Computationally prioritized drugs inhibit SARS-CoV-2 infection and syncytia formation

  1. Angela Serra
  2. Michele Fratello
  3. Antonio Federico
  4. Ravi Ojha
  5. Riccardo Provenzani
  6. Ervin Tasnadi
  7. Luca Cattelani
  8. Giusy del Giudice
  9. Pia A S Kinaret
  10. Laura A Saarimäki
  11. Alisa Pavel
  12. Suvi Kuivanen
  13. Vincenzo Cerullo
  14. Olli Vapalahti
  15. Peter Horvath
  16. Antonio Di Lieto
  17. Jari Yli-Kauhaluoma
  18. Giuseppe Balistreri
  19. Dario Greco

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Abstract

The pharmacological arsenal against the COVID-19 pandemic is largely based on generic anti-inflammatory strategies or poorly scalable solutions. Moreover, as the ongoing vaccination campaign is rolling slower than wished, affordable and effective therapeutics are needed. To this end, there is increasing attention toward computational methods for drug repositioning and de novo drug design.

Here, multiple data-driven computational approaches are systematically integrated to perform a virtual screening and prioritize candidate drugs for the treatment of COVID-19. From the list of prioritized drugs, a subset of representative candidates to test in human cells is selected. Two compounds, 7-hydroxystaurosporine and bafetinib, show synergistic antiviral effects in vitro and strongly inhibit viral-induced syncytia formation. Moreover, since existing drug repositioning methods provide limited usable information for de novo drug design, the relevant chemical substructures of the identified drugs are extracted to provide a chemical vocabulary that may help to design new effective drugs.

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  1. Version published to 10.1093/bib/bbab507
  2. ScreenIT

    SciScore for 10.1101/2021.04.15.440004: (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
    SentencesResources
    Following two washes in D-BSA, cells were incubated with a fluorescently labelled goat anti-rabbit antibody (Molecular Probes) at a dilution of 1:1000 for 1 h at room temperature.
    anti-rabbit
    suggested: None
    Experimental Models: Cell Lines
    SentencesResources
    The datasets are composed as following: human lung biopsies of SARS-CoV-2 infected patients and uninfected control; A549 cell line infected with SARS-CoV-2, A549 cell line infected with SARS-CoV-2 overexpressing ACE2, Calu-3 cells infected with SARS-CoV-2; NHBE cell line infected with SARS-CoV-2.
    A549
    suggested: None
    Calu-3
    suggested: KCLB Cat# 30055, RRID:CVCL_0609)
    Caco-2 cells stably expressing human ACE2 were generated by transduction with third generation lentivirus pLenti7.3 ACE2-EGFP, where the expression of EGFP if guided by an internal ribosome entry site downstream of the ACE2 coding sequence in the same messenger RNA.
    Caco-2
    suggested: None
    All cells were grown in DMEM media supplemented with 10% fetal calf serum (FCS), pen/strep, L-glutamine, and passaged 1:10 (HEK-293T-ACE2-TMPRSS2) or 1:6 (Caco-2-ACE2), every three days.
    Caco-2-ACE2
    suggested: None
    The virus was propagated once in Calu-1 cells and once in VeroE6-TMPRSS2 cells before sequencing and storage at −80 °C.
    Calu-1
    suggested: None
    VeroE6-TMPRSS2
    suggested: JCRB Cat# JCRB1819, RRID:CVCL_YQ49)
    Virus titers were determined by plaque assay in VeroE6 TMPRSS2 cells.
    VeroE6 TMPRSS2
    suggested: JCRB Cat# JCRB1819, RRID:CVCL_YQ49)
    Software and Algorithms
    SentencesResources
    RNA-Seq data preprocessing: Human transcriptomics datasets analysed in this study were retrieved from the Gene Expression Omnibus (GEO) repository, annotated with the GEO ID GSE147507 (Blanco-Melo et al., 2020; Daamen et al., 2021).
    Gene Expression Omnibus
    suggested: (Gene Expression Omnibus (GEO, RRID:SCR_005012)
    Filtered counts were then normalized through the upper quartile method implemented in the NOISeq package.
    NOISeq
    suggested: (NOISeq, RRID:SCR_003002)
    Differential expression analysis was carried out by using the DESeq2 Bioconductor package (Love et al., 2014) and the p values were adjusted using the Benjamini-Hochberg method (Benjamini and Hochberg, 1995).
    DESeq2
    suggested: (DESeq, RRID:SCR_000154)
    Bioconductor
    suggested: (Bioconductor, RRID:SCR_006442)
    Outliers were identified by using the RLE, NUSE from the affyPLM package (2005) and the slope of the RNA degradation curve implemented in the affy package (Gautier et al., 2004).
    affyPLM
    suggested: (affyPLM, RRID:SCR_001319)
    affy
    suggested: (affy, RRID:SCR_012835)
    The probes were annotated to Ensembl genes (by using the rat2302rnensgcdf (v. 22.0.0) annotation file from the brainarray website4, and the resulting expression matrix was quantile normalized by using the normalizeQuantile function from the limma package.
    limma
    suggested: (LIMMA, RRID:SCR_010943)
    DrugBank (Wishart et al., 2006, 2018) contains 13579 drug entries, including 2635 approved small molecule drugs, and over 6375 experimental drugs.
    DrugBank
    suggested: (DrugBank, RRID:SCR_002700)
    For the 8775 matched compounds, substructure fingerprints were retrieved from PubChem by querying it by CID identifiers.
    PubChem
    suggested: (PubChem, RRID:SCR_004284)
    We created an augmentation pipeline containing 7 main transformations where each transformation instance is applied with a probability of 0.5 implemented using the Numpy scientific programming library and the Pillow package10 for Python (Harris et al., 2020).
    Numpy
    suggested: (NumPy, RRID:SCR_008633)
    Python
    suggested: (IPython, RRID:SCR_001658)

    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.

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  3. Version published to 10.1101/2021.04.15.440004v1 on bioRxiv