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Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
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Systematic drug repurposing through text mining.

Luis B Tari1, Jagruti H Patel

  • 1Knowledge Discovery Lab, Software Science and Analytics, GE Global Research, 1 Research Circle, Niskayuna, NY, 12309, USA, luis.tari@asu.edu.

Methods in Molecular Biology (Clifton, N.J.)
|May 3, 2014
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Summary
This summary is machine-generated.

This study uses text mining to find new uses for existing drugs, a process called drug repurposing. This approach aims to reduce drug development costs and time by identifying alternative indications from biomedical abstracts.

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Area of Science:

  • Pharmacology
  • Bioinformatics
  • Computational Biology

Background:

  • Drug development is costly and time-consuming with high failure rates.
  • Increased regulatory scrutiny necessitates maximizing return on investment through life cycle extension.
  • Drug repurposing (or repositioning) identifies new indications for existing drugs.

Purpose of the Study:

  • To explore text mining for identifying novel drug indications.
  • To leverage publicly available knowledge resources and mechanism of action data.
  • To link existing drugs with new diseases using biomedical abstracts.

Main Methods:

  • Utilized text mining techniques.
  • Leveraged public knowledge bases and mechanism of action representations.
  • Analyzed biomedical abstracts to find drug-disease connections.

Main Results:

  • Generated potential biologically meaningful alternative drug indications.
  • Identified novel therapeutic uses for established pharmaceutical compounds.

Conclusions:

  • Text mining offers a viable strategy for drug repurposing.
  • This approach can accelerate the discovery of new therapeutic applications for existing drugs.
  • Biomedical abstract analysis is a key resource for identifying alternative drug indications.