Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/7448
Title: In silico prediction and in vitro and in vivo validation of acaricide fluazuron as a potential inhibitor of FGFR3 and a candidate anticancer drug for bladder carcinoma
Authors: Ke, Kunbin 
Li, Hongjian 
Yao, Hong 
Shi, Xi-Nan 
Dong, Chao 
Zhu, Ying 
Liu, Xu 
Li, Ling 
Prof. LEUNG Kwong Sak 
Wong, Man-Hon 
Liu, Xiao-Dong 
Kung, Hsiang-fu 
Lin, Marie Chia-mi 
Issue Date: 2017
Source: Chemical Biology and Drug Design, 2017, vol. 89(4), pp. 505-513
Journal: Chemical Biology and Drug Design 
Abstract: Bladder carcinoma (BC) is the ninth most common cause of cancer worldwide. Surgical resection and conventional chemotherapy and radiotherapy will ultimately fail due to tumor recurrence and resistance. Thus, the development of novel treatment is urgently needed. Fibroblast growth factor receptor 3 (FGFR3) is an important and well-established target for BC treatment. In this study, we utilized the free and open-source protein–ligand docking software idock to prospectively identify potential inhibitors of FGFR3 from 3,167 worldwide approved small-molecule drugs using a repositioning strategy. Six high-scoring compounds were purchased and tested in vitro. Among them, the acaricide drug fluazuron exhibited the highest antiproliferative effect in human BC cell lines RT112 and RT4. We further demonstrated that fluazuron treatment significantly increased the percentage of apoptosis cells, and decreased the phosphorylation level of FGFR3 and its downstream proteins FRS2-α, AKT, and ERK. We also investigated the anticancer effect of fluazuron in vivo in BALB/C nude mice subcutaneously xenografted with RT112 cells. Our results showed that oral treatment with fluazuron (80 mg/kg) significantly inhibited tumor growth. These results suggested for the first time that fluazuron is a potential inhibitor of FGFR3 and a candidate anticancer drug for the treatment of BC.
Type: Peer Reviewed Journal Article
URI: http://hdl.handle.net/20.500.11861/7448
DOI: 10.1111/cbdd.12872
Appears in Collections:Applied Data Science - Publication

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