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

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

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

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has 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 Haase, Ashley Hemmat, Mahya Masopust, David Odde, David Provenzano, Paolo Rajasingham, Radha Tignanelli, Christopher
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
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