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

Background: 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 AUC 0-72h by approximately 12-fold (AUCR = 11.65– 12.96), whereas rifampicin decreased C max and AUC 0-72h by 2.4- and 4.7-fold (C max R = 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

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Journal
Drug Design Development and Therapy
Published
2026-05-01
DOI
https://doi.org/10.2147/dddt.s574169
Primary Topic
Chronic Myeloid Leukemia Treatments
Type
article
Field-Weighted Citation Impact
0.00

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article

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

Qingfeng He, Chunyang Pan, Wen Yao Mak, Xinyan Zhu et al.
Drug Design Development and Therapy
Chronic Myeloid Leukemia Treatments
article

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

Qingfeng He, Chunyang Pan, Wen Yao Mak, Xinyan Zhu, Xiao Zhu, Xue Chu, Aole Zheng, Zheng Kuai, Muyesaier Alifu, Xiaoqiang Xiang
article en

Abstract

Background: 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 AUC 0-72h by approximately 12-fold (AUCR = 11.65– 12.96), whereas rifampicin decreased C max and AUC 0-72h by 2.4- and 4.7-fold (C max R = 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

Drug Design Development and TherapyVol. Volume 20
Fudan University (CN), Soochow University (CN), Second Affiliated Hospital of Soochow University (CN), Zhongshan Hospital (CN)
National Research Foundation, National Natural Science Foundation of China, National Research Foundation of Korea
Gender equality
Openalex Percentile: Top 6%
Chronic Myeloid Leukemia Treatments
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