Individualized Drug Repositioning For Rheumatoid Arthritis Using Weighted Kolmogorov-Smirnov Algorithm.

Background: Existing drugs are far from enough for investigators and patients to administrate the therapy of rheumatoid arthritis. Drug repositioning has drawn broad attention by reusing marketed drugs and clinical candidates for new uses.

Purpose: This study attempted to predict candidate drugs for rheumatoid arthritis treatment by mining the similarities of pathway aberrance induced by disease and various drugs, on a personalized or customized basis.

Methods: We firstly measured the individualized pathway aberrance induced by rheumatoid arthritis based on the microarray data and various drugs from CMap database, respectively. Then, the similarities of pathway aberrances between RA and various drugs were calculated using a Kolmogorov-Smirnov weighted enrichment score algorithm.

Results: Using this method, we identified 4 crucial pathways involved in rheumatoid arthritis development and predicted 9 underlying candidate drugs for rheumatoid arthritis treatment. Some candidates with current indications to treat other diseases might be repurposed to treat rheumatoid arthritis and complement the drug group for rheumatoid arthritis.

Conclusion: This study predicts candidate drugs for rheumatoid arthritis treatment through mining the similarities of pathway aberrance induced by disease and various drugs, on a personalized or customized basis. Our framework will provide novel insights in personalized drug discovery for rheumatoid arthritis and contribute to the future application of custom therapeutic decisions.