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

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 (AUC_{combo with ketoconazole}/AUC_{dosing 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.

GRAPHICAL ABSTRACT

KEYWORDS:

• Drug–drug interaction
• PBPK
• fraction metabolized
• CYP3A4
• CYP inhibition

Declaration of interest

T Chen is an employee of Shanghai PharmoGo Co., Ltd., an authorized distributor of Simulations Plus, the developer of GastroPlus, in China

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/17425255.2023.2263358

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Author contributions

H Ren, L Tang, H Wan, and M Li designed the research; J Pin, L Chu, and R Xu completed the determination of in vitro f_m; T Chen, H Ren, J Gao, L Wang, and Q Liu analyzed the data; H Ren, P Jiang, and T Chen wrote the manuscript; and L Tang, H wan, and M Li revised the manuscript.

Acknowledgments

The authors thank Simulations Plus, Inc. (Lancaster, CA, USA) for authorizing the use of the optimization module in this study. We thank Z Wu (Jinzhou Medical University, Jinzhou, China) for preparation of the graphic abstract.

Additional information

Funding

This paper was not funded.