Individualized Drug Repositioning For Rheumatoid Arthritis Using Weighted Kolmogorov–Smirnov Algorithm
Authors Hu RY, Tian XB, Li B, Luo R, Zhang B, Zhao JM
Received 11 September 2019
Accepted for publication 6 November 2019
Published 11 December 2019 Volume 2019:12 Pages 369—375
Checked for plagiarism Yes
Review by Single-blind
Peer reviewer comments 2
Editor who approved publication: Dr Martin H. Bluth
Ru-Yin Hu,1–3,* Xiao-Bin Tian,3,* Bo Li,3 Rui Luo,3 Bin Zhang,3 Jin-Min Zhao1
1Department of Orthopaedics, Guangxi Medical University, Nanning 530021, People’s Republic of China; 2Department of Orthopaedics, The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, People’s Republic of China; 3Department of Orthopaedics, Guizhou Provincial People’s Hospital, Guiyang 550002, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Jin-Min Zhao
Department of Orthopaedics, Guangxi Medical University, No. 22 Shuangyong Road, Nanning, Guangxi 530021, People’s Republic of China
Tel +86 771 13985048001
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.
Keywords: rheumatoid arthritis, drug repositioning, individualized pathway aberrance, differential pathway
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