Combined in silico docking and in vitro antiviral testing for drug repurposing identified lurasidone and elbasvir as SARS-CoV-2 and HCoV-OC43 inhibitors

  1. Mario Milani
  2. Manuela Donalisio
  3. Rafaela Milan Bonotto
  4. Edoardo Schneider
  5. Irene Arduino
  6. Francesco Boni
  7. David Lembo
  8. Alessandro Marcello
  9. Eloise Mastrangelo

Reviewed by

Read the full article See related articles

Discuss this preprint

Start a discussion What are Sciety discussions?

Listed in

Log in to save this article

Abstract

The current emergency of the novel coronavirus SARS-CoV-2 urged the need for broad-spectrum antiviral drugs as the first line of treatment. Coronaviruses are a large family of viruses that already challenged humanity in at least two other previous outbreaks and are likely to be a constant threat for the future. In this work we developed a pipeline based on in silico docking of known drugs on SARS-CoV RNA-dependent RNA polymerase combined with in vitro antiviral assays on both SARS-CoV-2 and the common cold human coronavirus HCoV-OC43. Results showed that certain drugs displayed activity for both viruses at a similar inhibitory concentration, while others were specific. In particular, the antipsychotic drug lurasidone and the antiviral drug elbasvir showed promising activity in the low micromolar range against both viruses with good selective index.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.11.12.379958: (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
    In silico docking: The virtual Library of DrugBank (https://www.drugbank.ca/) employed for the docking analysis (6996 compounds) includes commercially available FDA-approved drugs as well as experimental drugs going through the FDA approval process.
    DrugBank
    suggested: (DrugBank, RRID:SCR_002700)
    Hydrogen atoms and Kollman charges (Singh & Kollman, 1984) were added using the program Python Molecule Viewer 1.5.4 (MGL-tools package http://mgltools.scripps.edu/).
    Python
    suggested: (IPython, RRID:SCR_001658)
    MGL-tools
    suggested: None
    The in silico screen was divided into two runs: a fast procedure using AutoDock Vina (Trott & Olson, 2009) for the selection of the best compounds, followed by a more accurate screen using AutoDock4.2 (Morris et al., 2009).
    AutoDock
    suggested: (AutoDock, RRID:SCR_012746)

    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

    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 10.1101/2020.11.12.379958 on bioRxiv