Use of big data in drug development for precision medicine

Rosa S. Kim, Nicolas Goossens, Yujin Hoshida

Research output: Contribution to journalArticle

16 Scopus citations

Abstract

Drug development has been a costly and lengthy process with an extremely low success rate and lack of consideration of individual diversity in drug response and toxicity. Over the past decade, an alternative “big data” approach has been expanding at an unprecedented pace based on the development of electronic databases of chemical substances, disease gene/protein targets, functional readouts, and clinical information covering inter-individual genetic variations and toxicities. This paradigm shift has enabled systematic, high-throughput, and accelerated identification of novel drugs or repurposed indications of existing drugs for pathogenic molecular aberrations specifically present in each individual patient. The exploding interest from the information technology and direct-to-consumer genetic testing industries has been further facilitating the use of big data to achieve personalized Precision Medicine. Here we overview currently available resources and discuss future prospects.

Original languageEnglish (US)
Pages (from-to)245-253
Number of pages9
JournalExpert Review of Precision Medicine and Drug Development
Volume1
Issue number3
DOIs
StatePublished - May 3 2016
Externally publishedYes

Keywords

  • Big data
  • drug development
  • high-throughput screen
  • in silico drug discovery
  • precision medicine

ASJC Scopus subject areas

  • Molecular Medicine
  • Genetics
  • Pharmacology
  • Drug Discovery

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