Predicting the Drug-Drug Interaction Mediated by CYP3A4 Inhibition: Method Development and Performance Evaluation

The prediction of drug-drug interactions (DDIs) plays critical roles for the estimation of DDI risk caused by inhibition of CYP3A4. The aim of this paper is to develop a physiologically based pharmacokinetic (PBPK)-DDI model for prediction of the DDI co-administrated with ketoconazole in humans and evaluate the predictive performance of the model. The pharmacokinetic and biopharmaceutical properties of 35 approved drugs, as victims, were collected for the development of a PBPK model, which were linked to the PBPK model of ketoconazole for the DDI prediction. The PBPK model of victims and ketoconazole were validated by matching actual in vivo pharmacokinetic data. The predicted results of DDI were compared with actual data to evaluate the predictive performance. The percentage of predicted ratio of AUC (AUCR), C-max (CmaxR), and T-max (TmaxR) was 75%, 69%, and 91%, respectively, which were within the twofold threshold (range, 0.5-2.0x) of the observed values. Only 3% of the predicted AUCRs are obviously underestimated. After integration of the reported fraction of metabolism (f(m)) into the PBPK-DDI model for limited four cases, the model-predicted AUCRs were improved from the twofold range of the observed AUCRs to the 90% confidence interval. The developed method could reasonably predict drug-drug interaction with a low risk of underestimation. The present accuracy of the prediction was improved compared with that of static mechanistic models. The evaluation of predictive performance increases the confidence using the model to evaluate the risk of DDIs co-administrated with ketoconazole before the in vivo DDI study.

Automated summary

The study developed a PBPK-DDI model to predict drug-drug interactions co-administrated with ketoconazole in humans. Through verification with actual data, the model showed good predictive performance with predicted results within the observed range, and significantly better accuracy compared to traditional mechanistic models.