Suppr超能文献

关于现代毒性评估和药物毒性指数(DTI)的属性-反应视角。

A property-response perspective on modern toxicity assessment and drug toxicity index (DTI).

作者信息

Dixit Vaibhav A, Singh Pragati

机构信息

Department of Pharmacy, Birla Institute of Technology and Sciences Pilani (BITS Pilani), Vidya Vihar Campus, Street number 41, Pilani, Rajasthan 333031 India.

出版信息

In Silico Pharmacol. 2021 May 15;9(1):37. doi: 10.1007/s40203-021-00096-9. eCollection 2021.

Abstract

Toxicity related failures in drug discovery and clinical development have motivated scientists and regulators to develop a wide range of in-vitro, in-silico tools coupled with data science methods. Older drug discovery rules are being constantly modified to churn out any hidden predictive value. Nonetheless, the dose-response concepts remain central to all these methods. Over the last 2 decades medicinal chemists, and pharmacologists have observed that different physicochemical, and pharmacological properties capture trends in toxic responses. We propose that these observations should be viewed in a comprehensive property-response framework where dose is only a factor that modifies the inherent toxicity potential. We then introduce the recently proposed "Drug Toxicity Index (DTI)" and briefly summarize its applications. A webserver is available to calculate DTI values (https://all-tool-kit.github.io/Web-Tool.html).

摘要

药物研发和临床开发中与毒性相关的失败促使科学家和监管机构开发了一系列体外、计算机模拟工具以及数据科学方法。旧的药物研发规则不断被修改,以挖掘任何潜在的预测价值。尽管如此,剂量反应概念仍然是所有这些方法的核心。在过去20年里,药物化学家与药理学家观察到,不同的物理化学和药理特性反映出毒性反应的趋势。我们建议,应在一个综合的特性-反应框架中看待这些观察结果,其中剂量只是改变内在毒性潜力的一个因素。然后,我们介绍最近提出的“药物毒性指数(DTI)”并简要总结其应用。可通过一个网络服务器来计算DTI值(https://all-tool-kit.github.io/Web-Tool.html)。

相似文献

2
A simple model to solve a complex drug toxicity problem.一个解决复杂药物毒性问题的简单模型。
Toxicol Res (Camb). 2018 Nov 29;8(2):157-171. doi: 10.1039/c8tx00261d. eCollection 2019 Mar 1.

本文引用的文献

3
The Tox21 10K Compound Library: Collaborative Chemistry Advancing Toxicology.Tox21 十库化合物库:协作化学推动毒理学发展。
Chem Res Toxicol. 2021 Feb 15;34(2):189-216. doi: 10.1021/acs.chemrestox.0c00264. Epub 2020 Nov 3.
4
Ten simple rules to power drug discovery with data science.利用数据科学推动药物发现的十条简单规则。
PLoS Comput Biol. 2020 Aug 27;16(8):e1008126. doi: 10.1371/journal.pcbi.1008126. eCollection 2020 Aug.
7
A practical guide to secondary pharmacology in drug discovery.药物发现中的二次药理学实用指南。
J Pharmacol Toxicol Methods. 2020 Sep;105:106869. doi: 10.1016/j.vascn.2020.106869. Epub 2020 Apr 14.
8
Rethinking drug design in the artificial intelligence era.人工智能时代的药物设计再思考。
Nat Rev Drug Discov. 2020 May;19(5):353-364. doi: 10.1038/s41573-019-0050-3. Epub 2019 Dec 4.
10
Genome-wide off-targets of drugs: risks and opportunities.药物的全基因组脱靶效应:风险与机遇
Cell Biol Toxicol. 2019 Dec;35(6):485-487. doi: 10.1007/s10565-019-09491-7. Epub 2019 Aug 20.

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