文献类型：期刊文献

英文题名：In silico prediction of chemical mechanism of action via an improved network-based inference method

作者：Wu, Zengrui[1];Lu, Weiqiang[2,3];Wu, Dang[1];Luo, Anqi[1];Bian, Hanping[1];Li, Jie[1];Li, Weihua[1];Liu, Guixia[1];Huang, Jin[1];Cheng, Feixiong[4,5,6];Tang, Yun[1]

机构：[1]East China Univ Sci & Technol, Shanghai Key Lab New Drug Design, Sch Pharm, 130 Meilong Rd, Shanghai 200237, Peoples R China;[2]East China Normal Univ, Shanghai Key Lab Regulatory Biol, Inst Biomed Sci, Shanghai, Peoples R China;[3]East China Normal Univ, Shanghai Key Lab Regulatory Biol, Sch Life Sci, Shanghai, Peoples R China;[4]Sichuan Univ, West China Med Sch, Collaborat Innovat Ctr Biotherapy, State Key Lab Biotherapy,West China Hosp, Chengdu 610041, Sichuan, Peoples R China;[5]Harvard Med Sch, Dana Farber Canc Inst, CCSB, Boston, MA USA;[6]Northeastern Univ, Ctr Complex Networks Res, Boston, MA 02115 USA

年份：2016

卷号：173

期号：23

起止页码：3372

外文期刊名：BRITISH JOURNAL OF PHARMACOLOGY

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000387348900007)】；

基金：This work was supported by the National Natural Science Foundation of China (grants 81373328, 81573020 and 81673356), the National Key Research and Development Program (grant 2016YFA0502304) and the 111 Project (grant B07023).

语种：英文

摘要：Background and PurposeDeciphering chemical mechanism of action (MoA) enables the development of novel therapeutics (e.g. drug repositioning) and evaluation of drug side effects. Development of novel computational methods for chemical MoA assessment under a systems pharmacology framework would accelerate drug discovery and development with greater efficiency and low cost. Experimental ApproachIn this study, we proposed an improved network-based inference method, balanced substructure-drug-target network-based inference (bSDTNBI), to predict MoA for old drugs, clinically failed drugs and new chemical entities. Specifically, three parameters were introduced into network-based resource diffusion processes to adjust the initial resource allocation of different node types, the weighted values of different edge types and the influence of hub nodes. The performance of the method was systematically validated by benchmark datasets and bioassays. Key ResultsHigh performance was yielded for bSDTNBI in both 10-fold and leave-one-out cross validations. A global drug-target network was built to explore MoA of anticancer drugs and repurpose old drugs for 15 cancer types/subtypes. In a case study, 27 predicted candidates among 56 commercially available compounds were experimentally validated to have binding affinities on oestrogen receptor with IC50 or EC50 values 10M. Furthermore, two dual ligands with both agonistic and antagonistic activities 1M would provide potential lead compounds for the development of novel targeted therapy in breast cancer or osteoporosis. Conclusion and ImplicationsIn summary, bSDTNBI would provide a powerful tool for the MoA assessment on both old drugs and novel compounds in drug discovery and development.