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Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
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Network Medicine Framework for Identifying Drug Repurposing Opportunities for COVID-19
Deisy Morselli Gysi1,2, Ítalo Do Valle1, Marinka Zitnik3,4
1Network Science Institute and Department of Physics, Northeastern University, Boston, MA 02115, USA.
Arxiv
|June 19, 2020
Summary
Network medicine algorithms can prioritize drugs for viral infections like COVID-19. A consensus approach combining multiple methods reliably identifies effective repurposed drugs, even those not directly targeting viral proteins.
Area of Science:
- Computational biology
- Pharmacology
- Infectious disease research
Background:
- The COVID-19 pandemic necessitates rapid drug repurposing strategies.
- Network medicine offers predictive algorithms for identifying effective drugs based on gene-disease and drug-target relationships.
Approach:
- Deployed artificial intelligence, network diffusion, and network proximity algorithms to rank 6,340 drugs for SARS-CoV-2 efficacy.
- Validated predictions using experimental screening data and drugs in clinical trials for COVID-19.
- Developed a multimodal consensus approach by fusing predictions from individual algorithms.
Key Points:
- Most individual algorithms showed some predictive power, but no single method was consistently reliable across all datasets.
- The multimodal consensus approach significantly outperformed individual algorithms.
- 76 out of 77 effective drugs did not directly bind SARS-CoV-2 proteins, highlighting the importance of network-based mechanisms.
Conclusions:
- A consensus network medicine approach provides a reliable method for drug repurposing against viral pathogens.
- This strategy can accelerate the identification of treatments for future pandemics and neglected diseases.
- Network-based drug prioritization is crucial for discovering drugs with non-traditional mechanisms of action.
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