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Physiologically-based pharmacokinetic modeling for sequential metabolism: effect of CYP2C19
genetic polymorphism on clopidogrel and clopidogrel active metabolite pharmacokinetics
Nassim Djebli, David Fabre, Xavier Boulenc, Gérard Fabre, Eric Sultan, Fabrice Hurbin
Sanofi R&D, Drug Disposition, Disposition Safety and Animal Research, Montpellier, France
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Running title: PBPK modeling for clopidogrel and its active metabolite
Corresponding author:
Nassim Djebli,
Drug Disposition, Disposition Safety and Animal Research, sanofi Recherche et Développement,
371 rue du Professeur Blayac, Montpellier, France
Number of text pages: 40
Number of tables: 4
Number of figures: 9
Number of references: 40
Words in the Abstract: 244 (max = 250)
Words in the Introduction: 614 (max = 750)
Words in the Discussion: 913 (max = 1500)
Abbreviations: AUC, area under the plasma concentration versus time curve; Cmax, maximum
plasma concentration; CYP, cytochrome P450; clopi-H4, active metabolite isomer of clopidogrel
(H4); DDI, drug-drug interaction; EM, extensive metabolizer; IM, intermediate metabolizer;
PBPK, Physiologically-Based pharmacokinetic; EM, intermediate metabolizer; IM, intermediate
metabolizer; PM, poor metabolizer; UM, ultrarapid metabolizer; VPC, visual predictive check;
MBI, Mechanism-Based Inhibitor; fa, fraction absorbed; Ka, 1st-order rate constant; Peff, effective
permeability in human; Papp, in vitro Caco-2 permeability; fugut, unbound fraction in the gut; fup,
fraction unbound in plasma; MIIS, secondary metabolite of the substrate in the specific module;
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Fg-MIIS, fraction of secondary metabolite escaping first-pass metabolism in the gut; Qvilli, the villi
blood flow; fugut-MIIS, secondary metabolite unbound fraction in the gut ; CLintG-MIIS, the total gut
intrinsic clearance; AMIIS, The formation rate of the secondary metabolite in the gut; AP, the
formation rate of the primary metabolite in the gut; fugut-P, the unbound fraction of the primary
metabolite in the gut; CLintG-P, the total gut clearance of the primary metabolite; CLintG-P-n, the nth
metabolic pathway of the primary metabolite to form the secondary metabolite; VmaxG-P-n and
KMP-n, the gut metabolism kinetic parameters of the nth pathway; Ppv, the primary metabolite
concentration in portal vein; MIISsys, the secondary metabolite systemic vein plasma
concentration; MIISpv, the secondary metabolite portal vein plasma concentration; Fg-MIIS, the
secondary metabolite fraction escaping gut metabolism; Qpv , the portal vein blood flows; Qha, the
hepatic artery blood flow; UptakeP , active uptake into hepatocyte for the primary metabolite;
UptakeMIIS, the active uptake into hepatocyte for the secondary metabolite; fub-P , the unbound
fraction of drug in blood of the primary metabolite; fub-MIIS, the unbound fraction of drug in blood
of the secondary metabolite; PLiv, the primary metabolite concentration in the liver; MIISliv, the
liver concentration of the secondary metabolite; Vd-MIIS, the secondary metabolite volume of
distribution at steady-state; Qh, the hepatic blood flow; CLr-MIIS, the secondary metabolite renal
clearance; BPMIIS, the secondary metabolite blood to plasma ratio; Peff, the effective permeability
in human; CI, confidence interval; KI and Kinact , Mechanism-based inactivation parameters; SAC,
single adjusting compartment; Vss, volume of distribution at steady-state; B/P, blood-to-plasma
ratio; fumic, unbound fraction in microsomes; Vmax, maximum velocity of the metabolizing
enzyme; KM, Mickaelis-Menten coefficient; Kdeg, degradation rate constant.
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ABSTRACT
Clopidogrel is a prodrug that needs to be converted to its active metabolite (clopi-H4) in two
sequential cytochrome P450 (CYP)-dependent steps. In the present study, a dynamic
physiologically based pharmacokinetic (PBPK) model was developed in Simcyp for clopidogrel
and clopi-H4, using a specific sequential metabolite module in 4 populations with phenotypically
different CYP2C19 activity (poor, intermediate, extensive and ultrarapid metabolizers) receiving
a loading dose of 300 mg followed by a maintenance dose of 75 mg. This model was validated
using several approaches. First, a comparison of predicted to observed AUC0-24 obtained from a
randomized cross-over study conducted in four balanced CYP2C19-phenotype metabolizer
groups was performed using a visual predictive check method. Second, the inter-individual and
inter-trial variability (based on AUC0-24 comparisons) between the predicted trials and the
observed trial of individuals, for each phenotypic group, were compared. Finally, a further
validation, based on drug-drug interaction prediction, was performed by the comparison with
observed values of clopidogrel and clopi-H4 with or without dronedarone (moderate CYP3A4
inhibitor) co-administration using a previously developed and validated PBPK dronedarone
model. The PBPK model was well validated for both clopidogrel and its active metabolite clopi-
H4, in each CYP2C19-phenotypic group, whatever the treatment period (300 mg loading dose
and 75 mg last maintenance dose). This is the first study proposing a full dynamic PBPK model
able to accurately predict simultaneously the pharmacokinetics of the parent drug, its primary and
secondary metabolite, in populations with genetically different activity for a metabolizing
enzyme.
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INTRODUCTION
The antiplatelet agent clopidogrel is a prodrug, which is metabolized by two main metabolic
pathways: an esterase-dependent pathway leading to hydrolysis into an inactive carboxylic acid
derivative (85-92% of circulating metabolites) and a cytochrome P450 (CYP)-dependent pathway
leading to its active metabolite (clopi-H4) (Lins et al., 1999, Kazui et al., 2010, Dansette et al.,
2012, Tuffal et al., 2011). Clopi-H4 is formed in a two-step oxidative process (Figure 1)
mediated by CYP1A2, CYP2B6, CYP2C19 and CYP3A4 (Kazui et al., 2010). The clopi-H4
leads to inhibition of adenosine diphosphate-induced aggregation by irreversible binding of the
platelet P2Y12 receptor (Bhatt et al., 2003).
Polymorphisms of CYP2C19 affect both the pharmacodynamic and pharmacokinetic profiles of
clopi-H4 and it has been determined that this isoform is one of the major determinants of inter-
individual variability in clopidogrel pharmacodynamic and pharmacokinetic responsiveness (Kim
et al., 2008; Hulot et al., 2006; Mega et al., 2009; Umemura et al., 2008). CYP2C19 contribution
to the formation of clopi-H4 was confirmed in a randomized cross-over study conducted in four
balanced CYP2C19-phenotyped metabolizer groups (poor, intermediate, extensive and ultrarapid
metabolizers) (Simon et al., 2011). The authors of this study also performed a meta-analysis on
data from 396 healthy subjects and confirmed that CYP2C19 is the most important polymorphic
CYP involved in clopi-H4 formation and antiplatelet response, whereas CYP1A2, CYP2C9,
CYP2D6 and CYP3A5 played no significant roles. The in vivo impact of CYP3A4 on clopi-H4
pharmacokinetic variability appears to be minimal as observed after co-administration with
CYP3A4 inhibitors such as ketoconazole and dronedarone (Farid et al., 2007; Summary of
Product Characteristics for Multaq®, 2014).
We have previously reported a static model (Boulenc et al., 2012), which can be generalized for
more metabolic steps, in order to estimate the net contribution of a given polymorphic (or total
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inhibition of) enzyme to the secondary metabolite formation. We also used a dynamic model in
the Simcyp software to compare the predictions with the two types of models. The limitation, as
was stated in the publication, was that it was a preliminary physiologically-based
pharmacokinetic (PBPK) model and that it was not validated, strictly speaking, with a formal
comparison between observed and predicted exposure parameters. The aim of the investigation
was to use the same metabolized fraction values in the dynamic and static models for comparison
purpose of exposure ratios only, between the different CYP2C19-phenotyped populations. The
PBPK models are models consisting of a physiologically realistic compartmental structure into
which input parameters from different sources (e.g. in vitro and in vivo experiments and in silico
predictions) can be combined to predict plasma and tissue concentration-time profiles. PBPK
models have gained wide spread use as a mechanistic and realistic modeling approach in critical
areas of clinical pharmacology, including pediatrics (Barrett et al., 2012; Khalil et al. 2011;
Edginton et al. 2006; Leong et al. 2012), pharmacogenetics (Yeo et al. 2013; Djebli et al. 2009),
formulation effect (Jamei et al. 2009; Lukacova et al. 2009), organ impairment (Thompson et al.
2009; Johnson et al. 2010) and drug-drug interaction (DDI) (Rostami-Hodjegan 2004; Djebli et al.
2009; Rowland-Yeo et al. 2010; Boulenc et al. 2011; Boulenc et al. 2012; Vieira et al. 2012).
PBPK tools that incorporate inter-individual variability of intrinsic factors, such as Simcyp, can
help to better evaluate pharmacokinetic inter-individual variability and consequently anticipate
DDI impact and better determine optimal formulation, dosing regimen and sampling schemes in
the general population as well as in special populations (e.g. renal impaired patients, different
ethnic groups, etc).
In the present study, a dynamic PBPK model was developed and validated for clopidogrel and for
its active metabolite clopi-H4, using the specific sequential metabolite module, in the 4
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CYP2C19 phenotype metabolizers groups (poor, intermediate, extensive and ultrarapid
metabolizers).
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MATERIALS AND METHODS
Physiologically-based pharmacokinetics model building. Simcyp® algorithms (version 10.20
SE; Simcyp Ltd, Sheffield, UK) were used to predict clopidogrel and clopi-H4 exposures in
CYP2C19 PM (poor metabolizers), IM (intermediary metabolizers), EM (extensive metabolizers)
and UM (ultra-rapid metabolizers).
In addition, a specific module was implemented and used for the present analysis, through
collaboration between Simcyp® LTD (a CERTARA company) and Sanofi, in order to be able to
develop a Simcyp model for a compound with a secondary metabolite. This module is available
as free add-on package for all Simcyp® users.
Assumptions of the secondary metabolite module
The clopidogrel PBPK model involved the development of a module that incorporated a
secondary metabolite formed sequentially from a primary metabolite. The following assumptions
were made:
- The secondary metabolite is only formed from a primary metabolite of the substrate.
- The secondary metabolite is available for metabolism and inhibition instantaneously.
- The substrate is given orally or intravenously and can be administered as a single dose
or multiple doses.
- As for the primary metabolite, the gut transporters kinetic parameters could not be
applied for the secondary metabolite.
- The distribution of the secondary metabolite was described by a minimal PBPK model.
As a result, transporter kinetic models (e.g. hepatic transporters), if any, could not be
applied.
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- Mutual interactions (competitive inhibition, mechanism-based inhibition and induction)
between the secondary metabolite and other compounds (substrate, the primary
metabolite of the substrate, inhibitors, and the primary metabolite of the inhibitor) were
considered, as well as auto-inhibition, via mechanism-based inhibition and auto-
induction.
Implementation of the secondary metabolite module
It was assumed that the formed secondary metabolite was instantaneously available for further
elimination (metabolism and excretion) and interactions. MIIS was used to represent the
secondary metabolite of the substrate.
The fraction of secondary metabolite escaping first-pass metabolism in the gut, Fg-MIIS, could be
calculated in the same way as for the primary metabolite:
MIISGMIISgutvilli
villiMIISg CLfuQ
QF
−−− +
=int
(1)
where Qvilli was the villi blood flow, fugut-MIIS and CLintG-MIIS were the secondary metabolite
unbound fraction in the gut and the total gut intrinsic clearance, respectively. The formation rate
of the secondary metabolite in the gut was described by:
∑= −−
−−−
+=
M
n PGPgutgut
nPGPgutPMIIS CLfuQ
CLfuAA
1 int
int
(2)
where AP was the formation rate of the primary metabolite in the gut; fugut-P is the unbound
fraction of the primary metabolite in the gut; CLintG-P was the total gut clearance of the primary
metabolite; and CLintG-P-n was the nth metabolic pathway of the primary metabolite to form the
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secondary metabolite. The intrinsic clearances were corrected for non-specific binding and if
Vmax/Km values were provided, CLintG-P-n was computed as defined below:
pvPgutnP
nPGnPG PfuKm
VCL
−−
−−−− +
=max
int
(3)
where VmaxG-P-n and KMP-n were the gut metabolism kinetic parameters of the nth pathway, fugut-P
was the fraction unbound in the gut and Ppv was the primary metabolite concentration in portal
vein respectively.
The secondary metabolite portal vein concentration was determined using:
[ ]FMIISMIISgpvsyspvpv
pv POAFMIISMIISQVdt
dMIIS−+−= )(
1
(4)
where FPO was 0 when the parent drug was given by intravenous route, and 1, when the parent
drug was given by oral route. Also, MIISsys and MIISpv were the secondary metabolite systemic
and portal vein plasma concentrations and Fg-MIIS is the secondary metabolite fraction escaping
gut metabolism respectively.
The secondary metabolite liver was defined as below:
⎥⎥⎥
⎦
⎤
⎢⎢⎢
⎣
⎡
−−
++
=
−−
=−−− ∑
LivhlivMIISubMIISMIISH
M
nnPuHLivPubPsyshapvpv
Liv
Liv
MIISQMIISfUptakeCL
CLPfUptakeMIISQMIISQ
Vdt
dMIIS
int
1int
1
(5)
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where Qpv and Qha were the portal vein and hepatic artery blood flows, UptakeP and UptakeMIIS
were the active uptake into hepatocyte for the primary and secondary metabolites, fub-P and fub-MIIS
were the unbound fraction of drug in blood of the primary and secondary metabolites, PLiv was
the primary metabolite concentration in the liver, and MIISliv was the liver concentration of the
secondary metabolite, respectively.
The secondary metabolite systemic compartment was defined below:
( ) ⎥⎦
⎤⎢⎣
⎡−−= −
−sys
MIIS
MIISrsysLivh
MIISd
sys MIISBP
CLMIISMIISQ
Vdt
dMIIS 1
(6)
where Vd-MIIS was the secondary metabolite volume of distribution at steady-state, Qh was the
hepatic blood flow, CLr-MIIS was the secondary metabolite renal clearance and BPMIIS was the
secondary metabolite blood to plasma ratio.
Depending on the extent of sequential metabolism, a certain amount of the secondary-formed
metabolite will go to systemic circulation.
Input data
Simcyp® model was set up using clopidogrel and its metabolites (i.e. 2-oxo-clopidogrel, the
primary CYP-dependent metabolite and clopi-H4, the secondary metabolite), with the physico-
chemical, absorption, distribution and clearance parameters described in Table 1.
Physico-chemical parameters
As physico-chemical input parameters, the molecular weight, the chemical nature, the Pka and
the LogP values were used for clopidogrel, 2-oxo-clopidogrel and the clopi-H4.
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Absorption
The absorption process was described for clopidogrel only. The 1st-order absorption model was
selected with a fraction absorbed (fa), a 1st order rate constant (Ka) and an effective permeability
(Peff) in human were used as input parameters. The formulation was considered as a solution.
Distribution
Two PBPK distribution models were available in Simcyp: the minimal and the full PBPK models.
The minimal PBPK model can be described as a ‘lumped’ model which has only three
compartments, when there is no single adjusting compartment (SAC, i.e. peripheral
compartment), predicting the systemic, portal vein and liver concentrations. The full PBPK
distribution model proposed a number of time-based differential equations in order to simulate
the concentrations in various organ compartments: the blood (plasma), adipose, bone, brain, gut,
heart, kidney, liver, lung, muscle, skin and spleen. The inter-individual variability of tissue
volume is estimated taking account of age, sex, weight and height. The distribution is assumed to
be perfusion-limited, using the full PBPK model, unless the membrane transporters are taken into
account, whereby permeability-limited distribution is handled in the liver, the kidney and in the
brain.For the current analysis, the full PBPK distribution model was selected for clopidogrel and
the minimal PBPK model was selected for 2-oxo-clopidogrel and for clopi-H4. The volumes of
distribution at steady-state (Vss) were 0.217, 0.10 and 0.23 L/kg, for clopidogrel, 2-oxo-clopiogrel
and clopi-H4, respectively. These values were predicted using the model proposed by Rodgers
and Rowland (Rodgers et al. 2005a; 2005b; 2006; 2007), except for 2-oxo-clopidogrel where the
sensitivity analysis model was used to refine this value based on its impact on the observed
clopidogrel and clopi-H4 exposures (Cmax and AUC). The blood-to-plasma ratio (B/P) was
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predicted using the model proposed by Uchimura et al. (Uchimura et al. 2010): 0.72, 1.00 and
0.82 for clopidogrel, 2-oxo-clopidogrel and clopi-H4, respectively. The unbound fraction in
plasma (fup) was set to 0.02 for clopidogrel as stated in the analytical dossier and to 0.031 and
0.018 for 2-oxo-clopidogrel and clopi-H4, respectively, using the model proposed by Lobell and
Sivarajah (Lobell et al. 2003).
Elimination
For clopidogrel metabolism, enzyme kinetics information using human recombinant CYP
isoforms was selected. The Vmax and KM values, from Kazui et al. (Kazui et al. 2010) were used.
The unbound fraction in human hepatic microsomes (fumic) of 0.015 was predicted using the
QSAR model published by Gao et al. (Gao et al. 2008), in a first step, and refined using the
sensitivity analysis module based on observed clopidogrel and clopi-H4 exposures, in a second
step. Moreover, an additional systemic clearance of 600 L/h was considered, representing the
esterase-mediated clearance using the retrograde model (about 90% of clopidogrel total
clearance).
The enzyme kinetic information (Vmax and KM) from Kazui et al. (2010) using human
recombinant CYP isoforms was also used for 2-oxo-clopidogrel. Moreover, an additional
clearance of 50 µL/min/mg was considered for 2-oxo-clopidogrel, representing the esterase-
mediated clearance (about 50% of the total 2-oxo-clopidogrel clearance). An active uptake into
hepatocytes of 2 was set for 2-oxo-clopidogrel using the sensitivity analysis module.
Regarding clopi-H4, an in vivo clearance of 500 L/h was programmed into Simcyp. This value
represented the immediate direct irreversible binding of this active metabolite to platelets.
Dronedarone PBPK model
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The dronedarone Simcyp model has been previously developed and validated for a large range of
doses (200 to 1600 mg BID). The input parameters are detailed in Table 2.
This model accurately predicted the pharmacokinetics of dronedarone and correctly took into
account the non-linearity of dronedarone pharmacokinetics. This non-linearity resulted from the
moderate mechanism-based inhibition of CYP3A4, which is the main isoform involved in
dronedarone clearance itself. The main purpose of the dronedarone model was its application as a
guide for dose selection in pediatrics. This model was also used to evaluate the feasibility of a
sustained-release formulation for a 800 mg once-daily administration instead of the marketed 400
mg BID.
Simcyp simulations
The simulations were performed using 4 virtual populations of 100 (10 trials of 10 individuals
each) healthy volunteers aged between 20 and 50 with a Male/Female ratio of 50/50, in fasted
conditions, representing PM-, IM-, EM- and UM-CYP2C19 individuals. The number of virtual
subjects (10 trials of 10 subject in each trial) was selected based on the subjects number in study
1 (10 subjects in each CYP2C19-phenotyped group) in order to optimize the relevance of the
comparison to observed values at the model validation step.
The difference between these four CYP2C19-phenotyped groups was mainly based on the mean
liver CYP2C19 abundance. For EM-individuals, the mean liver abundance of CYP2C19 was set
in the Simcyp library to 14 pmole/mg of proteins, whereas it was defined as 0 pmole/mg for PM-
individuals. Regarding IM- and UM-individuals, the liver abundances of CYP2C19, after
repeated simulations using a conditional sensitivity analysis with mean value for IM-individuals
ranging between 0 and 14 pmol/mg (between PM- and EM-individuals) and for UM-individuals
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higher than 14 pmol/mg (higher than EM-individuals), were set to 10 and 18 pmole/mg,
respectively.
A customized design similar to that of the study 1 was used, with a 5 day-treatment duration: a
loading dose of 300 mg clopidogrel at Day 1 and a maintenance dose of 75 mg OD clopidogrel
from Day 2 to Day 5.
The simulations could be performed in Simcyp using either the “PK/PD parameters” or the
“PK/PD profiles” options. The “PK/PD parameters” option was the only way to estimate the
relative enzyme contribution (static modeling). This option excludes time- and, in some cases,
concentration-dependent phenomena. At the opposite, the “PK/PD profiles” options provided
time- and concentration-dependent predictions. The inter- and intra-moieties interactions
(metabolite, inhibitor, inducer, effect of the substrate on its own metabolism) were also taken into
account as well as the organ parameters (e.g. changes in enzymes synthesis or degradation rates
following administration of an inducer and/or a mechanism-based inhibitor).
In the present analysis, the “PK/PD parameters” option was used to estimate the relative enzyme
contribution for both clopidogrel and 2-oxo-clopidogrel metabolism, and the “PK/PD profiles”
option was used to predict the PK profiles of the compounds in the different CYP2C19-
phenotyped groups.
Clinical trials.
Two clinical studies were used for model validation purposes in the present analysis. The first
one aimed at validating the contribution of CYP2C19 in clopidogrel metabolism and clopi-H4
formation. The second study aimed at validating the model in terms of CYP3A4-based DDI.
The first clinical study (study 1) was conducted to compare clopidogrel and clopi-H4 in 4
CYP2C19-defined metabolizer groups. This single-center, randomized, placebo-controlled, 2-
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treatment, 2-period crossover study in 4 CYP2C19-defined metabolizer groups (PM, IM, EM and
UM) was conducted to determine whether CYP2C19 polymorphisms affected the
pharmacokinetics of clopidogrel and clopi-H4 after clopidogrel oral administration of 300 mg
loading dose followed by 75 mg for 4 days or 600 mg loading dose followed by 150 mg for
4 days (Simon et al., 2011). The number of subjects was 40 (10 per group).
The second clinical study (study 2) was conducted in order to evaluate the impact of dronedarone,
a moderate CYP3A4 inhibitor, on the pharmacokinetics of clopidogrel and clopi-H4
pharmacokinetics. This was a randomized, single-center, double-blind, placebo-controlled,
repeated-dose, two-treatment two-period, two-sequence crossover pharmacokinetic interaction
study conducted in France. The 2 treatment-periods, separated by a 7-day washout period, were
as follows: repeated doses of 400 mg BID dronedarone or placebo (for 14 days) on repeated
doses of clopidogrel (300 mg loading dose followed by 75 mg maintenance dose for 4 days)
started the 10th day after dronedarone initiation. Dronedarone was administered for 9 days before
clopidogrel initiation to achieve steady state pharmacokinetic conditions. Regardless of the
sequence, a washout duration between of the two periods was at least 7 days to ensure that
platelet aggregation returned to baseline during the ≥17 days between the last clopidogrel
administration (Day 14) in the first treatment period and the loading dose of clopidogrel (Day 10)
in the second treatment period. Healthy male subjects 18 - 65 years of age were eligible for
enrollment if they provided informed consent; had a body weight of 50 - 95 kg, body mass index
of 18 and 28 kg/m²; no contraindication to clopidogrel and dronedarone. Only CYP2C19 EM
individuals were considered in this analysis.
Clopidogrel and clopi-H4 analyses: plasma samples for pharmacokinetic assessment of
unchanged clopidogrel and clopi-H4 were collected after loading dose and last maintenance dose
of each of the two periods at T0 (time of clopidogrel administration) and at time points (in hours)
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T0.25, T0.5, T1, T1.5, T2, T3, T4, T6, T10, T16, and T24 under conditions already described for
study 1 (Simon et al., 2011). Clopidogrel and clopi-H4 plasma concentrations were assayed by
Sanofi (Bridgewater, NJ, USA, Malvern, PA, USA and Montpellier, France), using validated
liquid chromatography-tandem mass spectrometry with lower limits of quantification of 5 pg/mL
and 0.5 ng/mL, respectively (Tuffal et al., 2011).
Maximum plasma concentration (Cmax) and area under the plasma concentration versus time
curve from T0 to T24 using the trapezoidal rule (AUC0-24) were calculated using non-
compartmental techniques using PKDMS Version 2.0, incorporating WinNonlin Professional
Version 5.2.1 (Pharsight, Mountain View, CA, USA).
Validation of the PBPK model.
Comparison of predicted to observed clopidogrel and clopi-H4 AUC0-24 values
The predicted AUC0-24 values of clopidogrel and clopi-H4, using the PBPK model, at loading
dose (Day 1) and maintenance dose (Day 5) were compared to those observed in the study 1. This
comparison was performed for each CYP2C19-phenotyped group.
As previously mentioned, for each simulation (i.e. each CYP2C19-phenotyped group), 10 trials
of 10 virtual individuals in each trial were generated. The median AUC0-24 value and error bar of
the group of “real” subjects was presented together with the median and error bar of each virtual
trial. This representation allowed both the predicted inter-individual variability and inter-trial
variability to be well evaluated and confirmed that the group of “real” patients behaved as one of
the virtual trials.
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Visual Predictive Check
A visual predictive check (VPC) was adapted to PBPK and performed to allow a graphical
qualification of the clopidogrel/2-oxo-clopidogrel/clopi-H4 Simcyp model. This evaluation
method of the model’s predictive performance by comparing the predictions to clinical data was
described as the reference at the 2012 FDA Pediatric Advisory Committee (summary minutes of
Pediatric Advisory Committee). Briefly, in order to graphically validate the model’s
predictability, the 50th (median), 5th and 95th percentiles of predicted concentration-time profiles
(obtained from the Simcyp simulations of 100 generated virtual individuals for each CYP2C19-
phenotyped group and for each compound) were presented with the observed data obtained in
Study 1.
Validation based on DDI prediction
The last validation was based on DDI predictions using a previously developed and validated
dronedarone Simcyp model, given the fact that dronedarone is a CYP-dependent substrate and is
CYP3A4-Mechanism Based Inhibitor (MBI) (Multaq briefing document, 2009). This validation
was performed by the comparison of the Simcyp predictions to observed values (from clinical
study 2), with or without dronedarone co-administration, on clopidogrel and on the active
metabolite clopi-H4 plasma pharmacokinetics.
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RESULTS
Predicted contribution in Clopidogrel and 2-oxo-clopidogrel metabolism
The Simcyp simulation using the “PK/PD parameters” option with 100 virtual CYP2C19-EM
individuals, resulted in mean clopidogrel metabolism contributions for CYP1A2, CYP2B6,
CYP2C19 and additional systemic clearance as presented in the upper panel of Figure 3 and in
mean 2-oxo-clopidogrel metabolism contributions for CYP2B6, CYP2C9, CYP2C19, CYP3A4
and additional clearance as presented in the lower panel of Figure 3.
The mean contribution of non-CYP-mediated systemic clearance, i.e mainly esterase-dependent
hydrolysis to the overall clopidogrel clearance was about 90.2%. Regarding specifically CYP-
mediated oxidative clearance of clopidogrel and representing about 10% of overall clearance,
predicted relative mean contributions were of 29.0% for CYP1A2, 22.4% for CYP2B6 and
48.6% for CYP2C19.
For 2-oxo-clopidogrel metabolism, mean contribution of non-CYP-mediated clearance to the
overall 2-oxo-clopidogrel clearance was about 50.7%. Regarding specifically CYP-mediated
oxidative clearance of 2-oxo-clopidogrel, Simcyp predicted relative mean contributions of 39.0%
for CYP2B6, 6.47% for CYP2C9, 21.1% for CYP2C19 and 33.5% for CYP3A4. These
predictions are consistent with those published by Kazui et al. (Kazui et al. 2010).
Comparison of predicted to observed clopidogrel and Clopi-H4 AUC0-24 values
The predicted AUC0-24 values of clopidogrel and Clopi-H4, using the PBPK model, at loading
dose (Day 1) and maintenance dose (Day 5) were compared to those observed in Study 1. This
comparison was performed for each CYP2C19-phenotyped group. The median AUC0-24 value
and error bar of the group of observed subjects was presented together with the median and error
bar of each virtual trial. In addition, the corresponding global median and 5th and 95th percentiles,
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for each dose in each CYP2C19-phenotyped group, obtained from the 100 virtual subjects taken
together were presented. In addition to allowing a comparison of predicted to observed median
AUC0-24 values, this representation provided an accurate evaluation of both predicted inter-
individual and inter-trial variability. This is also a way to confirm that the group of observed
patients (n=10) behaved as one of the virtual trials (n=10 each).
Figures 4 and 5 present the results for clopidogrel 300 mg-loading (Day 1) and 75 mg-
maintenance doses (Day 5), respectively. Figures 6 and 7 are the results for clopi-H4 at these
doses.
These figures showed the good predictive performance of the Simcyp model for both clopidogrel
and clopi-H4, whatever the treatment period (for both loading- and maintenance-doses) and
whatever the CYP2C19-phenotyped group.
Visual Predictive Check (VPC)
The VPC was adapted to PBPK and performed to allow a graphical qualification of the Simcyp
model from Day 1 (300 mg loading dose) to Day 5 of treatment (75 mg-maintenance dose), for
clopidogrel and for clopi-H4 in each CYP2C19-phenotyped group. The results are presented in
Figure 8 for clopidogrel and Figure 9 for clopi-H4.
These results, obtained from the 100 virtual individuals for each CYP2C19-phenotyped group,
for both clopidogrel and clopi-H4 confirmed the accuracy of the predictions and the good
CYP2C19 contribution in both metabolic steps (clopidogrel to 2-oxo-clopidogrel and 2-oxo
clopidogrel to clopi-H4). In addition, these figures confirmed the accurate estimation of the inter-
individual variability for both compounds.
Model qualification based on Drug-Drug-Interaction prediction
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This model qualification was based on DDI prediction using a previously developed and
validated dronedarone Simcyp model (see Table 2 and Figure 2).
As previously mentioned, Study 2 was conducted in order to evaluate the impact of dronedarone
co-administration (400 mg BID repeated doses from Day -9 to Day 5) on the pharmacokinetics of
clopidogrel and clopi-H4 pharmacokinetics (300 mg clopidogrel loading dose at Day 1 followed
by 75 mg from Day 2 to Day 5), since dronedarone is a moderate CYP3A4- MBI. The Simcyp
simulations performed were based on the same design and similar population (healthy CYP2C19-
EM individuals) Study 2 in order to make the comparison the most reliable. The observed and
predicted ratio estimates and 90% confidence intervals (CIs) are presented in Table 3 and the
observed and predicted Clopi-H4 exposures are presented in Table 4.
Regarding clopidogrel, this comparison confirmed the absence of any DDI on clopidogrel when
co-administered with dronedarone. The observed ratio estimates ranged between 0.89 and 1.03
and predicted values ranged between 1.00 and 1.01.
For the active metabolite, i.e. Clopi-H4, the predicted ratio estimates (90% CI) were slightly
underestimated: observed Cmax ratio ranged between 0.81 (0.73-0.89) at Day 5 and 0.93 (0.84-
1.04) at Day 1, and the predicted Cmax ratio was about 0.72 (0.71-0.76) at Day 5 and 0.72 (0.71-
0.75) at Day 1; the observed AUC0-24 ratio ranged between 1.05 (0.67-1.22) at Day 5 and 1.09
(0.65-1.20) at Day 1 while the predicted value was about 0.72 (0.71-0.76) at Day 5 and 0.73
(0.72-0.76) at Day 1.
When looking at the Clopi-H4 exposures (see Table 4), the predicted Cmax values were very
similar to the observations when clopidogrel was administered without dronedarone
comedication and slightly underestimated when coadministered with dronedarone (for both Day 1
and Day 5). On the contrary, the predicted AUC0-24 seemed to be slightly overestimated when
clopidogrel was administered without dronedarone and very close to observations when
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coadministered with dronedarone (also observed for at both Day 1 and Day 5). Overall, the
predicted inter-individual variability on Clopi-H4 exposures (56.4% to 66.2%) was close to the
observations (40.0% to 80.7%).
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DISCUSSION
This is the first study proposing a full dynamic PBPK model able to accurately predict
simultaneously the pharmacokinetics of the parent drug, its primary and secondary metabolites,
in populations with genetically different activity for a metabolizing enzyme. This PBPK model
was developed for clopidogrel, its primary metabolite 2-oxo-clopidogrel and its secondary active
metabolite clopi-H4. The model qualification was performed on the parent drug clopidogrel and
on clopi-H4 whatever the treatment period (loading or maintenance doses) for each CYP2C19-
phenotyped group (PM, IM, EM and UM).
The model was not validated for 2-oxo-clopidogrel because of the absence of plasma
concentrations for this metabolite. The 2-oxo-clopidogrel plasma concentrations were not assayed
because of the weak stability and of the fleeting property of this metabolite.
The PBPK model was built using an approach integrating all of the available physico-chemical
and in vitro information of the three compounds, gathered via the enzyme kinetic parameters
which govern the two metabolic steps.
The observed data from two clinical studies were used for model qualification: (i) the first study
with well-balanced genetic polymorphic populations (CYP2C19-PMs, -IMs, -EMs and -UMs),
based on the important CYP2C19 involvement in both metabolic steps and (ii) the second study
with or without dronedarone co-administration for DDI prediction purpose, given that
dronedarone is a moderate CYP3A4-MBI and that CYP3A4 is involved in the second step of
clopidogrel metabolism, i.e. 2-oxo-clopidogrel to clopi-H4. Three qualification methods were
used for this PBPK model: the comparison of observed to predicted AUC0-24 coupled with an
estimation of the variability, the VPC method which was based on a visual inspection of the
predictive performance of the model and the last method based on DDI prediction.
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The first qualification method, presented in Figures 4 and 5 for clopidogrel and in Figures 6 and 7
for Clopi-H4, was based on a comparison of the observed median AUC0-24 values (and the
corresponding variability) in each CYP2C19-phenotyped group (with n=10 for each group) to the
predicted values of 10 virtual trials (10 individuals in each trial). This representation allowed, in
addition to evaluating the predictive performance of the model regarding median values,
estimating the inter-individual and inter-group variability. This method allowed validating the
model for both clopidogrel and Clopi-H4 in each CYP2C19-phenotyped group, whatever the
treatment period, and clearly showed that the clinical study, in terms of AUC0-24, behave as one
of the virtual trials.
The second qualification method was based on the adaptation of VPC method to PBPK.
Classically, the VPC method is performed to validate a model developed using the population
approach (population pharmacokinetics and pharmacodynamics), where hundreds of simulations
were launched once the final model was built, and at the end the observations were visually
compared to the statistics of the predictions (Holford 2005; Karlsson et al. 2008; Post et al. 2008).
This method is based on the presentation of the observed concentrations on a “time versus
concentrations” plot on which were superimposed the 5th, 50th (median) and 95th percentiles of
the predictions obtained from 100 virtual individuals. A plot was presented for each compound
and for each CYP2C19-phenotyped group (Figure 8 for clopidogrel and Figure 9 for clopi-H4).
In the present study, this method confirmed the good predictive performance of the PBPK model.
The complexity of clopidogrel pharmacokinetics, linked to the metabolic cascade with many
metabolic enzymes involved (different CYPs and esterases), was a strong incentive to perform a
model qualification based on DDI prediction. This method was based on the comparison of ratio
estimates of Cmax and AUC0-24 of clopidogrel and Clopi-H4, with and without dronedarone co-
administration. A PBPK model was previously developed and validated, accurately predicting the
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pharmacokinetics of dronedarone and correctly taking into account the non-linearity of
dronedarone pharmacokinetics resulting from the moderate mechanism-based inhibition of
CYP3A4, which is itself the main isoform involved in dronedarone clearance. Regarding
clopidogrel, this comparison confirmed the absence of any DDI interaction on Cmax and AUC0-24
of clopidogrel when co-administered with dronedarone. On the other hand, for the active
metabolite clopi-H4, the predicted ratio estimates were unexpectedly slightly underestimated.
There are some hypotheses that could explain this underestimation. The first one would be an
over-estimation of the impact of dronedarone CYP3A4 inhibition via inaccurate mechanism-
based inactivation input parameters (KI/Kinact) and/or CYP3A4 turnover (Kdeg) values in the
Simcyp population library. This hypothesis was evaluated since the Kdeg value used in this library
(0.0077 h-1) was discussed (Rowland-Yeo et al., 2011) suggesting that the use of a higher value
(0.0193 h-1) resulted in decreasing bias and increasing the precision of the predictions. At the end,
this hypothesis was unlikely since the dronedarone model was well validated using a large range
of doses (from 200 to 1600 mg BID) and accurately predicted the CYP3A4 saturation due to the
mechanism-based inhibition. The second hypothesis would be an over-estimation of CYP3A4
contribution to 2-oxo-clopidogrel metabolism. This idea is debatable given that the clopidogrel
model was well validated for the four CYP2C19-phenotyped groups, suggesting that the
contribution of CYP2C19 was well documented and consistent with the observed values. Given
that other CYP isoforms than CYP2C19 and CYP3A4 were involved in 2-oxo-clopidogrel
metabolism, a clinical study to validate this hypothesis would be of interest.
This work is the first study accurately describing the pharmacokinetics of a drug and its
sequential metabolite using the PBPK approach in different phenotypic groups. This can be
considered as the first step to build up a PBPK-PD model able to predict the therapeutic effect in
different sub-populations and/or different clinical conditions (Chetty et al., 2014).
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Authorship Contributions
Participated in research design: Djebli N., Boulenc X., Fabre D., Fabre G., Sultan E., Hurbin F.
Conducted in vitro experiments: Not applicable
Contributed new reagents or analytic tools: Not applicable
Performed data analysis: Djebli N,
Wrote or contributed to the writing of the manuscript: Djebli N., Boulenc X., Hurbin F.
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subjects. J Thromb Haemost 6:1439–1441.
Vieira ML, Zhao P, Berglund EG, Reynolds KS, Zhang L, Lesko LJ, and Huang SM (2012)
Predicting drug interaction potential with a physiologically based pharmacokinetic model: a
case study of telithromycin, a time-dependent CYP3A inhibitor. Clin Pharmacol Ther
91(4):700-708.
Yeo KR, Kenny JR, and Rostami-Hodjegan A (2013) Application of in vitro-in vivo
extrapolation (IVIVE) and physiologically based pharmacokinetic (PBPK) modelling to
investigate the impact of the CYP2C8 polymorphism on rosiglitazone exposure. Eur J Clin
Pharmacol. [Epub ahead of print]
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Figures
Figure 1 - Biotransformation pathway of clopidogrel leading to its pharmacologically active
metabolite (H4) via 2-oxo-clopidogrel
Figure 2 – Comparison of observed versus predicted dronedarone AUC0-12 and Cmax at steady-
state after 400 mg BID administration (A and B) and 200 to 1600 mg BID administration (C and
D)
Figure 3 - Mean predicted contribution of CYP isoforms and esterase (additional clearance) to
clopidogrel (A) and to 2-oxo-clopidogrel (B) metabolism in CYP2C19 extensive metabolizers
Figure 4 - Predicted and Observed median AUC0-24 and error bars for clopidogrel with the 300
mg loading dose (Day 1) in CYP2C19 poor, intermediate extensive and ultrarapid metabolizers
Figure 5 Predicted and Observed median AUC0-24 and error bars for clopidogrel with the 75 mg
maintenance dose at Day 5 in CYP2C19 poor, intermediate extensive and ultrarapid metabolizers
Figure 6 Predicted and Observed median AUC0-24 and error bars for Clopi-H4 with the 300 mg
loading dose (day 1) in CYP2C19 poor, intermediate extensive and ultrarapid metabolizers
Figure 7 Predicted and Observed median AUC0-24 and error bars for Clopi-H4 with the 75 mg
maintenance dose at Day 5 in CYP2C19 poor, intermediate extensive and ultrarapid metabolizers
Figure 8 Visual Predictive Check of clopidogrel in CYP2C19 poor, intermediate extensive and
ultrarapid metabolizers. Observed concentrations (blue dots) and median of predictions (red line)
and the ranges of 5th and 95th percentiles of predictions (pink area)
Figure 9 - Visual Predictive Check of clopi-H4 in CYP2C19 poor, intermediate extensive and
ultrarapid metabolizers. Observed concentrations (blue dots) and median of predictions (red line)
and the ranges of 5th and 95th percentiles of predictions (pink area)
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Tables
Table 1: Physico-chemical and in vitro ADME parameters used in Simcyp for clopidogrel, 2-oxo-
clopidogrel and active metabolite (Clopi-H4)
Parameters Value implemented in Simcyp Source Data
Clopidogrel
Physico-Chemical
MW (g/mol) 321.8
Internal data Log Po:w 3.89
Compound type monoprotic acid
Pka 4.55
Haematocrit (%) 45.0 Simcyp library
Absorption
Absorption model/input type 1st order -
fa; Ka (h-1) 0.5; 0.5 Internal data
Peff, man (10-4 cm/s) 0.466 Pred. Pcaco2 = 0.399×10-6 cm/s
Formulation solution -
fuGut 0.02 Set equal to fup
Distribution
Distribution model Full PBPK model -
Vss (L/kg) Predicted, 0.217 Prediction method
Rodgers and Rowland (2005a;
2005b; 2006 & 2007)
B/P ratio Predicted; 0.72 Prediction method
Uchimura et al. 2010
fup 0.02 Internal data
Metabolism
Clearance type Enzyme kinetics
Kazui et al. 2010
N.B.: fumic obtained using the prediction toolbox and refined by
sensitivity analysis
In vitro metabolic system Human recombinant CYP
isoforms
rhCYP1A2
Vmax (pmol/min/pmol)
2.27
KM (µM) 1.58
fumic 0.015
rhCYP2B6
Vmax (pmol/min/pmol)
7.66
KM (µM) 2.08
fumic 0.015
rhCYP2C19
Vmax (pmol/min/pmol)
7.52
KM (µM) 1.12
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Parameters Value implemented in Simcyp Source Data
fumic 0.015
Additional systemic clearance (L/h) 600 Representing about 90% of
clopidogrel clearance (esterase-dependent pathway)
2-oxo-clopidogrel (primary metabolite)
Physico-Chemical
MW (g/mol) 337.8
Internal data Log Po:w 2.96
Compound type Monoprotic acid
Pka 3.41
Haematocrit (%) 45.0 Simcyp library
Distribution
Distribution model Minimal PBPK model -
Vss (L/kg) 0.100 Sensitivity analysis
B/P ratio Predicted; 1.00 Prediction method
Uchimura et al., 2010
fup Predicted; 0.0310 Prediction method
Lobell & Sivarajah, 2003
Metabolism
Clearance type Enzyme kinetics
Kazui et al. 2010
N.B.: fumic obtained using the prediction toolbox and refined by
sensitivity analysis
In vitro metabolic system Human recombinant CYP
isoforms
rhCYP2B6
Vmax (pmol/min/pmol)
2.48
KM (µM) 1.62
fumic 0.180
rhCYP2C9
Vmax (pmol/min/pmol)
0.855
KM (µM) 18.1
fumic 0.180
rhCYP2C19
Vmax (pmol/min/pmol)
9.06
KM (µM) 12.1
fumic 0.180
rhCYP3A4
Vmax (pmol/min/pmol)
3.63
KM (µM) 27.8
fumic 0.180
Additional clearance
HLM Clint (µL/min/mg)
50 Representing about 50% of the total clearance (esterase-dependent
pathway) fumic 0.180
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Parameters Value implemented in Simcyp Source Data
Active uptake into hepatocyte 2 sensitivity analysis
Clopi-H4 (secondary metabolite = active metabolite)
Physico-Chemical
MW (g/mol) 355.8
Internal data Log Po:w 3.60
Compound type Diprotic acid
Pka 1; Pka 2 3.20; 5.10
Haematocrit (%) 45.0 Simcyp library
Distribution
Distribution model Minimal PBPK model -
Vss (L/kg) Predicted; 0.230 Prediction method
Rodgers and Rowland (2005a; 2005b; 2006 &
2007)
B/P ratio Predicted; 0.820 Prediction method
Uchimura et al., 2010
fup 0.018 Prediction method
Lobell & Sivarajah, 2003
Clearance Clearance type In vivo clearance Representing the direct irreversible
covalent binding to platelets CLpo (L/h) 500
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Table 2: Physico-chemical and in vitro ADME parameters used in Simcyp for dronedarone
Parameters Value implemented in Simcyp Source Data
Physico-Chemical
MW (g/mol) 557
Analytical dossier Log Po:w 7.80
Compound type monoprotic base
Pka 9.30
Haematocrit (%) 45.0 Simcyp library
Absorption
Absorption model/input type ADAM model -
fa; Ka (h-1) Predicted; 0.898; 0.816 Predicted using ADAM model
Peff, man (10-4 cm/s) 1.98 Predicted Pcaco2 = 5.30×10-6
cm/s
Formulation Solid; Controlled-
Released -
Dissolution-time profile
Time (h): 0, 0.083, 0.167,
0.25, 0.33, 0.42, 0.5, 0.75, 1 and
1.5
Dissolution (%): 0, 6.6, 12.8, 28.5, 38.9,, 47.7,
55.2, 75.9, 92.2 and 100 Analytical dossier
Solubility– pH profile
pH: 3, 4, 5, 6 and 7
Solubility (mg/mL): 1.6, 1.6, 1.5, 0.1 and 0.05
Analytical dossier
fuGut 1.00 -
Distribution
Distribution model Minimal PBPK model -
Vss (L/kg) 10 Analytical dossier; PopPk analysis
B:P ratio 1.00 Analytical dossier
fup 0.003
Metabolism
Clearance type Enzyme kinetics
Analytical dossier
N.B.: fumic obtained using the prediction toolbox and refined by
In vitro metabolic system Recombinant
rhCYP3A4
Vmax (pmol/min/pmol)
13.7
KM (µM) 4.2
fumic 0.0011
rhCYP3A5
Vmax (pmol/min/pmol)
4.87
KM (µM) 3.10
fumic 0.0011
Additional liver
Clint (µL/min/mg)
40
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Parameters Value implemented in Simcyp Source Data
clearance fumic 0.0011 sensitivity analysis
Interaction
CYP2B6 (comp.
inhibition)
Ki (µM) 12.0
Analytical dossier
fumic 0.0011
CYP2D6 (comp.
inhibition)
Ki (µM) 5.0
fumic 0.0011
CYP3A4 (MBI)
Kapp (µM) 2.44
Kinact (h-1) 9.16
fumic 0.0011
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Table 3: Observed versus predicted ratio estimates (90% CI) of Cmax and AUC0-24 for clopidogrel
and Clopi-H4 without and with dronedarone co-administration (400 mg BID) at Day 1 (300 mg
loading dose) and at Day 5 (75 mg maintenance dose)
Parameter Clopidogrel Secondary metabolite (Clopi-H4)
Observed (n=63) Simcyp (n=100)
Observed (n=63) Simcyp (n=100)
Ratio estimate (90% CI) at Day 1 (300 mg clopidogrel loading dose)
Cmax 0.89 (0.80-0.99) 1.01 (1.01-1.01) 0.93 (0.84-1.04) 0.72 (0.71-0.76)
AUC0-24 1.00 (0.94-1.07) 1.00 (1.00-1.00) 1.09 (0.65-1.20) 0.73 (0.72-0.76)
Ratio estimate (90% CI) at Day 5 (75 mg clopidogrel maintenance dose)
Cmax 0.96 (0.85-1.08) 1.00 (1.00-1.00) 0.81 (0.73-0.89) 0.72 (0.71-0.75)
AUC0-24 1.03 (0.96-1.11) 1.00 (1.00-1.00) 1.05 (0.67-1.22) 0.72 (0.71-0.76)
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Table 4: Observed versus predicted mean (SD) of Cmax and AUC0-24 of Clopi-H4 without and
with dronedarone co-administration (400 mg BID) at Day 1 (300 mg loading dose) and at Day 5
(75 mg last maintenance dose)
Parameter Without dronedarone With dronedarone
Obs. (n=63) Sim.
(n=100) Obs. (n=63) Sim. (n=100)
Day 1 (300 mg clopidogrel loading dose)
Cmax (ng/mL) 17.0 (10.5) 16.8 (10.6) 17.6 (14.2) 12.1 (6.82)
AUC0-24 (ng.h-1.mL-1) 37.9 (19.2) 59.5 (37.4) 43.1 (27.9) 43.2 (26.5)
Day 5 (last 75 mg clopidogrel last maintenance dose)
Cmax (ng/mL) 6.63 (4.08) 6.56 (4.43) 5.21 (2.89) 4.66 (2.95)
AUC0-24 (ng.h-1.mL-1) 12.8 (5.12) 20.4 (13.5) 14.2 (8.11) 14.7 (9.67)
Data are presented as mean (SD)
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