English  |  正體中文  |  简体中文  |  Items with full text/Total items : 12145/12927 (94%)
Visitors : 903525      Online Users : 349
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: http://ir.nhri.org.tw/handle/3990099045/12668


    Title: Artificial intelligence approach fighting COVID-19 with repurposing drugs
    Authors: Ke, YY;Peng, TT;Yeh, TK;Huang, WZ;Chang, SE;Wu, SH;Hung, HC;Hsu, TA;Lee, SJ;Song, JS;Lin, WH;Chiang, TJ;Lin, JH;Sytwu, HK;Chen, CT
    Contributors: Institute of Biotechnology and Pharmaceutical Research;Office of Environment, Safety, and Health;National Institute of Infectious Diseases and Vaccinology
    Abstract: Background :The ongoing COVID-19 pandemic has caused more than 193,825 deaths during the past few months. A quick-to-be-identified cure for the disease will be a therapeutic medicine that has prior use experiences in patients in order to resolve the current pandemic situation before it could become worsening. Artificial intelligence (AI) technology is hereby applied to identify the marketed drugs with potential for treating COVID-19. Methods: An AI platform was established to identify potential old drugs with anti-coronavirus activities by using two different learning databases; one consisted of the compounds reported or proven active against SARS-CoV, SARS-CoV-2, human immunodeficiency virus, influenza virus, and the other one containing the known 3C-like protease inhibitors. All AI predicted drugs were then tested for activities against a feline coronavirus in in vitro cell-based assay. These assay results were feedbacks to the AI system for relearning and thus to generate a modified AI model to search for old drugs again. Results: After a few runs of AI learning and prediction processes, the AI system identified 80 marketed drugs with potential. Among them, 8 drugs (bedaquiline, brequinar, celecoxib, clofazimine, conivaptan, gemcitabine, tolcapone, and vismodegib) showed in vitro activities against the proliferation of a feline infectious peritonitis (FIP) virus in Fcwf-4 cells. In addition, 5 other drugs (boceprevir, chloroquine, homoharringtonine, tilorone, and salinomycin) were also found active during the exercises of AI approaches. Conclusion: Having taken advantages of AI, we identified old drugs with activities against FIP coronavirus. Further studies are underway to demonstrate their activities against SARS-CoV-2 in vitro and in vivo at clinically achievable concentrations and doses. With prior use experiences in patients, these old drugs if proven active against SARS-CoV-2 can readily be applied for fighting COVID-19 pandemic.
    Date: 2020-08
    Relation: Biomedical Journal. 2020 Aug;43(4):355-362.
    Link to: http://dx.doi.org/10.1016/j.bj.2020.05.001
    JIF/Ranking 2023: http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=NHRI&SrcApp=NHRI_IR&KeyISSN=2319-4170&DestApp=IC2JCR
    Cited Times(WOS): https://www.webofscience.com/wos/woscc/full-record/WOS:000579445300009
    Cited Times(Scopus): https://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85085932433
    Appears in Collections:[陳炯東] 期刊論文
    [李秀珠] 期刊論文
    [徐祖安] 期刊論文
    [葉燈光] 期刊論文
    [其他] 期刊論文
    [司徒惠康] 期刊論文

    Files in This Item:

    File Description SizeFormat
    PUB32426387.pdf1251KbAdobe PDF372View/Open


    All items in NHRI are protected by copyright, with all rights reserved.

    Related Items in TAIR

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback