Evaluation of Various Static and Dynamic Modeling Methods to Predict Clinical CYP3A Induction Using In Vitro CYP3A4 mRNA Induction Data

TLDR

The study evaluated several drug–drug interaction prediction models for their ability to identify CYP3A induction liability using in vitro mRNA data. The authors compared correlation approaches (Cmax/EC50 ratio and relative induction score), a basic static model (R3), a mechanistic static model (net effect), and a mechanistic dynamic PBPK model to predict CYP3A substrate interaction magnitudes from 28 clinical trials. All models demonstrated high fidelity with few false positives or negatives, and correlation and basic static models, when total Cmax was incorporated, produced no false negatives and may be sufficient, whereas mechanistic models that also accounted for CYP inactivation were less accurate due to overprediction. Clinical Pharmacology & Therapeutics (2014); 95 2, 216–227.

Abstract

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. Clinical Pharmacology & Therapeutics (2014); 95 2, 216–227. doi:10.1038/clpt.2013.170