Objective: Using electronic health records (EHRs) and biomolecular data, we sought to discover drug pairs with synergistic repurposing potential. EHRs provide real-world treatment and outcome patterns, while complementary biomolecular data, including disease-specific gene expression and drug-protein interactions, provide mechanistic understanding. Method: We applied Group Lasso INTERaction NETwork (glinternet), an overlap group lasso penalty on a logistic regression model, with pairwise interactions to identify variables and interacting drug pairs associated with reduced 5-year mortality using EHRs of 9945 breast cancer patients. We identified differentially expressed genes from 14 case-control human breast cancer gene expression datasets and integrated them with drug-protein networks. Drugs in the network were scored according to their association with breast cancer individually or in pairs. Lastly, we determined whether synergistic drug pairs found in the EHRs were enriched among synergistic drug pairs from gene-expression data using a method similar to gene set enrichment analysis. Results: From EHRs, we discovered 3 drug-class pairs associated with lower mortality: anti-inflammatories and hormone antagonists, anti-inflammatories and lipid modifiers, and lipid modifiers and obstructive airway drugs. The first 2 pairs were also enriched among pairs discovered using gene expression data and are supported by molecular interactions in drug-protein networks and preclinical and epidemiologic evidence. Conclusions: This is a proof-of-concept study demonstrating that a combination of complementary data sources, such as EHRs and gene expression, can corroborate discoveries and provide mechanistic insight into drug synergism for repurposing.

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.

The majority of drugs currently used to treat rheumatoid arthritis (RA) act on a small number of immunomodulatory targets. We applied an integrative biomedical-informatics-based approach and in vivo testing to identify new drug candidates and potential therapeutic targets that could form the basis for future drug development in RA. A computational model of RA was constructed by integrating patient gene expression data, molecular interactions, and clinical drug-disease associations. Drug candidates were scored based on their predicted efficacy across these data types. Ten high-scoring candidates were subsequently screened in a collagen-induced arthritis model of RA. Treatment with exenatide, olopatadine, and TXR-112 significantly improved multiple preclinical endpoints, including animal mobility which was measured using a novel digital platform. These three drug candidates do not act on common RA therapeutic targets; however, links between known candidate pharmacology and pathological processes involved in RA suggest hypothetical mechanisms contributing to the observed efficacy.