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A Numerical Method for Analysis of In Vitro Time-Dependent Inhibition Data. Part 2. Application to Experimental Data

机译:一种用于分析体外时间依赖性抑制数据的数值方法。第2部分。对实验数据的应用

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摘要

Time-dependent inhibition (TDI) of cytochrome P450 enzymes is an important cause of drug-drug interactions. The standard approach to characterize the kinetics of TDI is to determine the rate of enzyme loss, kobs, at various inhibitor concentrations, [I], and replot the kobs versus [I] to obtain the key kinetic parameters, KI and kinact. In our companion manuscript (Part 1; ) in this issue of Drug Metabolism and Disposition, we used simulated datasets to develop and test a new numerical method to analyze in vitro TDI data. Here, we have applied this numerical method to five TDI datasets. Experimental datasets include the inactivation of CYP2B6, CYP2C8, and CYP3A4. None of the datasets exhibited Michaelis-Menten–only kinetics, and the numerical method allowed use of more complex models to fit each dataset. Quasi-irreversible as well as partial inhibition kinetics were observed and parameterized. Three datasets required the use of a multiple-inhibitor binding model. The mechanistic and clinical implications provided by these analyses are discussed. Together with the results in Part 1, we have developed and applied a new numerical method for analysis of in vitro TDI data. This method appears to be generally applicable to model in vitro TDI data with atypical and complex kinetic schemes.
机译:细胞色素P450酶的时间依赖性抑制(TDI)是药物相互作用的重要原因。表征TDI动力学的标准方法是确定各种抑制剂浓度[I]时酶损失的速率,并重新绘制[I]以获得关键的动力学参数KI和运动。在本期《药物代谢与处置》的配套手稿(第1部分)中,我们使用模拟数据集来开发和测试一种新的数值方法来分析体外TDI数据。在这里,我们已将此数值方法应用于五个TDI数据集。实验数据集包括CYP2B6,CYP2C8和CYP3A4的失活。所有数据集都没有表现出仅Michaelis-Menten的动力学,并且数值方法允许使用更复杂的模型来拟合每个数据集。观察了准不可逆以及部分抑制动力学并对其进行了参数化。三个数据集需要使用多重抑制剂结合模型。讨论了这些分析提供的机制和临床意义。连同第1部分中的结果,我们已经开发并应用了一种新的数值方法来分析体外TDI数据。该方法似乎通常适用于使用非典型和复杂动力学方案对体外TDI数据进行建模。

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