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

  1. Brian T. Castle
  2. Carissa Dock
  3. Mahya Hemmat
  4. Susan Kline
  5. Christopher Tignanelli
  6. Radha Rajasingham
  7. David Masopust
  8. Paolo Provenzano
  9. Ryan Langlois
  10. Timothy Schacker
  11. Ashley Haase
  12. David J. Odde

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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 (16 2 = 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.

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  1. Version published to 10.1101/2020.05.22.111237v2 on bioRxiv
  2. ScreenIT

    SciScore for 10.1101/2020.05.22.111237: (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

    Software and Algorithms
    SentencesResources
    The system of ODEs describing the viral cycle was solved using the ode15s solver in Matlab (R2019a; The Mathworks, Natick, MA).
    Matlab
    suggested: (MATLAB, RRID:SCR_001622)

    Results 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.

    About SciScore

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  3. Version published to 10.1101/2020.05.22.111237v1 on bioRxiv