Chen Ning, Pieter Annaert
Drug Delivery and Disposition, Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Herestraat 49-box 921, Leuven, Belgium
Introduction: Vicagrel, an innovative antiplatelet medication originating from Jiangsu Vcare PharmaTech Co. Ltd., China, is aimed at addressing clopidogrel resistance attributed to insufficient CYP2C19 activity in Asian populations. As a new molecular entity, vicagrel has undergone several Phase I and Phase II clinical trials in China and the United States and has filed a New Drug Application with the FDA. It shares the same metabolites and mechanism as clopidogrel but with a different initial bio-activation step mediated by esterase, thus avoiding resistance issues observed in patients with CYP2C19 loss-of-function alleles[1]. As one of the world’s best-selling drugs, clopidogrel has been reported for the risks related to drug-gene interactions and drug-drug interactions, with several PBPK models established for predicting these risks[2]. Due to similar metabolic pathways, the drug-drug-gene interaction risk of vicagrel may attract significant interest in future development. The objective of this study is to establish a physiologically-based pharmacokinetic (PBPK) model incorporating vicagrel and its metabolites to facilitate the further evaluation of the drug-drug-gene interaction risk of the novel antiplatelet agent.
Methods: The PBPK models were developed using PK-Sim® and MoBi® (version 11.2) within the framework of the Open Systems Pharmacology Suite (www.open-systems-pharmacology.org)[3]. Data for the model development was sourced from literature, encompassing physicochemical parameters and plasma concentration-time profiles for vicagrel, 2-oxo-clopidogrel, and clopidogrel active metabolite[4-8]. Available clinical data was divided into a training dataset for model development and a test dataset for subsequent verification. CYP2C19 activity was assumed to be 100% for extensive metabolizers, 50% for intermediate metabolizers, and 0% for poor metabolizers, based on reported activities.[9] An unspecific hepatic clearance of clopidogrel H4 was defined representing its irreversible binding to platelets[9].
Due to the lack of built-in data for non-CYP enzymes in PK-Sim (defaulting to 1μM/L), expression data for these enzymes were obtained from studies reporting absolute protein expression of human metabolic enzymes [10-15]. Compound physicochemical and in vitro ADME parameters (logP, fu) and metabolic kinetics were selected within the reported range values in the literature. The clearance of the clopidogrel active metabolite and partition coefficient calculation method were optimized based on parameter identification using PK profiles in the training dataset.
Results: Whole-body physiologically-based pharmacokinetic (PBPK) models were developed for vicagrel and its metabolites, 2-oxo-clopidogrel, and clopidogrel active metabolite. The model, optimized using the training dataset, demonstrated successful predictions of mean plasma concentrations in the validation dataset, including patients with various CYP2C19 activities. The geometric mean fold error (GMFE) was 1.37 for predicted versus observed AUC values and 1.32 for predicted versus observed Cmax values. Notably, all predicted AUC and Cmax values fell within the 2-fold error range, with 76% falling within the 1.5-fold error range.
Conclusions: A comprehensive parent-metabolite physiologically-based pharmacokinetic (PBPK) model for vicagrel and its metabolites, including 2-oxo-clopidogrel and the active metabolite of clopidogrel, has been successfully developed. The model confirms that the novel antiplatelet medication vicagrel effectively overcomes resistance associated with CYP2C19 loss-of-function alleles, showing comparable exposure to the active metabolite at 1/12 of the administered clopidogrel doses. Given the absence of clinical data for 2-oxo-clopidogrel, further validation of the model’s utility in predicting drug-drug-gene interactions involving 2-oxo-clopidogrel will be necessary as more clinical data becomes available. In vitro and preclinical studies have observed drug-drug interactions between vicagrel and both statins and aspirin.[16, 17] The extension of the PBPK model to predict the risk of drug-drug interactions during clinical use can be achieved when more in vitro interaction kinetics and perpetrator models are accessible.
References:
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Reference: PAGE 32 (2024) Abstr 11216 [www.page-meeting.org/?abstract=11216]
Poster: Drug/Disease Modelling - Absorption & PBPK