In silico methods for drug repurposing and pharmacology

Rachel A. Hodos, Brian A. Kidd, Khader Shameer, Benjamin Readhead, Joel T. Dudley

Research output: Contribution to journalArticle

67 Scopus citations

Abstract

Data in the biological, chemical, and clinical domains are accumulating at ever-increasing rates and have the potential to accelerate and inform drug development in new ways. Challenges and opportunities now lie in developing analytic tools to transform these often complex and heterogeneous data into testable hypotheses and actionable insights. This is the aim of computational pharmacology, which uses in silico techniques to better understand and predict how drugs affect biological systems, which can in turn improve clinical use, avoid unwanted side effects, and guide selection and development of better treatments. One exciting application of computational pharmacology is drug repurposing-finding new uses for existing drugs. Already yielding many promising candidates, this strategy has the potential to improve the efficiency of the drug development process and reach patient populations with previously unmet needs such as those with rare diseases. While current techniques in computational pharmacology and drug repurposing often focus on just a single data modality such as gene expression or drug-target interactions, we argue that methods such as matrix factorization that can integrate data within and across diverse data types have the potential to improve predictive performance and provide a fuller picture of a drug's pharmacological action.

Original languageEnglish (US)
Pages (from-to)186-210
Number of pages25
JournalWiley Interdisciplinary Reviews: Systems Biology and Medicine
Volume8
Issue number3
DOIs
StatePublished - May 1 2016
Externally publishedYes

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ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • Biochemistry, Genetics and Molecular Biology (miscellaneous)

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