Evaluation of Various Static and Dynamic Modeling Methods to Predict Clinical CYP3A Induction Using In Vitro CYP3A4 mRNA Induction Data
Evaluation of Various Static and Dynamic Modeling Methods to Predict Clinical CYP3A Induction Using In Vitro CYP3A4 mRNA Induction Data
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DOI：
10.1038/clpt.2013.170
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发表时间：
2014-02-01
影响因子：
6.7
通讯作者：
Zhao, P.
中科院分区：
文献类型：
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作者：
Einolfl, H. J.;Chen, L.;Zhao, P.
基于体外mRNA数据，评估几种药物-药物相互作用（DDI）预测模型识别细胞色素P450 (CYP)3A诱导倾向药物的能力。28项临床试验中CYP3A底物的药物相互作用程度采用(i)相关方法（体内峰值血浆浓度（C-max）与体外半最大有效浓度（EC50）之比）进行预测；（ii）基本静态模型（计算R-3值），（iii）机制静态模型（净效应），（iv）机制动态（基于生理的药代动力学）建模。所有模型都具有高保真度，预测的假阴性或假阳性很少。采用相关方法和基本静态模型计算总C-max时均无假阴性结果；这些模型可能足以保守地确定临床CYP3A诱导倾向。除了诱导外，还包括CYP失活的机制模型导致DDI预测的准确性较低，这可能是由于对失活效应的过度预测。
Several drug-drug interaction (DDI) prediction models were evaluated for their ability to identify drugs with cytochrome P450 (CYP)3A induction liability based on in vitro mRNA data. The drug interaction magnitudes of CYP3A substrates from 28 clinical trials were predicted using (i) correlation approaches (ratio of the in vivo peak plasma concentration (C-max) to in vitro half-maximal effective concentration (EC50); and relative induction score), (ii) a basic static model (calculated R-3 value), (iii) a mechanistic static model (net effect), and (iv) mechanistic dynamic (physiologically based pharmacokinetic) modeling. All models performed with high fidelity and predicted few false negatives or false positives. The correlation approaches and basic static model resulted in no false negatives when total C-max was incorporated; these models may be sufficient to conservatively identify clinical CYP3A induction liability. Mechanistic models that include CYP inactivation in addition to induction resulted in DDI predictions with less accuracy, likely due to an overprediction of the inactivation effect.