Physiologically-based pharmacokinetic modeling for...

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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

This article has not been copyedited and formatted. The final version may differ from this version.DMD Fast Forward. Published on January 21, 2015 as DOI: 10.1124/dmd.114.062596

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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

[email protected]

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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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


7.66

KM (µM) 2.08

fumic 0.015

rhCYP2C19


7.52

KM (µM) 1.12


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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


Compound type Monoprotic acid

Pka 3.41


Distribution

Distribution model Minimal PBPK model -

Vss (L/kg) 0.100 Sensitivity analysis


Uchimura et al., 2010

fup Predicted; 0.0310 Prediction method

Lobell & Sivarajah, 2003

Metabolism


Kazui et al. 2010


sensitivity analysis

In vitro metabolic system Human recombinant CYP

isoforms

rhCYP2B6


2.48

KM (µM) 1.62

fumic 0.180

rhCYP2C9


0.855

KM (µM) 18.1

fumic 0.180

rhCYP2C19


9.06

KM (µM) 12.1

fumic 0.180

rhCYP3A4


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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Active uptake into hepatocyte 2 sensitivity analysis

Clopi-H4 (secondary metabolite = active metabolite)

Physico-Chemical

MW (g/mol) 355.8


Compound type Diprotic acid

Pka 1; Pka 2 3.20; 5.10


Distribution


Vss (L/kg) Predicted; 0.230 Prediction method

Rodgers and Rowland (2005a; 2005b; 2006 &

2007)


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


Physico-Chemical

MW (g/mol) 557

Analytical dossier Log Po:w 7.80

Compound type monoprotic base

Pka 9.30


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


Vss (L/kg) 10 Analytical dossier; PopPk analysis

B:P ratio 1.00 Analytical dossier

fup 0.003

Metabolism


Analytical dossier


In vitro metabolic system Recombinant

rhCYP3A4


13.7

KM (µM) 4.2

fumic 0.0011

rhCYP3A5


4.87

KM (µM) 3.10

fumic 0.0011

Additional liver

Clint (µL/min/mg)

40


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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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