摘要
目的介绍可评估化合物成药性的在线程序,为提高新药研发效率提供参考。方法列举各类成药性评估的在线程序,重点介绍其中pkCSM、SwissADME、Molsoft三个网站的提交方法,分析化合物的吸收、分布、代谢、排泄以及毒性性质数据,并以上市药物布洛芬为例,解读三个网站的预测结果,综合评估其成药性,包括pkCSM网站的7种表征吸收项目、4种表征分布项目、7种表征代谢项目、2种表征消除项目、10种表征毒性项目的数据解读;SwissADME网站的鸡蛋图、生物利用度雷达、药物化学性质预测等数据解读;Molsoft网站的类药性评分等数据解读。结果与结论基于ADME/T理论的预测模型,能够简单、快速、准确地判断化合物的成药性和安全性,建议科研工作者能够充分利用在线程序,加快新药研发进程。
The purpose of this paper is to introduce online programs that can evaluate the drug-making properties of compounds and provide a reference for improving the efficiency of new drug development.We listed various online procedures for drug evaluation, focusing on the submission methods of pkCSM,SwissADME,and Molsoft, as well as the interpretation of compound absorption, distribution, metabolism, excretion and toxicity data.Taking drug ibuprofen as an example, we interpret the prediction results of three websites, and comprehensively evaluate the compound′s druggability, including data interpretation of seven characterization absorption projects, four characterization distribution projects, seven characterization metabolism projects, two characterization elimination projects, and ten characterization toxicity projects of the pkCSM website;boiled eggs map, bioavailability radar, medicinal chemical property prediction on SwissADME website;and drug-like scores on the Molsoft website.The predictive model based on ADME/T theory can easily, quickly and accurately judge the drug-making properties and safety of compounds.It is recommended that researchers can make full use of online programs to speed up the development of new drugs.
作者
胡百淳
田金鑫
张逸腾
李玉娟
王健
HU Bai-chun;TIAN Jin-xin;ZHANG Yi-teng;LI Yu-juan;WANG Jian(Key Laboratory of Structure-Based Drug Design&Discovery of Ministry of Education,Shenyang Pharmaceutical University,Shenyang 110016,China;School of Pharmaceutical Engineering,Shenyang Pharmaceutical University,Shenyang 110016,China;School of Pharmacy,Shenyang Pharmaceutical University,Shenyang 110016,China;School of Medical Device,Shenyang Pharmaceutical University,Shenyang 110016,China)
出处
《中国药物化学杂志》
CAS
CSCD
2022年第2期90-101,共12页
Chinese Journal of Medicinal Chemistry
关键词
成药性
预测模型
理化性质
ADME/T
drug-likeness
prediction model
physicochemical properties
ADME/T
