Monitoring for SARS-CoV-2 drug resistance mutations in broad viral populations

  1. Mayya Sedova
  2. Lukasz Jaroszewski
  3. Mallika Iyer
  4. Adam Godzik

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

The search for drugs against COVID-19 and other diseases caused by coronaviruses focuses on the most conserved and essential proteins, mainly the main (M pro ) and the papain-like (PL pro ) proteases and the RNA-dependent RNA polymerase (RdRp). Nirmatrelvir, an inhibitor for M pro , was recently approved by FDA as a part of a two-drug combination, Paxlovid, and many more drugs are in various stages of development. Multiple candidates for the PL pro inhibitors are being studied, but none have yet progressed to clinical trials. Several repurposed inhibitors of RdRp are already in use. We can expect that once anti-COVID-19 drugs become widely used, resistant variants of SARS-CoV-2 will emerge, and we already see that for the drugs targeting SARS-CoV-2 RdRp. We hypothesize that emergence of such variants can be anticipated by identifying possible escape mutations already present in the existing populations of viruses. Our group previously developed the coronavirus3D server ( https://coronavirus3d.org ), tracking the evolution of SARS-CoV-2 in the context of the three-dimensional structures of its proteins. Here we introduce dedicated pages tracking the emergence of potential drug resistant mutations to M pro and PL pro , showing that such mutations are already circulating in the SARS-CoV-2 viral population. With regular updates, the drug resistance tracker provides an easy way to monitor and potentially predict the emergence of drug resistance-conferring mutations in the SARS-CoV-2 virus.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2022.05.27.493798: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: Thank you for sharing your data.

    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.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2022.05.27.493798v1 on bioRxiv