Abstract
BackgroundRheumatoid arthritis (RA) is a chronic autoimmune disease characterized by inflammation and destruction of synovial joints. RA affects up to 1 % of the population worldwide. Currently, there are no drugs that can cure RA or achieve sustained remission. The unknown cause of the disease represents a significant challenge in the drug development. In this study, we address this challenge by proposing an alternative drug discovery approach that integrates and reasons over genetic interrelationships between RA and other genetic diseases as well as a large amount of higher-level drug treatment data.We first constructed a genetic disease network using disease genetics data from Genome-Wide Association Studies (GWAS). We developed a network-based ranking algorithm to prioritize diseases genetically-related to RA (RA-related diseases). We then developed a drug prioritization algorithm to reposition drugs from RA-related diseases to treat RA.ResultsOur algorithm found 74 of the 80 FDA-approved RA drugs and ranked them highly (recall: 0.925, median ranking: 8.93 %), demonstrating the validity of our strategy. When compared to a study that used GWAS data to directly connect RA-associated genes to drug targets (“direct genetics-based” approach), our algorithm (“indirect genetics-based”) achieved a comparable overall performance, but complementary precision and recall in retrospective validation (precision: 0.22, recall: 0.36; F1: 0.27 vs. precision: 0.74, recall: 0.16; F1: 0.28). Our approach performed significantly better in novel predictions when evaluated using 165 not-yet-FDA-approved RA drugs (precision: 0.46, recall: 0.50; F1: 0.47 vs. precision: 0.40, recall: 0.006; F1: 0.01).ConclusionsIn summary, although the fundamental pathophysiological mechanisms remain uncharacterized, our proposed computation-based drug discovery approach to analyzing genetic and treatment interrelationships among thousands of diseases and drugs can facilitate the discovery of innovative drugs for treating RA.
Highlights
Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by inflammation and destruction of synovial joints
Several recent studies have indicated that disease genetics identified by Genome-Wide Association Studies (GWAS) may lead to translational opportunities for drug discovery [5,6,7,8,9,10,11]
To capitalize on complex human disease genetics identified through GWAS, the National Center for Advancing Translational Science (NCATS) was established to use genomic information to determine whether drugs approved to treat one disease could be effective in treating others [8]
Summary
Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by inflammation and destruction of synovial joints. We first constructed a genetic disease network using disease genetics data from Genome-Wide Association Studies (GWAS). Several recent studies have indicated that disease genetics identified by GWAS may lead to translational opportunities for drug discovery [5,6,7,8,9,10,11]. A recent study showed that RA risk loci identified through meta-analysis of GWAS data provided therapeutic opportunities for the repositioning of existing drugs for the treatment of RA [7]. To capitalize on complex human disease genetics identified through GWAS, the National Center for Advancing Translational Science (NCATS) was established to use genomic information to determine whether drugs approved to treat one disease could be effective in treating others [8]. Significant challenges exist in directly translating disease-associated genetic variants identified by GWAS into novel therapeutics [4]
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