Clinical Pharmacy, Saarland University, Saarbrücken, Germany.
Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany.
CPT Pharmacometrics Syst Pharmacol. 2024 Jun;13(6):926-940. doi: 10.1002/psp4.13127. Epub 2024 Mar 14.
The first-generation tyrosine kinase inhibitor imatinib has revolutionized the development of targeted cancer therapy and remains among the frontline treatments, for example, against chronic myeloid leukemia. As a substrate of cytochrome P450 (CYP) 2C8, CYP3A4, and various transporters, imatinib is highly susceptible to drug-drug interactions (DDIs) when co-administered with corresponding perpetrator drugs. Additionally, imatinib and its main metabolite N-desmethyl imatinib (NDMI) act as inhibitors of CYP2C8, CYP2D6, and CYP3A4 affecting their own metabolism as well as the exposure of co-medications. This work presents the development of a parent-metabolite whole-body physiologically based pharmacokinetic (PBPK) model for imatinib and NDMI used for the investigation and prediction of different DDI scenarios centered around imatinib as both a victim and perpetrator drug. Model development was performed in PK-Sim® using a total of 60 plasma concentration-time profiles of imatinib and NDMI in healthy subjects and cancer patients. Metabolism of both compounds was integrated via CYP2C8 and CYP3A4, with imatinib additionally transported via P-glycoprotein. The subsequently developed DDI network demonstrated good predictive performance. DDIs involving imatinib and NDMI were simulated with perpetrator drugs rifampicin, ketoconazole, and gemfibrozil as well as victim drugs simvastatin and metoprolol. Overall, 12/12 predicted DDI area under the curve determined between first and last plasma concentration measurements (AUC) ratios and 12/12 predicted DDI maximum plasma concentration (C) ratios were within twofold of the respective observed ratios. Potential applications of the final model include model-informed drug development or the support of model-informed precision dosing.
第一代酪氨酸激酶抑制剂伊马替尼彻底改变了靶向癌症治疗的发展，并且仍然是一线治疗方法，例如治疗慢性髓性白血病。作为细胞色素 P450（CYP）2C8、CYP3A4 和各种转运体的底物，当与相应的引发药物共同给药时，伊马替尼非常容易发生药物相互作用（DDI）。此外，伊马替尼及其主要代谢物 N-去甲基伊马替尼（NDMI）作为 CYP2C8、CYP2D6 和 CYP3A4 的抑制剂，影响其自身代谢以及合并用药的暴露。本工作开发了一个用于伊马替尼和 NDMI 的母体-代谢物全身体生理基于药代动力学（PBPK）模型，用于研究和预测以伊马替尼为受害和引发药物的不同 DDI 情况。模型开发在 PK-Sim®中进行，使用了总共 60 个健康受试者和癌症患者的伊马替尼和 NDMI 的血浆浓度-时间曲线。通过 CYP2C8 和 CYP3A4 整合了这两种化合物的代谢，伊马替尼还通过 P-糖蛋白进行转运。随后开发的 DDI 网络显示出良好的预测性能。用引发药物利福平、酮康唑和吉非贝齐以及受害药物辛伐他汀和美托洛尔模拟涉及伊马替尼和 NDMI 的 DDI。总体而言，12/12 预测的 DDI 从第一次和最后一次血浆浓度测量（AUC）比值的面积和 12/12 预测的 DDI 最大血浆浓度（C）比值都在观察到的比值的两倍以内。最终模型的潜在应用包括模型指导药物开发或模型指导精准剂量支持。
