Network medicine framework for identifying drug-repurposing opportunities for COVID-19
Abstract
The COVID-19 pandemic has highlighted the importance of prioritizing approved drugs to treat severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. Here, we deployed algorithms relying on artificial intelligence, network diffusion, and network proximity to rank 6,340 drugs for their expected efficacy against SARS-CoV-2. We experimentally screened 918 drugs, allowing us to evaluate the performance of the existing drug-repurposing methodologies, and used a consensus algorithm to increase the accuracy of the predictions. Finally, we screened in human cells the top-ranked drugs, identifying six drugs that reduced viral infection, four of which could be repurposed to treat COVID-19. The developed strategy has significance beyond COVID-19, allowing us to identify drug-repurposing candidates for neglected diseases.
- Publication:
-
Proceedings of the National Academy of Science
- Pub Date:
- May 2021
- DOI:
- 10.1073/pnas.2025581118
- arXiv:
- arXiv:2004.07229
- Bibcode:
- 2021PNAS..11825581M
- Keywords:
-
- Quantitative Biology - Molecular Networks;
- Computer Science - Machine Learning;
- Quantitative Biology - Quantitative Methods;
- Statistics - Machine Learning
- E-Print:
- doi:10.1073/pnas.2025581118