A quantitative model used to compare within-host SARS-CoV-2, MERS-CoV, and SARS-CoV dynamics provides insights into the pathogenesis and treatment of SARS-CoV-2
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Abstract
The scientific community is focused on developing antiviral therapies to mitigate the impacts of the ongoing novel coronavirus disease 2019 (COVID-19) outbreak. This will be facilitated by improved understanding of viral dynamics within infected hosts. Here, using a mathematical model in combination with published viral load data, we compare within-host viral dynamics of SARS-CoV-2 with analogous dynamics of MERS-CoV and SARS-CoV. Our quantitative analyses using a mathematical model revealed that the within-host reproduction number at symptom onset of SARS-CoV-2 was statistically significantly larger than that of MERS-CoV and similar to that of SARS-CoV. In addition, the time from symptom onset to the viral load peak for SARS-CoV-2 infection was shorter than those of MERS-CoV and SARS-CoV. These findings suggest the difficulty of controlling SARS-CoV-2 infection by antivirals. We further used the viral dynamics model to predict the efficacy of potential antiviral drugs that have different modes of action. The efficacy was measured by the reduction in the viral load area under the curve (AUC). Our results indicate that therapies that block de novo infection or virus production are likely to be effective if and only if initiated before the viral load peak (which appears 2–3 days after symptom onset), but therapies that promote cytotoxicity of infected cells are likely to have effects with less sensitivity to the timing of treatment initiation. Furthermore, combining a therapy that promotes cytotoxicity and one that blocks de novo infection or virus production synergistically reduces the AUC with early treatment. Our unique modeling approach provides insights into the pathogenesis of SARS-CoV-2 and may be useful for development of antiviral therapies.
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SciScore for 10.1101/2020.03.23.20040493: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Antibodies Sentences Resources In silico experiments for possible anti-SARS-CoV-2 therapies: By utilising our novel mathematical model and the estimated parameter values, we investigated the antiviral effects of unlicensed but developing (promising) drugs with the following different mechanisms of action, depending on inhibition rates and timings of therapy initiation: (i) blocking de novo infection (e.g. via human neutralising antibodies, viral entry-inhibitors and antibody levels raised by vaccination (13, 14)); (ii) blocking virus production (such as lopinavir/ritonavir (HIV protease inhibitors), remdesivir (an anti-Ebola … SciScore for 10.1101/2020.03.23.20040493: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Antibodies Sentences Resources In silico experiments for possible anti-SARS-CoV-2 therapies: By utilising our novel mathematical model and the estimated parameter values, we investigated the antiviral effects of unlicensed but developing (promising) drugs with the following different mechanisms of action, depending on inhibition rates and timings of therapy initiation: (i) blocking de novo infection (e.g. via human neutralising antibodies, viral entry-inhibitors and antibody levels raised by vaccination (13, 14)); (ii) blocking virus production (such as lopinavir/ritonavir (HIV protease inhibitors), remdesivir (an anti-Ebola virus candidate) and other nucleoside analogues, and interferon (15, 16)); and (iii) promoting cytotoxicity (by adaptive immunity such as cytotoxic T lymphocytes). anti-Ebola virus candidatesuggested: NoneResults from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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