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.
SARS-CoV-2 is an enveloped, positive-sense, single-stranded RNA beta-coronavirus that emerged in November 2019. The associated disease, COVID-19, has an array of symptoms, ranging from flu-like illness and gastrointestinal distress to acute respiratory distress syndrome, heart arrhythmias, stroke, and death. Drug repurposing has played an essential role in the search for COVID-19 therapies. Recently, the FDA issued emergency approval of remdesivir (GS-5734), a prodrug of a nucleoside inhibitor developed for Ebola virus treatment, and hydroxychloroquine, an aminoquinoline derivative first developed in the 1940s for the treatment of malaria, for patients with severe COVID-19.

However, there are no established prophylactic strategies or direct antiviral treatments available to limit SARS-CoV-2 infections and to prevent/cure the associated disease COVID-19. Repurposing of FDA-approved drugs is a promising strategy for identifying rapidly deployable treatments for COVID-19 given they already have known safety profiles, robust supply chains, and there is a short time-frame necessary for development.

A complementary approach to standard in vitro antiviral assays is high-content imaging-based morphological cell profiling. Morphological cell profiling can identify pathways and novel biology underlying infection, thus allowing for targeted screening around a particular biological process or targeting of host processes that limit viral infection. Multiple antiviral mechanisms can be identified, allowing for the rational design of drug combinations. Conversely, this can also reveal drugs that exacerbate infectivity or are associated with cytotoxicity.

Here, the Sexton Lab developed a pipeline for the quantitative high-throughput image-based screening of SARS-CoV-2 infection. The Sexton Lab leveraged machine learning approaches to create an assay metric that accurately and robustly identifies features that predict antiviral efficacy and mechanism of action (MOA). They identified several FDA-approved drugs and clinical candidates with unique antiviral activity.

The Sexton Lab further demonstrated that lactoferrin inhibits viral entry and replication, enhances antiviral host cell response, and potentiates the effects of remdesivir and hydroxychloroquine. Furthermore, they identified currently prescribed drugs that exacerbate viral infectivity.

Collectively, they present 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.