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

机译:一种基于半生物学的药代动力学模型,其特征是基于机制的自动抑制,以预测人类的立体选择性药代动力学及其在人类中的代谢物诺伦米尔

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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.
机译:鉴定维拉帕米及其主要的代谢物诺伦替纳米尔是Cyp3a的基于机制的抑制剂和底物,并据报道临床中的非线性药代动力学。在合并的人肝微粒体中首先测量了Verapamil和Norverapmil的代谢间隙以及它们对CYP3A活性的影响。结果表明,S-异构体更优先于对维拉帕米和Norverapamil的R-异构体来代谢,它们对CYP3A活性的抑制作用也与S-异构体比R-异构体更有效。基于半生理学的药代动力学模型(半PBPK)表征了基于机理的自动抑制,以预测单一或多个口服剂量后维拉帕米和Norverapamil的立体选择性药代动力学谱。获得了良好的仿真,这表明开发的半PBPK模型可以同时预测S-Verapamil,R-Verapamil,S-Norverapamil和R-Norverapamil的药代动力学曲线。在第38次口腔剂量的维拉帕米缓释片剂(每日240毫克)之后还研究了对维拉帕米和诺伦比米累积的自动抑制和诺伦比米累积的贡献。预测的累积率约为1.3-1.5倍,接近观察到的1.4-2.1倍。最后,开发的半PBPK模型进一步应用于预测维拉帕米和其他三种CYP3A底物之间的药物 - 药物相互作用(DDI),包括Midazolam,Simvastatin和环孢菌素A.也获得了成功的预测,这表明开发的半PBPK掺入自动抑制的模型还对CYP3A基板的DDI预测显示出很大的优势。

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