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一种用于分析体外时间依赖性抑制数据的数值方法。第1部分。理论考量。

A numerical method for analysis of in vitro time-dependent inhibition data. Part 1. Theoretical considerations.

作者信息

Nagar Swati, Jones Jeffrey P, Korzekwa Ken

机构信息

Department of Pharmaceutical Sciences, Temple University School of Pharmacy, Philadelphia, Pennsylvania (S.N., K.K.); and Department of Chemistry, Washington State University, Pullman, Washington (J.P.J.).

Department of Pharmaceutical Sciences, Temple University School of Pharmacy, Philadelphia, Pennsylvania (S.N., K.K.); and Department of Chemistry, Washington State University, Pullman, Washington (J.P.J.)

出版信息

Drug Metab Dispos. 2014 Sep;42(9):1575-86. doi: 10.1124/dmd.114.058289. Epub 2014 Jun 17.

Abstract

Inhibition of cytochromes P450 by time-dependent inhibitors (TDI) is a major cause of clinical drug-drug interactions. It is often difficult to predict in vivo drug interactions based on in vitro TDI data. In part 1 of these manuscripts, we describe a numerical method that can directly estimate TDI parameters for a number of kinetic schemes. Datasets were simulated for Michaelis-Menten (MM) and several atypical kinetic schemes. Ordinary differential equations were solved directly to parameterize kinetic constants. For MM kinetics, much better estimates of KI can be obtained with the numerical method, and even IC50 shift data can provide meaningful estimates of TDI kinetic parameters. The standard replot method can be modified to fit non-MM data, but normal experimental error precludes this approach. Non-MM kinetic schemes can be easily incorporated into the numerical method, and the numerical method consistently predicts the correct model at errors of 10% or less. Quasi-irreversible inactivation and partial inactivation can be modeled easily with the numerical method. The utility of the numerical method for the analyses of experimental TDI data is provided in our companion manuscript in this issue of Drug Metabolism and Disposition (Korzekwa et al., 2014b).

摘要

时间依赖性抑制剂(TDI)对细胞色素P450的抑制作用是临床药物相互作用的主要原因。基于体外TDI数据往往难以预测体内药物相互作用。在这些手稿的第1部分中,我们描述了一种数值方法,该方法可以直接估算多种动力学方案的TDI参数。针对米氏(MM)和几种非典型动力学方案模拟了数据集。直接求解常微分方程以参数化动力学常数。对于MM动力学,使用数值方法可以获得更好的KI估计值,甚至IC50偏移数据也可以提供TDI动力学参数的有意义估计值。标准重绘方法可以修改以拟合非MM数据,但正常的实验误差排除了这种方法。非MM动力学方案可以轻松纳入数值方法,并且数值方法在误差为10%或更低时始终能预测正确的模型。准不可逆失活和部分失活可以用数值方法轻松建模。本期《药物代谢与处置》(Korzekwa等人,2014b)中我们的配套手稿提供了数值方法在分析实验TDI数据方面的实用性。

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