文献类型：期刊文献

英文题名：Drug repositioning by prediction of drug's anatomical therapeutic chemical code via network-based inference approaches

作者：Peng, Yayuan[1];Wang, Manjiong[1];Xu, Yixiang[1];Wu, Zengrui[1];Wang, Jiye[1];Zhang, Chao[1];Liu, Guixia[1];Li, Weihua[1];Li, Jian[1];Tang, Yun[1]

机构：[1]East China Univ Sci & Technol, Shanghai, Peoples R China

年份：2021

卷号：22

期号：2

起止页码：2058

外文期刊名：BRIEFINGS IN BIOINFORMATICS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000642298000109)】；

基金：National Key Research and Development Program of China (2016YFA0502304); the National Natural Science Foundation of China (81673356, 81872800); and the Chinese Special Fund for State Key Laboratory of Bioreactor Engineering (2060204).

语种：英文

外文关键词：anatomical therapeutic chemical code; drug repositioning; network-based inference

摘要：Drug discovery and development is a time-consuming and costly process. Therefore, drug repositioning has become an effective approach to address the issues by identifying new therapeutic or pharmacological actions for existing drugs. The drug's anatomical therapeutic chemical (ATC) code is a hierarchical classification system categorized as five levels according to the organs or systems that drugs act and the pharmacology, therapeutic and chemical properties of drugs. The 2nd-, 3rd-and 4th-level ATC codes reserved the therapeutic and pharmacological information of drugs. With the hypothesis that drugs with similar structures or targets would possess similar ATC codes, we exploited a network-based approach to predict the 2nd-, 3rd- and 4th-level ATC codes by constructing substructure drug-ATC (SD-ATC), target drug-ATC (TD-ATC) and Substructure&Target drug-ATC (STD-ATC) networks. After 10-fold cross validation and two external validations, the STD-ATC models outperformed the SD-ATC and TD-ATC ones. Furthermore, with KR as fingerprint, the STD-ATC model was identified as the optimal model with AUC values at 0.899 +/- 0.015, 0.916 and 0.893 for 10-fold cross validation, external validation set 1 and external validation set 2, respectively. To illustrate the predictive capability of the STD-ATC model with KR fingerprint, as a case study, we predicted 25 FDA-approved drugs (22 drugs were actually purchased) to have potential activities on heart failure using that model. Experiments in vitro confirmed that 8 of the 22 old drugs have shown mild to potent cardioprotective activities on both hypoxia model and oxygen-glucose deprivation model, which demonstrated that our STD-ATC prediction model would be an effective tool for drug repositioning.