Evaluation of various static and dynamic modeling methods to predict clinical CYP3A induction using in vitro CYP3A4 mRNA induction data

Einolf HJ1, Chen L2, Fahmi OA3, Gibson CR4, Obach RS3, Shebley M5, Silva J6, Sinz MW7, Unadkat JD8, Zhang L9, Zhao P9
Source: Other
Publication Date: (2014)
Issue: 95(2): 179-188
Cells used in publication:
Hepatocyte, mouse
Species: mouse
Tissue Origin: liver
Hepatocyte, rat
Species: rat
Tissue Origin: liver
Hepatocyte, human
Species: human
Tissue Origin: liver
Hepatocyte, Cynomolgus
Species: monkey
Tissue Origin: liver
Hepatocyte, Rhesus
Species: monkey
Tissue Origin: liver
Hepatocyte, canine
Species: canine
Tissue Origin:
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 (Cmax) to in vitro half-maximal effective concentration (EC50); and relative induction score), (ii) a basic static model (calculated R3 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 Cmax 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.