Mechanistic Physiologically Based Pharmacokinetic Modeling to Predict CYP3A4-Mediated Drug–Drug Interactions of Flumatinib as Both a Victim and a Perpetrator

Muyesaier Alifu,1,* Xue Chu,2,* Xinyan Zhu,1 Wen Yao Mak,1 Aole Zheng,1 Chunyang Pan,1 Qingfeng He,1 Xiao Zhu,1 Zheng Kuai,3 Xiaoqiang Xiang1 1Department of Clinical Pharmacy and Pharmacy Administration, School of Pharmaceutical Sciences, Fudan University, Shanghai, People’s Republic of China; 2Department of Oncology, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, People’s Republic of China; 3Department of Geriatrics, Zhongshan Hospital, Fudan University, Shanghai, People’s Republic of China*These authors contributed equally to this workCorrespondence: Zheng Kuai, Department of Geriatrics, Zhongshan Hospital, Fudan University, Shanghai, 200032, People’s Republic of China, Email [email protected] Xiaoqiang Xiang, Department of Clinical Pharmacy and Pharmacy Administration, School of Pharmaceutical Sciences, Fudan University, Shanghai, 201203, People’s Republic of China, Email [email protected]: Flumatinib is an orally administered tyrosine kinase inhibitor (TKI) that selectively targets the BCR-ABL fusion protein and is approved in China for treating adult patients with chronic-phase chronic myeloid leukemia (CML-CP). It is mainly metabolized by CYP3A4 and CYP2C8 and exhibits time-dependent inhibition (TDI) of CYP3A4. However, its clinical drug–drug interaction (DDI) potential remains insufficiently characterized.Methods: A series of in vitro experiments were conducted using human liver microsomes and Caco-2 cells to characterize the pharmacokinetic properties of flumatinib, including CYP3A4 time-dependent inhibition and key physicochemical inputs relevant to systemic disposition. These data, together with in silico estimates and clinical pharmacokinetic profiles, were integrated into a mechanistic physiologically based pharmacokinetic (PBPK) model for Chinese subjects. Predictive performance was assessed by comparing simulated and clinically observed exposure ratios.Results: The PBPK model demonstrated robust performance, with predicted-to-observed exposure ratios predominantly within the 0.5– 2.0-fold range. DDI simulations indicated that, as a perpetrator, flumatinib exerted minimal impact on CYP3A4 substrate exposure. However, when acting as a victim, co-administration with strong CYP3A4 inhibitors (itraconazole, ketoconazole) increased flumatinib AUC0-72h by approximately 12-fold (AUCR = 11.65– 12.96), whereas rifampicin decreased Cmax and AUC0-72h by 2.4- and 4.7-fold (CmaxR = 0.41, AUCR = 0.21), respectively.Conclusion: Flumatinib shows negligible perpetrator potential but is highly sensitive to CYP3A4 modulation. PBPK-informed DDI assessment supports cautious co-administration with strong CYP3A4 inhibitors or inducers and guides its rational clinical use.Keywords: flumatinib, PBPK model, drug–drug interaction, CYP3A4 inhibitor, CYP3A4 inducer