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DRUG METABOLISM AND DISPOSITION
Copyright ª 2015 by The American Society for Pharmacology and Experimental Therapeutics
http://dx.doi.org/10.1124/dmd.115.064303
Drug Metab Dispos 43:1108–1118, July 2015
Quantitative Rationalization of Gemfibrozil Drug Interactions:
Consideration of Transporters-Enzyme Interplay and the Role
of Circulating Metabolite Gemfibrozil 1-O-b-Glucuronide
Manthena V. S. Varma, Jian Lin, Yi-an Bi, Emi Kimoto, and A. David Rodrigues
Pharmacokinetics Dynamics and Metabolism, Pfizer Global Research and Development, Pfizer Inc., Groton, Connecticut
Received March 13, 2015; accepted May 4, 2015
ABSTRACT
the 48 cases evaluated using the static models, about 75% and
98% of the DDIs were predicted within 1.5- and 2-fold of the
observed values, respectively, when incorporating the interaction
potential of both gemfibrozil and its 1-O-b-glucuronide. Moreover,
the PBPK model was able to recover the plasma profiles of
rosiglitazone, pioglitazone, cerivastatin, and repaglinide under
control and gemfibrozil treatment conditions. Analyses suggest
that Gem-Glu is the major contributor to the DDIs, and its exposure
needed to bring about complete inactivation of CYP2C8 is only
a fraction of that achieved in the clinic after a therapeutic
gemfibrozil dose. Overall, the complex interactions of gemfibrozil
can be quantitatively rationalized, and the learnings from this
analysis can be applied in support of future predictions of
gemfibrozil DDIs.
Introduction
$25% of the parent area under the plasma concentration-time curve
(AUC) (CDER, 2012) or $25% of the parent AUC and $10% of
the total drug-related AUC (CHMP, 2012). Under the auspices of
the Drug Metabolism Leadership Group of the Innovative and
Quality Consortium (IQ-DMLG), the Metabolite-mediated DDI
Scholarship Group (MDSG)—tasked to examine the application of
these regulatory recommendations—was formed with scientists
representing 18 pharmaceutic companies (Yu et al., 2015). Based on
the analysis of 137 frequently prescribed drugs, the MDSG
concluded that the risk for unexpected clinical DDI as a result of
not assessing in vitro cytochrome P450 (P450) inhibition by
metabolite(s) is low, which is consistent with earlier reports (Yeung
et al., 2011; Callegari et al., 2013). However, five of the 137
perpetrator drugs (amiodarone, bupropion, gemfibrozil, sertraline,
and capecitabine) were noted to be false-negative predictions
(parent in vivo inhibition negative, but in vivo inhibition positive)
and showed evidence that metabolites contribute to the observed in
vivo P450-based DDIs. To further investigate these perpetrator
drugs and assess the quantitative contribution of the circulating
metabolite(s), a Metabolite Scholarship PBPK Subteam was formed
Drug-drug interactions (DDIs) may significantly alter drug
exposure and subsequently influence the efficacy and safety profiles
of a drug. Therefore, the ability to quantitatively predict DDIs,
primarily involving drug-metabolizing enzymes and transporters, is
essential for risk assessment at drug discovery and early development
stages. However, drug interactions may be mechanistically complex
due to inhibition and/or induction of multiple enzymes and/or
transporters, concomitant use of multiple perpetrator drugs, and the
presence of perpetrator metabolites (Sager et al., 2014; Varma et al.,
2015). Considerable attention has been drawn to the contribution of
perpetrator metabolites to DDIs due to inhibition of enzymes and
transporters (Isoherranen et al., 2009; Yeung et al., 2011; ZamekGliszczynski et al., 2014).
The current European Medicines Agency (EMA) and U.S. Food
and Drug Administration (FDA) guidelines on DDIs recommend in
vitro investigation of metabolite’s interaction potential if present at
dx.doi.org/10.1124/dmd.115.064303.
ABBREVIATIONS: ADME, absorption, distribution, metabolism, and excretion; AUC, area under the plasma concentration-time curve; AUCR, area
under the plasma concentration-time curve ratio; CLint,bile, biliary intrinsic clearance; CLint,met, intrinsic metabolic clearance; CLint,h, intrinsic hepatic
clearance; CLh, hepatic clearance; CLr, renal clearance; DDI, drug-drug interactions; Fa, fraction absorbed; Fg, fraction of drug escaping gut-wall
extraction; Fh, fraction of drug escaping hepatic extraction; fu,gut, fraction unbound in the gut; Gem-Glu, gemfibrozil 1-O-b-glucuronide; Iu,max,
maximum unbound plasma perpetrator concentration; Iu,max,in, maximum unbound perpetrator concentration at the inlet to liver; Ka, absorption rate
constant; kdeg,h, apparent first-order degradation rate constant; Ki, inhibition constant; kinact, maximal inactivation rate constant; KI, inhibitor
concentration that supports half the maximal rate of inactivation; OAT, organic anion transporter; OATP, organic anion transporting polypeptide;
P450, cytochrome P450; PBPK, physiologically based pharmacokinetic; PSactive, active uptake clearance; PSpd, passive diffusion; Qgut, enterocytic
blood flow; Rb, blood-to-plasma ratio; SFactive, scaling factor for active uptake; TDI, time-dependent inhibition; Vss, volume at steady-state.
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Gemfibrozil has been suggested as a sensitive cytochrome P450
2C8 (CYP2C8) inhibitor for clinical investigation by the U.S. Food
and Drug Administration and the European Medicines Agency.
However, gemfibrozil drug-drug interactions (DDIs) are complex; its
major circulating metabolite, gemfibrozil 1-O-b-glucuronide (GemGlu), exhibits time-dependent inhibition of CYP2C8, and both parent
and metabolite also behave as moderate inhibitors of organic anion
transporting polypeptide 1B1 (OATP1B1) in vitro. Additionally,
parent and metabolite also inhibit renal transport mediated by
OAT3. Here, in vitro inhibition data for gemfibrozil and Gem-Glu
were used to assess their impact on the pharmacokinetics of
several victim drugs (including rosiglitazone, pioglitazone, cerivastatin, and repaglinide) by employing both static mechanistic and
dynamic physiologically based pharmacokinetic (PBPK) models. Of
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Quantitative Rationalization of Gemfibrozil DDIs
Materials and Methods
Clinical DDI and In Vitro Data Collection
Clinical DDI data with gemfibrozil as perpetrator drug were primarily
extracted from the University of Washington metabolism and transporter
drug interaction database (www.druginteractioninfo.org). An additional
exhaustive literature search was conducted to enrich the clinical DDI
dataset. In vitro interaction potency data were collected from the scientific
literature.
Static Model Predictions
The area under the plasma concentration-time curve ratio (AUCR) of an oral
victim drug, in the presence (AUCʹpo) and absence (AUCpo) of perpetrator, can
be described by the following equations:
AUCR ¼ Fa9 Fg9 Fh9 ðCLh þ CLr Þ
×
×
×
Fa Fg Fh ðCLh 9 þ CLr 9Þ
ð1Þ
where
CLh
Qh
Qh :f u;b :CLint;h
CLh ¼
Qh þ f u;b :CLint;h
Fh ¼ 1 2
CLr ¼ f u;b :GFR þ CLsec Fa, Fg, and Fh represent the fraction of drug absorbed, fraction of drug
escaping gut-wall extraction, and hepatic extraction, respectively. Faʹ, Fgʹ, and
Fhʹ are the corresponding parameters in the presence of the perpetrator. CLh,
CLr, CLhʹ, and CLrʹ represent hepatic and renal blood clearance in the absence
and presence of the perpetrator, respectively. CLint,h is the intrinsic hepatic
clearance, fu,b is the fraction unbound in blood, and Qh is the hepatic blood flow
[20.7 ml/min/kg (Kato et al., 2003)]. Assuming no or negligible tubular
reabsorption, renal clearance is expressed as a function of the glomerular
filtration rate (GFR, 1.78 ml/min/kg) and active secretion (CLsec).
Net-Effect Model. Gemfibrozil interactions occur primarily at the level of
the liver (OATP1B1 and CYP2C8 inhibition) and kidney (OAT3 inhibition).
Therefore, for victim drugs not subjected to active hepatic uptake, the AUCR
was predicted using the static net-effect model:
AUCR ¼ Fa9 Fg9
×
× Fa Fg
f Hepatic ×
1
sec
þ f Renal × ðRIfOAT3
+RICYPfm×CYP
þ
1
2
+fm
þ f GFR Þ
CYP
TDICYP
ð2Þ
where
f Hepatic þ f Renal ¼ 1
+fmCYP þ 1 2 +fmCYP ¼ 1
and
f sec þ f GFR ¼ 1 The terms fHepatic and fRenal are the fraction of parent drug cleared by hepatic
metabolism and renal clearance after oral dosing in the absence of inhibitor
drug, respectively. The term fmCYP is the fraction of the fHepatic metabolized by
a particular CYP enzyme. Assuming no tubular reabsorption, fsec and fGFR are
the fraction (of the fRenal) of parent drug cleared by active secretion and
glomerular filtration, respectively. For the victim drug with no renal clearance
(i.e., fRenal = 0), eq. 2 is reduced to eq. 3, which is commonly used to describe
P450-mediated DDI predictions (Obach et al., 2006):
AUCR ¼ 1
Fa9 Fg9
×
× Fa Fg +RI fm×CYP
þ
1 2 +fmCYP
CYP TDICYP
ð3Þ
RICYP is the competitive inhibition term (eq. 6), TDICYP is the time-dependent
inhibition term (eq. 7) against hepatic P450 enzymes, and RIOAT3 is the
competitive inhibition term (eq. 6) against renal basolateral transporter OAT3.
Extended Net-Effect Model Accounting for Transporter-Enzyme Interplay. For victim drugs that are subjected to active hepatic uptake (e.g.,
statins and glinides), CLint,h is mathematically defined by the extended
clearance concept (Liu and Pang, 2005; Shitara et al., 2006; Shitara and
Sugiyama, 2006; Camenisch and Umehara, 2012; Barton et al., 2013; Li et al.,
2014):
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to focus on the development of mechanistic models for the perpetrator
drug/metabolite pairs for amiodarone (Chen et al., 2015), bupropion,
gemfibrozil, and sertraline.
Gemfibrozil, when administered at a total daily dose of 900 or
1200 mg, improves lipid and apolipoprotein profiles, particularly verylow-density lipoprotein triglyceride and high-density lipoprotein
cholesterol levels, in all types of dyslipidemia (except type I) (Spencer
and Barradell, 1996). Gemfibrozil is thus a highly prescribed and
well-tolerated drug; however, its concomitant use with repaglinide is
contraindicated because of documented drug interactions (Niemi et al.,
2003b). As a perpetrator drug, gemfibrozil presents a complex profile
involving inhibition of multiple transporters and cytochrome P450
2C8 (CYP2C8) by parent and a circulating metabolite, gemfibrozil
1-O-b-glucuronide (Gem-Glu). Cerivastatin was withdrawn from the
worldwide market in 2001 because of a higher incidence of fatal
rhabdomyolysis linked to increased exposure caused by gemfibrozil
(Farmer, 2001). Reports suggested that 12 of the 31 patients who died
had also been taking gemfibrozil along with cerivastatin.
Subsequently, several studies were conducted to understand the
mechanisms involved, and it was determined that gemfibrozil (twice
a day for 5 days) increases cerivastatin oral AUC about 5-fold, which
however was not assumed from the interaction potency of gemfibrozil
alone (Backman et al., 2002). Similar DDIs have been observed with
repaglinide (;7-fold increase in AUC) (Niemi et al., 2003b;
Honkalammi et al., 2011a), pioglitazone (;4-fold increase in AUC)
(Aquilante et al., 2013), and rosiglitazone (;2.5-fold increase in
AUC) (Niemi et al., 2003a). The results of complimentary in vitro
mechanistic studies suggest that gemfibrozil dosing leads to CYP2C8
inhibition, which is attributed to time-dependent inhibition (TDI) by
its major circulating metabolite, Gem-Glu (Ogilvie et al., 2006).
Furthermore, both parent and metabolite have also been shown to
behave as moderate inhibitors of organic anion transporting polypeptide 1B1 (OATP1B1), renal organic anion transporter 3 (OAT3),
and weakly inhibit cytochrome P450 2C9 (CYP2C9) and cytochrome
P450 3A4 (CYP3A4) (Fujino et al., 2003; Shitara et al., 2004).
Consequently, quantitative rationalization of gemfibrozil DDIs is
challenging, and requires extensive in vitro characterization and
mechanistic integration of all associated interaction components.
Rich clinical data are available, with several DDI studies reported
for gemfibrozil, which is now suggested as the clinical probe
inhibitor for CYP2C8 activity by the FDA, the EMA, and other
agencies (CDER, 2012; CHMP, 2012). Given the complexity of
gemfibrozil DDIs, the main objectives of the present study are 1)
to mechanistically rationalize the clinical DDIs with a goal to apply
the quantitative understanding in the prospective prediction of
gemfibrozil interactions before human dosing and enable subsequent
clinical study planning and 2) assess the contribution of the circulating
metabolite to the magnitude of DDIs. By leveraging information
from 48 different clinical DDI studies reported for gemfibrozil, in
conjunction with the available in vitro inhibition data for parent and
metabolite, we could employ both static mechanistic and dynamic
physiologically based pharmacokinetic (PBPK) modeling approaches
to predict the impact of gemfibrozil on the oral pharmacokinetics of
several victim drugs, including rosiglitazone, pioglitazone, cerivastatin, and repaglinide.
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Varma et al.
CLint;h ¼ SFactive
+CLint;CYP þ CLint;bile
: PSactive þ PSpd × PSpd þ +CLint;CYP þ CLint;bile
ð4Þ
CLint;h 9¼
SFactive : PSactive
þ PSpd :
RIOATP
CL
+RICYP : int;CYP
TDICYP þ CLint;bile
CL
PSpd þ +RICYP : int;CYP
þ
CL
int;bile
TDICYP
ð5Þ
½Iu;max;in ½Iu;max;in ½Iu;max ; RICYP ¼ 1 þ +
; RIOAT3 ¼ 1 þ +
KiOATP
KiCYP
KiOAT3
ð6Þ
TDICYP ¼
kdeg;h þ
½Iu;max;in : kinact
½Iu;max;in þ KI
kdeg;h
ð7Þ
Ki (KiOATP, KiCYP, and KiOAT3) is the inhibition constant, Iu,max is the maximum
unbound plasma perpetrator concentration, and Iu,max,in is the maximum
unbound perpetrator concentration at the inlet to liver, calculated using eq. 8.
The kinact and KI are the maximal inactivation rate constant and the inhibitor
concentration that supports half the maximal rate of inactivation, respectively,
and kdeg,h is the apparent first-order degradation rate constant of the affected
hepatic enzyme. The following interaction parameters were used: gemfibrozil
KiOATP (2.54 mM), Gem-Glu KiOATP (7.9 mM), gemfibrozil KiCYP2C8 corrected
for binding to microsomal protein (6.9 mM), Gem-Glu KI corrected for binding
to microsomal protein and kinact against CYP2C8 (7.9 mM and 12.6 h21),
gemfibrozil KiOAT3 (3.4 mM), Gem-Glu KiOAT (9.9 mM), and itraconazole and
4-hydroxy itraconazole KiCYP3A (0.0013 mM and 0.014 mM, respectively)
(Schneck et al., 2004; Nakagomi-Hagihara et al., 2007a,b; VandenBrink et al.,
2011; Barton et al., 2013).
Because of the minor effect on CYP3A and intestinal transporters,
gemfibrozil is assumed to have no effect on Fa.Fg, thus the change in intestinal
absorption and extraction is minimal (Faʹ.Fgʹ/Fa.Fg ;1.0). However, the change
in Fg due to CYP3A inhibition [as described elsewhere (Fahmi et al., 2008)] by
itraconazole was considered when predicting DDIs involving the combination
of gemfibrozil and itraconazole. Iu,gut, the free intestinal concentration of the
perpetrator, was estimated by eq. 9:
½Iu;max;in ¼ f u;b :
½Iu;gut ¼
!
Dose : Ka : Fa :Fg
Imax;b þ
Qh
Dose : Ka : Fa : f u;gut
Qgut
CLsec
ð10Þ
I
1 þ Kimax;u
OAT3
In vivo CLint,h was calculated using the well-stirred liver model (Pang and
Rowland, 1977):
CLint;h ¼
f u;b
CLh
h
: 1- CL
Qh
ð11Þ
where CLh [= (CLp 2 CLr)/Rb] is the hepatic blood clearance obtained from
intravenous total plasma clearance corrected for renal clearance and blood-toplasma ratio (Rb).
PBPK Modeling and Simulations
RIOATP is the competitive inhibition term (eq. 6) for active hepatic uptake
(Giacomini et al., 2010; CDER, 2012; Barton et al., 2013). RICYP, TDICYP, and
RIOAT were defined above:
RIOATP ¼ 1 þ +
CLsec 9 ¼ ð8Þ
ð9Þ
Modeling and simulations were performed using the population-based
absorption, distribution, metabolism, and excretion (ADME) simulator Simcyp
(version 13; Simcyp Ltd., Sheffield, United Kingdom). Each simulation was
performed for 50 virtual populations of healthy subjects (5 trials 10 subjects).
The PBPK models for gemfibrozil and Gem-Glu were similar to those
described elsewhere (Varma et al., 2012; Barton et al., 2013). Briefly, a model
for gemfibrozil was constructed using the clinical first-order absorption rate
(Fa) observed volume of distribution (0.08 l/kg) and oral clearance (6.0 l/h)
(Table 1). Based on the in vitro data, the model considered 79% of gemfibrozil
dose underwent glucuronidation to form Gem-Glu, and the rest was accounted
for by CYP3A-dependent metabolism (Kilford et al., 2009). A lag-time of
15 minutes was considered for oral absorption based on the oral pharmacokinetic profiles (Varma et al., 2012). A model for Gem-Glu was built based on
metabolite formation rate from gemfibrozil, fu, and the blood/plasma ratio. Due
to the lack of disposition data for the metabolite, the Vss (0.1 l/kg) and biliary
intrinsic clearance (5.2 ml/min/106cells) for the metabolite were estimated
(assuming complete elimination via bile) by “retrograde” fitting to the observed
plasma concentration-time profile, assuming perfusion-limited disposition.
The following interaction parameters were applied in the PBPK model:
gemfibrozil KiOATP (2.54 mM), Gem-Glu KiOATP (7.9 mM), gemfibrozil
KiCYP2C8 corrected for binding to microsomal protein (6.9 mM), Gem-Glu KI
corrected for binding to microsomal protein and kinact against CYP2C8 (7.9 mM
and 12.6 h21), and gemfibrozil KiCYP3A (68.1 mM) and Gem-Glu KiCYP3A
(103.7 mM) (Schneck et al., 2004; Nakagomi-Hagihara et al., 2007b;
VandenBrink et al., 2011; Barton et al., 2013). Inhibition of renal transporters
was not included in the PBPK model as the victim drugs evaluated with PBPK
modeling are not eliminated in the urine.
For pioglitazone, a model was developed assuming rapid-equilibrium
between liver and blood compartments using the physicochemical properties
and in vitro preclinical data such as human plasma fu, blood-to-plasma ratio,
and metabolic intrinsic clearance values (Table 1). In the case of rosiglitazone,
the model (also assumed rapid equilibrium) was adopted from Simcyp V13
compound files. Models for the OATP1B1 substrates, cerivastatin and
repaglinide, were built using the approach similar to that described earlier
(Varma et al., 2012; Barton et al., 2013; Jamei et al., 2014). A full-PBPK model
using the Rodgers and Rowland (2006) method considering rapid equilibrium
between blood and tissues was adopted to obtain the distribution into all organs
except the liver. Permeability-limiting distribution into liver was considered, for
which sinusoidal active uptake intrinsic clearance and passive diffusion
obtained from sandwich-cultured human hepatocytes studies were incorporated
to capture hepatic disposition. The intrinsic transport clearance values were
adopted from an earlier report (Varma et al., 2014). A scaling factor for the
active uptake intrinsic clearance, which was initially assumed as unity, was
estimated by a “top-down” approach of fitting to the intravenous plasma
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PSactive and PSpd are sinusoidal active uptake clearance and passive
diffusion, respectively. Active uptake was assumed to be primarily OATP1B1mediated transport (basolateral active efflux is not considered). CLint,bile is
biliary intrinsic clearance. +CLint,CYP represents the sum of intrinsic metabolic
clearances by individual CYP enzymes and can also be expressed as: CLint,met.
fmCYP-X + CLint,met.(1 2 fmCYP-X), where CLint,met is the total intrinsic
metabolic clearance. SFactive represents the empirical scaling factor for active
uptake estimated by matching the in vitro CLint,h (eq. 4) to the in vivo CLint,h
obtained from intravenous pharmacokinetics (eq. 11). The geometric mean
SFactive of 10.6, established by utilizing sandwich-cultured human primary
hepatocytes (three hepatocyte lots) for 10 different OATP substrates, was
applied (Varma et al., 2014). The in vitro intrinsic values were scaled assuming
the following: 118 106 hepatocytes g21 liver, 39.8 mg microsomal protein g21
liver, and 24.5 g liver kg21 body weight (Varma et al., 2013b).
In the presence of perpetrator, the expected net-effect of reversible
inhibition, time-dependent inhibition and induction, can be represented by
Dose, Imax,b, Ka, fu,gut, and Qgut [248 ml/min (Fahmi et al., 2008)] represent
the total dose given orally, maximum total blood concentration, absorption rate
constant, fraction unbound in the gut, and enterocytic blood flow, respectively.
Consistent with the earlier reports, kdeg,h was assumed to be 0.019 h21 for
hepatic CYP2C8 (Fahmi et al., 2008; Lai et al., 2009).
Changes in active renal secretion (CLʹsec) caused by inhibition of OAT3 by
gemfibrozil is described by a basic model (Feng et al., 2013):
(Jaakkola et al., 2006)
27.5 (HLM)
1.5 (HLM)
6.1 (CYP2C19-HLM)
Minimal PBPK
0.253
1
0.32
102 (CYP2C9-HLM)
191 (HLM)
Minimal PBPK
0.12
1
1st order
1 (Ka = 3.6 h21)
ADAM
0.98
31
357.4
2.6
Ampholyte
6.25 and 6.32
0.002
0.57
Rosiglitazoneb
356.4
3.5
Monoprotic base
5.53
0.015
1
Pioglitazonea
0.99
26.1
1.873
1
0.143/0.028
22
35.5
18.7f
(Varma et al., 2013a)
93 (HLM)
38 (HLM)
Full PBPK (method 2)
0.256
ADAM
452.6
4.87
Ampholyte
4.19 and 5.78
0.015
0.62
Repaglinidec
0.97/0.025
17.5
16.8
30.5f
(Muck et al., 1997; Varma et al., 2014)
19.1 (HLM)
12.7 (HLM)
Full PBPK (method 2) (Kp scalar 1.5)
0.31
ADAM
0.75
10
1.873
1
459
1.8
Monoprotic acid
5
0.013
0.76
Cerivastatina
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ADAM, advanced dissolution, absorption, and metabolism model; P, partition coefficient; pKa, acid dissociation constant.
a
Models developed in this current study based on the literature in vitro data (pioglitazone) or in-house in vitro data (cerivastatin).
b
Rosiglitazone model was adopted from Simcyp V13 compound files.
c
Model adopted from a previous report (Barton et al., 2013).
d
Model modified from a previous report (Barton et al., 2013).
e
CLint,CYP3A4 in ml/min/pmol.
f
Estimated by fitting to intravenous data. See Materials and Methods.
Physicochemical properties
Molecular weight (g/mol)
Log P
Compound type
pKa
Fraction unbound
Blood/plasma ratio
Absorption
Absorption type
Fraction absorbed
Caco-2 permeability (1026 cm/s)
Absorption scalar
fuGut
Distribution
Distribution model
Vss (l/kg)
Elimination
CLint,CYP2C8 (ml/min/mg)
CLint,CYP3A4 (ml/min/mg)
CLint,others (ml/min/mg)
Biliary CLint (ml/min/1026cells)
Renal clearance (l/h)
Hepatobiliary transport
Liver unbound fraction (Intra-/extracellular)
Passive diffusion (ml/min/1026cells)
CLint,active (ml/min/1026cells)
Scaling factor (active uptake)
References
Parameters
TABLE 1
Summary of victim drug input parameters for PBPK modeling and simulations
(Varma et al., 2013a)
0.29 (rCYP)e
65 (UGT2B7-HLM)
Minimal PBPK
0.08
1
1st order
1 (Ka = 3 h21)
250.3
4.3
Monoprotic acid
4.75
0.008
0.825
Gemfibrozild
(Varma et al., 2013a)
5.2
Minimal PBPK
0.1
1
426.5
3.3
Monoprotic acid
2.68
0.115
0.825
Gemfibrozil-1-O-b-Glucuronided
Quantitative Rationalization of Gemfibrozil DDIs
1111
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Varma et al.
concentration-time profile while fixing the rest of the parameters (Varma et al.,
2012; Barton et al., 2013). Absorption phase in the model was captured by the
ADAM model using Caco-2 permeability data.
Results
Discussion
Gemfibrozil is a widely prescribed drug and is also the
recommended inhibitor to probe the role of CYP2C8 in the clearance
Fig. 1. Prediction of gemfibrozil drug interactions based on static net-effect models.
Observed versus predict AUCRs when considering the interaction potential of parent alone
(A) and when assuming the combined effect of
both parent and Gem-Glu (B). Static predictions are based on the net-effect model for
victim drugs that are not substrates to active
hepatic uptake (circles) and using an extended
net-effect model for victim drugs that are
OATP1B1 substrates (triangles). Open data
points represent interactions with gemfibrozil
alone treatment. Filled data points represent
interactions with gemfibrozil and itraconazole
combination treatment. Diagonal solid, dotted,
and dashed lines indicate unity, 1.5-fold, and 2fold range, respectively. Vertical and horizontal
dotted lines drawn at AUCR of 1.25 represent
bioequivalence boundaries.
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Prediction of Gemfibrozil Interactions Using Static Models.
Two mechanistic static models were used to predict gemfibrozil DDIs
depending upon the disposition characteristics of the victim drugs:
a net-effect model (Fahmi et al., 2008), assuming rapid equilibrium
between liver and blood compartments for victim drug (not substrate
to OATPs), and an extended net-effect model (Varma et al., 2013b,
2014), considering the extended clearance concept for the victim drug
(OATPs substrate). Assuming inhibition of hepatic OATP1B1 and
CYP2C8 and renal OAT3 by both gemfibrozil and Gem-Glu, the
AUCR in 75% of cases was predicted within 1.5-fold of the clinically
observed AUCRs; 98% were predicted within 2-fold of the observed
AUCRs (Fig. 1). Furthermore, on the basis of bioequivalence limits,
only three false-positive (predicted .1.25-fold, observed #1.25-fold)
and three false-negative (predicted #1.25-fold, observed .1.25-fold)
predictions were noted among the 48 DDIs assessed. Imatinib,
zopiclone, and (S)-ibuprofen were false-positive predictions, although
the predicted AUCRs are below 1.5.
The three false-negative predictions involved simvastatin, ezetimibe, and empagliflozin, which are suggested to possess some degree
of hepatic active uptake transport (Oswald et al., 2008; Macha et al.,
2014)—not captured in the current assessment due to limited
transporter data.
Inhibition of OAT3-mediated renal secretion by the parent and
metabolite contributed significantly to the predicted AUCR of
sitagliptin, pravastatin, and rosuvastatin (Table 2). However, such an
effect was not seen with other victim drugs due to their minimal renal
secretion.
When combined with gemfibrozil, itraconazole (a potent CYP3A
inhibitor) increased the AUCR of victim drugs including repaglinide
and loperamide. Static models predicted these complex effects within
1.5-fold of the observed values. Clearly, parent (gemfibrozil) alone
significantly underpredicted the AUCR (Fig. 1A), whereas inclusion
of the metabolite (Gem-Glu) in the static models recovered the
observed AUCRs (Fig. 1B).
PBPK Modeling and Quantitative DDI Predictions. A PBPK
model was developed based on the data in Table 1, which described the
plasma concentration-time profiles of gemfibrozil and Gem-Glu (Fig.
2). On the basis of in vitro data, model predictions suggest that the
presence of Gem-Glu almost completely inhibited CYP2C8 activity
after the first dose of 600 mg of gemfibrozil. Moreover, consideration of
Gem-Glu increased the inhibition of OATP1B1 (Fig. 2C).
PBPK models for the victim drugs not subjected to active hepatic
uptake, pioglitazone and rosiglitazone, were developed assuming rapid
equilibrium between blood and all tissue compartments (Table 1).
However, permeability-limited hepatic disposition was assumed for
the OATP1B1 and CYP2C8/3A4 dual substrates repaglinide and
cerivastatin. Models were constructed using the in silico, in vitro, and
intravenous pharmacokinetic data and were further verified using the
oral pharmacokinetic profiles (Fig. 3). The model predictions of
pioglitazone-gemfibrozil interaction are similar to those observed in
the clinic when considering TDI by Gem-Glu, without which no
significant AUC change was predicted (Fig. 3A). Similarly, in the case
of rosiglitazone interactions, the parent alone predicted no AUC
change (,1.25-fold) whereas incorporation of Gem-Glu in the
predictions recovered the observed AUC ratio (Fig. 3B). Repaglinide
and cerivastatin interactions with gemfibrozil are well recovered when
both parent and metabolite are considered (Fig. 3, C and D).
Gemfibrozil alone showed weak DDI (,1.5-fold) for the OATP1B1
substrates repaglinide and cerivastatin.
A sensitivity analysis on the impact of metabolite-to-parent
exposure on gemfibrozil DDIs suggested that less than 10% of the
circulating Gem-Glu after 600 mg of gemfibrozil is enough to move
the DDI category from weak or no interaction (,2-fold) to a moderate
risk category (2- to 5-fold) (Fig. 4). Change in pioglitazone and
rosiglitazone AUC was minimal above the metabolite-to-parent ratio
of 0.1, whereas a significant increase in the cerivastatin and
repaglinide AUC was predicted as the metabolite-to-parent ratio
increase due to competitive inhibition of OATP1B1-mediated hepatic
uptake.
(S)-warfarin
Lovastatin
Imatinib
(R)-warfarin
Brivaracetam
Lovastatin
Zopiclone
Zafirlukast
(S)-ibuprofen
Alogliptin
Glimepiride
(R)-ibuprofen
Simvastatin
Atorvastatin acid
Ezetimibe
Sitagliptin
Pitavastatin
Empagliflozin
Repaglinide
Rosuvastatin
Pravastatin
Loperamide
Rosiglitazone
Pioglitazone
Pioglitazone
Pioglitazone
Repaglinide
Pioglitazone
Pioglitazone
Montelukast
Enzalutamide
Montelukast
Cerivastatin
Montelukast
Repaglinide
Pioglitazone
Repaglinide
Repaglinide
Repaglinide
Repaglinide
Repaglinide
Repaglinide
Repaglinide
Repaglinide
Repaglinide
Repaglinide
Loperamide
Repaglinide
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
ENE
NE
NE
ENE
NE
ENE
ENE
ENE
NE
NE
NE
NE
NE
ENE
NE
NE (+Itra)
NE (+Itra)
NE
NE
ENE
NE
ENE
NE
ENE
ENE
ENE
ENE
ENE
ENE
ENE
ENE
ENE
ENE
NE (+Itra)
ENE (+Itra)
600
600
600
600
mg (8 d)
mg (3 d)
mg (6 d)
mg (8 d)
mg (7 d)
mg (3 d)
mg (3 d)
mg (5 d)
mg (3 d)
mg (7 d)
mg (2.5 d)
mg (3 d)
mg (3 d)
mg (7 d)
mg (7 d)
mg (3 d)
mg (7 d)
mg (5 d)
30 mg
600 mg (7 d)
600 mg (3 d)
600 mg (5 d)
600 mg (4 d)
600 mg (4 d)
600 mg (4 d)
600 mg (1 wk)
30 mg (5 d)
600 mg (4 d)
mg + 100 mg Itra
mg + 100 mg Itra
600 mg (21 d)
600 mg (5 d)
600 mg (3 d)
600 mg (3 d)
100 mg
600 mg (4 d)
600 mg
100 mg (5 d)
600 mg
300 mg
600 mg (2.5 d)
600 mg (5 d)
600 mg (3 d)
600 mg (3 d)
600 mg (3 d)
900 mg
mg + 100 mg Itra
mg + 100 mg Itra
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
Gemfibrozil Dose
(5 d)
(3 d)
(4 d)
(5 d)
0
0
0.25
0
0
0
0.3
0
0.23
0
0.2
0.25
0
0
0
0
0
0
0.71
0
0
0.38
0.65
0.68
0.68
0.68
0.71
0.68
0.68
0.81
0.75
0.81
0.6
0.81
0.71
0.68
0.71
0.71
0.71
0.71
0.71
0.71
0.71
0.71
0.71
0.71
0.38
0.71
(0.53)
(0.24)
(0.1)
(0.0)
fmCYP2C8a
0.46
0
0.38
0.73
0.26
0.03
0.71
0.7
0.16
Fsec
0.05
FRenal
0.9
0.9
0.9
0.9
0.9
1.0
1.0
1.0
1.1
1.1
1.2
1.3
1.4
1.4
1.5
1.5
1.5
1.6
1.8
1.9
2.0
2.2
2.3
3.1
3.2
3.4
3.4
3.8
3.9
4.0
4.3
4.3
4.4
4.5
4.5
4.7
5.0
5.6
6.4
6.7
7.0
7.0
7.6
7.7
8.1
8.3
12.6
19.3
Observed
AUCR
1.0
1.0
1.1
1.0
1.0
1.0
1.1
1.0
1.1
1.1
1.1
1.1
1.0
1.8
1.0
1.2
1.8
1.0
1.0
1.8
1.7
1.1
1.2
1.2
1.2
1.2
1.0
1.2
1.3
1.2
1.2
1.2
1.9
1.2
1.1
1.2
1.9
1.1
1.9
1.4
1.9
1.9
1.9
1.9
1.9
2.3
3.6
3.5
Predicted
(Gemfibrozil
Only)
1.0
1.0
1.3
1.0
1.0
1.0
1.4
1.0
1.3
1.2
1.2
1.3
1.0
2.7
1.0
1.4
2.8
1.0
1.9
2.7
2.2
1.6
2.8
3.1
3.1
3.1
1.9
3.1
4.5
5.2
4.0
5.2
4.3
5.2
2.3
3.1
4.8
2.3
4.8
3.4
4.8
4.8
4.8
4.8
4.8
6.0
13.4
26.5
Predicted
(Gemfibrozil
and Gem-Glu)
References
(Karonen et al., 2012)
(Backman et al., 2002)
(Karonen et al., 2010)
(Kajosaari et al., 2005a; Honkalammi et al., 2011a)
(Jaakkola et al., 2006; Aquilante et al., 2013)
(Kajosaari et al., 2005a; Honkalammi et al., 2011b)
(Kajosaari et al., 2005a; Honkalammi et al., 2012)
(Kajosaari et al., 2005a; Honkalammi et al., 2011b)
(Kajosaari et al., 2005a; Honkalammi et al., 2011a)
(Kajosaari et al., 2005a; Tornio et al., 2008)
(Kajosaari et al., 2005a; Honkalammi et al., 2012)
(Kajosaari et al., 2005a; Backman et al., 2009)
(Kajosaari et al., 2005a; Kalliokoski et al., 2008)
(Niemi et al., 2003b; Kajosaari et al., 2005a)
(Kajosaari et al., 2005a; Honkalammi et al., 2011a)
(Kim et al., 2004; Niemi et al., 2006)
(Niemi et al., 2003b; Kajosaari et al., 2005a)
(Macha et al., 2014)
(Honkalammi et al., 2011a)
(Schneck et al., 2004)
(Kyrklund et al., 2003)
(Kim et al., 2004; Niemi et al., 2006)
(Baldwin et al., 1999; Niemi et al., 2003a)
(Jaakkola et al., 2006; Aquilante et al., 2013)
(Jaakkola et al., 2005, 2006)
(Deng et al., 2005; Jaakkola et al., 2006)
(Honkalammi et al., 2012)
(Jaakkola et al., 2006; Aquilante et al., 2013)
(Jaakkola et al., 2005)
(Karonen et al., 2012)
(Niemi et al., 2001)
(Tornio et al., 2007; Chang et al., 2008)
(Backman et al., 2000; Prueksaritanont et al., 2003)
(Whitfield et al., 2011)
(Ghosal et al., 2004; Reyderman et al., 2004; Kosoglou et al., 2005)
(Vincent et al., 2007; Arun et al., 2012)
(Kaminsky and Zhang, 1997; Lilja et al., 2005)
(Wang et al., 1991; Chen et al., 2012)
(Nebot et al., 2010; Filppula et al., 2013)
(Kaminsky and Zhang, 1997; Lilja et al., 2005)
(Nicolas et al., 2012)
(Wang et al., 1991; Kyrklund et al., 2001)
(Becquemont et al., 1999; Tornio et al., 2006)
(Dekhuijzen and Koopmans, 2002; Karonen et al., 2011)
(Tornio et al., 2007; Chang et al., 2008)
Downloaded from dmd.aspetjournals.org at ASPET Journals on June 18, 2017
ENE, extended net-effect model; Itra, itraconazole; NE, net effect model.
a
Value in parentheses indicates fmCYP3A4.
Victim Drug
Static Model
TABLE 2
Summary of victim-gemfibrozil DDI predictions using mechanistic static models
Quantitative Rationalization of Gemfibrozil DDIs
1113
1114
Varma et al.
of investigational drugs. Here we attempted to rationalize a wide array
of clinical DDIs of gemfibrozil using a set of 48 data points, so as to,
1) implement the learnings for the prospective predictions of
gemfibrozil interactions early in drug development and strategize the
clinical studies, 2) understand the quantitative role of a major
circulating metabolite in governing the magnitude of the DDI, and 3)
assess the ability of mechanistic static and dynamic PBPK models to
predict complex drug interactions involving transporter-enzyme
interplay and multiple inhibitory species (parent and metabolite).
Gemfibrozil and its metabolite, Gem-Glu, can bring about DDIs by
multiple mechanisms, which include CYP2C8, OATP1B1, and OAT3
inhibition. These mechanisms are simultaneously implemented in the
mechanistic models to predict the DDIs of victim drugs with a wide
range of disposition characteristics. For a dataset of 48 DDIs, the static
models predicted 75% and 98% of the cases within 1.5- and 2-fold of
the observed AUCR values (Fig. 1). Similarly, for representative drugs
(pioglitazone, rosiglitazone, cerivastatin, and repaglinide), PBPK
modeling and simulations well recovered the plasma concentrationtime profiles in control and gemfibrozil treatment conditions (Fig. 3).
It was obvious that the inclusion of Gem-Glu in the static and PBPK
models greatly improved the accuracy of the predictions—
consideration of parent only led to a significant underprediction of
AUCR (Fig. 1 and 3). Furthermore, model predictions suggest that
Gem-Glu has a much larger inhibitory effect than that of the parent in
vivo (Fig. 4).
Repaglinide and cerivastatin are mainly metabolized by CYP2C8
and CYP3A4 to their respective major oxidative metabolites, and they
are subjected to OATP1B1-mediated sinusoidal uptake also (Kajosaari
et al., 2005b; Niemi et al., 2005; Jones et al., 2012). Consequently, one
would have to anticipate complex DDIs when these drugs are dosed
with perpetrators impacting one or more of these processes. We have
characterized the hepatic transport of repaglinide and cerivastatin
using sandwich-cultured human hepatocytes and have developed
mechanistic models incorporating transporter-enzyme interplay. In
terms of extended clearance concept (Shitara et al., 2006; Li et al.,
2014), the total hepatic uptake for repaglinide and cerivastatin is
significantly higher than the passive uptake, and the passive uptake
and metabolic intrinsic clearance are within a similar range (Table 1).
Therefore, the systemic clearance of these drugs decreases by
inhibiting active uptake or metabolism, with a much larger impact
caused by simultaneous inhibition of both processes (Varma et al.,
2015).
Earlier studies suggested an in vitro–in vivo disconnect in the
repaglinide fmCYP2C8 on the basis of a collection of clinical
repaglinide-gemfibrozil DDI reports (Bidstrup et al., 2003; Kajosaari
et al., 2005a; Honkalammi et al., 2011a, 2012). However, with the
current mechanistic models (extended net-effect and PBPK models)
accounting for transporter-enzyme interplay, repaglinide-gemfibrozil
interactions are closely predicted using fmCYP2C8 determined in vitro.
Repaglinide and cerivastatin share similar ADME characteristics: both
are highly permeable OATP1B1 and CYP2C8/3A4 substrates with
intrinsic transport and metabolic clearances in a similar range
(Table 1). With the static and PBPK models, cerivastatingemfibrozil interaction of ;5-fold can be recovered with in vitro
fmCYP2C8 of only 0.65 (Fig. 3). Overall, consistent with the in vitro
observations, both CYP2C8 and CYP3A contribute to the metabolism
Downloaded from dmd.aspetjournals.org at ASPET Journals on June 18, 2017
Fig. 2. Pharmacokinetics and CYP2C8/OATP1B1 interaction profiles of gemfibrozil. PBPK model prediction of the plasma concentration-time profiles of gemfibrozil (A)
and Gem-Glu (B) after oral dosing of 600 mg twice a day of gemfibrozil. Data points are mean observed values from separate studies (Tornio et al., 2008; Honkalammi et al.,
2011b). (C) Predicted in vivo CYP2C8 inhibition potential (red curves) and OATP1B1 inhibition potential (green curves) of parent alone (dashed lines) and parent/metabolite
pair (solid lines) after 600 mg twice a day of gemfibrozil. (D) CYP2C8 inhibition potential (red curves) and OATP1B1 inhibition potential (green curves) of parent/
metabolite pair after 30 mg twice a day (dashed lines), 100 mg twice a day (dashed-dotted lines), and 600 mg twice a day (solid lines) of gemfibrozil.
Quantitative Rationalization of Gemfibrozil DDIs
1115
of repaglinide and cerivastatin in vivo, while the larger than expected
clinical interaction with gemfibrozil is due to simultaneous inhibition
of both OATP1B1-mediated active uptake and CYP2C8-based
metabolism.
Recent regulatory guidelines have suggested the use of repaglinide
as a clinical probe for the assessment of CYP2C8 and OATP1B1
inhibition (CDER, 2012; CHMP, 2012). However, based on the in
vitro findings and the mechanistic evaluation, in vivo fmCYP2C8 could
be smaller than previously thought, and repaglinide may not be an
ideal substrate to probe CYP2C8 inhibition in vivo. It is expected that
a potent OATP1B1 inhibitor or potent CYP3A and OATP1B1
inhibitor combination can also result in a comparable increase in
repaglinide AUC (Varma et al., 2013a). On the other hand,
investigational drugs with moderate reversible inhibition of CYP2C8
and no OATP1B1 inhibition will likely precipitate weak or no clinical
DDI with repaglinide as the victim drug. Due to the lack of alternative
sensitive probes, CYP2C8 substrates with no active hepatic uptake
that show an observed and predicted DDI in the moderate to high
range, such as pioglitazone and montelukast, should be considered for
clinical studies (Table 2).
One of the aims of this study was to determine the quantitative
contribution of the major metabolite(s) to the in vivo DDIs, and to
examine the application of the regulatory recommendations to trigger
in vitro investigation of the interaction potential of metabolites when
present at $25% of the parent AUC. Sensitivity analysis on the impact
of the metabolite-to-parent ratio on AUCR suggested that Gem-Glu
exposure at ;10% of the parent moved the DDI category from no or
weak (AUCR ,2) to moderate (2 , AUCR,5) for all four victim
drugs (Fig. 4). This suggests that the Gem-Glu exposure needed to
bring about complete inactivation of CYP2C8 is only a fraction of that
achieved in the clinic after a 600-mg gemfibrozil dose. However, due
to moderate OATP1B1 inhibition by the metabolite, an increase in the
metabolite-to-parent ratio further increased the AUCR for repaglinide
and cerivastatin (Fig. 4, C and D). These model predictions are
consistent with clinical findings where a subtherapeutic gemfibrozil
dose (30 mg; 20 times lower than therapeutic dose) caused a ;3.4-fold
increase in repaglinide AUC while a further increase in AUCR was
noted with higher doses (100-mg and 600-mg gemfibrozil doses
yielded repaglinide AUCR of ;5.5 and ;7, respectively) (Honkalammi
et al., 2012). Overall, the default metabolite-to-parent exposure cutoff
($25%) may not firmly reflect the DDI potential of gemfibrozil due to
the CYP2C8 TDI component. Furthermore, the DDI risk in vivo when
metabolites inhibit both uptake transport and metabolism is expected to
be large. So the early risk assessment of metabolite-mediated DDIs
should consider structural alerts for TDI, in vitro interaction potential
against enzymes and transporters, and systemic exposure of both parent
and metabolites (Callegari et al., 2013; Yu et al., 2015). The resulting
data can then be integrated in the static or PBPK models for DDI
predictions, as described here.
Gemfibrozil has been proposed as strong inhibitor to assess the role
of CYP2C8 in the clinical pharmacokinetics of the investigational
drugs (CDER, 2012; CHMP, 2012). However, due to its multiple
interaction mechanisms, the interpretation of the clinical study data
will be challenging for investigational drugs that serve as OATP1B1
substrates also. Our predictions suggest the use of subtherapeutic
doses of gemfibrozil (100 mg twice a day) in such situations, where
the competitive inhibition of transporters is insignificant but the
CYP2C8 inactivation is almost complete (Figs. 2D and 4).
Downloaded from dmd.aspetjournals.org at ASPET Journals on June 18, 2017
Fig. 3. PBPK prediction of pharmacokinetic interactions of the CYP2C8 substrates pioglitazone (A) and rosiglitazone (B) and the OATP1B1-CYP2C8 dual substrates
cerivastatin (C) and repaglinide (D) after 600 mg twice a day of gemfibrozil. Shown are the predicted mean plasma concentration-time profiles in control (dashed curves) and
gemfibrozil treatment when considering interaction potential of parent alone (dotted curves) or assuming the combined effect of both parent and Gem-Glu (solid curves).
Open and filled data points are the mean observed plasma concentrations in the control and gemfibrozil treatment subjects (Niemi et al., 2003a,b; Deng et al., 2005; Jaakkola
et al., 2005; Honkalammi et al., 2011a, 2012; Aquilante et al., 2013). Where available, normalized data from multiple reports are shown.
1116
Varma et al.
The CYP2C8 inactivation mechanism of Gem-Glu (phase II
metabolite) has focused some attention on acyl-glucuronides as
TDIs of P450s, particularly CYP2C8. To date, however, inhibition
of CYP2C8 has been observed only with Gem-Glu, and more
recently with clopidogrel-acyl-b-D-glucuronide (Jenkins et al.,
2011; Tornio et al., 2014). On the other hand, ezetimibe
glucuronide, with exposure ;4 times that of the ezetimibe, has
been shown to inhibit OATPs in vitro; however, no DDI with statins
is evident (Patino-Rodriguez et al., 2014). Beyond Gem-Glu, only
clopidogrel-acyl-b-D-glucuronide has shown similar interaction
mechanisms manifesting in clinically relevant DDI. In this instance,
it has been noted that the incidence of cerivastatin-induced
rhabdomyolysis is higher (odds ratio of ;30) in clopidogrel users
(Floyd et al., 2012), potentially due to a ;5-fold increase in
cerivastatin exposure as a result of CYP2C8 inactivation and
competitive inhibition of OATP1B1 by clopidogrel and its
metabolites including clopidogrel-acyl-b-D-glucuronide (Tornio
et al., 2014). Further investigation is warranted in identifying the
structural attributes of gemfibrozil and clopidogrel acyl-glucuronides
that render CYP2C8 inactivation—which could flag probable glucuronides for early in vitro DDI assessment.
Our study reinforces the utility of in vitro data and the modeling
approaches that mechanistically integrate the multiple components
in the DDI risk assessment. The importance of understanding
transporter-enzyme interplay and the role of perpetrator metabolites needs to be emphasized. In a true prospective sense, it is
challenging to predict plasma concentrations of the metabolites at
the discovery stage due to limited in vitro information on their
disposition and/or species differences in parent/metabolite
handling. Moreover, the disposition of phase II metabolites like
acyl-glucuronides is primarily determined by hepatic and renal
transporters, which further complicate pharmacokinetic predictions. We therefore used the observed plasma concentration data
of Gem-Glu in the static models and in the process of building the
metabolite PBPK model. Early predictions that consider the sensitivity
analysis of uncertain parameters, particularly for metabolite exposure, could be helpful at the drug discovery stage, but there is need to
refine the models of both parent and metabolite(s) as the clinical
pharmacokinetic and human ADME data become available. The
mechanistic static models described here will be valuable in the early
discovery and development stage, and the PBPK models can be
applied for more refined predictions based on the initial clinical data.
Importantly, once developed and verified, such static and PBPK
models can be used to project DDIs for new victim drugs, support
risk assessment, and inform clinical DDI study planning and
prioritization.
In summary, the complex DDIs involving perpetrator parent/
metabolite pair and multiple inhibitory mechanisms (transporters and
enzymes) can be quantitatively rationalized by informing mechanistic
static and PBPK models with thoroughly characterized interaction
parameters of the perpetrator species and the disposition attributes
(transporter-enzyme interplay) of the victim drug. The mechanistic
static and PBPK model developed and validated leveraging a large
clinical dataset should provide confidence in the DDI risk assessment
involving gemfibrozil coadministration, and potentially avoid unnecessary clinical studies.
Downloaded from dmd.aspetjournals.org at ASPET Journals on June 18, 2017
Fig. 4. PBPK model based prediction of the effect of metabolite-to-parent ratio on the exposure increase of victim drugs pioglitazone (A), rosiglitazone (B), cerivastatin (C),
and repaglinide (D). Data points are the mean observed AUCR (filled) and Cmax ratio (open) from separate studies when available. Vertical dotted lines represent 25%
metabolite-to-parent exposure cutoff, above which in vitro investigation of metabolites’ interaction potential is recommended. Horizontal dotted lines represent DDI
categories (,1.25, no interaction; 1.25–2, weak interaction; 2–5, modest interaction; .5, strong interaction).
Quantitative Rationalization of Gemfibrozil DDIs
Acknowledgments
The authors thank the members of the Metabolite Scholarship PBPK
Subteam for their contributions: Drs. Ian Templeton, Hongbin Yu, Yuan Chen,
Jialin Mao, Mohamad Shebley, Chuang Lu, Chris MacLauchlin, Grant
Generaux, Edgard Schuck, Guanfa Gan, Jin Zhou, Sheila Peters, and Susanna
Tse.
Authorship Contributions
Participated in research design: Varma, Lin, Bi, Kimoto, Rodrigues.
Performed data analysis: Varma, Lin.
Wrote or contributed to the writing of the manuscript: Varma, Lin, Bi,
Kimoto, Rodrigues.
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