The Repurposed Drugs Suramin and Quinacrine Cooperatively Inhibit SARS-CoV-2 3CLpro In Vitro

  1. Raphael J. Eberle
  2. Danilo S. Olivier
  3. Marcos S. Amaral
  4. Ian Gering
  5. Dieter Willbold
  6. Raghuvir K. Arni
  7. Monika A. Coronado

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Abstract

Since the first report of a new pneumonia disease in December 2019 (Wuhan, China) the WHO reported more than 148 million confirmed cases and 3.1 million losses globally up to now. The causative agent of COVID-19 (SARS-CoV-2) has spread worldwide, resulting in a pandemic of unprecedented magnitude. To date, several clinically safe and efficient vaccines (e.g., Pfizer-BioNTech, Moderna, Johnson & Johnson, and AstraZeneca COVID-19 vaccines) as well as drugs for emergency use have been approved. However, increasing numbers of SARS-Cov-2 variants make it imminent to identify an alternative way to treat SARS-CoV-2 infections. A well-known strategy to identify molecules with inhibitory potential against SARS-CoV-2 proteins is repurposing clinically developed drugs, e.g., antiparasitic drugs. The results described in this study demonstrated the inhibitory potential of quinacrine and suramin against SARS-CoV-2 main protease (3CLpro). Quinacrine and suramin molecules presented a competitive and noncompetitive inhibition mode, respectively, with IC50 values in the low micromolar range. Surface plasmon resonance (SPR) experiments demonstrated that quinacrine and suramin alone possessed a moderate or weak affinity with SARS-CoV-2 3CLpro but suramin binding increased quinacrine interaction by around a factor of eight. Using docking and molecular dynamics simulations, we identified a possible binding mode and the amino acids involved in these interactions. Our results suggested that suramin, in combination with quinacrine, showed promising synergistic efficacy to inhibit SARS-CoV-2 3CLpro. We suppose that the identification of effective, synergistic drug combinations could lead to the design of better treatments for the COVID-19 disease and repurposable drug candidates offer fast therapeutic breakthroughs, mainly in a pandemic moment.

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  1. Version published to 10.3390/v13050873
  2. ScreenIT

    SciScore for 10.1101/2020.11.11.378018: (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
    2.1 Cloning, expression and purification of SARS-CoV-2 3CLpro: The codon optimized cDNA encoding SARS-CoV-2 3CLpro (Uniprot entry: P0DTD1) was synthesized and implemented in the ampicillin resistant vector pGEX-6P-3 (BioCat GmbH, Heidelberg, Germany).
    BioCat
    suggested: (BioCAT, RRID:SCR_001440)
    The initial velocity was plotted against the substrate concentration with the classic Michaelis-Menten equation using GraphPad Prism5 software and Kcat was obtained using the equation (1):

    while Vmax is the experimental determined maximal velocity and [E] is the enzyme concentration in the experiment (Berg et al., 2002).

    GraphPad Prism5
    suggested: None
    Molecular docking was used to select the best pose for the ligand-protein interaction using Autodock Vina program (Trott and Olson, 2010).
    Autodock Vina
    suggested: (AutoDock Vina, RRID:SCR_011958)
    Differences were considered significant when a P value was less than 0.05 (Zhang et al., 1999) All statistical analyses were performed with GraphPad Prism software version 5 (San Diego, CA, USA).
    GraphPad Prism
    suggested: (GraphPad Prism, RRID:SCR_002798)

    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 found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

    Results from JetFighter: Please consider improving the rainbow (“jet”) colormap(s) used on pages 32 and 35. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.

    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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  3. Version published to 10.1101/2020.11.11.378018v1 on bioRxiv