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

Abstract

BACKGROUND AND PURPOSE Enhancing the precision of drug-drug interaction (DDI) prediction is essential for mitigating potential drug interactions and enhancing drug safety and efficacy. This study aims to investigate the impact of in vitro and in silico approaches for calculating the fraction metabolized by CY3A4 (fm) on DDI prediction accuracy and identified the most effective method for improving DDI prediction using physiologically based pharmacokinetic (PBPK) models. EXPERIMENTAL APPROACH Both in vitro and in silico methods were utilized to determine fm values for 33 approved drugs, or fm values were assumed to be 100%. These fm values were then integrated into PBPK models. Subsequently, the PBPK models were combined with a PBPK model of ketoconazole to predict potential DDIs. Finally, the accuracy of these predictions was assessed. KEY RESULTS The integration of in vitro fm had remarkable precision in predicting CmaxR of 31 drugs and accurately predicting AUCRs of 28 drugs out of 33 drugs, both within 2 times of the measured values. However, using 100% fm and in silico fm resulted in lower prediction accuracy that was comparable to each other. CONCLUSIONS AND IMPLICATIONS Our study highlights the importance of incorporating in vitro fm data into PBPK models to improve the accuracy of predicting DDIs. While in silico fm may have some potential, its influence on predictions appears to be limited. Additionally, our findings suggest that drugs with high Clliver levels (>15 L·h-1) and high fm (>75%) are particularly susceptible to the impact of CYP3A4 inhibitor ketoconazole.