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首页> 外文期刊>Pharmacology and Therapeutics: The Journal of the International Encyclopedia of Pharmacology and Therapeutics >Time-dependent enzyme inactivation: Numerical analyses of in vitro data and prediction of drug-drug interactions
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Time-dependent enzyme inactivation: Numerical analyses of in vitro data and prediction of drug-drug interactions

机译：时间依赖性酶失活：对体外数据的数值分析和药物 - 药物相互作用的预测

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摘要

Cytochrome P450 (CYP) enzyme kinetics often do not conform to Michaelis-Menten assumptions, and time-dependent inactivation (TDI) of CYPs displays complexities such as multiple substrate binding, partial inactivation, quasi-irreversible inactivation, and sequential metabolism. Additionally, in vitro experimental issues such as lipid partitioning, enzyme concentrations, and inactivator depletion can further complicate the parameterization of in vitro TDI. The traditional replot method used to analyze in vitro TDI datasets is unable to handle complexities in CYP kinetics, and numerical approaches using ordinary differential equations of the kinetic schemes offer several advantages. Improvement in the parameterization of CYP in vitro kinetics has the potential to improve prediction of clinical drug-drug interactions (DDIs). This manuscript discusses various complexities in TDI kinetics of CYPs, and numerical approaches to model these complexities. The extrapolation of CYP in vitro TDI parameters to predict in vivo DDIs with static and dynamic modeling is discussed, along with a discussion on current gaps in knowledge and future directions to improve the prediction of DDI with in vitro data for CYP catalyzed drug metabolism. (C) 2019 Elsevier Inc. All rights reserved.
机译：细胞色素P450（CYP）酶动力学通常不符合Michaelis-Menten的假设，Cyps的时间依赖性灭活（TDI）显示诸如多个底物结合，部分失活，准不可逆的灭活和连续代谢的复杂性。另外，在体外实验问题，例如脂质分配，酶浓度和灭失剂耗尽可以进一步使体外TDI的参数化复杂化。用于分析体外TDI数据集的传统Reploot方法无法处理Cyp动力学中的复杂性，并且使用动力学方案的常微分方程的数值方法提供了几个优点。体外动力学中CYP参数化的改善具有改善临床药物 - 药物相互作用（DDIS）预测的潜力。该手稿讨论了CYP的TDI动力学中的各种复杂性，以及模拟这些复杂性的数值方法。讨论了CYP的外推，以静态和动态建模在体内DDI中预测的情况下，以及关于知识和未来方向上的电流间隙的讨论，以改善DDI对CYP催化药物代谢的体外数据的预测。 （c）2019 Elsevier Inc.保留所有权利。

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