About: Biophysical modeling of the SARS-CoV-2 viral cycle reveals ideal antiviral targets

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.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	Biophysical modeling of the SARS-CoV-2 viral cycle reveals ideal antiviral targets
Creator	Schacker, Timothy Kline, Susan Langlois, Ryan Castle, Brian Dock, Carissa »more»
source	BioRxiv
abstract	Effective therapies for COVID-19 are urgently needed. Presently there are more than 800 COVID-19 clinical trials globally, many with drug combinations, resulting in an empirical process with an enormous number of possible combinations. To identify the most promising potential therapies, we developed a biophysical model for the SARS-CoV-2 viral cycle and performed a sensitivity analysis for individual model parameters and all possible pairwise parameter changes (162 = 256 possibilities). We found that model-predicted virion production is fairly insensitive to changes in most viral entry, assembly, and release parameters, but highly sensitive to some viral transcription and translation parameters. Furthermore, we found a cooperative benefit to pairwise targeting of transcription and translation, predicting that combined targeting of these processes will be especially effective in inhibiting viral production.
has issue date	2020-06-16(xsd:dateTime)
bibo:doi	10.1101/2020.05.22.111237
has license	biorxiv
sha1sum (hex)	0840ba89628a7321665e339d49c78c95e4ca94a0
schema:url	https://doi.org/10.1101/2020.05.22.111237
resource representing a document's title	Biophysical modeling of the SARS-CoV-2 viral cycle reveals ideal antiviral targets
schema:publication	bioRxiv
resource representing a document's body	covid:0840ba89628a7321665e339d49c78c95e4ca94a0#body_text
is schema:about of	named entity 'assembly' named entity 'viral' named entity 'model' named entity 'transcription' named entity 'NEEDED' »more»

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