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核心技术专利:CN118964589B侵权必究
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一种基于生理的药代动力学精准给药方法,用于管理达沙替尼的药物相互作用。

A physiologically-based pharmacokinetic precision dosing approach to manage dasatinib drug-drug interactions.

机构信息

Clinical Pharmacy, Saarland University, Saarbrücken, Germany.

Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany.

出版信息

CPT Pharmacometrics Syst Pharmacol. 2024 Jul;13(7):1144-1159. doi: 10.1002/psp4.13146. Epub 2024 May 1.


DOI:10.1002/psp4.13146
PMID:38693610
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11247110/
Abstract

Dasatinib, a second-generation tyrosine kinase inhibitor, is approved for treating chronic myeloid and acute lymphoblastic leukemia. As a sensitive cytochrome P450 (CYP) 3A4 substrate and weak base with strong pH-sensitive solubility, dasatinib is susceptible to enzyme-mediated drug-drug interactions (DDIs) with CYP3A4 perpetrators and pH-dependent DDIs with acid-reducing agents. This work aimed to develop a whole-body physiologically-based pharmacokinetic (PBPK) model of dasatinib to describe and predict enzyme-mediated and pH-dependent DDIs, to evaluate the impact of strong and moderate CYP3A4 inhibitors and inducers on dasatinib exposure and to support optimized dasatinib dosing. Overall, 63 plasma profiles from perorally administered dasatinib in healthy volunteers and cancer patients were used for model development. The model accurately described and predicted plasma profiles with geometric mean fold errors (GMFEs) for area under the concentration-time curve from the first to the last timepoint of measurement (AUC) and maximum plasma concentration (C) of 1.27 and 1.29, respectively. Regarding the DDI studies used for model development, all (8/8) predicted AUC and C ratios were within twofold of observed ratios. Application of the PBPK model for dose adaptations within various DDIs revealed dasatinib dose reductions of 50%-80% for strong and 0%-70% for moderate CYP3A4 inhibitors and a 2.3-3.1-fold increase of the daily dasatinib dose for CYP3A4 inducers to match the exposure of dasatinib administered alone. The developed model can be further employed to personalize dasatinib therapy, thereby help coping with clinical challenges resulting from DDIs and patient-related factors, such as elevated gastric pH.

摘要

达沙替尼是一种第二代酪氨酸激酶抑制剂,获批用于治疗慢性髓性白血病和急性淋巴细胞白血病。作为一种敏感的细胞色素 P450(CYP)3A4 底物和具有强 pH 敏感性的弱碱,达沙替尼易受到 CYP3A4 介导的药物相互作用(DDI)和与酸还原剂的 pH 依赖性 DDI 的影响。本研究旨在建立达沙替尼的全身生理基于药代动力学(PBPK)模型,以描述和预测酶介导和 pH 依赖性 DDI,评估强和中度 CYP3A4 抑制剂和诱导剂对达沙替尼暴露的影响,并支持优化达沙替尼的给药方案。总体而言,使用了 63 例健康志愿者和癌症患者口服给予达沙替尼的血浆谱来进行模型开发。该模型准确地描述和预测了血浆谱,对于从第一个到最后一个测量时间点的浓度-时间曲线下面积(AUC)和最大血浆浓度(C)的几何平均误差(GMFE)分别为 1.27 和 1.29。关于用于模型开发的 DDI 研究,所有(8/8)预测的 AUC 和 C 比值均在观察比值的两倍以内。在各种 DDI 中应用 PBPK 模型进行剂量调整,结果显示对于强 CYP3A4 抑制剂,达沙替尼的剂量减少 50%-80%,对于中度 CYP3A4 抑制剂,剂量减少 0%-70%,对于 CYP3A4 诱导剂,达沙替尼的日剂量增加 2.3-3.1 倍,以匹配单独给予达沙替尼的暴露量。该模型可进一步用于个性化达沙替尼治疗,从而有助于应对由 DDI 和与患者相关的因素(如胃内 pH 值升高)引起的临床挑战。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f80d/11247110/67fa2dea1e0a/PSP4-13-1144-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f80d/11247110/41e77b9878dd/PSP4-13-1144-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f80d/11247110/6827ea9cf1b0/PSP4-13-1144-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f80d/11247110/225e5b9824db/PSP4-13-1144-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f80d/11247110/24d6af3aa8c1/PSP4-13-1144-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f80d/11247110/f1586df5caba/PSP4-13-1144-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f80d/11247110/2a51b643e394/PSP4-13-1144-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f80d/11247110/67fa2dea1e0a/PSP4-13-1144-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f80d/11247110/41e77b9878dd/PSP4-13-1144-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f80d/11247110/6827ea9cf1b0/PSP4-13-1144-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f80d/11247110/225e5b9824db/PSP4-13-1144-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f80d/11247110/24d6af3aa8c1/PSP4-13-1144-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f80d/11247110/f1586df5caba/PSP4-13-1144-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f80d/11247110/2a51b643e394/PSP4-13-1144-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f80d/11247110/67fa2dea1e0a/PSP4-13-1144-g007.jpg

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引用本文的文献

[1]
Pulmonary arterial hypertension caused by coadministration of dasatinib and voriconazole: A case report and literature review.

Medicine (Baltimore). 2025-7-18

[2]
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Int J Toxicol. 2025-6-11

[3]
Severe Impact of Omeprazole Timing on pH-Sensitive Dasatinib Absorption: Unveiling Substantial Drug-Drug Interaction.

J Clin Pharmacol. 2025-5

本文引用的文献

[1]
Prediction of drug-drug interactions between roflumilast and CYP3A4/1A2 perpetrators using a physiologically-based pharmacokinetic (PBPK) approach.

BMC Pharmacol Toxicol. 2024-1-2

[2]
Physiologically Based Pharmacokinetic Modeling of Bergamottin and 6,7-Dihydroxybergamottin to Describe CYP3A4 Mediated Grapefruit-Drug Interactions.

Clin Pharmacol Ther. 2023-8

[3]
A Physiologically Based Pharmacokinetic Model of Ketoconazole and Its Metabolites as Drug-Drug Interaction Perpetrators.

Pharmaceutics. 2023-2-17

[4]
Physiologically based pharmacokinetic modeling of tacrolimus for food-drug and CYP3A drug-drug-gene interaction predictions.

CPT Pharmacometrics Syst Pharmacol. 2023-5

[5]
Prediction of drug-drug interaction potential mediated by transporters between dasatinib and metformin, pravastatin, and rosuvastatin using physiologically based pharmacokinetic modeling.

Cancer Chemother Pharmacol. 2022-3

[6]
Pharmacokinetics of the CYP3A4 and CYP2B6 Inducer Carbamazepine and Its Drug-Drug Interaction Potential: A Physiologically Based Pharmacokinetic Modeling Approach.

Pharmaceutics. 2021-2-17

[7]
Physiologically Based Precision Dosing Approach for Drug-Drug-Gene Interactions: A Simvastatin Network Analysis.

Clin Pharmacol Ther. 2021-1

[8]
Variables associated with self-reported anxiety and depression symptoms in patients with chronic myeloid leukemia receiving tyrosine kinase inhibitor therapy.

Leuk Lymphoma. 2021-3

[9]
An Overview of the Management of the Most Important Invasive Fungal Infections in Patients with Blood Malignancies.

Infect Drug Resist. 2020-7-14

[10]
Application of Physiologically-Based Pharmacokinetic Modeling to Predict Gastric pH-Dependent Drug-Drug Interactions for Weak Base Drugs.

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