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基于生理的药代动力学模型预测恩考芬尼的药物相互作用。第一部分。模型构建、验证和与酶抑制剂、诱导剂和转运体抑制剂的前瞻性预测。

Physiologically based pharmacokinetic modelling to predict drug-drug interactions for encorafenib. Part I. Model building, validation, and prospective predictions with enzyme inhibitors, inducers, and transporter inhibitors.

机构信息

KL College of Pharmacy, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, India.

Biopharmaceutics Group, Global Clinical Management, Dr. Reddy's Laboratories Ltd., Integrated Product Development Organization (IPDO), Hyderabad, Telangana, India.

出版信息

Xenobiotica. 2023 May;53(5):366-381. doi: 10.1080/00498254.2023.2250856. Epub 2023 Sep 4.


DOI:10.1080/00498254.2023.2250856
PMID:37609899
Abstract

Encorafenib, a potent BRAF kinase inhibitor undergoes significant metabolism by CYP3A4 (83%) and CYP2C19 (16%) and also a substrate of P-glycoprotein (P-gp). Because of this, encorafenib possesses potential for enzyme-transporter related interactions. Clinically, its drug-drug interactions (DDIs) with CYP3A4 inhibitors (posaconazole, diltiazem) were reported and hence there is a necessity to study DDIs with multiple enzyme inhibitors, inducers, and P-gp inhibitors.USFDA recommended clinical CYP3A4, CYP2C19, P-gp inhibitors, CYP3A4 inducers were selected and prospective DDIs were simulated using physiologically based pharmacokinetic modelling (PBPK). Impact of dose (50 mg vs. 300 mg) and staggering of administrations (0-10 h) on the DDIs were predicted.PBPK models for encorafenib, perpetrators simulated PK parameters within twofold prediction error. Clinically reported DDIs with posaconazole and diltiazem were successfully predicted.CYP2C19 inhibitors did not result in significant DDI whereas strong CYP3A4 inhibitors resulted in DDI ratio up to 4.5. Combining CYP3A4, CYP2C19 inhibitors yielded DDI equivalent CYP3A4 alone. Strong CYP3A4 inducers yielded DDI ratio up to 0.3 and no impact of P-gp inhibitors on DDIs was observed. The DDIs were not impacted by dose and staggering of administration. Overall, this work indicated significance of PBPK modelling for evaluating clinical DDIs with enzymes, transporters and interplay.

摘要

恩考芬尼是一种强效的 BRAF 激酶抑制剂,主要通过 CYP3A4(83%)和 CYP2C19(16%)代谢,也是 P-糖蛋白(P-gp)的底物。因此,恩考芬尼具有与酶-转运体相关相互作用的潜力。临床上,已报道其与 CYP3A4 抑制剂(酮康唑、地尔硫卓)的药物相互作用(DDI),因此有必要研究与多种酶抑制剂、诱导剂和 P-gp 抑制剂的 DDI。美国 FDA 推荐选择临床 CYP3A4、CYP2C19、P-gp 抑制剂、CYP3A4 诱导剂,并使用基于生理学的药代动力学模型(PBPK)模拟前瞻性 DDI。预测了剂量(50mg 与 300mg)和给药时间间隔(0-10 小时)对 DDI 的影响。恩考芬尼的 PBPK 模型,模拟了两个预测误差范围内的 PK 参数。成功预测了酮康唑和地尔硫卓的临床报告 DDI。CYP2C19 抑制剂不会导致明显的 DDI,而强 CYP3A4 抑制剂会导致 DDI 比值高达 4.5。联合使用 CYP3A4 和 CYP2C19 抑制剂会产生相当于单独使用 CYP3A4 的 DDI。强 CYP3A4 诱导剂会导致 DDI 比值高达 0.3,并且观察到 P-gp 抑制剂对 DDI 没有影响。DDI 不受剂量和给药时间间隔的影响。总的来说,这项工作表明 PBPK 模型在评估酶、转运体和相互作用的临床 DDI 方面具有重要意义。

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Physiologically based pharmacokinetic modelling to predict drug-drug interactions for encorafenib. Part I. Model building, validation, and prospective predictions with enzyme inhibitors, inducers, and transporter inhibitors.

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