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Identification of potential treatments for COVID-19 through artificial intelligence-enabled phenomic analysis of human cells infected with SARS-CoV-2
Preprint
in English
| bioRxiv
| ID: ppbiorxiv-054387
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, neither chloroquine nor hydroxychloroquine have any beneficial effect in this human cell model, and 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.
cc_by_nc
Full text:
Available
Collection:
Preprints
Database:
bioRxiv
Type of study:
Experimental_studies
/
Prognostic study
Language:
English
Year:
2020
Document type:
Preprint
