Physiologically Based Pharmacokinetic Modeling for Sequential Metabolism: Effect of CYP2C19 Genetic Polymorphism on Clopidogrel and Clopidogrel Active Metabolite Pharmacokinetics

Abstract

Clopidogrel is a prodrug that needs to be converted to its active metabolite (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 for clopidogrel and clopi-H4 using a specific sequential metabolite module in four 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 area under the curve (AUC) 0-24 obtained from a randomized crossover study conducted in four balanced CYP2C19-phenotype metabolizer groups was performed using a visual predictive check method. Second, the interindividual and intertrial variability (on the basis of AUC 0-24 comparisons) between the predicted trials and the observed trial of individuals, for each phenotypic group, were compared. Finally, a further validation, on the basis of drug-drug-interaction prediction, was performed by comparing observed values of clopidogrel and clopi-H4 with or without dronedarone (moderate CYP3A4 inhibitor) coadministration using a previously developed and validated physiologically based 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 and of its primary and secondary metabolites in populations with genetically different activity for a metabolizing enzyme.

Development of a physiology-directed population pharmacokinetic and pharmacodynamic model for characterizing the impact of genetic and demographic factors on clopidogrel response in healthy adults

“…For example, the work published by Lee et al (2012) as well as Yousef et al (2013) primarily focused on the characterization of inactive metabolite kinetics, which is unlikely to be reflective of clopidogrel’s therapeutic effect. There are also studies (Djebli et al, 2015; Yun et al, 2014) that focus on an isolated PK or PD question or use parameterizations (e.g. nonlinear absorption processes to characterize saturable bioactivation) that are not physiologically relevant (Ernest et al, 2008).…”

“…Using another method proposed by Turner et al (2006), the predicted unbound fraction of clopidogrel and 2-oxo-clopidogrel in the microsomal system was 0.74 (0.83–1.54 μM) and 0.97 (1.57–27.0 μM), respectively. However, a recent published clopidogrel PBPK model developed with Simcyp (Djebli et al, 2015) reported that the estimated unbound fraction values 0.015 and 0.18, which turned out to best characterize unbound fraction of clopidogrel and 2-oxo-clopidogrel in the microsomal system, respectively, were significantly lower than the unbound fraction values obtained from prediction methods derived from historical data (Hallifax and Houston, 2006; Turner et al, 2006); and the resultant unbound K m values were 0.017–0.039 μM and 0.29–5.00 μM for step 1 and step 2 of the 2-step clopidogrel bioactivation process, respectively. Interestingly, this set of unbound K m values (0.017–5.00 μM) was within the same range of our model estimated free in vivo K m value (0.154 μM), which is a lumped value that characterizes the overall 2-step bioactivation process, indicating that our model may provide a reasonable characterization of free in vivo K m value.…”

Identifying clinically relevant sources of variability: The clopidogrel challenge

“…A well‐stirred model accounting for nonspecific in vitro binding ( fu inc ), blood to plasma ratio (B/P), and the fraction unbound in plasma ( fu p ) best described CLOP's hepatic biotransformation to CLOP‐AM (A 5 ) via the formation of CLOP intermediate metabolite, 2‐oxo‐clopidogrel (2‐OC) (A 4 ) in the liver. The model also accounted for the mechanism of hydrolytic inactivation of CLOP and its metabolites by CES1 ( Figure ) ( Supplementary Table S1 ) . Governed by the law of mass balance, our model described the change over time in amount of CLOP (A 3 ) and CLOP‐AM (A 6 ) in the systemic circulation and the corresponding change in MPA, normalized to MPA 0 (A 7 ) due to irreversible binding of CLOP‐AM to the circulating platelets.…”

Physiologically Based Pharmacokinetic Modeling of Palbociclib

“…In this regard a useful mechanistic modeling tool that can be applied to accomplish this objective involves physiologically based pharmacokinetic (PBPK) modeling and simulation, which provides a quantitative framework to assess potential DDI . In addition, recent advances in PBPK modeling and simulation have enabled this approach to be used as a mechanistic modeling tool for the evaluation of the possible impact of various intrinsic and extrinsic factors affecting the pharmacokinetics of drugs such as drug‐disease interactions in organ impairment patients, pharmacogenetics, drug formulation, and pediatrics …”