An Integrative Bioinformatics Approach Identifies In Vivo Validated Drug Candidates with Novel Mechanisms of Action in Rheumatoid Arthritis

Abstract

Rheumatoid arthritis (RA) is an area of active drug development, with over 100 candidates in clinical trials. However, most act on a small number of immunomodulatory targets. Drug candidates that act through new targets or mechanisms could expand treatment options for RA. We applied a data-driven bioinformatics approach and in vivo screen to identify and test new drug candidates and targets that could form the basis of future drug development in RA. A computational model of RA was constructed by integrating patient gene expression data, molecular interactions, chemical structures, and clinical drug-disease associations. Candidates were scored based on their predicted efficacy in the computational model. FDA-approved treatments for RA were significantly enriched among the top-ranked candidates. Ten high scoring novel candidates were then screened in the collagen-induced arthritis model of RA in rats. Therapeutic treatment with three candidates significantly reduced ankle size, alleviated limb inflammation, improved joint histopathology, and reduced mobility impairments tracked by a novel digital motion endpoint. These candidates are currently approved for metabolic, allergic, and psychiatric indications, and do not act on common RA therapeutic targets. However, links between known candidate pharmacology and pathological processes in RA suggest hypothetical mechanisms that could contribute to efficacy. Future studies will inform the druggable targets, pathways, and mechanisms that could contribute to each candidate’s efficacy in RA. The candidates could themselves be modified and optimized to increase efficacy in RA. Novel targets identified in these studies could also be the basis of new drug discovery initiatives.