A Workflow of Integrated Resources to Catalyze Network Pharmacology Driven COVID-19 Research

  1. Gergely Zahoránszky-Kőhalmi
  2. Vishal B. Siramshetty
  3. Praveen Kumar
  4. Manideep Gurumurthy
  5. Busola Grillo
  6. Biju Mathew
  7. Dimitrios Metaxatos
  8. Mark Backus
  9. Tim Mierzwa
  10. Reid Simon
  11. Ivan Grishagin
  12. Laura Brovold
  13. Ewy A. Mathé
  14. Matthew D. Hall
  15. Samuel G. Michael
  16. Alexander G. Godfrey
  17. Jordi Mestres
  18. Lars J. Jensen
  19. Tudor I. Oprea

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Abstract

Motivation

In the event of an outbreak due to an emerging pathogen, time is of the essence to contain or to mitigate the spread of the disease. Drug repositioning is one of the strategies that has the potential to deliver therapeutics relatively quickly. The SARS-CoV-2 pandemic has shown that integrating critical data resources to drive drug-repositioning studies, involving host-host, hostpathogen and drug-target interactions, remains a time-consuming effort that translates to a delay in the development and delivery of a life-saving therapy.

Results

Here, we describe a workflow we designed for a semi-automated integration of rapidly emerging datasets that can be generally adopted in a broad network pharmacology research setting. The workflow was used to construct a COVID-19 focused multimodal network that integrates 487 host-pathogen, 74,805 host-host protein and 1,265 drug-target interactions. The resultant Neo4j graph database named “Neo4COVID19” is accessible via a web interface and via API calls based on the Bolt protocol. We believe that our Neo4COVID19 database will be a valuable asset to the research community and will catalyze the discovery of therapeutics to fight COVID-19.

Availability

https://neo4covid19.ncats.io

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    SciScore for 10.1101/2020.11.04.369041: (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
    Furthermore, the DrugCentral database also provides information on DTIs; 15,397 human DTIs and 4,910 nonhuman DTIs; of these 2,752 (2,328 human) are MoA drug-target associations.
    DrugCentral
    suggested: (DrugCentral, RRID:SCR_015663)
    The DTIs were originally extracted from scientific literature, drug labels and other data sources such as, ChEMBL [4], IUPHAR Guide2Pharmacology [59], WOMBAT-PK [
    ChEMBL
    suggested: (ChEMBL, RRID:SCR_014042)
    60], DrugBank [61] and KEGG Drug [62].
    DrugBank
    suggested: (DrugBank, RRID:SCR_002700)
    KEGG
    suggested: (KEGG, RRID:SCR_012773)

    Results from OddPub: Thank you for sharing your code and 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.

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  2. Version published to 10.1101/2020.11.04.369041 on bioRxiv