Frequent failures of experimental medicines in clinical trials question current abilities for predicting drug effects in the human body. Therefore, the approach of drug repositioning is an important consideration for any life science organization. By using knowledge-driven systems in the form of large data stores and applying rational in silico experimental design, researchers have generated workflows that are capable of identifying novel uses for drugs that span the therapeutic pipeline and beyond. Both broadly accessible data, such as Medline and Chembank, in addition to internal proprietary data of the company in the form of gene chip experiments, compound screening databases, and clinical trial information play an important role in the success of drug repositioning. By reviewing how current and past successes have beenaccomplished along with the data used, important stratagems emerge that can provide a wealth of ideas for novel workflows, as well as provide a guide for future discoveries.

Cheminformatic/bioinformatic mining of large corporate databases for drug repurposing

244th National Meeting (2012)

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