A network-based model for drug repurposing in Rheumatoid Arthritis

Abstract

BackgroundThe identification of drug repositioning targets through computational methods has the potential to provide a fast, inexpensive alternative to traditional drug discovery process. Diseases where complicated pathophysiology evolves over time are excellent targets for such methods. One such disease is Rheumatoid Arthritis (RA), a chronic inflammatory autoimmune disease, where the drug survival at 5-years is less than 50% for patients treated with disease-modifying anti-rheumatic drugs (DMARDs).\n\nMethods and FindingsWe have developed a network-based approach for drug repositioning that takes into account the human interactome network, proximity measures between drug targets and disease-associated genes, potential side-effects, genome-wide gene expression and disease modules that emerge through pertinent analysis. We found that all DMARDs, except for hydroxychloroquine (HCQ), were found to be significantly proximal to RA-related genes. Application of the method on anti-diabetic agents, statins and H2 receptor blockers identified anti-diabetic agents - gliclazide, sitagliptin and metformin - that have similar network signatures with the DMARDs. Subsequent in-vitro experiments on mouse fibroblast NIH-3T3 cells validated the findings and the down regulation of six key RA-related inflammatory genes. Our analysis further argues that the combination of HCQ and/or sulfasalazine with methotrexate (MTX) is predicted to have an additive synergistic effect in treatment based on network complementarity. Similarly, leflunomide and tofacitinib were found to be suitable alternatives upon chemoresistance to MTX-based double/triple therapy, given the complementary network signatures and overlapping critical target hubs.\n\nConclusionsOur results corroborate that computational methods that are based on network proximity, among other contextual information can help narrow down the drug candidates for drug repositioning, as well as support decisions for combinatorial drug treatment that is tailored to patients needs.\n\nAuthor summaryThe network-based proximity between drug targets and disease genes can provide novel insights on the repercussion, interplay, and reposition of drugs in the context of disease. Disease-modifying anti-rheumatic drugs (DMARDs) located significantly close to rheumatoid arthritis (RA)-associated genes and RA-relevant pathways. Three anti-diabetic agents were identified to have an anti-inflammatory effect like DMARDs. We built RA disease module encompassing the emerging small-molecule targets and functional neighbors, which better explained the RA pathophysiology. By proximity and network robustness, tofacitinib and tocilizumab were the most potent for RA disease module as a single agent, and this is consistent with clinical observation. Side effects of clinical importance are predictable by measuring network-based proximity between drug targets and side effect protein. Network-based drug-disease proximity offers a novel and clinically actionable information about drugs and opens the new possibility to drug combination and reposition.