Abstract
The process of discovering new drugs has been extremely costly and slow in the last decades despite enormous investment in pharmaceutical research. Drug repurposing enables researchers to speed up the process of discovering other conditions that existing drugs can effectively treat, with low cost and fast FDA approval. Here, we introduce ‘RE:fine Drugs’, a freely available interactive website for integrated search and discovery of drug repurposing candidates from GWAS and PheWAS repurposing datasets constructed using previously reported methods in Nature Biotechnology. ‘RE:fine Drugs’ demonstrates the possibilities to identify and prioritize novelty of candidates for drug repurposing based on the theory of transitive Drug–Gene–Disease triads. This public website provides a starting point for research, industry, clinical and regulatory communities to accelerate the investigation and validation of new therapeutic use of old drugs.Database URL: http://drug-repurposing.nationwidechildrens.org
Highlights
Drug discovery and development is typically a 7–12 year process that requires investment of billions of dollars and abundant clinical trials but results in unpredictable return on investment [1, 2]
Since repurposing relies on previously approved studies that already passed multiple toxicity and other tests, new discoveries tend to be ready for clinical trials quickly and be reviewed by Food and Drug Administration (FDA) faster [4]
We provided a public Phenome-Wide Association Study (PheWAS) dataset of 52 966 drug–disease candidates for drug repurposing [14]
Summary
Drug discovery and development is typically a 7–12 year process that requires investment of billions of dollars and abundant clinical trials but results in unpredictable return on investment [1, 2]. There have been previous studies using Machine Learning-based methods [16] and literature analysis [17] for drug repurposing, we extracted and prioritized a new dataset of candidate drug repurposing pairs using previously established data integration methods [3, 14, 15] using Transitive Property of Equality between drug–gene and gene–disease pairs (Figure 1). We applied the same method using data extracted from GWAS catalog and verified co-occurrence of 38.8% (3087 out of 7945) drug–disease pairs in Medline abstracts while 6.2% co-occurrence identified using shuffled drug–disease pairs These findings suggest that PheWAS and GWAS data can significantly enrich drug– disease pairs to be further considered for the potential of reposition. Note that the lack of occurrence of both drug and disease terms together in the literature could potentially indicate a novel finding, a bias of literature annotations or a true lack of association [20, 21]
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