Ru-Yin Hu1,2,3, Xiao-Bin Tian3, Bo Li3, Rui Luo3, Bin Zhang3, Jin-Min Zhao1. 1. Department of Orthopaedics, Guangxi Medical University, Nanning 530021, People's Republic of China. 2. Department of Orthopaedics, The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, People's Republic of China. 3. Department of Orthopaedics, Guizhou Provincial People's Hospital, Guiyang 550002, People's Republic of China.
Abstract
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.
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.
Authors: Michael J Keiser; Vincent Setola; John J Irwin; Christian Laggner; Atheir I Abbas; Sandra J Hufeisen; Niels H Jensen; Michael B Kuijer; Roberto C Matos; Thuy B Tran; Ryan Whaley; Richard A Glennon; Jérôme Hert; Kelan L H Thomas; Douglas D Edwards; Brian K Shoichet; Bryan L Roth Journal: Nature Date: 2009-11-01 Impact factor: 49.962
Authors: Josef S Smolen; Ferdinand C Breedveld; Gerd R Burmester; Vivian Bykerk; Maxime Dougados; Paul Emery; Tore K Kvien; M Victoria Navarro-Compán; Susan Oliver; Monika Schoels; Marieke Scholte-Voshaar; Tanja Stamm; Michaela Stoffer; Tsutomu Takeuchi; Daniel Aletaha; Jose Louis Andreu; Martin Aringer; Martin Bergman; Neil Betteridge; Hans Bijlsma; Harald Burkhardt; Mario Cardiel; Bernard Combe; Patrick Durez; Joao Eurico Fonseca; Alan Gibofsky; Juan J Gomez-Reino; Winfried Graninger; Pekka Hannonen; Boulos Haraoui; Marios Kouloumas; Robert Landewe; Emilio Martin-Mola; Peter Nash; Mikkel Ostergaard; Andrew Östör; Pam Richards; Tuulikki Sokka-Isler; Carter Thorne; Athanasios G Tzioufas; Ronald van Vollenhoven; Martinus de Wit; Desirée van der Heijde Journal: Ann Rheum Dis Date: 2015-05-12 Impact factor: 19.103