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.
Highlights
The cost of developing a new therapeutic drug has been estimated at 1.4 billion dollars (DiMasi et al, 2016), the process typically takes 15 years from lead compound to market (Reichert, 2003), and the likelihood of success is stunningly low (Hay et al, 2014)
This information has been extracted from the literature by human curators and compiled into databases such as DrugBank, ChEMBL, DrugCentral, and BindingDB
The predictions successfully prioritized two external validation sets: novel indications from DrugCentral (AUROC = 85.5%) and novel indications in clinical trial (AUROC = 70.0%). These findings indicate that Project Rephetio has the ability to recognize efficacious compound–disease pairs
Summary
The cost of developing a new therapeutic drug has been estimated at 1.4 billion dollars (DiMasi et al, 2016), the process typically takes 15 years from lead compound to market (Reichert, 2003), and the likelihood of success is stunningly low (Hay et al, 2014). Drug repurposing — identifying novel uses for existing therapeutics — can drastically reduce the duration, failure rates, and costs of approval (Ashburn and Thor, 2004).
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