Korzekwa Ken, Tweedie Donald, Argikar Upendra A, Whitcher-Johnstone Andrea, Bell Leslie, Bickford Shari, Nagar Swati
Department of Pharmaceutical Sciences, Temple University School of Pharmacy, Philadelphia, Pennsylvania (K.K., S.N.); Drug Metabolism and Pharmacokinetics, Boehringer Ingelheim, Ridgefield, Connecticut (D.T., A.W.-J.); and Analytical Sciences and Imaging (U.A.A.) and Metabolism and Pharmacokinetics (L.B., S.B.), Novartis Institutes for BioMedical Research Inc., Cambridge, Massachusetts.
Department of Pharmaceutical Sciences, Temple University School of Pharmacy, Philadelphia, Pennsylvania (K.K., S.N.); Drug Metabolism and Pharmacokinetics, Boehringer Ingelheim, Ridgefield, Connecticut (D.T., A.W.-J.); and Analytical Sciences and Imaging (U.A.A.) and Metabolism and Pharmacokinetics (L.B., S.B.), Novartis Institutes for BioMedical Research Inc., Cambridge, Massachusetts
Drug Metab Dispos. 2014 Sep;42(9):1587-95. doi: 10.1124/dmd.114.058297. Epub 2014 Jun 17.
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; Nagar et al., 2014) 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.
细胞色素P450酶的时间依赖性抑制(TDI)是药物相互作用的一个重要原因。表征TDI动力学的标准方法是在各种抑制剂浓度[I]下确定酶损失速率kobs,并重新绘制kobs与[I]的关系图以获得关键动力学参数KI和kinact。在本期《药物代谢与处置》中我们的配套论文(第1部分;Nagar等人,2014年)中,我们使用模拟数据集开发并测试了一种分析体外TDI数据的新数值方法。在此,我们将这种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。没有一个数据集表现出仅符合米氏动力学,并且该数值方法允许使用更复杂的模型来拟合每个数据集。观察并参数化了准不可逆以及部分抑制动力学。三个数据集需要使用多抑制剂结合模型。讨论了这些分析所提供的机制和临床意义。连同第1部分的结果,我们开发并应用了一种分析体外TDI数据的新数值方法。该方法似乎普遍适用于对具有非典型和复杂动力学方案的体外TDI数据进行建模。