The COVID-19 PHARMACOME: A method for the rational selection of drug repurposing candidates from multimodal knowledge harmonization

  1. Bruce Schultz
  2. Andrea Zaliani
  3. Christian Ebeling
  4. Jeanette Reinshagen
  5. Denisa Bojkova
  6. Vanessa Lage-Rupprecht
  7. Reagon Karki
  8. Sören Lukassen
  9. Yojana Gadiya
  10. Neal G. Ravindra
  11. Sayoni Das
  12. Shounak Baksi
  13. Daniel Domingo-Fernández
  14. Manuel Lentzen
  15. Mark Strivens
  16. Tamara Raschka
  17. Jindrich Cinatl
  18. Lauren Nicole DeLong
  19. Phil Gribbon
  20. Gerd Geisslinger
  21. Sandra Ciesek
  22. David van Dijk
  23. Steve Gardner
  24. Alpha Tom Kodamullil
  25. Holger Fröhlich
  26. Manuel Peitsch
  27. Marc Jacobs
  28. Julia Hoeng
  29. Roland Eils
  30. Carsten Claussen
  31. Martin Hofmann-Apitius

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Abstract

The SARS-CoV-2 pandemic has challenged researchers at a global scale. The scientific community’s massive response has resulted in a flood of experiments, analyses, hypotheses, and publications, especially in the field of drug repurposing. However, many of the proposed therapeutic compounds obtained from SARS-CoV-2 specific assays are not in agreement and thus demonstrate the need for a singular source of COVID-19 related information from which a rational selection of drug repurposing candidates can be made. In this paper, we present the COVID-19 PHARMACOME, a comprehensive drug-target-mechanism graph generated from a compilation of 10 separate disease maps and sources of experimental data focused on SARS-CoV-2 / COVID-19 pathophysiology. By applying our systematic approach, we were able to predict the synergistic effect of specific drug pairs, such as Remdesivir and Thioguanosine or Nelfinavir and Raloxifene, on SARS-CoV-2 infection. Experimental validation of our results demonstrate that our graph can be used to not only explore the involved mechanistic pathways, but also to identify novel combinations of drug repurposing candidates.

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  1. ScreenIT

    SciScore for 10.1101/2020.09.23.308239: (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

    Experimental Models: Cell Lines
    SentencesResources
    Drug combinations assessment with anti-cytopathic effect measured in Caco-2 cells: As described in Ellinger et al.,38 we challenged four combinations of five different compounds with the SARS-CoV-2 virus in four 96-well plates containing two drugs each.
    Caco-2
    suggested: None
    Software and Algorithms
    SentencesResources
    Additionally, by enriching the COVID-19 supergraph with drug-target information linked from highly curated drug-target databases (DrugBank, ChEMBL, PubChem), we created an initial version of the COVID-19 PHARMACOME, a comprehensive drug-target-mechanism graph representing COVID-19 pathophysiology mechanisms that includes both drug targets and their ligands (Figure 2).
    DrugBank
    suggested: (DrugBank, RRID:SCR_002700)
    ChEMBL
    suggested: (ChEMBL, RRID:SCR_014042)
    Pathway enrichment: Associated pathways for subgraphs and significant targets were identified using the Enrichr34 feature of the gseapy Python package35.
    Python
    suggested: (IPython, RRID:SCR_001658)
    Briefly, gene symbol lists were assembled from their respective subgraph or dataset and compared against multiple pathway gene set libraries including Reactome, KEGG, and WikiPathways.
    Reactome
    suggested: (Reactome, RRID:SCR_003485)
    KEGG
    suggested: (KEGG, RRID:SCR_012773)
    WikiPathways
    suggested: (WikiPathways, RRID:SCR_002134)

    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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  2. Version published to 10.1101/2020.09.23.308239 on bioRxiv