DMPK的计算方法：对当前方法及其实际影响的现实评估。第一部分：理化和体外特性。

IF 7.5 2区医学 Q1 PHARMACOLOGY & PHARMACY

Drug Discovery Today Pub Date : 2025-06-30 DOI:10.1016/j.drudis.2025.104422

Koichi Handa , Mariko Hirano , Michiharu Kageyama , Andreas Bender

{"title":"DMPK的计算方法：对当前方法及其实际影响的现实评估。第一部分：理化和体外特性。","authors":"Koichi Handa , Mariko Hirano , Michiharu Kageyama , Andreas Bender","doi":"10.1016/j.drudis.2025.104422","DOIUrl":null,"url":null,"abstract":"<div><div>Artificial intelligence and computational approaches have received considerable interest in recent years, and here we assess their real-world utility in drug discovery projects. We review recent <em>in silico</em> models in the area of drug metabolism and pharmacokinetics (DMPK), especially for physicochemical properties (pKa and logD) and <em>in vitro</em> assays [solubility (DMSO, Dried-DMSO, Powder), permeability (PAMPA, Caco-2, MDCK), metabolic stability (liver microsome, hepatocyte), and protein binding (plasma, microsome, brain)]. We discuss which are currently fit for purpose (and which are not), bridging both computational and experimental aspects in the early drug discovery stages. The review includes diverse aspects of obtaining data and model generation, as well as modeler/experimentalist interplay.</div></div>","PeriodicalId":301,"journal":{"name":"Drug Discovery Today","volume":"30 8","pages":"Article 104422"},"PeriodicalIF":7.5000,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Computational approaches to DMPK: A realistic assessment of current methods and their practical impact. Part I: Physicochemical and in vitro properties\",\"authors\":\"Koichi Handa , Mariko Hirano , Michiharu Kageyama , Andreas Bender\",\"doi\":\"10.1016/j.drudis.2025.104422\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Artificial intelligence and computational approaches have received considerable interest in recent years, and here we assess their real-world utility in drug discovery projects. We review recent <em>in silico</em> models in the area of drug metabolism and pharmacokinetics (DMPK), especially for physicochemical properties (pKa and logD) and <em>in vitro</em> assays [solubility (DMSO, Dried-DMSO, Powder), permeability (PAMPA, Caco-2, MDCK), metabolic stability (liver microsome, hepatocyte), and protein binding (plasma, microsome, brain)]. We discuss which are currently fit for purpose (and which are not), bridging both computational and experimental aspects in the early drug discovery stages. The review includes diverse aspects of obtaining data and model generation, as well as modeler/experimentalist interplay.</div></div>\",\"PeriodicalId\":301,\"journal\":{\"name\":\"Drug Discovery Today\",\"volume\":\"30 8\",\"pages\":\"Article 104422\"},\"PeriodicalIF\":7.5000,\"publicationDate\":\"2025-06-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Drug Discovery Today\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1359644625001357\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"PHARMACOLOGY & PHARMACY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Drug Discovery Today","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1359644625001357","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PHARMACOLOGY & PHARMACY","Score":null,"Total":0}

引用次数: 0

摘要

近年来，人工智能和计算方法获得了相当大的兴趣，在这里我们评估它们在药物发现项目中的实际效用。我们回顾了最近在药物代谢和药代动力学（DMPK）领域的计算机模型，特别是物理化学性质（pKa和logD）和体外测定[溶解度（DMSO，干燥DMSO，粉末），渗透性（PAMPA, Caco-2， MDCK），代谢稳定性（肝微粒体，肝细胞）和蛋白质结合（血浆，微粒体，脑）]。我们讨论哪些是目前适合的目的（哪些不是），在早期药物发现阶段架起计算和实验方面的桥梁。该综述包括获取数据和模型生成的各个方面，以及建模者/实验者的相互作用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Computational approaches to DMPK: A realistic assessment of current methods and their practical impact. Part I: Physicochemical and in vitro properties

Artificial intelligence and computational approaches have received considerable interest in recent years, and here we assess their real-world utility in drug discovery projects. We review recent in silico models in the area of drug metabolism and pharmacokinetics (DMPK), especially for physicochemical properties (pKa and logD) and in vitro assays [solubility (DMSO, Dried-DMSO, Powder), permeability (PAMPA, Caco-2, MDCK), metabolic stability (liver microsome, hepatocyte), and protein binding (plasma, microsome, brain)]. We discuss which are currently fit for purpose (and which are not), bridging both computational and experimental aspects in the early drug discovery stages. The review includes diverse aspects of obtaining data and model generation, as well as modeler/experimentalist interplay.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Drug Discovery Today 医学-药学

CiteScore

14.80

自引率

2.70%

发文量

293

审稿时长

6 months

期刊介绍： Drug Discovery Today delivers informed and highly current reviews for the discovery community. The magazine addresses not only the rapid scientific developments in drug discovery associated technologies but also the management, commercial and regulatory issues that increasingly play a part in how R&D is planned, structured and executed. Features include comment by international experts, news and analysis of important developments, reviews of key scientific and strategic issues, overviews of recent progress in specific therapeutic areas and conference reports.