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.
Keywords: anatomical therapeutic chemical code; drug repositioning; network-based inference.
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Network predicting drug's anatomical therapeutic chemical code.Bioinformatics. 2013 May 15;29(10):1317-24. doi: 10.1093/bioinformatics/btt158. Epub 2013 Apr 5. Bioinformatics. 2013. PMID: 23564845
Prediction of drug's Anatomical Therapeutic Chemical (ATC) code by integrating drug-domain network.J Biomed Inform. 2015 Dec;58:80-88. doi: 10.1016/j.jbi.2015.09.016. Epub 2015 Oct 3. J Biomed Inform. 2015. PMID: 26434987
Similarity-based prediction for Anatomical Therapeutic Chemical classification of drugs by integrating multiple data sources.Bioinformatics. 2015 Jun 1;31(11):1788-95. doi: 10.1093/bioinformatics/btv055. Epub 2015 Jan 31. Bioinformatics. 2015. PMID: 25638810
Convolutional Neural Networks for ATC Classification.Curr Pharm Des. 2018;24(34):4007-4012. doi: 10.2174/1381612824666181112113438. Curr Pharm Des. 2018. PMID: 30417778 Review.
Computational Study of Drugs by Integrating Omics Data with Kernel Methods.Mol Inform. 2013 Dec;32(11-12):930-41. doi: 10.1002/minf.201300090. Epub 2013 Dec 11. Mol Inform. 2013. PMID: 27481139 Review.