Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

The promise of drug repurposing is that existing drugs may be used for new disease indications in order to curb the high costs and time for approval. The goal of computational methods for drug repurposing is to enable solutions for safer, cheaper and faster drug discovery. Towards this end, we developed a novel method that integrates genetic and clinical phenotype data from large-scale GWAS and PheWAS studies with detailed drug information on the concept of transitive Drug-Gene-Disease triads. We created "RE:fine Drugs," a freely available, interactive dashboard that automates gene, disease and drug-based searches to identify drug repurposing candidates. This web-based tool supports a user-friendly interface that includes an array of advanced search and export options. Results can be prioritized in a variety of ways, including but not limited to, biomedical literature support, strength and statistical significance of GWAS and/or PheWAS associations, disease indications and molecular drug targets. Here we provide a protocol that illustrates the functionalities available in the "RE:fine Drugs" system and explores the different advanced options through a case study.

[1]  Stephen T. C. Wong,et al.  Toward better drug repositioning: prioritizing and integrating existing methods into efficient pipelines. , 2014, Drug discovery today.

[2]  Ying Li,et al.  Validating drug repurposing signals using electronic health records: a case study of metformin associated with reduced cancer mortality , 2014, J. Am. Medical Informatics Assoc..

[3]  Philip R. O. Payne,et al.  ‘RE:fine drugs’: an interactive dashboard to access drug repurposing opportunities , 2016, Database J. Biol. Databases Curation.

[4]  Melissa A. Basford,et al.  Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data , 2013, Nature Biotechnology.

[5]  Jian Wang,et al.  High-throughput synergy screening identifies microbial metabolites as combination agents for the treatment of fungal infections , 2007, Proceedings of the National Academy of Sciences.

[6]  L. Cardon,et al.  Use of genome-wide association studies for drug repositioning , 2012, Nature Biotechnology.

[7]  P. Sanseau,et al.  Computational Drug Repositioning: From Data to Therapeutics , 2013, Clinical pharmacology and therapeutics.

[8]  Z. Cao,et al.  Mining drug–disease relationships as a complement to medical genetics‐based drug repositioning: Where a recommendation system meets genome‐wide association studies , 2015, Clinical pharmacology and therapeutics.

[9]  Simon M Lin,et al.  Opportunities for drug repositioning from phenome-wide association studies , 2015, Nature Biotechnology.

[10]  Van V. Brantner,et al.  Estimating the cost of new drug development: is it really 802 million dollars? , 2006, Health affairs.

[11]  J. Lehár,et al.  Systematic discovery of multicomponent therapeutics , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[12]  M. Bunnage Getting pharmaceutical R&D back on target. , 2011, Nature chemical biology.

[13]  Zhiyong Lu,et al.  A survey of current trends in computational drug repositioning , 2016, Briefings Bioinform..

[14]  Yang Yang,et al.  Use of Genome-Wide Association Studies for Cancer Research and Drug Repositioning , 2015, PloS one.

[15]  T. Ashburn,et al.  Drug repositioning: identifying and developing new uses for existing drugs , 2004, Nature Reviews Drug Discovery.