In this webinar, Professor Jonny Sexton discusses a pipeline, developed in the Sexton lab, for the quantitative high-throughput image-based screening of SARS-CoV-2 infection to identify potential antiviral mechanisms and allow selection of appropriate drug combinations to treat COVID-19. This webinar presents evidence that morphological profiling can robustly identify new potential therapeutics against SARS-CoV-2 infection as well as drugs that potentially worsen COVID-19 outcomes.
In this webinar, you will discover:
- The machine learning approaches leveraged by the Sexton Lab to create an assay metric that accurately and robustly identifies features that predict antiviral efficacy and mechanism of action (MOA).
- Several FDA-approved drugs and clinical candidates with a unique antiviral activity identified using this approach.
- How lactoferrin inhibits viral entry and replication, enhances antiviral host cell response, and potentiates the effects of remdesivir and hydroxychloroquine.
- How currently prescribed drugs that exacerbate viral infectivity were also identified.
Date and time
Wednesday, August 26th, 2020, 12 pm (EST) 9 am (PST) 5 pm (London)
Assistant Professor, Internal Medicine, Division of Gastroenterology and Hepatology
Assistant Professor, College of Pharmacy, Medicinal Chemistry
Faculty Lead, Michigan Institute for Clinical & Health Research, MICHR, Drug Repurposing
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