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SAveRUNNER: A network-based algorithm for drug repurposing and its application to COVID-19.

Giulia Fiscon1,2, Federica Conte1, Lorenzo Farina3

  • 1Institute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Rome, Italy.

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|February 5, 2021
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Summary
This summary is machine-generated.

A new network algorithm, SAveRUNNER, identifies existing drugs for repurposing against COVID-19 and related diseases. It prioritizes 24 potential anti-SARS-CoV drugs, including ACE-inhibitors and monoclonal antibodies, for faster treatment development.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Drug Discovery

Background:

  • The COVID-19 pandemic necessitates rapid identification of effective treatments.
  • Drug repositioning offers a faster, cost-effective alternative to de novo drug discovery.
  • Existing drugs repurposed for COVID-19 treatment show promise but require systematic identification.

Purpose of the Study:

  • To develop and apply a novel network-based algorithm, SAveRUNNER, for predicting drug-disease associations.
  • To identify repurposable drugs for COVID-19 and genetically similar or comorbid diseases.
  • To prioritize potential drug candidates for SARS-CoV infections through network analysis.

Main Methods:

  • Developed SAveRUNNER, a network-based algorithm quantifying interplay between drug targets and disease proteins.
  • Applied SAveRUNNER to 14 diseases related to COVID-19 (genetic similarity, comorbidity, drug association).
  • Utilized network neighborhoods and gene set enrichment analysis for in-silico validation.

Main Results:

  • Identified 282 repurposable drugs for SARS, including known COVID-19 treatments and a 5-drug combination therapy.
  • Prioritized 24 top-ranked potential anti-SARS-CoV drugs, including ACE-inhibitors, monoclonal antibodies, and thrombin inhibitors.
  • In-silico validation confirmed potential therapeutic effects of network-predicted drugs against human coronavirus infections.

Conclusions:

  • SAveRUNNER effectively predicts drug-disease associations for drug repositioning.
  • The study identified promising drug candidates and combinations for COVID-19 and related conditions.
  • Network-based approaches can accelerate the discovery of treatments for emerging infectious diseases.