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Finding new uses for old drugs is a strategy embraced by the pharmaceutical industry, with increasing participation from the academic sector. Drug repurposing efforts focus on identifying novel modes of action, but not in a systematic manner. With intensive data mining and curation, we aim to apply bio- and cheminformatics tools using the DRUGS database, containing 3837 unique small molecules annotated on 1750 proteins. These are likely to serve as drug targets and antitargets (i.e., associated with side effects, SE). The academic community, the pharmaceutical sector and clinicians alike could benefit from an integrated, semantic-web compliant computer-aided drug repurposing (CADR) effort, one that would enable deep data mining of associations between approved drugs (D), targets (T), clinical outcomes (CO) and SE. We report preliminary results from text mining and multivariate statistics, based on 7684 approved drug labels, ADL (Dailymed) via text mining. From the ADL corresponding to 988 unique drugs, the "adverse reactions" section was mapped onto 174 SE, then clustered via principal component analysis into a 5 x 5 self-organizing map that was integrated into a Cytoscape network of SE-D-T-CO. This type of data can be used to streamline drug repurposing and may result in novel insights that can lead to the identification of novel drug actions.
Original languageEnglish
JournalMolecular Informatics
Volume30
Issue number2-3
Pages (from-to)100-111
ISSN1868-1743
DOIs
StatePublished - 2011
Peer-reviewedYes

Conference

Conference18th European Symposium on Quantitative Structure-Activity Relationships
Number18
CountryGreece
CityRhodes
Period19/09/201024/09/2010
CitationsWeb of Science® Times Cited: 37

Keywords

  • Text mining, Principal component analysis, Drug discovery, Drug side effects, Drug targets
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