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

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About: Identification of potential treatments for COVID-19 through artificial intelligence-enabled phenomic analysis of human cells infected with SARS-CoV-2 Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Identification of potential treatments for COVID-19 through artificial intelligence-enabled phenomic analysis of human cells infected with SARS-CoV-2
Creator	Alfa¹, Ronald Chong¹, Yolanda Earnshaw¹, Berton Fogelson¹, Ben Gordon¹, Hannah Haque¹, Imran Jacobson¹, Pamela Low¹, Adeline Victors¹, Mason Authors, ¹ ⁺co-First Davis⁺¹, Chadwick Gibson, Christopher Heiser⁺¹, Katie Hurst², Brett Mclean⁺¹, Peter Miller¹, Ben
source	BioRxiv
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 β-blockers, mTOR/PI3K inhibitors and Vitamin D analogues and a mild amplification of the viral phenotype with β-agonists.
has issue date	2020-04-23(xsd:dateTime)
bibo:doi	10.1101/2020.04.21.054387
has license	biorxiv
sha1sum (hex)	408fee3533b80fd809fd95f55fd19908bc681ba0
schema:url	https://doi.org/10.1101/2020.04.21.054387
resource representing a document's title	Identification of potential treatments for COVID-19 through artificial intelligence-enabled phenomic analysis of human cells infected with SARS-CoV-2
schema:publication	bioRxiv
resource representing a document's body	covid:408fee3533b80fd809fd95f55fd19908bc681ba0#body_text
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