Concepedia

Systematic integration of biomedical knowledge prioritizes drugs for repurposing

Daniel Himmelstein, Antoine Lizée, Christine Hessler, Leo Brueggeman, Sabrina Chen, Dexter Hadley, Ari Green, Pouya Khankhanian, Sergio E. Baranzini

bioRxiv (Cold Spring Harbor Laboratory) · 2016 · 43 citations · 114 references

DOI Full text

Open access

TL;DR

Computational prediction of whether a compound treats a disease could improve the economy and success rate of drug approval. This study introduces Project Rephetio, which systematically models drug efficacy using 755 existing treatments. The authors constructed Hetionet, an integrative network linking 47,031 nodes of 11 types and 2,250,197 relationships of 24 types from 29 public resources, identified network patterns distinguishing treatments from non‑treatments, and used these patterns to predict treatment probabilities for 209,168 compound–disease pairs, with the project open to community feedback. Hetionet v1.0’s predictions, validated on two external treatment sets, provided pharmacological insights into epilepsy and demonstrate the method’s potential to prioritize drug repurposing candidates.

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 were 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.