Pathway Analysis for Drug Repositioning Based on Public Database Mining

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• 作者：Yongmei Pan ; Tiejun Cheng ; Yanli Wang ; Stephen H. Bryant
• 刊名：Journal of Chemical Information and Modeling
• 出版年：2014
• 出版时间：February 24, 2014
• 年：2014
• 卷：54
• 期：2
• 页码：407-418
• 全文大小：340K
• 年卷期：v.54,no.2(February 24, 2014)
• ISSN：1549-960X

文摘

Sixteen FDA-approved drugs were investigated to elucidate their mechanisms of action (MOAs) and clinical functions by pathway analysis based on retrieved drug targets interacting with or affected by the investigated drugs. Protein and gene targets and associated pathways were obtained by data-mining of public databases including the MMDB, PubChem BioAssay, GEO DataSets, and the BioSystems databases. Entrez E-Utilities were applied, and in-house Ruby scripts were developed for data retrieval and pathway analysis to identify and evaluate relevant pathways common to the retrieved drug targets. Pathways pertinent to clinical uses or MOAs were obtained for most drugs. Interestingly, some drugs identified pathways responsible for other diseases than their current therapeutic uses, and these pathways were verified retrospectively by in vitro tests, in vivo tests, or clinical trials. The pathway enrichment analysis based on drug target information from public databases could provide a novel approach for elucidating drug MOAs and repositioning, therefore benefiting the discovery of new therapeutic treatments for diseases.