title: Construction of drug network based on side effects and its application for drug repositioning.
abstract: Drugs with similar side-effect profiles may share similar therapeutic properties through related mechanisms of action. In this study, a drug-drug network was constructed based on the similarities between their clinical side effects. The indications of a drug may be inferred by the enriched FDA-approved functions of its neighbouring drugs in the network. We systematically screened new indications for 1234 drugs with more than 2 network neighbours, 36.87% of the drugs achieved a performance score of Normalized Discounted Cumulative Gain in the top 5 positions (NDCG@5) ≥ 0.7, which means most of the known FDA-approved indications were well predicted at the top 5 positions. In particular, drugs for diabetes, obesity, laxatives and antimycobacterials had extremely high performance with more than 80% of them achieving NDCG@5 ≥ 0.7. Additionally, by manually checking the predicted 1858 drug-indication pairs with Expression Analysis Systematic Explorer (EASE) score ≤ 10(-5) (EASE score is a rigorously modified Fisher exact test p value), we found that 80.73% of such pairs could be verified by preclinical/clinical studies or scientific literature. Furthermore, our method could be extended to predict drugs not covered in the network. We took 98 external drugs not covered in the network as the test sample set. Based on our similarity criteria using side effects, we identified 41 drugs with significant similarities to other drugs in the network. Among them, 36.59% of the drugs achieved NDCG@5 ≥ 0.7. In all of the 106 drug-indication pairs with an EASE score ≤ 0.05, 50.94% of them are supported by FDA approval or preclinical/clinical studies. In summary, our method which is based on the indications enriched by network neighbors may provide new clues for drug repositioning using side effects.
authors: R&D Information, AstraZeneca, Shanghai, China.
authors: Hao
authors: H
authors: Ye
authors: School of Life Sciences and Technology, Tongji University, Shanghai, China.
authors: Qi
authors: Q
authors: Liu
authors: R&D Information, AstraZeneca, Shanghai, China.
authors: Jia
authors: J
authors: Wei
mesh_terms: D000465:Algorithms; D062313:Databases, Pharmaceutical; D058492:Drug Repositioning; D064420:Drug-Related Side Effects and Adverse Reactions; D006801:Humans; D008962:Models, Theoretical; D015203:Reproducibility of Results