DRUG METABOLISM AND DISPOSITION
U.S. Government work not protected by U.S. copyright
Drug Metab Dispos 41:801–813, April 2013
A Physiologically Based Pharmacokinetic Model to Predict
Disposition of CYP2D6 and CYP1A2 Metabolized Drugs
in Pregnant Womens
Alice Ban Ke, Srikanth C. Nallani, Ping Zhao, Amin Rostami-Hodjegan, Nina Isoherranen,
and Jashvant D. Unadkat
Department of Pharmaceutics, University of Washington, Seattle, Washington (A.B.K., N.I., J.D.U.); Office of Clinical Pharmacology,
Office of Translational Sciences, Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland
(A.B.K., S.C.N., P.Z.); School of Pharmacy and Pharmaceutical Sciences, University of Manchester, Manchester, United Kingdom
(A.R.-H.); and Simcyp Limited, now part of Certara, Sheffield, United Kingdom (A.R-.H.)
Received November 19, 2012; accepted January 25, 2013
Conducting pharmacokinetic (PK) studies in pregnant women is
challenging. Therefore, we asked if a physiologically based phar-
macokinetic (PBPK) model could be used to evaluate different
dosing regimens for pregnant women. We refined and verified our
previously published pregnancy PBPK model by incorporating
cytochrome P450 CYP1A2 suppression (based on caffeine PK)
and CYP2D6 induction (based on metoprolol PK) into the model.
This model accounts for gestational age–dependent changes in
maternal physiology and hepatic CYP3A activity. For verification,
the disposition of CYP1A2–metabolized drug theophylline (THEO)
and CYP2D6–metabolized drugs paroxetine (PAR), dextromethor-
phan (DEX), and clonidine (CLO) during pregnancy was predicted.
Our PBPK model successfully predicted THEO disposition during the
third trimester (T3). Predicted mean postpartum to third trimester
(PP:T3) ratios of THEO area under the curve (AUC), maximum
plasma concentration, and minimum plasma concentration were
0.76, 0.95, and 0.66 versus observed values 0.75, 0.89, and 0.72,
respectively. The predicted mean PAR steady-state plasma con-
centration (Css) ratio (PP:T3) was 7.1 versus the observed value 3.7.
Predicted mean DEX urinary ratio (UR) (PP:T3) was 2.9 versus the
observed value 1.9. Predicted mean CLO AUC ratio (PP:T3) was 2.2
versus the observed value 1.7. Sensitivity analysis suggested that
a 100% induction of CYP2D6 during T3was required to recover the
observed PP:T3ratios of PAR Css, DEX UR, and CLO AUC. Based on
these data, it is prudent to conclude that the magnitude of hepatic
CYP2D6 induction during T3ranges from 100 to 200%. Our PBPK
model can predict the disposition of CYP1A2, 2D6, and 3A drugs
Pregnancy can affect the absorption (e.g., gastric pH, transporters),
distribution (e.g., plasma protein binding and transporters), metabo-
lism [e.g., cytochrome P450 (P450) metabolism], and excretion (e.g.,
renal secretion via transporters) (ADME) of drugs. Such changes can
result in reduced efficacy (e.g., antiepileptics, antivirals) or increased
toxicity of a drug. Considerable clinical data in the literature suggest
that the magnitude of change in maternal hepatic enzyme activity,
as reflected in the change in exposure to probe drugs, is P450
isoform–specific and gestational age–dependent (Hodge and Tracy,
2007). Many of these studies have used model (probe) drugs that
report P450 enzyme activities to delineate the magnitude of change in
the activity of major P450 enzymes, mostly during the third trimester
(e.g., caffeine for CYP1A2, metoprolol for CYP2D6, midazolam for
CYP3A, and phenytoin for CYP2C9) (Anderson, 2005).
Hepatic CYP1A2 enzyme activity, as measured by caffeine salivary
clearance, is suppressed throughout pregnancy, with the greatest
suppression of up to ;65% observed in the third trimester (T3) versus
postpartum (PP) (Tracy et al., 2005). Consistent with these data, the
oral clearance (CLORAL) of another CYP1A2 probe substrate,
theophylline (THEO), is reduced by ;30% during T3versus PP, but
This work was supported by the Food and Drug Administration Office of
Women’s Health and a visiting fellowship from Simcyp Limited (now part of
Certara). The clonidine pharmacokinetics study in pregnancy was supported in
part by a grant from the National Institutes of Health Eunice Kennedy Shriver
National Institute of Child Health and Human Development [Grant U10HD047892].
The content is solely the responsibility of the authors and does not necessarily
represent the official views of the Eunice Kennedy Shriver National Institute of
Child Health and Human Development or the National Institutes of Health.
s This article has supplemental material available at dmd.aspetjournals.org.
ABBREVIATIONS: ADME, absorption, distribution, metabolism, and excretion; AUC, area under the curve; AUCR, AUC ratio; B/P, blood-to-
plasma concentration ratio; CI, confidence interval; CL, clearance; CLH, hepatic metabolic clearance; CLint,u, unbound intrinsic clearance; CLORAL,
oral clearance; CLr, renal clearance; CLO, clonidine; Cmax, maximum plasma concentration; Cmin, minimum plasma concentration; Css, steady-state
plasma concentration; DEX, dextromethorphan; DXO, dextrorphan; EM, extensive metabolizer; Fa, fraction absorbed; Fg, intestinal bioavailability;
Fh, hepatic bioavailability; fm, fraction metabolized of total body clearance; fu,p, fraction unbound in plasma; GFR, glomerular filtration rate; IVIVE, in
vitro-in vivo extrapolation; ka, first-order absorption rate constant; Kp, tissue-to-plasma partition coefficient; MET, metoprolol; P450, cytochrome
P450; PAR, paroxetine; PBPK model, physiologically based pharmacokinetic model; PK, pharmacokinetic; PM, poor metabolizer; PP, postpartum;
SD, single dose; SS, steady state; T1, T2, and T3, first, second, and third trimesters; THEO, theophylline; UR, urinary metabolic ratio.
the CLORALduring the first (T1) and second (T2) trimesters is not
affected (Gardner et al., 1987). In contrast, the activity of hepatic
CYP2D6 appears to be increased during pregnancy. In pregnant
women, the mean CLORAL of metoprolol (MET) (100 mg orally)
during T3was almost 4-fold of that during postpartum (Hogstedt et al.,
1985). However, the i.v. clearance or the plasma protein binding of
MET (10 mg) is not affected by pregnancy. Subsequently, two studies
assessed CYP2D6 activity during pregnancy using the dextromethorphan/
dextrorphan (DEX/DXO) metabolic ratio in pregnant women. The
plasma DEX/DXO metabolic ratio (2 hours post dose, ;Tmax) during
T3is significantly reduced (;2.3-fold PP:T3) among extensive me-
tabolizers (EMs), indicating increased CYP2D6 activity (Wadelius
et al., 1997). Similarly, the 24-hour DEX urinary metabolic ratio
(UR: DEX/DXO) was significantly reduced throughout pregnancy in
subjects phenotyped as EMs, with the greatest reduction (;1.9-fold
PP:T3) observed during T3(Tracy et al., 2005). In accordance with
the increased CYP2D6 activity during pregnancy, paroxetine (PAR)
plasma concentrations steadily decrease over the course of pre-
gnancy in women genotyped as CYP2D6 EMs (Ververs et al., 2009).
The most pronounced effect (73% reduction compared with post-
partum) on PAR steady-state plasma concentrations is observed
during T3. Finally, the CLORALof clonidine (CLO) is 1.7-fold of that
in nonpregnant subjects (Buchanan et al., 2009). This increase in
CLO CLORALis most likely due to increased CYP2D6 activity, as
CYP2D6 plays a major role in in vitro CLO metabolism (Claessens
et al., 2010), and the renal clearance (CLr) of clonidine appears not to
be affected by pregnancy.
The above-described changes in P450 activities during pregnancy
are postulated to reduce the efficacy or enhance the toxicity of drugs
during pregnancy. Since it is logistically impossible to delineate the
changes in the pharmacokinetics (PK) of all drugs administered to
pregnant women, alternative approaches that can generalize across
drugs and predict drug disposition in pregnancy are highly desirable.
Physiologically based pharmacokinetic (PBPK) modeling has the
advantage of incorporating both physiologic parameters that are
important for ADME processes and drug-specific parameters (e.g.,
physicochemical and drug disposition characteristics) into a quantita-
tive predictive model (Jamei et al., 2009; Rowland et al., 2011). A
maternal PBPK model incorporating known physiologic parameters as
well as maternal hepatic P450 activity in each trimester was recently
developed (Abduljalil et al., 2012; Gaohua et al., 2012; Lu et al.,
2012). We refined this PBPK model and showed that the PBPK model
populated with CYP3A activity change, based on CLORALof midazolam,
could accurately predict the T3disposition of other CYP3A–metabolized
drugs, nifedipine and indinavir (Ke et al., 2012). A sensitivity
analysis suggested that CYP3A induction in T3is most likely hepatic
and not intestinal. In the current study, we expanded and verified the
established PBPK model by incorporating CYP1A2 suppression and
CYP2D6 induction based on disposition of caffeine (Tracy et al.,
2005) and metoprolol data (Hogstedt et al., 1985). The model was then
used to predict the disposition during pregnancy of CYP1A2–
metabolized drug THEO, and CYP2D6–metabolized drugs PAR,
DEX, and CLO.
Materials and Methods
General Workflow of PBPK Model Development and Verification
Criterion. A general workflow of PBPK modeling and simulation of test
compounds in nonpregnant subjects consisted of the following steps. First,
mean plasma concentration-time profiles simulated using the Simcyp
Population-based Simulator (version 11.1; Simcyp Limited, Sheffield, UK)
were compared with those obtained from in vivo studies including i.v. dosing,
single and multiple oral dosing. The 13-compartment full PBPK model was
used. Second, the drug-specific parameters (e.g., fm) underwent refinement
(hence referred to as the modified model) if the prediction in the first step
deviates significantly (,0.8-fold or .1.25-fold) from that observed. Such
refinements were often based on changes in mean area under the curve (AUC)
and mean concentration-time profiles in the presence of inhibitors or genetic
polymorphism of the enzymes clearing the drug. Third, the time-varying full
PBPK model constructed in Matlab version 7.10 (2010; Mathworks, Natick,
MA) was populated with these qualified drug-specific parameters and pregnancy-
induced P450 activity changes (see the following sections).
Verification of the established PBPK model was primarily based on AUC
(for DEX data, urinary data were used as AUC data were not available) because
achieving equivalent drug exposure in pregnant and nonpregnant women was
our primary focus. The term “verification” is used in place of “validation” to
acknowledge the complexity of the PBPK model that requires more than
plasma data to accomplish proper validation. As secondary criteria, prediction
of maximum plasma concentration (Cmax) and minimum plasma concentration
(Cmin) was considered, because achieving similar drug Cmaxand Cminmay be
important for some drugs where these measures are related to drug efficacy and/
or toxicity. For model verification, 1) mean AUC, Cmax, and Cminof THEO; 2)
average steady-state concentration (Css) of PAR; 3) mean DEX/DXO UR; and
4) mean steady-state AUC of CLO during pregnancy were predicted and
compared with published studies in pregnant, CYP2D6 EM subjects. We chose
the criterion of PK bioequivalence as the criterion for successful verification of
the model, namely, the predicted mean population PK parameters of the drug
(as described earlier), should fall within 80–125% of the observed value,
i.e., 0.80 # predicted/observed # 1.25.
General Pregnancy PBPK Model Structure and Key Assumptions. The
general pregnancy PBPK model structure and key assumptions were described
in detail previously (Gaohua et al., 2012; Ke et al., 2012). Briefly, the
gestational age–dependent changes in physiologic parameters (e.g., cardiac
output, glomerular filtration rate, etc.) were incorporated into an existing PBPK
scheme (Jamei et al., 2009). Maternal glomerular filtration rate (GFR) was
assumed to increase during pregnancy by 19, 40, and 37% during T1, T2, and
T3, respectively (Abduljalil et al., 2012). Renal secretion clearance mediated by
organic cation transporter was assumed to increase by 50% during T3
(metformin CLsecretion) (Eyal et al., 2010). The fraction reabsorbed was
assumed to remain constant through gestation. The change in drug unbound
fraction in plasma (fu,p) during pregnancy, as a function of serum albumin or
a1-acid glycoprotein concentrations, was accounted for in the model as
described previously (Ke et al., 2012). The established PBPK model also
assumed that hepatic CYP3A activity increased by 99% (measured by
midazolam CLORAL) during T3(Ke et al., 2012).
The PBPK model was further expanded to incorporate pregnancy-induced
CYP1A2 suppression and CYP2D6 induction as described below. Maternal
hepatic CYP1A2 was assumed to decrease during pregnancy by 33, 48, and
65% (salivary caffeine clearance) during the first (T1), second (T2), and third
trimesters, respectively (Tracy et al., 2005). Maternal CYP2D6 activity was
assumed to increase by 200% (reported by metoprolol CLORAL) during T3
(Hogstedt et al., 1985). This value of 200% was obtained through sensitivity
analysis by varying CYP2D6 activity in the range of 50–350% induction to
recover the observed metoprolol data. Reliable assessment of the magnitude of
CYP2D6 induction in earlier trimesters has not been conducted. All other
maternal hepatic P450 activities were assumed to remain constant throughout
pregnancy. These changes were accomplished in Matlab v7.10.
THEO PBPK Model Construction. THEO physiochemical and protein
binding parameters [log Po:w(octanol–water partition coefficient), pKa, blood-
to-plasma concentration ratio (B/P ratio)], absorption [fraction absorbed (Fa),
intestinal bioavailability (Fg)], and distribution [tissue-to-plasma partition
coefficient (Kp)] were obtained from Simcyp (version 11.1). Initial scaling of in
vitro metabolic data to metabolic clearance (CLH) overpredicted THEO CLH
observed in vivo. Therefore, unbound intrinsic clearance (CLint,u) was back-
calculated from observed CLH using a well-stirred liver model. The
contributions from individual CYPs to total metabolic clearance of THEO
obtained in vitro were 91.7, 8, and 0.06% for 1A2, 2E1, and 3A, respectively
(Tjia et al., 1996). However, in vivo drug-drug interation studies using
diltiazem and verapamil as the perpetrator (both are mechanism-based
inactivators of CYP3A) reported an AUC percent change of 12–18% (Sirmans
et al., 1988; Stringer et al., 1992), suggesting that CYP3A plays a greater role in
Ke et al.
Participated in research design: Ke, Nallani, Zhao, Rostami-Hodjegan,
Conducted experiments: Ke.
Contributed new reagents or analytic tools: Rostami-Hodjegan.
Performed data analysis: Ke.
Wrote or contributed to the writing of the manuscript: Ke, Nallani, Zhao,
Rostami-Hodjegan, Isoherranen, Unadkat.
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Address correspondence to: Dr. Jashvant D. Unadkat, Department of
Pharmaceutics, University of Washington, Box 357610, Seattle, WA 98195.
PBPK Prediction of PK Changes During Pregnancy