Physiologically based pharmacokinetic (PBPK) modeling of drug-drug interactions between suraxavir marboxil and CYP3A4 inhibitors: Quantitative prediction of pharmacokinetic effects on active metabolite GP1707D07

Abstract

Suraxavir marboxil (GP681) is a promising novel prodrug influenza polymerase acidic (PA) inhibitor whose active metabolite, suraxavir (GP1707D07), is primarily metabolized by cytochrome P450 3A4 (CYP3A4), raising concerns about drug-drug interactions (DDI) with CYP3A4 inhibitors. Traditional DDI assessment methods are limited for evaluating all potential combinations. This study aimed to develop a physiologically based pharmacokinetic (PBPK) model to predict the DDI risk between GP681 and CYP3A4 inhibitors of varying potency. The model was developed based on physicochemical and in vitro parameters, as well as clinical data, including a phase I single ascending dose study of GP681 tablets and a single-center phase I study evaluating the DDI between GP681 and strong CYP3A4 inhibitor itraconazole. The model was successfully verified against clinical data, with predicted-to-observed ratios for GP1707D07 exposure under itraconazole co-administration of AUC and C_max  of 1.042 and 1.357, respectively. Simulations using the validated model predicted a substantial increase in GP1707D07 exposure when co-administered with moderate inhibitors fluconazole (AUC ratio 2.820 ; C_max  ratio 1.509) and verapamil (AUC ratio 2.347;C_max  ratio 1.645), comparable to the effect of itraconazole. Weak inhibitors showed negligible effects. Consequently, clinical monitoring and potential dose adjustment of GP681 are recommended when co-administered with strong inhibitors and the moderate inhibitors. The study demonstrates the utility of PBPK modeling for efficient and predictive DDI assessment of complex prodrug systems, guiding the safe clinical use of GP681.

Keywords

Physiologically based pharmacokinetic / modeling / Drug-drug interaction / Suraxavir marboxil / CYP3A4 inhibitors / Pharmacokinetics

CRediT authorship contribution statement

Lan Yang: Writing - original draft, Software. Fan Yang: Writing original draft, Methodology. Chao-Zhuang Shen: Visualization, Software. Yan-Xin Wang: Supervision, Software. Xiao-Lin Wang: Methodology. Lang lv: Investigation, Conceptualization. Yue-E Wu: Investigation. Pan-Pan Ye: Methodology. Bo-Hao Tang: Investigation. Guo-Xiang Hao: Investigation. Shou-Sheng Yan: Writing - review & editing, Project administration. Wei Zhao: Writing - review & editing, Project administration. Yi Zheng: Writing - review & editing, Project administration.

Data sharing statement

The data underlying this article will be shared on reasonable request to the corresponding author.

Ethics approval

This observational study adhered to the principles of the Declaration of Helsinki and Ethical Guidelines for Medical and Health Research Involving Human Subjects. The clinical study protocol and the informed consent form, as well as their amendments, had been reviewed and approved by the Ethics Committee of the China-Japan Friendship Hospital before implementation. Written informed consent was obtained from all enrolled patients.

Clinical trial registration

This study was registered on https://ClinicalTrials.gov (identifier: NCT05789342) on February 15, 2023.

Declaration of generative AI in scientific writing

No generative AI tools have been used throughout the entire writing process of this manuscript.

Funding

This work was supported by the National Natural Science Foundation of China 82474005, Natural Science Foundation of Shandong Province (ZR2022QH004), Taishan Scholar Program of Shandong Province (NO. tstp20230660).

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This study was supported by Jiangxi Qingfeng Pharmaceutical Co., Ltd. The authors are grateful to all the study participants and the medical staff at the participating sites for their contributions.

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.pscia.2025.100095.

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