Early Prediction and Impact Assessment of CYP3A4-Related Drug-Drug Interactions for Small-Molecule Anticancer Drugs Using Human-CYP3A4-Transgenic Mouse Models.

Abstract

Early detection of drug-drug interactions (DDIs) can facilitate timely drug development decisions, prevent unnecessary restrictions on patient enrollment, resulting in clinical study populations that are not representative of the indicated study population, and allow for appropriate dose adjustments to ensure safety in clinical trials. All of these factors contribute to a streamlined drug approval process and enhanced patient safety. Here we describe a new approach for early prediction of the magnitude of change in exposure for cytochrome P450 (P450) CYP3A4-related DDIs of small-molecule anticancer drugs based on the model-based extrapolation of human-CYP3A4-transgenic mice pharmacokinetics to humans. Victim drugs brigatinib and lorlatinib were evaluated with the new approach in combination with the perpetrator drugs itraconazole and rifampicin. Predictions of the magnitude of change in exposure deviated at most 0.99- to 1.31-fold from clinical trial results for inhibition with itraconazole, whereas exposure predictions for the induction with rifampicin were less accurate, with deviations of 0.22- to 0.48-fold. Results for the early prediction of DDIs and their clinical impact appear promising for CYP3A4 inhibition, but validation with more victim and perpetrator drugs is essential to evaluate the performance of the new method. SIGNIFICANCE STATEMENT: The described method offers an alternative for the early detection and assessment of potential clinical impact of CYP3A4-related drug-drug interactions. The model was able to adequately describe the inhibition of CYP3A4 metabolism and the subsequent magnitude of change in exposure. However, it was unable to accurately predict the magnitude of change in exposure of victim drugs in combination with an inducer.