Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/7443
Title: Identification of clinically approved drugs indacaterol and canagliflozin for repurposing to treat epidermal growth factor tyrosine kinase inhibitor-resistant lung cancer
Authors: Li, Hongjian 
Tong, Christy Wing-Sum 
Leung, Yee 
Wong, Man-Hon 
To, Kenneth Kin-Wah 
Prof. LEUNG Kwong Sak 
Issue Date: 2017
Source: Frontiers in Oncology, 2017, vol.7 (29), article number 288
Journal: Frontiers in Oncology 
Abstract: In advanced lung cancer, epidermal growth factor tyrosine kinase inhibitors (EGFR TKIs) have extraordinary clinical efficacy. However, their usefulness is severely compromised by drug resistance mediated by various mechanisms, the most important of which is the secondary EGFR T790M mutation. The mutation blocks the binding of EGFR TKIs to the receptor kinase, thereby abolishing the therapeutic efficacy. In this study, we used our free and open-source protein-ligand docking software idock to screen worldwide approved small-molecule drugs against EGFR T790M. The computationally selected drug candidates were evaluated in vitro in resistant non-small cell lung cancer (NSCLC) cell lines. The specificity of the drugs toward the mutant EGFR was demonstrated by cell-free kinase inhibition assay. The inhibition of EGFR kinase activity and its downstream signaling pathways in NSCLC cells was shown by immunoblot analysis. The positive hints were revealed to be indacaterol, canagliflozin, and cis-flupenthixol, all of which were shown to induce apoptosis in NSCLC cells harboring the EGFR T790M mutation. Moreover, the combination of indacaterol with gefitinib was also found to produce synergistic anticancer effect in NSCLC cells bearing EGFR T790M. The observed synergistic effect was likely contributed by the enhanced inhibition of EGFR and its downstream signaling molecules. © 2017 Li, Tong, Leung, Wong, To and Leung.
Type: Peer Reviewed Journal Article
URI: http://hdl.handle.net/20.500.11861/7443
ISSN: 2234943X
DOI: 10.3389/fonc.2017.00288
Appears in Collections:Applied Data Science - Publication

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