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A Physiologically Based Pharmacokinetic Model for Butadiene and Its Metabolite Butadiene Monoxide In Rat and Mouse and Its Significance for Risk Extrapolation

A Physiologically Based Pharmacokinetic Model for Butadiene and Its Metabolite Butadiene Monoxide In Rat and Mouse and Its Significance for Risk Extrapolation
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  1521-009X/43/4/510 – 522$25.00 RUG  M ETABOLISM AND  D ISPOSITION  Drug Metab Dispos 43:510 – 522, April 2015Copyright   ª  2015 by The American Society for Pharmacology and Experimental Therapeutics Physiologically Based Pharmacokinetic Modeling for SequentialMetabolism: Effect of CYP2C19 Genetic Polymorphism onClopidogrel and Clopidogrel Active Metabolite Pharmacokinetics Nassim Djebli, David Fabre, Xavier Boulenc, Gérard Fabre, Eric Sultan, and Fabrice Hurbin Sanofi R&D, Drug Disposition, Disposition Safety and Animal Research, Montpellier, France Received December 9, 2014; accepted January 21, 2015  ABSTRACTClopidogrel is a prodrug that needs to be converted to its activemetabolite (clopi-H4) in two sequential cytochrome P450 (P450)-dependent steps. In the present study, a dynamic physiologically based pharmacokinetic (PBPK) model was developed in Simcyp forclopidogrelandclopi-H4usingaspecificsequentialmetabolitemodulein four populations with phenotypically different CYP2C19 activity (poor, intermediate, extensive, and ultrarapid metabolizers) receivinga loading dose of 300 mg followed by a maintenance dose of 75 mg.This model was validated using several approaches. First, a compar-ison of predicted-to-observedarea under the curve (AUC) 0 – 24  obtainedfrom a randomized crossover study conducted in four balancedCYP2C19-phenotypemetabolizergroupswasperformedusingavisualpredictive check method. Second, the interindividual and intertrialvariability(onthebasis ofAUC 0 – 24 comparisons)betweenthepredictedtrials and the observed trial of individuals, for each phenotypicgroup, were compared. Finally, a further validation, on the basis of drug-drug – interaction prediction, was performed by comparingobserved values of clopidogrel and clopi-H4 with or withoutdronedarone (moderate CYP3A4 inhibitor) coadministration usinga previously developed and validated physiologically based PBPK dronedarone model. The PBPK model was well validated for bothclopidogrel and its active metabolite clopi-H4, in each CYP2C19-phenotypic group, whatever the treatment period (300-mg loadingdose and 75-mg last maintenance dose). This is the first study proposing a full dynamic PBPK model able to accurately predictsimultaneously the pharmacokinetics of the parent drug and of itsprimary and secondary metabolites in populations with geneti-cally different activity for a metabolizing enzyme. Introduction The antiplatelet agent clopidogrel is a prodrug that is metabolizedby two main metabolic pathways: an esterase-dependent pathwayleading to hydrolysis into an inactive carboxylic acid derivative(85 – 92% of circulating metabolites) and a cytochrome P450 (P450)-dependent pathway leading to its active metabolite (clopi-H4) (Linset al., 1999; Kazui et al., 2010; Tuffal et al., 2011; Dansette et al.,2012). Clopi-H4 is formed in a two-step oxidative process (Fig. 1)mediated by CYP1A2, CYP2B6, CYP2C19, and CYP3A4 (Kazuiet al., 2010). Clopi-H4 leads to inhibition of adenosine diphosphate – induced aggregation by irreversible binding of the platelet P2Y12 receptor (Bhatt and Topol, 2003).Polymorphisms of CYP2C19 affect both the pharmacodynamic andpharmacokinetic profiles of clopi-H4, and it has been determined that this isoform is one of the major determinants of interindividual variabilityin clopidogrel pharmacodynamic and pharmacokinetic responsiveness(Hulot et al., 2006; Kim et al., 2008; Umemura et al., 2008; Mega et al.,2009). CYP2C19 contribution to the formation of clopi-H4 was confirmedin a randomized crossover study conducted in four balanced CYP2C19-phenotyped metabolizer groups (poor, intermediate, extensive, andultrarapid metabolizers) (Simon et al., 2011). The authors of this studyalso performed a meta-analysis on data from 396 healthy subjects andconfirmed that CYP2C19 is the most important polymorphic P450involved in clopi-H4 formation and antiplatelet response, whereasCYP1A2, CYP2C9, CYP2D6, and CYP3A5 played no significant roles. The in vivo impact of CYP3A4 on clopi-H4 pharmacokineticvariability appears to be minimal as observed after coadministrationwith CYP3A4 inhibitors such as ketoconazole and dronedarone (Faridet al., 2007; Sanofi, 2014).We have previously reported a static model (Boulenc et al., 2012),which can be generalized for more metabolic steps, to estimate the net contribution of a given polymorphic enzyme to secondary metaboliteformation (or its total inhibition). We also used a dynamic model inthe Simcyp software to compare predictions with the two types of models. The limitation, as was stated in the publication, was that it wasa preliminary physiologically based pharmacokinetic (PBPK) modeland that it was not validated, strictly speaking, with a formal comparisonbetween observed and predicted exposure parameters. The aim of theinvestigation was to use the same metabolized fraction values in thedynamic and static models for comparison of exposure ratios only be-tween the different CYP2C19-phenotyped populations. The PBPK models are models consisting of a physiologically realistic compart-mental structure into which input parameters from different sources(e.g., in vitro and in vivo experiments and in silico predictions) can becombined to predict plasma and tissue concentration-time profiles.  ABBREVIATIONS:  AUC, area under the plasma concentration-versus-time curve; BID, twice a day; clopi-H4, active metabolite isomer of clopidogrel(H4);  C  max  , maximum plasma concentration; DDI, drug-drug interaction; EM, extensive metabolizer; IM, intermediate metabolizer; MBI, mechanism-based inhibitor; MIIS, secondary metabolite of the substrate in the specific module; P450, cytochrome P450; PBPK, physiologically basedpharmacokinetic; PM, poor metabolizer; SAC, single-adjusting compartment; UM, ultrarapid metabolizer;  V   max  , maximum velocity of the metabolizingenzyme; VPC, visual predictive check.510   a  t  A S P E T  J   o ur n a l   s  on J   a n u a r  y1  9  ,2  0 1  7  d m d  . a  s  p e  t   j   o ur n a l   s  . or  gD o wnl   o a  d  e  d f  r  om   PBPK models have gained widespread use as a mechanistic and realisticmodeling approach in critical areas of clinical pharmacology, includingpediatrics (Edginton et al., 2006; Khalil and Läer, 2011; Barrett et al.,2012; Leong et al., 2012), pharmacogenetics (Djebli et al., 2009; Yeoet al., 2013), 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 et al. 2004; Djebliet al., 2009; Rowland-Yeo et al., 2010; Boulenc and Barberan, 2011,2012; Vieira et al., 2012). PBPK tools that incorporate interindividualvariability of intrinsic factors, such as Simcyp, can improve evaluationof pharmacokinetic interindividual variability and consequentlyanticipate DDI impact and better determine optimal formulation,dosing regimen, and sampling schemes in the general population aswell as in special populations (e.g., renal impaired patients, different ethnic groups, etc.).In the present study, a dynamic PBPK model was developed andvalidated for clopidogrel and for its active metabolite clopi-H4, usingthe specific sequential metabolite module, in the four CYP2C19 phenotypemetabolizers groups (poor, intermediate, extensive, and ultrarapidmetabolizers). Materials and MethodsPhysiologically Based Pharmacokinetics Model Building Simcyp algorithms (version 10.20 SE; Simcyp Limited, Sheffield, UK, a CERTARA company) were used to predict clopidogrel and clopi-H4 exposuresin CYP2C19 PM (poor metabolizers), intermediate metabolizers (IM ), extensivemetabolizers (EM), and ultra-rapid metabolizers (UM).In addition, a specific module was implemented, through collaborationbetween Simcyp Limited and Sanofi, and used for the present analysis to enablemodeling of a compound with a secondary metabolite. This module is availableas 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 secondarymetabolite formed sequentially from a primary metabolite. The following assumptionswere made:The secondary metabolite is only formed from a primary metabolite of thesubstrate.The secondary metabolite is available for metabolism and inhibitioninstantaneously.The substrate is given orally or intravenously and can be administered asa single dose or multiple doses.As for the primary metabolite, the gut transporters kinetic parameterscould not be applied for the secondary metabolite.The distribution of the secondary metabolite was described by a minimalPBPK model. As a result, transporter kinetic models (e.g., hepatictransporters), if any, could not be applied.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 theprimary metabolite of the inhibitor) were considered, as well asautoinhibition, via mechanism-based inhibition and autoinduction. Implementation of the Secondary Metabolite Module.  It was assumedthat the formed secondary metabolite was instantaneously available for further elimination (metabolism and excretion) and interactions. MIIS was used torepresent the secondary metabolite of the substrate.The fraction of secondary metabolite escaping first-pass metabolism in thegut,  F  g  –   MIIS  , could be calculated in the same way as for the primary metabolite: F  g 2  MIIS   ¼  Q villi Q villi þ  fu gut  2  MIIS  CL  int  G 2  MIIS  ð 1 Þ where  Q villi  was the villi blood flow,  fu gut   –   MIIS  , and  CL  intG   –   MIIS   were thesecondary metabolite unbound fraction in the gut and the total gut intrinsicclearance, respectively. The formation rate of the secondary metabolite in thegut was described by:  A  MIIS   ¼  A P   +  M n ¼ 1  fu gut  2 P CL  int  G 2 P 2 n Q gut  þ  fu gut  2 P CL  int  G 2 P ð 2 Þ where  A P  was the formation rate of the primary metabolite in the gut,  fu gut   –  P  isthe unbound fraction of the primary metabolite in the gut,  Q gut   was the gut blood flow,  CL  intG   –  P  was the total gut clearance of the primary metabolite, and CL  intG   –  P  –  n  was the  n th metabolic pathway of the primary metabolite to form thesecondary metabolite. The intrinsic clearances were corrected for nonspecificbinding and if   V  max   /Km  values were provided,  CL  intG   –  P  –  n  was computed as: CL  int   G 2 P 2 n ¼  V  max G 2 P 2 n Km P 2 n þ  fu gut  2 P P  pv ð 3 Þ where  V  maxG   –  P  –  n  and  K   MP  –  n  were the gut metabolism kinetic parameters of the n th pathway,  fu gut   –  P  was the fraction unbound in the gut, and  P  pv  was theprimary metabolite concentration in portal vein.The secondary metabolite portal vein concentration was determined using: dMIIS   pv dt  ¼  1 V   pv  Q  pv ð  MIIS  sys 2  MIIS   pv Þþ F  g 2  MIIS   A  MIIS  PO F    ð 4 Þ where  PO F   was 0 when the parent drug was given by intravenous route, and 1when the parent drug was given by oral route. Also,  MIIS  sys  and  MIIS   pv  werethe secondary metabolite systemic and portal vein plasma concentrations and F  g  –   MIIS   is the secondary metabolite fraction escaping gut metabolism, and  Q  pv ,the portal vein blood flow.The secondary metabolite liver was defined as: dMIIS   Liv dt  ¼  1 V   Liv Q  pv  MIIS   pv þ Q ha  MIIS  sys þ Uptake P    f  ub 2 P P  Liv  +  M n ¼ 1 CL  int  uH  2 P 2 n 2 CL  int   H  2  MIIS    Uptake  MIIS   f  ub 2  MIIS   MIIS  liv 2 Q h  MIIS   Liv 264375 ð 5 Þ where  Q  pv  and  Q ha  were the portal vein and hepatic artery blood flows, Uptake P , and  Uptake  MIIS   were the active uptake into hepatocyte for the primaryand secondary metabolites,  f  ub  –  P  and  f  ub  –   MIIS   were the unbound fraction of drugin blood of the primary and secondary metabolites,  PL  iv  was the primarymetabolite concentration in the liver, and  MIIS  liv  was the liver concentration of the secondary metabolite. Fig. 1.  Biotransformation pathway of clopidogrel leading to its pharmacologically active metabolite (H4) via 2-oxo-clopidogrel. PBPK Modeling for Clopidogrel and Its Active Metabolite  511   a  t  A S P E T  J   o ur n a l   s  on J   a n u a r  y1  9  ,2  0 1  7  d m d  . a  s  p e  t   j   o ur n a l   s  . or  gD o wnl   o a  d  e  d f  r  om   TABLE 1Physicochemical 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  PhysicochemicalMW (g/mol) 321.8 Internal data Log P o:w  3.89Compound type Monoprotic acidPka 4.55Hematocrit (%) 45.0 Simcyp libraryAbsorptionAbsorption model/input type First order   —  fa ;  Ka  (h 2 1 ) 0.5; 0.5 Internal data  P eff  , man  (10 2 4 cm/s) 0.466 Predicted P caco2  = 0.399  10 2 6 cm/sFormulation Solution  —  fu Gut   0.02 Set equal to  fu  p DistributionDistribution model Full PBPK model  — Vss  (l/kg) Predicted, 0.217 Prediction method Rodgers et al. (2005a, b); Rodgers andRowland (2006, 2007)B/P ratio Predicted; 0.72 Prediction method Uchimura et al. (2010)  fu  p  0.02 Internal data Metabolism Clearance type Enzyme kineticsIn vitro metabolic system Human recombinant P450 isoforms Kazui et al. (2010)rhCYP1A2  V  max   (pmol/minper pmol)2.27 K   M   ( m M) 1.58  fu mic  0.015rhCYP2B6  V  max   (pmol/minper pmol)7.66 K   M   ( m M) 2.08  fu mic  0.015rhCYP2C19  V  max   (pmol/minper pmol)7.52 K   M   ( m M) 1.12  fu mic  0.015 N.B.:  fu mic  obtained using the prediction toolbox and refined bysensitivity analysisAdditional systemic clearance (l/h) 600 Representing about 90% of clopidogrel clearance (esterase-dependent pathway) 2-Oxo-clopidogrel (primary metabolite) PhysicochemicalMW (g/mol) 337.8 Internal data Log P o:w  2.96Compound type Monoprotic acidPka 3.41Hematocrit (%) 45.0 Simcyp libraryDistributionDistribution model Minimal PBPK model  — Vss  (l/kg) 0.100 Sensitivity analysisB/P ratio Predicted; 1.00 Prediction method Uchimura et al. (2010)  fu  p  Predicted; 0.0310 Prediction method Lobell and Sivarajah (2003)Metabolism Clearance type Enzyme kineticsIn vitro metabolic system Human recombinant P450 isoforms Kazui et al. (2010)rhCYP2B6  V  max   (pmol/minper pmol)2.48 K   M   ( m M) 1.62  fu mic  0.180rhCYP2C9  V  max   (pmol/minper pmol)0.855 K   M   ( m M) 18.1  fu mic  0.180rhCYP2C19  V  max   (pmol/minper pmol)9.06 K   M   ( m M) 12.1  fu mic  0.180rhCYP3A4  V  max   (pmol/minper pmol)3.63 K   M   ( m M) 27.8  fu mic  0.180 N.B.:  fu mic  obtained using the prediction toolboxand refined by sensitivity analysisAdditional clearance HLM  Cl  int   ( m l/minper mg)50 Representing about 50% of the total clearance(esterase-dependent pathway)  fu mic  0.180Active uptake into hepatocyte 2 Sensitivity analysis Clopi-H4 (secondary metabolite = active metabolite) Physicochemical MW (g/mol) 355.8 Internal data Log P o:w  3.60( continued  ) 512  Djebli et al.   a  t  A S P E T  J   o ur n a l   s  on J   a n u a r  y1  9  ,2  0 1  7  d m d  . a  s  p e  t   j   o ur n a l   s  . or  gD o wnl   o a  d  e  d f  r  om   The secondary metabolite systemic compartment was defined as: dMIIS  sys dt  ¼  1 V  d  2  MIIS   Q h   MIIS   Liv 2  MIIS  sys  2 CL  r  2  MIIS   BP  MIIS   MIIS  sys   ð 6 Þ where  V  d   –   MIIS   was the secondary metabolite volume of distribution at steady-state,  Q h  was the hepatic blood flow,  CL  r   –   MIIS   was the secondarymetabolite renal clearance and  BP  MIIS   was the secondary metabolite blood-to-plasma ratio,  MIIS   Liv  was the liver concentration of the secondarymetabolite, and  MIIS  sys  was the secondary metabolite systemic vein plasma concentration;Depending on the extent of sequential metabolism, a certain amount of thesecondarily 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 P450-dependent metabolite, and clopi-H4, the TABLE 1 — Continued  Parameters Value Implemented in Simcyp Source Data  Compound type Diprotic acidPka 1; Pka 2 3.20; 5.10Hematocrit (%) 45.0 Simcyp libraryDistributionDistribution model Minimal PBPK model  — Vss  (l/kg) Predicted; 0.230 Prediction method Rodgers et al. (2005a, b); Rodgers andRowland (2006, 2007)B/P ratio Predicted; 0.820 Prediction method Uchimura et al. (2010)  fu  p  0.018 Prediction method Lobell and Sivarajah (2003)Clearance Clearance type In vivo clearance Representing the direct irreversible covalent binding to platelets CL   po  (l/h) 500 B/P, blood-to-plasma ratio;  Cl  int  , intrinsic clearance;  CL   po , oral clearance;  fa , fraction absorbed;  fu mic , unbound fraction in microsomes;  fu  p , unbound fraction in plasma;  K   M  , Michaelis-Mentencoefficient;  P eff  , effective permeability;  P o :w, octanol/water partition coefficient;  Vss , steady state. TABLE 2Physicochemical and in vitro ADME parameters used in Simcyp for dronedarone Parameters Value Implemented in Simcyp Source Data  PhysicochemicalMW (g/mol) 557 Analytical dossier Log P o:w  7.80Compound type Monoprotic basePka 9.30Hematocrit (%) 45.0 Simcyp libraryAbsorptionAbsorption model/input type ADAM model  —  fa ;  Ka  (h 2 1 ) Predicted; 0.898; 0.816 Predicted using ADAM model P eff,  (10 2 4 cm/s) 1.98 Predicted P caco2  = 5.30   10 2 6 cm/sFormulation Solid; Controlled-Released  — Dissolution-timeprofileTime (h): 0, 0.083, 0.167, 0.25, 0.33, 0.42,0.5, 0.75, 1 and 1.5Dissolution (%): 0, 6.6, 12.8, 28.5, 38.9, 47.7,55.2, 75.9, 92.2 and 100Analytical dossier Solubility –  pHprofilepH: 3, 4, 5, 6 and 7 Solubility (mg/ml): 1.6, 1.6, 1.5, 0.1 and 0.05 Analytical dossier   fu Gut   1.00  — DistributionDistribution model Minimal PBPK model  — Vss  (l/kg) 10 Analytical dossier;  Pop Pk  analysisB:P ratio 1.00 Analytical dossier   fu  p  0.003Metabolism Clearance type Enzyme kineticsIn vitro metabolic system Recombinant Analytical dossier rhCYP3A4  V  max  (pmol/min per pmol)13.7 K   M   ( m M) 4.2  fu mic  0.0011rhCYP3A5  V  max  (pmol/min per pmol)4.87 K   M   ( m M) 3.10  fu mic  0.0011Additional liver clearance Cl  int  (l/min per mg)40  fu mic  0.0011 N.B.:  fu mic  obtained using theprediction toolbox and refinedby sensitivity analysisInteractionCYP2B6 (comp.inhibition) K  i  ( m M) 12.0 Analytical dossier   fu mic  0.0011CYP2D6 (comp.inhibition) K  i  ( m M) 5.0  fu mic  0.0011CYP3A4 (MBI)  K  i  ( m M) 2.44 K  inact   (h 2 1 ) 9.16  fu mic  0.0011  fa , fraction absorbed ; fu  p , unbound fraction in plasma;  fu mic , unbound fraction in microsomes ; Ka , first-order rate constant;  K  i  and  K  inact  , mechanism-based inactivation parameters;  P eff  , effectivepermeability in human;  Vss , steady state. PBPK Modeling for Clopidogrel and Its Active Metabolite  513   a  t  A S P E T  J   o ur n a l   s  on J   a n u a r  y1  9  ,2  0 1  7  d m d  . a  s  p e  t   j   o ur n a l   s  . or  gD o wnl   o a  d  e  d f  r  om   secondary metabolite), with the physicochemical, absorption, distribution, andclearance parameters described in Table 1. Physicochemical Parameters.  As physicochemical input parameters, themolecular weight, the chemical nature, the Pka, and the Log P  values were usedfor clopidogrel, 2-oxo-clopidogrel, and clopi-H4. Absorption.  The absorption process was described for clopidogrel only. Thefirst-order absorption model was selected with a fraction absorbed (  fa ), a first-order rate constant ( Ka ), and an effective permeability ( P eff  ) in human wereused as input parameters. The formulation was considered as a solution. Distribution.  Two PBPK distribution models were available in Simcyp: theminimal and the full PBPK models. The minimal PBPK model can be describedas a   “ lumped ”  model that has only three compartments, when there is no single-adjusting compartment (i.e., peripheral compartment), predicting the systemic,portal vein, and liver concentrations. The full PBPK distribution model proposeda number of time-based differential equations to simulate the concentrations invarious organ compartments: blood (plasma), adipose, bone, brain, gut, heart,kidney, liver, lung, muscle, skin, and spleen. The interindividual variability of tissue volume was estimated taking account of age, sex, weight, and height. Thedistribution is assumed to be perfusion-limited, using the full PBPK model, unlessthe membrane transporters are taken into account, whereby permeability-limiteddistribution is handled in the liver, kidney, and brain. For the current analysis, thefull PBPK distribution model was selected for clopidogrel, and the minimalPBPK model was selected for 2-oxo-clopidogrel and for clopi-H4. The volumesof 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 werepredicted using the model proposed by Rodgers and Rowland (Rodgers et al.,2005a, b; Rodgers and Rowland 2006, 2007), with the exception of 2-oxo-clopidogrel, for which the sensitivity analysis model was used to refine this valueon the basis of its impact on the observed clopidogrel and clopi-H4 exposures[maximum plasma concentration ( C  max  ) and area under the curve (AUC)]. Theblood-to-plasma ratio was predicted using the model proposed by 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 (  fu  p ) was set to 0.02 for clopidogrelas stated in the analytical dossier and to 0.031 and 0.018 for 2-oxo-clopidogreland clopi-H4, respectively, using the model proposed by Lobell and Sivarajah(2003). Elimination.  For clopidogrel metabolism, enzyme kinetics informationusing human recombinant P450 isoforms was selected. The maximum velocityof the metabolizing enzyme ( V  max  ) , and Michaelis-Menten coefficient value( K   M  ) from Kazui et al. (2010) were used. The unbound fraction in humanhepatic microsomes (  fu mic ) of 0.015 was predicted using the QSAR modelpublished by Gao et al. (2008), in a first step, and refined using the sensitivityanalysis module on the basis of observed clopidogrel and clopi-H4 exposuresin a second step. Moreover, an additional systemic clearance of 600 l/h wasconsidered, representing the esterase-mediated clearance using the retrogrademodel (about 90% of clopidogrel total clearance).The enzyme kinetic information ( V  max   and  K   M  ) from Kazui et al. (2010)using human recombinant P450 isoforms was also used for 2-oxo-clopidogrel.Moreover, an additional clearance of 50  m l/min per milligram was consideredfor 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 intoSimcyp. This value represented the immediate direct irreversible binding of thisactive metabolite to platelets. Dronedarone PBPK Model.  The dronedarone Simcyp model has beenpreviously developed and validated for a large range of doses (200 – 1600 mgBID). The input parameters are detailed in Table 2.This model accurately predicted the pharmacokinetics of dronedarone andcorrectly took into account the nonlinearity of dronedarone pharmacokinetics(Fig. 2). This nonlinearity resulted from the moderate mechanism-based inhibitionof 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  Fig. 2.  Comparison of observed versus predicted dronedarone AUC 0 – 12  and  C  max   at steady state after 400-mg BID administration (A and B) and 200 – 1600-mg BIDadministration (C and D). 514  Djebli et al.   a  t  A S P E T  J   o ur n a l   s  on J   a n u a r  y1  9  ,2  0 1  7  d m d  . a  s  p e  t   j   o ur n a l   s  . or  gD o wnl   o a  d  e  d f  r  om 
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