A semi-physiologically-based pharmacokinetic model characterizing mechanism-based auto-inhibition to predict stereoselective pharmacokinetics of verapamil and its metabolite norverapamil in human

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• 作者：Jian Wang ; Sumei Xia ; Weifang Xue ; Dawei Wang ; Yang Sai ; Li Liu ; Xiaodong Liu
• 关键词：Verapamil ; Semi-physiologically-based pharmacokinetic model ; Auto-inhibition ; Stereoselective pharmacokinetics ; Drug&ndash ; drug interaction ; CYP3A
• 刊名：European Journal of Pharmaceutical Sciences
• 出版年：2013
• 出版时间：20 November, 2013
• 年：2013
• 卷：50
• 期：3-4
• 页码：290-302
• 全文大小：1511 K

文摘

Verapamil and its major metabolite norverapamil were identified to be both mechanism-based inhibitors and substrates of CYP3A and reported to have non-linear pharmacokinetics in clinic. Metabolic clearances of verapamil and norverapmil as well as their effects on CYP3A activity were firstly measured in pooled human liver microsomes. The results showed that S-isomers were more preferential to be metabolized than R-isomers for both verapamil and norverapamil, and their inhibitory effects on CYP3A activity were also stereoselective with S-isomers more potent than R-isomers. A semi-physiologically based pharmacokinetic model (semi-PBPK) characterizing mechanism-based auto-inhibition was developed to predict the stereoselective pharmacokinetic profiles of verapamil and norverapamil following single or multiple oral doses. Good simulation was obtained, which indicated that the developed semi-PBPK model can simultaneously predict pharmacokinetic profiles of S-verapamil, R-verapamil, S-norverapamil and R-norverapamil. Contributions of auto-inhibition to verapamil and norverapamil accumulation were also investigated following the 38th oral dose of verapamil sustained-release tablet (240 mg once daily). The predicted accumulation ratio was about 1.3-1.5 fold, which was close to the observed data of 1.4-2.1-fold. Finally, the developed semi-PBPK model was further applied to predict drug-drug interactions (DDI) between verapamil and other three CYP3A substrates including midazolam, simvastatin, and cyclosporine A. Successful prediction was also obtained, which indicated that the developed semi-PBPK model incorporating auto-inhibition also showed great advantage on DDI prediction with CYP3A substrates.