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毒理学和药代动力学性质预测的计算方法。

Computational approaches for toxicology and Pharmacokinetic properties prediction.

作者信息

Kaboudi Navid, Shekari Tara, Shayanfar Ali, Pimentel Andre Silva

机构信息

Faculty of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran.

Department of Pharmaceutics, Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran, Iran.

出版信息

J Pharmacokinet Pharmacodyn. 2025 Sep 4;52(5):51. doi: 10.1007/s10928-025-09999-y.

DOI:10.1007/s10928-025-09999-y
PMID:40908375
Abstract

Pharmacokinetics and toxicological studies how the body reacts to a specific administered substance, such as a drug, toxin, or food. Each substance experiences these four steps: absorption, distribution, metabolism, and excretion, which are the main parameters in pharmacokinetics studies. Many toxic endpoints exist. There are three main ways to measure toxicology and pharmacokinetic parameters: in vivo, in vitro, and in-silico. Knowing toxicological and pharmacokinetic parameters before developing a new drug candidate could save time and resources, as clinical studies are highly cost-demanding. This review aims to gather studies using in-silico methodologies to predict pharmacokinetic properties.

摘要

药代动力学和毒理学研究身体对特定给药物质(如药物、毒素或食物)的反应。每种物质都经历吸收、分布、代谢和排泄这四个步骤,它们是药代动力学研究的主要参数。存在许多毒性终点。测量毒理学和药代动力学参数有三种主要方法:体内、体外和计算机模拟。在开发新的候选药物之前了解毒理学和药代动力学参数可以节省时间和资源,因为临床研究成本很高。本综述旨在收集使用计算机模拟方法预测药代动力学性质的研究。

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本文引用的文献

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Physicochemical Descriptors in Biodistribution and Clearance of Contrast Agents.造影剂生物分布与清除中的物理化学描述符
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Rationalizing Predictions of Isoform-Selective Phosphoinositide 3-Kinase Inhibitors Using MolAnchor Analysis.使用MolAnchor分析对亚型选择性磷酸肌醇3-激酶抑制剂的预测进行合理化
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实践中的机器学习ADME模型:来自一个成功的先导化合物优化案例研究的四条准则。
ACS Med Chem Lett. 2024 Jul 25;15(8):1169-1173. doi: 10.1021/acsmedchemlett.4c00290. eCollection 2024 Aug 8.
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A Combination of Machine Learning and PBPK Modeling Approach for Pharmacokinetics Prediction of Small Molecules in Humans.机器学习与 PBPK 模型结合用于小分子在人体中的药代动力学预测。
Pharm Res. 2024 Jul;41(7):1369-1379. doi: 10.1007/s11095-024-03725-y. Epub 2024 Jun 25.
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Machine learning framework to predict pharmacokinetic profile of small molecule drugs based on chemical structure.基于化学结构预测小分子药物药代动力学特征的机器学习框架。
Clin Transl Sci. 2024 May;17(5):e13824. doi: 10.1111/cts.13824.
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A Method to Redesign and Simplify Schedules of Assessment and Quantify the Impacts. Applications to Merck Protocols.一种重新设计和简化评估计划并量化影响的方法。在默克方案中的应用。
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Interpreting Neural Network Models for Toxicity Prediction by Extracting Learned Chemical Features.通过提取学习到的化学特征来解释神经网络模型在毒性预测中的作用。
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