Apr 23, 2020

Identification of potential treatments for COVID-19 through artificial intelligence-enabled phenomic analysis of human cells infected with SARS-CoV-2

BioRxiv : the Preprint Server for Biology
K. HeiserChristopher C Gibson

Abstract

To identify potential therapeutic stop-gaps for SARS-CoV-2, we evaluated a library of 1,670 approved and reference compounds in an unbiased, cellular image-based screen for their ability to suppress the broad impacts of the SARS-CoV-2 virus on phenomic profiles of human renal cortical epithelial cells using deep learning. In our assay remdesivir is the only antiviral tested with strong efficacy, that neither chloroquine nor hydroxychloroquine have any beneficial effect in this human cell model, and that a small number of compounds not currently being pursued clinically for SARS-CoV-2 have efficacy. We observed weak but beneficial class effects of {beta}-blockers, mTOR/PI3K inhibitors and Vitamin D analogues and a mild amplification of the viral phenotype with {beta}-agonists.

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Mentioned in this Paper

Monoclonal Antibodies
Monoclonal antibodies, antineoplastic
Histone antigen
M Protein, multiple myeloma
Post-Translational Protein Processing
Polyclonal antibody
Mouse Cell Line
Chromatin Immunoprecipitation
Antigens
Histone Modification

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