Enhancing drug-drug Interaction Prediction by Integrating Physiologically-Based Pharmacokinetic Model with Fraction Metabolized by CYP3A4

Abstract

ABSTRACT Background Enhancing the precision of drug–drug interaction (DDI) prediction is essential for improving drug safety and efficacy. The aim is to identify the most effective fraction metabolized by CY3A4 (f m ) for improving DDI prediction using physiologically based pharmacokinetic (PBPK) models. Research Design and Methods The f m values were determined for 33 approved drugs using a human liver microsome for in vitro measurements and the ADMET Predictor software for in silico predictions. Subsequently, these f m values were integrated into PBPK models using the GastroPlus platform. The PBPK models, combined with a ketoconazole model, were utilized to predict AUCR (AUCcombo with ketoconazole/AUCdosing alone), and the accuracy of these predictions was evaluated by comparison with observed AUCR. Results The integration of in vitro f m method demonstrates superior performance compared to the in silico f m method and f m of 100% method. Under the Guest-limits criteria, the integration of in vitro f m achieves an accuracy of 76%, while the in silico f m and f m of 100% methods achieve accuracies of 67% and 58%, respectively. Conclusions Our study highlights the importance of in vitro f m data to improve the accuracy of predicting DDIs and demonstrates the promising potential of in silico f m in predicting DDIs.