Systematic integration of biomedical knowledge prioritizes drugs for repurposing
- University of California, San Francisco, United States
- Cited 45
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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.
Article and author information
Author details
Daniel Scott Himmelstein
Sergio E Baranzini
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
- Alfonso Valencia, Barcelona Supercomputing Center - BSC, Spain
Publication history
- Received: March 11, 2017
- Accepted: September 11, 2017
- Accepted Manuscript published: September 22, 2017 (version 1)
- 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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