文献类型：期刊文献

英文题名：In silico ADME/T modelling for rational drug design

作者：Wang, Yulan[1];Xing, Jing[1];Xu, Yuan[1];Zhou, Nannan[2,3];Peng, Jianlong[1];Xiong, Zhaoping[4];Liu, Xian[1];Luo, Xiaomin[1];Luo, Cheng[1];Chen, Kaixian[1];Zheng, Mingyue[1];Jiang, Hualiang[1,2,3,4]

机构：[1]Chinese Acad Sci, Shanghai Inst Mat Med, Drug Discovery & Design Ctr, State Key Lab Drug Res, Shanghai 201203, Peoples R China;[2]E China Univ Sci & Technol, State Key Lab Bioreactor Engn, Sch Pharm, Shanghai 200237, Peoples R China;[3]E China Univ Sci & Technol, Shanghai Key Lab Chem Biol, Sch Pharm, Shanghai 200237, Peoples R China;[4]Shanghai Tech Univ, Sch Life Sci & Technol, Shanghai 200031, Peoples R China

年份：2015

卷号：48

期号：4

起止页码：488

外文期刊名：QUARTERLY REVIEWS OF BIOPHYSICS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000364764300014)】；

基金：We gratefully acknowledge the financial support from the National Natural Science Foundation of China (Grants 21210003 and 81230076 to H.J., Grant 81430084 to K.C.), the Hi-Tech Research and Development Program of China (Grant 2012AA020308 to X.L. and 2014AA01A302 to M.Z.), and the National Science and Technology Major Project 'Key New Drug Creation and Manufacturing Program' (Grant 2014ZX09507002-005-012 to M.Z.).

语种：英文

外文关键词：ADME/T; Drug Design; Pharmacokinetics; Predictive Toxicology; QSAR

摘要：In recent decades, in silico absorption, distribution, metabolism, excretion (ADME), and toxicity (T) modelling as a tool for rational drug design has received considerable attention from pharmaceutical scientists, and various ADME/T-related prediction models have been reported. The high-throughput and low-cost nature of these models permits a more streamlined drug development process in which the identification of hits or their structural optimization can be guided based on a parallel investigation of bioavailability and safety, along with activity. However, the effectiveness of these tools is highly dependent on their capacity to cope with needs at different stages, e.g. their use in candidate selection has been limited due to their lack of the required predictability. For some events or endpoints involving more complex mechanisms, the current in silico approaches still need further improvement. In this review, we will briefly introduce the development of in silico models for some physicochemical parameters, ADME properties and toxicity evaluation, with an emphasis on the modelling approaches thereof, their application in drug discovery, and the potential merits or deficiencies of these models. Finally, the outlook for future ADME/T modelling based on big data analysis and systems sciences will be discussed.