文献类型：期刊文献

英文题名：Quantitative and Systems Pharmacology. 1. In Silico Prediction of Drug-Target Interactions of Natural Products Enables New Targeted Cancer Therapy

作者：Fang, Jiansong[1];Wu, Zengrui[2];Cai, Chuipu[1];Wang, Qi[1];Tang, Yun[2];Cheng, Feixiong[3,4]

机构：[1]Guangzhou Univ Chinese Med, Inst Clin Pharmacol, 12 Jichang Rd, Guangzhou 510405, Guangdong, Peoples R China;[2]East China Univ Sci & Technol, Sch Pharm, Shanghai Key Lab New Drug Design, 130 Meilong Rd, Shanghai 200237, Peoples R China;[3]Harvard Med Sch, Dana Farber Canc Inst, CCSB, Boston, MA 02215 USA;[4]Northeastern Univ, CCNR, Boston, MA 02115 USA

年份：2017

卷号：57

期号：11

起止页码：2657

外文期刊名：JOURNAL OF CHEMICAL INFORMATION AND MODELING

收录：;EI(收录号：20174904482833);WOS:【SCI-EXPANDED(收录号:WOS:000416614900005)】；

基金：This work was supported by the National Key Research and Development Program of China (Grant 2016YFA0502304) and the National Natural Science Foundation of China (Grants 81603318, 81603026, 81673356). This work was also supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health under Award Number K99HL138272 to F.C. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

语种：英文

外文关键词：Oncology - Scaffolds - Flavonoids - Drug interactions - Diseases - Proteins

摘要：Natural products with diverse chemical scaffolds have been recognized as an invaluable source of compounds in drug discovery and development. However, systematic identification of drug targets for natural products at the human proteome level via various experimental assays is highly expensive and time-consuming. In this study, we proposed a systems pharmacology infrastructure to predict new drug targets and anticancer indications of natural products. Specifically, we reconstructed a global drug target network with 7,314 interactions connecting 751 targets and 2,388 natural products and built predictive network models via a balanced substructure drug target network-based inference approach. A high area under receiver operating characteristic curve of 0.96 was yielded for predicting new targets of natural products during cross-validation. The newly predicted targets of natural products (e.g., resveratrol, genistein, and kaempferol) with high scores were validated by various literature studies. We further built the statistical network models for identification of new anticancer indications of natural products through integration of both experimentally validated and computationally predicted drug target interactions of natural products with known cancer proteins. We showed that the significantly predicted anticancer indications of multiple natural products (e.g., naringenin, disulfiram, and metformin) with new mechanism-of-action were validated by various published experimental evidence. In summary, this study offers powerful computational systems pharmacology approaches and tools for the development of novel targeted cancer therapies by exploiting the polypharmacology of natural products.