0.045

0.571 6 0.045

0.572 6 0.045

fBCLint,all or fBCLint (L/h/kg)

0.251 6 0.028

0.469 6 0.030

0.0151 6 0.0006

0.123 6 0.004

0.123 6 0.004

0.123 6 0.004

PSdif,E (L/h/kg)

0.161 6 0.079

-

-

-

-

-

Vcentral (L/kg)-

0.571 6 0.050

-

-

-

-

(ng/mL)146 6 45

-

-

-

-

-

(lmol/L)

0.177 6 0.055

-

-

-

-

-

(ng/mL)52.6 6 7.5

-

-

-

-

-

(lmol/L)

0.0639 6 0.0091

-

-

-

-

-

Km,u for hepatic uptakec
EC50,u for enzyme inductionaTolbutamideb 5 0.2

b 5 0.5b 5 0.8-0.445 6 0.053

0.445 6 0.053

0.445 6 0.053

Emax for UGT auto-induction

1.34 6 0.50

-

-

-

-

-

Emax for CYP3A induction

4.57 6 0.18

-

-

-

-

-

Emax for CYP2C9 induction

2.41 6 0.06

-

-

-

-

-

b 5 CLint,all/(PSact,inf1PSdif,inf); CLint, hepatic intrinsic clearance; CLint,all, overall hepatic intrinsic clearance; EC50,u, unbound concentration for half maximum
induction effect; Emax, maximum induction effect; fB, unbound fraction in blood; ka, absorption rate constant; Km,u, unbound Michaelis-Menten constant; Kp, tissue/blood concentration ratio; PSact,inf, active uptake intrinsic clearance on sinusoidal membrane; PSdif,inf, influx intrinsic clearance by passive diffusion throughsinusoidal membrane; PSdif,E, intrinsic clearance by passive diffusion through basolateral membrane of enterocytes; Rdif 5 PSdif,inf/PSact,inf; SFKp, common scaling factor for in silico Kp values in each tissue; Tlag, lag time in intestinal absorption; Vcentral, volume of central compartment; Vmax, maximum uptake rate.
Values are shown as the mean 6 SD.
aOptimized parameters of midazolam were obtained from the analyses of Figure 4b,c.
bInitial value was set as 1.
cCorresponding Vmax value was calculated to be 2,086 lg/h/kg based on the following equation; Vmax 5 Km,u/(1 1 Rdif) 3 CLint,all/b.

was estimated using the nonlinear blood concentration-time
profiles after single oral doses of 150–600 mg rifampicin.1
For the auto-induction parameters, unbound EC50 (EC50,u)and Emax values for CYP3A induction by rifampicin were firstestimated using blood midazolam concentration-time profiles
after repeated oral rifampicin doses of 5–75 mg once daily.22
The obtained EC50,u value for CYP3A induction was assumed
to be equal to that for UGT auto-induction, based on the common involvement of pregnane X receptor-mediated induction
mechanism (i.e., EC50,u values reflecting the binding affinityof rifampicin to the pregnane X receptor may be shared for
induction of metabolic enzymes).3 The Emax value for UGTauto-induction by rifampicin was estimated using its bloodconcentration-time profiles during 14-day repeated oral dosing of 300–900 mg rifampicin.2 Additional rifampicin-relatedparameters (ka, Tlag, fBCLint,all, and PSdif,E) were optimized asnecessary, whereas the rest of the parameters of rifampicin
were set as fixed.
The estimation processes of the aforementioned EC50,u andEmax values for CYP3A induction were as follows. Midazolam
parameters (ka, Tlag, SFKp, fBCLint, and Vcentral) were firstestimated using the blood midazolam concentration-time
profiles after single intravenous and oral doses of midazolam
only (i.e., control condition). Thereafter, the EC50,u and Emax
values for CYP3A induction were estimated using the blood
midazolam concentration-time profiles after rifampicin treatment (i.e., DDI condition). Specifically, the EC50,u and Emax
values were determined using the DDI data of midazolam
after a wide range of rifampicin doses (5–75 mg).22 Using
the obtained EC50,u value as fixed, the Emax value for UGTauto-induction was estimated. Considering that the Emax
value for CYP3A induction might not be adequately captured

with low rifampicin doses, the Emax value was estimatedagain using the different DDI data of midazolam after a highrifampicin dose (600 mg).23 In these analyses of the DDI
conditions, all of midazolam-related parameters were set as
fixed with those obtained under control conditions. All ofrifampicin-related parameters except for EC50,u and Emax
were fixed with those obtained from auto-induction cases.
For the Emax value for CYP2C9 induction by rifampicin, similar stepwise optimization was performed using the DDI data
between rifampicin and tolbutamide.24
Validation of the constructed PBPK models
To validate the constructed PBPK model of rifampicin, thefollowing simulations were performed and compared withthe observed data: (i) simulations of the blood rifampicin
concentration-time profiles after single oral or intravenous
doses of 100–900 mg rifampicin; (ii) simulations of the CYP3A
induction-mediated DDIs between rifampicin and midazolam;and (iii) simulations of the CYP3A/2C9 induction-mediated and
OATP inhibition-mediated DDIs between rifampicin andglibenclamide.
RESULTS
Determination of rifampicin parameters for saturable
hepatic uptake and auto-induction
Several rifampicin-related parameters were obtained by
stepwise optimization (the results are summarized in Table 1).
To adjust the distribution volume of rifampicin, the SFKp valueof rifampicin was estimated using the observed bloodconcentration-time profiles after single intravenous doses ofrifampicin (Figure 2a).21 The Km,u value for hepatic uptakeof rifampicin was determined to be 146 ng/mL using thewww.psp-journal.com

Rifampicin PBPK Model to Predict Complex DDIs
Asaumi et al.
190

Figure 2 Optimized and observed rifampicin concentration-time profiles in blood and unbound rifampicin concentration-time profiles inblood, hepatocytes, and enterocytes after various rifampicin dosing regimens. The lines and closed circles represent optimized andobserved concentration–time profiles of rifampicin, respectively. (a) Blood rifampicin concentration–time profiles after single intravenous
infusion doses of 300 mg (blue) or 600 mg (red) rifampicin.21 Observed data were shown as mean 1 SD. (b) Blood rifampicin concentration–time profiles after single oral doses of 150 mg (black), 300 mg (blue), 450 mg (green), or 600 mg (red) rifampicin.1 (c) Bloodrifampicin concentration–time profiles during 14-day repeated oral doses of rifampicin 300 mg twice daily (black), 600 mg once daily(green), or 900 mg once daily (red).2 (d) Unbound rifampicin concentration–time profiles in blood (solid line), the first hepatocytes compartment (dashed line), and enterocytes (dotted line) after the last dose of 14-day repeated oral doses of rifampicin 300 mg twice daily(black), 600 mg once daily (green), or 900 mg once daily (red). The concentration–time profiles were shown with the b value of rifampicin of 0.2. Because the results shown in panels a and b were obtained in the optimization processes of PBPK model of rifampicin,
auto-induction process was not incorporated.

observed nonlinear blood concentration-time profiles after
single oral doses of 150–600 mg rifampicin (Figure 2b).1
The parameters for saturable hepatic uptake were successfully optimized with all three b values (0.2, 0.5, and 0.8) during sensitivity analyses. However, the optimization for autoinduction cases was successful only with the b value of 0.2.
The Emax value for UGT auto-induction by rifampicin was
obtained to be 1.34 using the observed profiles with 14-day
repeated oral dosing of 300, 600, or 900 mg rifampicin (Figure 2c).2 In this case, the estimated trough concentrations
decreased with the multiple dosing and seemed to reach aplateau on day 6. When the rifampicin concentrations at 12hours postdosing were compared between days 6 and 1, theratios were 0.35, 0.42, and 0.48 for rifampicin doses of 300,
600, and 900 mg, respectively. These calculated values
were in good agreement with the observed values,
0.40 6 0.20, 0.37 6 0.12, and 0.39 6 0.27 for the rifampicin
doses of 300, 600, and 900 mg, respectively. Figure 2dshows the estimated unbound rifampicin concentration-time
CPT: Pharmacometrics & Systems Pharmacology

profiles in blood, hepatocytes, and enterocytes after the lastdose in the auto-induction case.
To validate the obtained parameters of rifampicin related
to its nonlinearity and auto-induction, the blood rifampicin
concentration-time profiles after single oral or intravenous
doses of 100–900 mg rifampicin were simulated. The simulated profiles were mostly in good agreement with theobserved profiles (Figure 31,2,7,14,21,25–31).
Determination of rifampicin parameters for CYP3A andCYP2C9 induction
The midazolam-related parameters were first estimated using
the observed blood concentration-time profiles of midazolam
under control conditions. Subsequently, the EC50,u and Emax
values for CYP3A induction by rifampicin were determined to
be 52.6 ng/mL and 4.29, respectively, using the observed midazolam profiles after repeated oral doses of 5–75 mg rifampicin (EC50,u adopted and listed in Table 1, Figure 4a).22 In
estimating Emax, the obtained EC50,u value (52.6 ng/mL) was

Rifampicin PBPK Model to Predict Complex DDIs
Asaumi et al.
191

Figure 3 Simulated and observed blood rifampicin concentration-time profiles after single oral or intravenous infusion doses of
100 mg to 900 mg rifampicin. The solid lines represent simulated blood rifampicin concentration-time profiles after single oral(a–h) or intravenous infusion (i–n) doses of rifampicin. Dosing regimens are indicated in each figure. The closed circles andsquares represent observed mean blood concentrations (with SD) of rifampicin after single oral or intravenous infusion doses ofrifampicin, respectively. The observed data were extracted from the literature reports shown in refs. 1,2,7,14,21,25–31. IF, interview form.

used as fixed and different DDI data of midazolam after
repeated oral doses of 600 mg rifampicin were analyzed.23
The estimated Emax value was 4.57 (adopted and listed inTable 1, Figure 4b-c). During the rifampicin treatment shownin Figure 4b,c, time-dependent changes of the relative
CYP3A activities in hepatocytes and enterocytes were shownin Figure 4d.
Similarly, the Emax value for CYP2C9 induction by rifampicin was determined to be 2.41 using the reported DDI data
between rifampicin and tolbutamide (Figure 4e).24 During
the rifampicin treatment in Figure 4e, time-dependentchanges of the relative CYP2C9 activities in hepatocytes
were shown in Figure 4f.

Validation of the constructed PBPK models
Simulation of CYP3A induction effect by rifampicin
Using the obtained EC50,u (52.6 ng/mL) and Emax (4.57)
values for CYP3A induction, simulations were performed to
predict the CYP3A induction-mediated DDIs with midazolam. The simulated blood concentration-time profiles and
AUC ratios (AUCRs) of midazolam after its single
www.psp-journal.com

Rifampicin PBPK Model to Predict Complex DDIs
Asaumi et al.
192

Figure 4 Optimized and observed blood concentration-time profiles of midazolam and tolbutamide before and after multiple rifampicin
dosing, and the time-dependent changes in the relative cytochrome P450 (CYP)3A and CYP2C9 activities during multiple rifampicin
dosing. Solid lines and closed circles represent optimized and observed data, respectively. (a) Blood midazolam concentration-time profiles after single oral dosing of 3 mg midazolam before (black) and after repeated oral dosing of 5 mg (blue), 10 mg (green), 25 mg(orange), or 75 mg (red) rifampicin.22 (b and c) Blood midazolam concentration-time profiles after single intravenous infusion dosing of
0.05 mg/kg midazolam (b) or single oral dosing of 4 or 6 mg midazolam (c) before (black) and after (red) repeated oral dosing of
600 mg rifampicin.23 Observed data were shown as mean 1 SD. (d) Estimated time courses of the relative CYP3A activities in the first
hepatocyte compartment (red) and enterocytes (blue) during repeated oral dosing of 600 mg rifampicin. (e) Blood tolbutamide
concentration-time profiles after single intravenous infusion dosing of 20 mg/kg tolbutamide before (black) and after (red) repeated oral
dosing of 1,200 mg rifampicin.24 (f) Estimated time course of the relative CYP2C9 activities in the first hepatocyte compartment (red)during repeated oral dosing of 1,200 mg rifampicin. The closed inverted triangles represent the timing of the doses of rifampicin (red),midazolam (black in d), and tolbutamide (black in f). Note: The results shown on the panel a was obtained during the optimization processes of CYP3A induction parameters by rifampicin (thus, using the unbound concentration for the half maximum induction effect(EC50,u) and maximum induction effect (Emax) values of 52.6 ng/mL and 4.29, respectively).

intravenous doses after repeated oral doses of 5–75 mg
rifampicin were in good agreement with the observed data
(Figure 5 and Supplementary Figure S1).22
Simulation of CYP3A/2C9 induction and/or OATP
inhibition effects by rifampicin
To simulate complex DDIs with glibenclamide, several
glibenclamide-related parameters were first obtained by fitting to the observed profiles with glibenclamide dosing only
(Table 1 and Supplementary Figure S2).32 Thereafter, thecomplex DDIs were simulated using the parameters of rifampicin for CYP3A and CYP2C9 induction (obtained from thecurrent study) and the previously optimized OATP inhibition
parameter (unbound Ki (Ki,u) of 0.226 lmol/L).15 For the caseof oral glibenclamide dosing with single intravenous rifampicin dosing, the simulated AUCR (2.08) of glibenclamide forthe b value of 0.2 was in very good agreement with theobserved AUCR of 2.18 6 1.09 (Figure 6).8 In the case inwhich oral rifampicin dosing was repeated for 6 days followedby oral glibenclamide dosing together with single intravenous
rifampicin dosing on day 7, the simulated AUCR (0.90) of
CPT: Pharmacometrics & Systems Pharmacology

glibenclamide with the b value of 0.2 was close to theobserved AUCR of 0.72 6 0.32 (Figure 6).8 In the case inwhich repeated rifampicin dosing for 7 days followed by oral
glibenclamide dosing on day 9, the simulated AUCR (0.50) ofglibenclamide with the b value of 0.2 was close to theobserved AUCR of 0.35 6 0.19 (Figure 6).8

DISCUSSION
The present study aimed to construct a comprehensive
PBPK model of rifampicin that can correctly and quantitatively predict complex DDIs involving the induction of
CYP3A/2C9 and the inhibition of OATPs. The parameters forrifampicin and the victim drugs were individually determinedusing the respective blood concentration-time profiles undercontrol conditions. Then, the rifampicin parameters for
CYP3A and CYP2C9 induction were determined using theblood concentration-time profiles of midazolam and tolbutamide, respectively, after rifampicin treatment (Figures 2–4,
Table 1). Using the obtained CYP3A induction parameters,

Rifampicin PBPK Model to Predict Complex DDIs
Asaumi et al.
193

Figure 5 Optimized, simulated, and observed area under the curve ratios (AUCRs) of midazolam after repeated oral dosing of 5–
600 mg rifampicin. The closed circles and squares represent observed mean AUCRs 6 SD of midazolam after rifampicin treatment.
The open circles and cross signs represent simulated and optimized AUCRs of midazolam after rifampicin treatment using the finallyadopted estimated unbound concentration for the half maximum induction effect (EC50,u; 52.6 ng/mL) and maximum induction effect(Emax; 4.57) values. Because the drug-drug interaction (DDI) data between rifampicin (PO 5, 10, 25, or 75 mg) and midazolam (PO3 mg) were analyzed in the optimization processes with EC50,u (52.6 ng/mL) and Emax (4.29) (Figure 4a), the AUCRs were simulatedusing the finally adopted values. As the DDI data between rifampicin (PO 600 mg) and midazolam (PO 4 or 6 mg or i.v. 0.05 mg/kg)
were analyzed in the final optimization process (Figure 4b,c), the AUCRs were calculated from Figure 4b,c. Because the DDI data
between rifampicin (PO 5, 10, 25, or 75 mg) and midazolam (i.v. 1 mg) were used in the simulation process (Supplementary FigureS1), the AUCRs were calculated from Supplementary Figure S1. Dosing regimens of rifampicin and midazolam are indicated on bothsides.

simulations for the CYP3A induction-mediated DDIs withmidazolam were performed. The simulated AUCRs of midazolam were in very good agreement with the observed data
(Figure 5). For further validation, the prediction accuracy
was examined using the complex DDI cases in which intravenous and/or repeated oral doses of rifampicin were coadministered with glibenclamide. The simulated AUCRs of
glibenclamide were again in quite good accordance with theobserved data (Figure 6). Taken together, these results support the robustness and utility of our current PBPK model for
rifampicin in quantitatively predicting complex DDIs withother victim drugs.
In our current study, the cases of complex DDIs between
rifampicin and glibenclamide were successfully predicted
for the most part. The differing impact of the glibenclamideb value on the prediction accuracy was, however, noted
and it may offer an indirect clue for the rate-limiting step invivo. With single intravenous rifampicin dosing (mostly
incurring OATP inhibition), the simulated AUCRs of glibenclamide were independent of the b values used and very
close to the observed AUCR (Figure 6). After multiple
rifampicin dosing (mostly incurring CYP induction), the simulated AUCRs of glibenclamide varied somewhat dependingon the b values used and yielded the closest prediction
with the b value of 0.2 (Figure 6). Based on the simulation
results with sensitivity analyses, the low b value ofglibenclamide likely reflects in vivo situations in which therate-limiting steps in the hepatic elimination of glibenclamide may be not only hepatic uptake, but also metabolism(see the Methods section). Our constructed PBPK models
can adequately reflect such cases of complex DDIs.
After repeated oral dosing of rifampicin, its unbound concentrations were higher in hepatocytes than those in blood,reflecting “concentrative” hepatic uptake of rifampicin (Figure 2d). Moreover, the concentrative hepatic uptake wasless pronounced as the unbound blood concentrations ofrifampicin increased. These results support that the saturation of rifampicin hepatic uptake was adequately incorporated into the model. Of note, the optimized Km,u value forhepatic uptake (146 ng/mL) differed by approximately eightfold from the reported in vitro Km value (1.2 lg/mL).13
Although the exact reasons for this difference are currently
unknown, albumin-facilitated hepatic uptake may contributein part to lowering the in vivo Km value. For some of thehighly albumin-bound drugs, their enhanced uptake into
hepatocytes by the albumin-bound form has beenreported.33 Interestingly, similar findings were also reportedwith Ki values for OATP inhibition by rifampicin, in that thein vivo Ki value was about a one-fourth of the in vitro Ki values.15 For UGT auto-induction by rifampicin, the estimated
EC50,u (52.6 ng/mL) and Emax (1.34) values successfully
captured the auto-induction profiles, including the loweredwww.psp-journal.com

Rifampicin PBPK Model to Predict Complex DDIs
Asaumi et al.
194

Figure 6 Simulated and observed area under the curve ratios(AUCRs) of glibenclamide with co-administration of oral and/or
intravenous rifampicin (RIF) dosing. The closed circles represent
observed mean AUCRs 6 SD of glibenclamide with or without
various co-administration of rifampicin. The open circles, diamonds, and triangles represent simulated AUCRs of glibenclamide with the b values of 0.2, 0.5, and 0.8, respectively, as asensitivity analysis. Control: oral dosing of 1.25 mg glibenclamide(GLB) only. RIF IV: oral glibenclamide dosing with single i.v. dosing of 600 mg rifampicin. RIF PO/IV: repeated oral dosing of
600 mg rifampicin for 6 days, followed by oral glibenclamide dosing with single i.v. dosing of 600 mg rifampicin on day 7. RIF
PO: repeated dosing of 600 mg rifampicin for 7 days, followed byoral glibenclamide dosing on day 9.

trough concentrations of rifampicin. However, further verification will be necessary using clinical DDI data between
UGT substrates and rifampicin or other data on UGT isoforms involved in the metabolism of rifampicin.
The optimized EC50,u (52.6 ng/mL, 0.0639 lmol/L) andEmax (4.57) values for CYP3A induction by rifampicin werelower than the previously reported in vivo values (EC50,u,0.32 lmol/L; Emax, 15).34 Using the obtained parameters inour current study, our PBPK model of rifampicin showed agood relationship between CYP3A induction and rifampicin
doses at a wide range of 5–600 mg (Figure 5). However, thepreviously reported PBPK model did not show such relationship due to the lack of simulated DDI data with CYP3A substrates following low doses of rifampicin (<450 mg). Thus,
our estimated induction parameters may be considered morereliable, but further investigations will be necessary to evaluate the prediction accuracy for CYP3A induction-mediated
DDIs with other CYP3A substrates.
Overall, the obtained induction parameters for CYP3A andCYP2C9 by rifampicin yielded predictions that are in goodaccordance with the observed DDI data with midazolam orglibenclamide (Figures 5 and 6). After repeated oral dosingof 600 mg rifampicin, the estimated relative CYP3A activities
were increased up to 3.8-fold and 3.6-fold in hepatocytes andenterocytes, respectively (Figure 4d). In the previous reports
with repeated oral dosing of 600 mg rifampicin, the CYP3A
protein levels in liver microsomes prepared from biopsy
samples were increased by 2.4-fold to 4.7-fold.35 Similarly,
hepatic intrinsic clearance of various CYP3A substrates calculated from clinical DDI data after rifampicin treatments
increased by 3.3-fold.36 In enterocytes, the CYP3A protein
levels obtained from a perfusion method or biopsy samples
were increased by 3.3-fold37 or 2.2-fold to 4.4-fold,38
CPT: Pharmacometrics & Systems Pharmacology

respectively, after repeated oral dosing of 600 mg rifampicin.
Given that the estimated relative CYP3A activities werequite comparable to the experimentally observed values,
these results support the utility of the optimized CYP3A
induction parameters of rifampicin at both the liver and small
intestines. Furthermore, the estimated relative CYP2C9
activities increased up to 2.5-fold in hepatocytes during
rifampicin treatment (Figure 4f). Their increase was consistent with the increase of hepatic intrinsic clearance for various CYP2C9 substrates (by 1.3-fold to 3.7-fold) after
repeated rifampicin dosing (Supplementary Text andSupplementary Table S3). These results also provide
confidence for further use of these induction parameters infuture investigations.
Regarding the major CYP isoform(s) responsible for glibenclamide metabolism, some apparent discrepancies
exist. The following in vivo data support the major role of
CYP2C9 in the hepatic metabolism of glibenclamide: (i)CYP2C9 polymorphisms significantly affected the pharmacokinetics of glibenclamide39–42 (4.9-fold increase in its
exposure in the CYP2C9 *3/*3 genotype compared to thewild type42); and (ii) co-administration of CYP3A inhibitors(clarithromycin, verapamil, or erythromycin) only marginally
increased the glibenclamide exposure (by 1.35-fold or
less43–45). Therefore, the estimated fm values of glibenclamide for CYP2C9/CYP3A in the liver were 0.85/0.15, 0.94/
0.06, and 1.00/0.0, respectively, when the glibenclamide b
value was 0.2, 0.5, and 0.8 (see Supplementary Text,
Supplementary Table S2, and Supplementary Figure S3).
Using these fm values and the CLint ratios of each CYP2C9
genotype to the wild type,46 the simulated AUCRs of glibenclamide for each CYP2C9 genotype to the wild typewere in good accordance with the observed AUCRs(Supplementary Figure S4). These results further support
the robustness of our constructed PBPK model of glibenclamide. Currently, information is limited on the observed AUCRs
with CYP2C9 polymorphisms and the different fm values ofglibenclamide for CYP2C9 were used with varying b values.
Further efforts may be needed to identify in vivo b value ofglibenclamide. However, considering the results of DDI simulations with rifampicin, we reason that the in vivo b value ofglibenclamide may be low. It may be worth noting that the previously reported PBPK model of glibenclamide did not succeed in predicting the AUCRs of glibenclamide for individual
CYP2C9 genotypes.47 Possible reasons for its poor prediction
include: (i) the fm value (0.35, based on the in vitro data) of glibenclamide for CYP2C9 was underestimated; and (ii) the bvalue (calculated to be >0.8) of glibenclamide was set to behigh. Comparison of these different PBPK models of glibenclamide reiterates the importance of quantitative and accurate
determination of the elimination mechanisms. When CYPexpressing microsomes or human liver microsomes wereused, glibenclamide was reported to be metabolized primarily
by CYP3A, with a minor or negligible contribution of
CYP2C9.48,49 This discrepancy in the main CYP isoforms
responsible for glibenclamide metabolism between in vivo
and in vitro conditions may arise from the underestimation of
the CYP2C9-mediated metabolic clearance from in vitro
experiments, in which long-chain fatty acids presented inRifampicin PBPK Model to Predict Complex DDIs
Asaumi et al.
195

liver microsomes are found to suppress the activities of
CYP2C9.50
In conclusion, the present study constructed a comprehensive PBPK model of rifampicin that can quantitatively
predict complex DDIs involving CYP3A/2C9 induction and/or OATP inhibition. The constructed PBPK model adequately captured the processes of saturable hepatic uptake
and auto-induction of rifampicin. The parameters of rifampicin related to CYP3A and CYP2C9 induction were obtainedby stepwise optimization that used clinical DDI data with aprobe substrate for CYP3A (midazolam) and CYP2C9 (tolbutamide), respectively. Using the optimized parameters,
the CYP3A/2C9 induction-mediated and/or OATP inhibitionmediated DDIs with glibenclamide were successfully predicted. To our knowledge, this is the first report thatsuccessfully incorporated multiple aspects of rifampicin
pharmacokinetics into a unified PBPK model. The established rifampicin PBPK model presents advancement
toward accurate and quantitative prediction of DDIs withvarious substrates of CYP3A, CYP2C9, and/or OATPs.
Acknowledgments. The authors thank Ryo Fujino and Shinsuke
Aoyama of Sekisui Medical Company, Ltd. for their experimental supportof obtaining in vitro data of glibenclamide.

12.

13.

14.
15.

16.
17.

18.
19.
20.

21.
22.

23.

Source of Funding. No funding was received for this work.
24.

Conflict of Interest. The authors declared no conflict of interest.
25.

Author Contributions. R.A., K.N., H.I., W.L., and Y.S. wrote themanuscript. R.A., K.T., and Y.S. designed the research. R.A., K.T., Y.T.,
K.H., and Y.S. performed the research. R.A. and Y.S. analyzed the data.

26.
27.

1.

Acocella, G. Clinical pharmacokinetics of rifampicin. Clin. Pharmacokinet. 3, 108–127(1978).
2. Acocella, G., Pagani, V., Marchetti, M., Baroni, G.C. & Nicolis, F.B. Kinetic studies
on rifampicin. I. Serum concentration analysis in subjects treated with different oral
doses over a period of two weeks. Chemotherapy 16, 356–370 (1971).
3. Niemi, M., Backman, J.T., Fromm, M.F., Neuvonen, P.J. & Kivist€o, K.T. Pharmacokinetic interactions with rifampicin: clinical relevance. Clin. Pharmacokinet. 42, 819–850(2003).
4. Maeda, K. Organic anion transporting polypeptide (OATP)1B1 and OATP1B3 as
important regulators of the pharmacokinetics of substrate drugs. Biol. Pharm. Bull.
38, 155–168 (2015).
5. Wienkers, L.C. & Heath, T.G. Predicting in vivo drug interactions from in vitro drug
discovery data. Nat. Rev. Drug. Discov. 4, 825–833 (2005).
6. Maeda, K. et al. Identification of the rate-determining process in the hepatic clearanceof atorvastatin in a clinical cassette microdosing study. Clin. Pharmacol. Ther. 90,
575–581 (2011).
7. Prueksaritanont, T. et al. Pitavastatin is a more sensitive and selective organic aniontransporting polypeptide 1B clinical probe than rosuvastatin. Br. J. Clin. Pharmacol.
78, 587–598 (2014).
8. Zheng, H.X., Huang, Y., Frassetto, L.A. & Benet, L.Z. Elucidating rifampin’s inducing
and inhibiting effects on glyburide pharmacokinetics and blood glucose in healthy volunteers: unmasking the differential effects of enzyme induction and transporter inhibition for a drug and its primary metabolite. Clin. Pharmacol. Ther. 85, 78–85 (2009).
9. Bidstrup, T.B., Stilling, N., Damkier, P., Scharling, B., Thomsen, M.S. & Brøsen, K.
Rifampicin seems to act as both an inducer and an inhibitor of the metabolism of
repaglinide. Eur. J. Clin. Pharmacol. 60, 109–114 (2004).
10. Center for Drug Evaluation and Research. In Vitro Metabolism- and TransporterMediated Drug-Drug Interaction Studies Guidance for Industry: Draft Guidance, Foodand Drug Administration, Silver Spring, MD. <https://www.fda.gov/downloads/drugs/guidancecomplianceregulatoryinformation/guidances/ucm581965.pdf> (2017). Accessed
25 October 2017.
11. Committee for Human Medicinal Products. Guideline on the Investigation of Drug
Interactions, European Medicines Agency, London. <http://www.ema.europa.eu/docs/

28.

29.
30.

31.

32.

33.

34.

35.

36.

37.

en_GB/document_library/Scientific_guideline/2012/07/WC500129606.pdf>(2012).
Accessed 21 June 2012.
Loos, U., Musch, E., Jensen, J.C., Mikus, G., Schwabe, H.K. & Eichelbaum, M. Pharmacokinetics of oral and intravenous rifampicin during chronic administration. Klin.
Wochenschr. 63, 1205–1211 (1985).
Tirona, R.G., Leake, B.F., Wolkoff, A.W. & Kim, R.B. Human organic anion transporting polypeptide-C (SLC21A6) is a major determinant of rifampin-mediated pregnane
X receptor activation. J. Pharmacol. Exp. Ther. 304, 223–228 (2003).
Riess, W. The optimum dosage schedule for Rimactane. A Symposium on Rimactane.
(Luzius Dettli, L. & CIBA Limited, Basel) 36–44 (Ciba Ltd, Basel, Switzerland, 1968).
Yoshikado, T. et al. Quantitative analyses of hepatic OATP-mediated interactions
between statins and inhibitors using PBPK modeling with a parameter optimization
method. Clin. Pharmacol. Ther. 100, 513–523 (2016).
Nakagawa, H., Umene, Z. & Sunahara, S. Increased desacetylation of rifampicin andan adverse reaction. Kekkaku. 56, 577–586 (1981).
Hisaka, A. & Sugiyama, Y. Analysis of nonlinear and nonsteady state hepatic extraction with the dispersion model using the finite difference method. J. Pharmacokinet.
Biopharm. 26, 495–519 (1998).
Chow, EC. & Pang, KS. Why we need proper PBPK models to examine intestineand liver oral drug absorption. Curr. Drug Metab. 14, 57–79 (2013).
Yang, J., Jamei, M., Yeo, K.R., Tucker, G.T. & Rostami-Hodjegan, A. Prediction ofintestinal first-pass drug metabolism. Curr. Drug Metab. 8, 676–684 (2007).
Acocella, G., Mattiussi, R. & Segre, G. Multicompartmental analysis of serum, urine
and bile concentrations of rifampicin and desacetyl-rifampicin in subjects treated for
one week. Pharmacol. Res. Commun. 10, 271–288 (1978).
V
Rifampin (Rifadin ) prescribing information. New Jersey (NJ): Sanofi-Aventis U.S.
LLC.
Kharasch, E.D., Francis, A., London, A., Frey, K., Kim, T. & Blood, J. Sensitivity of
intravenous and oral alfentanil and pupillary miosis as minimal and noninvasiveprobes for hepatic and first-pass CYP3A induction. Clin. Pharmacol. Ther. 90,
100–108 (2011).
Gorski, J.C. et al. The effect of age, sex, and rifampin administration on intestinal
and hepatic cytochrome P450 3A activity. Clin. Pharmacol. Ther. 74, 275–287(2003).
Zilly, W., Breimer, D.D. & Richter, E. Induction of drug metabolism in man after rifampicin treatment measured by increased hexobarbital and tolbutamide clearance. Eur.
J. Clin. Pharmacol. 9, 219–227 (1975).
Furesz, S., Scotti, R., Pallanza, R. & Mapelli, E. Rifampicin: a new rifamycin. 3.
Absorption, distribution, and elimination in man. Arzneimittelforschung. 17, 534–537(1967).
V
Rifampin (Rifadin capsules) Japanese interview form information. Daiichi SankyoCo., Ltd., Tokyo.
Lai, Y. et al. Coproporphyrins in plasma and urine can be appropriate clinicalbiomarkers to recapitulate drug-drug interactions mediated by organic aniontransporting polypeptide inhibition. J. Pharmacol. Exp. Ther. 358, 397–404(2016).
Peloquin, C.A., Jaresko, G.S., Yong, C.L., Keung, A.C., Bulpitt, A.E. & Jelliffe, R.W.
Population pharmacokinetic modeling of isoniazid, rifampin, and pyrazinamide. Antimicrob. Agents Chemother. 41, 2670–2679 (1997).
Kohno, H., Hata, B., Tsuchiya, T., Kubo, H. & Kobayashi, Y. Pharmacokinetics ofrifampicin. Rinshoyakuri. 13, 403–412 (1982).
Acocella, G., Bonollo, L., Mainardi, M., Margaroli, P. & Tenconi, L.T. Serum and urine
concentrations of rifampicin administered by intravenous infusion in man. Arzneimittelforschung. 27, 1221–1226 (1977).
Lau, Y.Y., Huang, Y., Frassetto, L., & Benet, L.Z. Effect of OATP1B transporter inhibition on the pharmacokinetics of atorvastatin in healthy volunteers. Clin. Pharmacol.
Ther. 81,194–204 (2007).
Neugebauer, G., Betzien, G., Hrstka, V., Kaufmann, B., von M€ollendorff, E. &Abshagen, U. Absolute bioavailability and bioequivalence of glibenclamide (SemiEuglucon N). Int. J. Clin. Pharmacol. Ther. Toxicol. 23, 453–460 (1985).
Poulin, P. & Haddad, S. Albumin and uptake of drugs in cells: additional validation
exercises of a recently published equation that quantifies the albumin-facilitateduptake mechanism(s) in physiologically based pharmacokinetic and pharmacodynamic
modeling research. J. Pharm. Sci. 104, 4448–4458 (2015).
Almond, L.M. et al. Prediction of drug-drug interactions arising from CYP3A induction
using a physiologically based dynamic model. Drug Metab. Dispos. 44, 821–832(2016).
Combalbert, J. et al. Metabolism of cyclosporin A. IV. Purification and identification of the rifampicin-inducible human liver cytochrome P-450 (cyclosporin A oxidase) as a product of p450IIIA gene subfamily. Drug Metab. Dispos. 17, 197–207(1988).
Kato, M., Chiba, K., Horikawa, M. & Sugiyama Y. The quantitative prediction of invivo enzyme-induction caused by drug exposure from in vitro information on humanhepatocytes. Drug Metab. Pharmacokinet. 20, 236–243 (2005).
Glaeser, H., Drescher, S., Eichelbaum, M. & Fromm, M.F. Influence of rifampicin on
the expression and function of human intestinal cytochrome P450 enzymes. Br. J.
Clin. Pharmacol. 59, 199–206 (2005).
RR

www.psp-journal.com

Rifampicin PBPK Model to Predict Complex DDIs
Asaumi et al.
196
38. Greinre, B. et al. The role of intestinal P-glycoprotein in the interaction of digoxin andrifampin. J. Clin. Invest. 104, 147–153 (1999).
39. Niemi, M., Cascorbi, I., Timm, R., Kroemer, H.K., Neuvonen, P.J. & Kivist€o, K.T. Glyburide and glimepiride pharmacokinetics in subjects with different CYP2C9 genotypes.
Clin. Pharmacol. Ther. 72, 326–332 (2002).
40. Ieiri, I. et al. Pharmacogenomic/pharmacokinetic assessment of a four-probe cocktail
for CYPs and OATPs following oral microdosing. Int. J. Clin. Pharmacol. Ther. 50,
689–700 (2012).
41. Yin, O.Q., Tomlinson, B. & Chow, M.S. CYP2C9, but not CYP2C19, polymorphisms
affect the pharmacokinetics and pharmacodynamics of glyburide in Chinese subjects.
Clin. Pharmacol. Ther. 78, 370–377 (2005).
42. Kirchheiner, J. et al. Impact of CYP2C9 amino acid polymorphisms on glyburide
kinetics and on the insulin and glucose response in healthy volunteers. Clin. Pharmacol. Ther. 71, 286–296 (2002).
43. Lilja, J.J., Niemi, M., Fredrikson, H. & Neuvonen, P.J. Effects of clarithromycin andgrapefruit juice on the pharmacokinetics of glibenclamide. Br. J. Clin. Pharmacol. 63,
732–740 (2007).
44. Semple, C.G., Omile, C., Buchanan, K.D., Beastall, G.H. & Paterson, K.R. Effect oforal verapamil on glibenclamide stimulated insulin secretion. Br. J. Clin. Pharmacol.
22, 187–190 (1986).
45. Fleishaker, J.C. & Phillips, J.P. Evaluation of a potential interaction between erythromycin and glyburide in diabetic volunteers. J. Clin. Pharmacol. 31, 259–262 (1991).
46. Kusama, M., Maeda, K., Chiba, K., Aoyama, A. & Sugiyama, Y. Prediction of theeffects of genetic polymorphism on the pharmacokinetics of CYP2C9 substrates fromin vitro data. Pharm. Res. 26, 822–835 (2009).

47. Varma, M.V. et al. Mechanism-based pharmacokinetic modeling to evaluate
transporter-enzyme interplay in drug interactions and pharmacogenetics of glyburide.
AAPS J. 16, 736–748 (2014).
48. Zharikova, O.L. et al. Identification of the major human hepatic and placental
enzymes responsible for the biotransformation of glyburide. Biochem. Pharmacol. 78,
1483–1490 (2009).
49. Zhou, L. et al. Contributions of human cytochrome P450 enzymes to glyburide
metabolism. Biopharm. Drug Dispos. 31, 228–242 (2010).
50. Rowland, A., Elliot, D.J., Knights, K.M., Mackenzie, P.I. & Miners, J.O. The ‘‘albumin
effect’’ and in vitro-in vivo extrapolation: sequestration of long-chain unsaturated fatty
acids enhances phenytoin hydroxylation by human liver microsomal and recombinantcytochrome P450 2C9. Drug Metab. Dispos. 36, 870–877 (2008).
C 2018 The Authors CPT: Pharmacometrics & Systems
V

Pharmacology published by Wiley Periodicals, Inc. on
behalf of American Society for Clinical Pharmacology andTherapeutics. This is an open access article under the
terms
of
the
Creative
Commons
AttributionNonCommercial License, which permits use, distribution
and reproduction in any medium, provided the original
work is properly cited and is not used for commercial
purposes.

Supplementary information accompanies this paper on the CPT: Pharmacometrics & Systems Pharmacology website(http://psp-journal.com)

CPT: Pharmacometrics & Systems Pharmacology