Comparison of Different Algorithms for Predicting Clinical Drug-Drug Interactions, Based on the Use of CYP3A4 in Vitro Data: Predictions of Compounds as Precipitants of Interaction
Comparison of Different Algorithms for Predicting Clinical Drug-Drug Interactions, Based on the Use of CYP3A4 in Vitro Data: Predictions of Compounds as Precipitants of Interaction
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DOI：
10.1124/dmd.108.026252
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发表时间：
2009-08-01
影响因子：
3.9
通讯作者：
Obach, R. Scott
中科院分区：
文献类型：
--
作者：
Fahmi, Odette A.;Hurst, Susan;Obach, R. Scott
细胞色素P450 3A4 （CYP3A4）是药物代谢中最重要的酶，因为它是药代动力学药物-药物相互作用（ddi）最常见的靶标，因此非常希望能够从体外数据预测基于CYP3A4的ddi。在本研究中，通过体外抑制、失活和诱导数据，预测了30种药物对CYP3A4探针底物咪达唑仑（midazolam）药代动力学的临床ddi。使用了两种DDI预测方法，它们同时考虑了肝脏和肠道的影响。第一个模型同时结合了可逆抑制、时间依赖性失活和诱导数据，以及沉淀药物相关体内浓度的静态估计，以提供咪达唑仑暴露平均变化幅度的点估计。该模型识别ddi的成功率为88%，平均误差为1.74。第二个模型是基于计算生理学的药代动力学模型，该模型使用沉淀性药物的体内浓度的动态估计，并考虑种群间的个体差异（Simcyp）。该模型的成功率为88%和90%（分别适用于“稳态”和“基于时间”的方法），平均误差为1.59和1.47。从这些发现可以得出结论，CYP3A4的体内ddi可以从体外数据预测，即使同时发生多种生化现象。
Cytochrome P450 3A4 (CYP3A4) is the most important enzyme in drug metabolism and because it is the most frequent target for pharmacokinetic drug-drug interactions (DDIs) it is highly desirable to be able to predict CYP3A4-based DDIs from in vitro data. In this study, the prediction of clinical DDIs for 30 drugs on the pharmacokinetics of midazolam, a probe substrate for CYP3A4, was done using in vitro inhibition, inactivation, and induction data. Two DDI prediction approaches were used, which account for effects at both the liver and intestine. The first was a model that simultaneously combines reversible inhibition, time-dependent inactivation, and induction data with static estimates of relevant in vivo concentrations of the precipitant drug to provide point estimates of the average magnitude of change in midazolam exposure. This model yielded a success rate of 88% in discerning DDIs with a mean-fold error of 1.74. The second model was a computational physiologically based pharmacokinetic model that uses dynamic estimates of in vivo concentrations of the precipitant drug and accounts for interindividual variability among the population (Simcyp). This model yielded success rates of 88 and 90% (for "steady-state" and "time-based" approaches, respectively) and mean-fold errors of 1.59 and 1.47. From these findings it can be concluded that in vivo DDIs for CYP3A4 can be predicted from in vitro data, even when more than one biochemical phenomenon occurs simultaneously.