Abstract

The ability to computationally predict whether a compound treats a disease would improve the economy and success rate of drug approval. This study describes Project Rephetio to systematically model drug efficacy based on 755 existing treatments. First, we constructed Hetionet (neo4j.het.io), an integrative network encoding knowledge from millions of biomedical studies. Hetionet v1.0 consists of 47,031 nodes of 11 types and 2,250,197 relationships of 24 types. Data was integrated from 29 public resources to connect compounds, diseases, genes, anatomies, pathways, biological processes, molecular functions, cellular components, pharmacologic classes, side effects, and symptoms. Next, we identified network patterns that distinguish treatments from non-treatments. Then we predicted the probability of treatment for 209,168 compound-disease pairs (het.io/repurpose). Our predictions validated on two external sets of treatment and provided pharmacological insights on epilepsy, suggesting they will help prioritize drug repurposing candidates. This study was entirely open and received realtime feedback from 40 community members.

Data availability

The following previously published data sets were used

Article and author information

Author details

  1. Daniel Scott Himmelstein

    Program in Biological and Medical Informatics, University of California, San Francisco, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3012-7446
  2. Antoine Lizee

    Department of Neurology, University of California, San Francisco, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Christine Hessler

    Department of Neurology, University of California, San Francisco, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Leo Brueggeman

    Department of Neurology, University of California, San Francisco, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Sabrina L Chen

    Department of Neurology, University of California, San Francisco, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Dexter Hadley

    Department of Pediatrics, Institute for Computational Health Sciences, University of California, San Francisco, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Ari Green

    Department of Neurology, University of California, San Francisco, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  8. Pouya Khankhanian

    Department of Neurology, University of California, San Francisco, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  9. Sergio E Baranzini

    Program in Biological and Medical Informatics, University of California, San Francisco, San Francisco, United States
    For correspondence
    sergio.baranzini@ucsf.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0067-194X

Funding

National Science Foundation (1144247)

  • Daniel Scott Himmelstein

Heidrich Family and Friends Foundation

  • Sergio E Baranzini

National Institutes of Health (5R01NS088155)

  • Sergio E Baranzini

National Cancer Institute (UH2CA203792)

  • Dexter Hadley

U.S. National Library of Medicine (1U01LM012675)

  • Dexter Hadley

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Alfonso Valencia, Barcelona Supercomputing Center - BSC, Spain

Version history

  1. Received: March 11, 2017
  2. Accepted: September 11, 2017
  3. Accepted Manuscript published: September 22, 2017 (version 1)
  4. Version of Record published: October 13, 2017 (version 2)

Copyright

© 2017, Himmelstein et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Daniel Scott Himmelstein
  2. Antoine Lizee
  3. Christine Hessler
  4. Leo Brueggeman
  5. Sabrina L Chen
  6. Dexter Hadley
  7. Ari Green
  8. Pouya Khankhanian
  9. Sergio E Baranzini
(2017)
Systematic integration of biomedical knowledge prioritizes drugs for repurposing
eLife 6:e26726.
https://doi.org/10.7554/eLife.26726

Share this article

https://doi.org/10.7554/eLife.26726

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