• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

基于生理学的精准剂量设计方法在药物-药物-基因相互作用中的应用:辛伐他汀网络分析。

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

机构信息

Clinical Pharmacy, Saarland University, Saarbrücken, Germany.

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

出版信息

Clin Pharmacol Ther. 2021 Jan;109(1):201-211. doi: 10.1002/cpt.2111. Epub 2020 Dec 6.

DOI:10.1002/cpt.2111
PMID:33280091
Abstract

Drug-drug interactions (DDIs) and drug-gene interactions (DGIs) are well known mediators for adverse drug reactions (ADRs), which are among the leading causes of death in many countries. Because physiologically based pharmacokinetic (PBPK) modeling has demonstrated to be a valuable tool to improve pharmacotherapy affected by DDIs or DGIs, it might also be useful for precision dosing in extensive interaction network scenarios. The presented work proposes a novel approach to extend the prediction capabilities of PBPK modeling to complex drug-drug-gene interaction (DDGI) scenarios. Here, a whole-body PBPK network of simvastatin was established, including three polymorphisms (SLCO1B1 (rs4149056), ABCG2 (rs2231142), and CYP3A5 (rs776746)) and four perpetrator drugs (clarithromycin, gemfibrozil, itraconazole, and rifampicin). Exhaustive network simulations were performed and ranked to optimize 10,368 DDGI scenarios based on an exposure marker cost function. The derived dose recommendations were translated in a digital decision support system, which is available at simvastatin.precisiondosing.de. Although the network covers only a fraction of possible simvastatin DDGIs, it provides guidance on how PBPK modeling could be used to individualize pharmacotherapy in the future. Furthermore, the network model is easily extendable to cover additional DDGIs. Overall, the presented work is a first step toward a vision on comprehensive precision dosing based on PBPK models in daily clinical practice, where it could drastically reduce the risk of ADRs.

摘要

药物-药物相互作用(DDIs)和药物-基因相互作用(DGIs)是已知的药物不良反应(ADRs)的主要介导因素,在许多国家,ADRs 是导致死亡的主要原因之一。由于基于生理的药代动力学(PBPK)模型已被证明是改善受 DDIs 或 DGIs 影响的药物治疗的有价值的工具,因此它也可能对广泛相互作用网络情况下的精准剂量调整有用。本文提出了一种新方法,旨在扩展 PBPK 模型的预测能力,以应对复杂的药物-药物-基因相互作用(DDGI)情况。在此,建立了辛伐他汀的全身 PBPK 网络,其中包括三个多态性(SLCO1B1(rs4149056)、ABCG2(rs2231142)和 CYP3A5(rs776746))和四种致剂药物(克拉霉素、吉非贝齐、伊曲康唑和利福平)。进行了详尽的网络模拟并进行了排序,根据暴露标志物成本函数对 10368 种 DDGI 情况进行了优化。所得的剂量建议被转换为数字决策支持系统,可在 simvastatin.precisiondosing.de 上获得。尽管该网络仅涵盖了辛伐他汀可能的 DDGIs 的一小部分,但它为 PBPK 模型如何在未来用于个体化药物治疗提供了指导。此外,该网络模型易于扩展以涵盖其他 DDGIs。总的来说,本文的工作是朝着在日常临床实践中基于 PBPK 模型实现全面精准剂量调整的愿景迈出的第一步,这将极大地降低 ADRs 的风险。

相似文献

1
Physiologically Based Precision Dosing Approach for Drug-Drug-Gene Interactions: A Simvastatin Network Analysis.基于生理学的精准剂量设计方法在药物-药物-基因相互作用中的应用:辛伐他汀网络分析。
Clin Pharmacol Ther. 2021 Jan;109(1):201-211. doi: 10.1002/cpt.2111. Epub 2020 Dec 6.
2
Physiologically Based Pharmacokinetic Models for Prediction of Complex CYP2C8 and OATP1B1 (SLCO1B1) Drug-Drug-Gene Interactions: A Modeling Network of Gemfibrozil, Repaglinide, Pioglitazone, Rifampicin, Clarithromycin and Itraconazole.基于生理学的药代动力学模型预测复杂 CYP2C8 和 OATP1B1(SLCO1B1)的药物-药物-基因相互作用:吉非贝齐、瑞格列奈、吡格列酮、利福平、克拉霉素和伊曲康唑的建模网络。
Clin Pharmacokinet. 2019 Dec;58(12):1595-1607. doi: 10.1007/s40262-019-00777-x.
3
Impact of CYP2D6, CYP3A5, CYP2C19, CYP2A6, SLCO1B1, ABCB1, and ABCG2 gene polymorphisms on the pharmacokinetics of simvastatin and simvastatin acid.CYP2D6、CYP3A5、CYP2C19、CYP2A6、SLCO1B1、ABCB1和ABCG2基因多态性对辛伐他汀及辛伐他汀酸药代动力学的影响
Pharmacogenet Genomics. 2015 Dec;25(12):595-608. doi: 10.1097/FPC.0000000000000176.
4
Physiologically-Based Pharmacokinetic Model-Informed Drug Development for Fenebrutinib: Understanding Complex Drug-Drug Interactions.基于生理的药代动力学模型指导芬苯替尼药物开发:深入理解复杂的药物-药物相互作用。
CPT Pharmacometrics Syst Pharmacol. 2020 Jun;9(6):332-341. doi: 10.1002/psp4.12515. Epub 2020 May 29.
5
Does the choice of applied physiologically-based pharmacokinetics platform matter? A case study on simvastatin disposition and drug-drug interaction.应用生理药代动力学平台的选择是否重要?以辛伐他汀处置和药物相互作用为例的研究。
CPT Pharmacometrics Syst Pharmacol. 2022 Sep;11(9):1194-1209. doi: 10.1002/psp4.12837. Epub 2022 Jul 16.
6
Mechanistic Evaluation of the Complex Drug-Drug Interactions of Maraviroc: Contribution of Cytochrome P450 3A, P-Glycoprotein and Organic Anion Transporting Polypeptide 1B1.马拉维若与药物相互作用的机制评估:细胞色素 P4503A、P-糖蛋白和有机阴离子转运多肽 1B1 的贡献。
Drug Metab Dispos. 2019 May;47(5):493-503. doi: 10.1124/dmd.118.085241. Epub 2019 Mar 12.
7
PBPK Models for CYP3A4 and P-gp DDI Prediction: A Modeling Network of Rifampicin, Itraconazole, Clarithromycin, Midazolam, Alfentanil, and Digoxin.基于 CYP3A4 和 P-糖蛋白相互作用预测的 PBPK 模型:利福平、伊曲康唑、克拉霉素、咪达唑仑、阿芬太尼和地高辛的建模网络。
CPT Pharmacometrics Syst Pharmacol. 2018 Oct;7(10):647-659. doi: 10.1002/psp4.12343. Epub 2018 Sep 7.
8
Comprehensive Evaluation of OATP- and BCRP-Mediated Drug-Drug Interactions of Methotrexate Using Physiologically-Based Pharmacokinetic Modeling.基于生理的药代动力学模型对甲氨蝶呤的 OATP 和 BCRP 介导的药物相互作用的综合评价。
Clin Pharmacol Ther. 2024 Oct;116(4):1013-1022. doi: 10.1002/cpt.3329. Epub 2024 Jun 11.
9
Predictive pharmacogenetic biomarkers for breast cancer recurrence prevention by simvastatin.辛伐他汀预防乳腺癌复发的预测性遗传生物标志物。
Acta Oncol. 2020 Sep;59(9):1009-1015. doi: 10.1080/0284186X.2020.1759820. Epub 2020 Apr 30.
10
The influences of SLCO1B1 and ABCB1 genotypes on the pharmacokinetics of simvastatin, in relation to CYP3A4 inhibition.SLCO1B1和ABCB1基因多态性对辛伐他汀药代动力学的影响及其与CYP3A4抑制作用的关系。
Pharmacogenomics. 2017 Apr;18(5):459-469. doi: 10.2217/pgs-2016-0199. Epub 2017 Mar 28.

引用本文的文献

1
Formulation Strategy of BCS-II Drugs by Coupling Mechanistic In-Vitro and Nonclinical In-Vivo Data with PBPK: Fundamentals of Absorption-Dissolution to Parameterization of Modelling and Simulation.通过将体外机制性数据和非临床体内数据与生理药代动力学(PBPK)相结合来制定BCS-II类药物的策略:从吸收-溶出基础到建模与模拟的参数化
AAPS PharmSciTech. 2025 Apr 17;26(5):106. doi: 10.1208/s12249-025-03093-9.
2
A Comprehensive CYP2D6 Drug-Drug-Gene Interaction Network for Application in Precision Dosing and Drug Development.用于精准给药和药物研发的综合CYP2D6药物-药物-基因相互作用网络
Clin Pharmacol Ther. 2025 Jun;117(6):1718-1731. doi: 10.1002/cpt.3604. Epub 2025 Feb 14.
3
Physiologically based pharmacokinetic modeling to predict the effect of risperidone on aripiprazole pharmacokinetics in subjects with different CYP2D6 genotypes and individuals with hepatic impairment.
基于生理的药代动力学建模,以预测利培酮对不同CYP2D6基因型受试者及肝功能损害个体中阿立哌唑药代动力学的影响。
Ther Adv Drug Saf. 2024 Dec 18;15:20420986241303432. doi: 10.1177/20420986241303432. eCollection 2024.
4
A physiologically-based pharmacokinetic precision dosing approach to manage dasatinib drug-drug interactions.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。
CPT Pharmacometrics Syst Pharmacol. 2024 Jul;13(7):1144-1159. doi: 10.1002/psp4.13146. Epub 2024 May 1.
5
Physiologically based pharmacokinetic modeling of imatinib and N-desmethyl imatinib for drug-drug interaction predictions.基于生理的伊马替尼和 N-去甲基伊马替尼药代动力学模型用于药物相互作用预测。
CPT Pharmacometrics Syst Pharmacol. 2024 Jun;13(6):926-940. doi: 10.1002/psp4.13127. Epub 2024 Mar 14.
6
Implementation of pre-emptive testing of a pharmacogenomic panel in clinical practice: Where do we stand?临床实践中药基因组学检测预测试验的实施:我们目前的进展如何?
Br J Clin Pharmacol. 2025 Feb;91(2):270-282. doi: 10.1111/bcp.15956. Epub 2023 Dec 21.
7
Effects of Genetic Variant on Metabolite Profile in Participants on Simvastatin Treatment.基因变异对接受辛伐他汀治疗参与者代谢物谱的影响。
Metabolites. 2022 Nov 22;12(12):1159. doi: 10.3390/metabo12121159.
8
Efficacy difference of antipsychotics in Alzheimer's disease and schizophrenia: explained with network efficiency and pathway analysis methods.抗精神病药在阿尔茨海默病和精神分裂症中的疗效差异:用网络效率和途径分析方法解释。
Brief Bioinform. 2022 Nov 19;23(6). doi: 10.1093/bib/bbac394.
9
Physiologically based pharmacokinetic modelling to predict the pharmacokinetics of metoprolol in different CYP2D6 genotypes.基于生理学的药代动力学模型预测不同 CYP2D6 基因型中美托洛尔的药代动力学。
Arch Pharm Res. 2022 Jun;45(6):433-445. doi: 10.1007/s12272-022-01394-2. Epub 2022 Jun 28.
10
Does the choice of applied physiologically-based pharmacokinetics platform matter? A case study on simvastatin disposition and drug-drug interaction.应用生理药代动力学平台的选择是否重要?以辛伐他汀处置和药物相互作用为例的研究。
CPT Pharmacometrics Syst Pharmacol. 2022 Sep;11(9):1194-1209. doi: 10.1002/psp4.12837. Epub 2022 Jul 16.