Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/9578
Title: MET-targeting anticancer drugs—de novo design and identification by drug repurposing
Authors: To, Kenneth Kin-Wah 
Prof. LEUNG Kwong Sak 
Cho, William Chi Shing 
Issue Date: 2023
Source: Drugs Drug Candidates, 2023, vol. 2(3), pp. 591-623.
Journal: Drugs Drug Candidates 
Abstract: The Met protein is a cell surface receptor tyrosine kinase predominantly expressed in epithelial cells. Aberrant regulation of MET is manifested by numerous mechanisms including amplification, mutations, deletion, fusion of the MET proto-oncogene, and protein overexpression. They represent the common causes of drug resistance to conventional and targeted chemotherapy in numerous cancer types. There is also accumulating evidence that MET/HGF signaling drives an immunosuppressive tumor microenvironment and dampens the efficacy of cancer immunotherapy. Substantial research effort has been invested in designing Met-targeting drugs with different mechanisms of action. In this review, we summarized the current preclinical and clinical research about the development of Met-targeting drugs for cancer therapeutics. Early attempts to evaluate Met-targeted therapies in clinical trials without selecting the appropriate patient population did not produce satisfactory outcomes. In the era of personalized medicine, cancer patients harboring MET exon 14 alterations or MET amplification have been found to respond well to Met-inhibitor therapy. The application of Met inhibitors to overcome drug resistance in cancer patients is discussed in this paper. Given that kinases play critical roles in cancer development, numerous kinase-mediated signaling pathways are attractive targets for cancer therapy. Existing kinase inhibitors have also been repurposed to new kinase targets or new indications in cancer. On the other hand, non-oncology drugs have also been repurposed for treating cancer through kinase inhibition as one of their reported anticancer mechanisms.
Type: Peer Reviewed Journal Article
URI: http://hdl.handle.net/20.500.11861/9578
ISSN: 2813-2998
DOI: https://doi.org/10.3390/ddc2030031
Appears in Collections:Applied Data Science - Publication

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