A public-private partnership for the express development of antiviral leads: a perspective view

The COVID-19 pandemic raises the question of strategic readiness for emergent pathogens. The current case illustrates that the cost of inaction can be higher in the future. The perspective article proposes a dedicated, government-sponsored agency developing anti-viral leads against all potentially dangerous pathogen species. The author explores the methods of computational drug screening and in-silico synthesis and proposes a specialized government-sponsored agency focusing on leads and functioning in collaboration with a network of labs, pharma, biotech firms, and academia, in order to test each lead against multiple viral species. The agency will employ artificial intelligence and machine learning tools to cut the costs further. The algorithms are expected to receive continuous feedback from the network of partners conducting the tests. The author proposes a bionic principle, emulating antibody response by producing a combinatorial diversity of high q uality generic antiviral leads, suitable for multiple potentially emerging species. The availability of multiple pre-tested agents and an even greater number of combinations would reduce the impact of the next outbreak. The methodologies developed in this effort are likely to find utility in the design of chronic disease therapeutics.

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PID https://www.doi.org/10.6084/m9.figshare.12907666.v1
PID https://www.doi.org/10.1080/17460441.2020.1811676
PID https://www.doi.org/10.6084/m9.figshare.12907666
URL http://dx.doi.org/10.1080/17460441.2020.1811676
URL http://dx.doi.org/10.6084/m9.figshare.12907666.v1
URL http://dx.doi.org/10.6084/m9.figshare.12907666
URL https://www.tandfonline.com/doi/pdf/10.1080/17460441.2020.1811676
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Access Right Restricted
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Author Mayburd, Anatoly
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Collected From Datacite; figshare; Crossref
Hosted By Expert Opinion on Drug Discovery; figshare
Publication Date 2020-09-02
Publisher Taylor & Francis
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Language UNKNOWN
Resource Type Other literature type; Article
keyword FOS: Biological sciences
keyword FOS: Computer and information sciences
keyword FOS: Health sciences
system:type publication
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Source https://science-innovation-policy.openaire.eu/search/publication?articleId=dedup_wf_001::d4019dbeefe6bccfe40993d2c8ef6f96
Author jsonws_user
Last Updated 25 December 2020, 02:52 (CET)
Created 25 December 2020, 02:52 (CET)