Network medicine framework for identifying drug-repurposing opportunities for COVID-19

The COVID-19 pandemic has highlighted the need to quickly and reliably prioritize clinically approved compounds for their potential effectiveness for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. Here, we deployed algorithms relying on artificial intelligence, network diff...

Full description

Saved in:
Bibliographic Details
Published in:Proceedings of the National Academy of Sciences - PNAS 2021-05, Vol.118 (19), p.1
Main Authors: Morselli Gysi, Deisy, do Valle, Ítalo, Zitnik, Marinka, Ameli, Asher, Gan, Xiao, Varol, Onur, Ghiassian, Susan Dina, Patten, J J, Davey, Robert A, Loscalzo, Joseph, Barabási, Albert-László
Format: Article
Language:English
Subjects:
Algorithms
Animals
Antiviral Agents - administration & dosage
Antiviral Agents - pharmacology
Antiviral Agents - therapeutic use
Artificial intelligence
Biological Sciences
Chlorocebus aethiops
Clinical trials
Coronaviruses
COVID-19
COVID-19 Drug Treatment
Databases, Pharmaceutical
Drug development
Drug Repositioning - methods
Drugs
Humans
Infections
Neural Networks, Computer
Pandemics
Predictions
Protein Binding
Respiratory diseases
Severe acute respiratory syndrome
Severe acute respiratory syndrome coronavirus 2
Systems Biology - methods
Vero Cells
Viral diseases
Viral infections
Viral Proteins - metabolism
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The COVID-19 pandemic has highlighted the need to quickly and reliably prioritize clinically approved compounds for their potential effectiveness for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. Here, we deployed algorithms relying on artificial intelligence, network diffusion, and network proximity, tasking each of them to rank 6,340 drugs for their expected efficacy against SARS-CoV-2. To test the predictions, we used as ground truth 918 drugs experimentally screened in VeroE6 cells, as well as the list of drugs in clinical trials that capture the medical community's assessment of drugs with potential COVID-19 efficacy. We find that no single predictive algorithm offers consistently reliable outcomes across all datasets and metrics. This outcome prompted us to develop a multimodal technology that fuses the predictions of all algorithms, finding that a consensus among the different predictive methods consistently exceeds the performance of the best individual pipelines. We screened in human cells the top-ranked drugs, obtaining a 62% success rate, in contrast to the 0.8% hit rate of nonguided screenings. Of the six drugs that reduced viral infection, four could be directly repurposed to treat COVID-19, proposing novel treatments for COVID-19. We also found that 76 of the 77 drugs that successfully reduced viral infection do not bind the proteins targeted by SARS-CoV-2, indicating that these network drugs rely on network-based mechanisms that cannot be identified using docking-based strategies. These advances offer a methodological pathway to identify repurposable drugs for future pathogens and neglected diseases underserved by the costs and extended timeline of de novo drug development.
ISSN:0027-8424
1091-6490
DOI:10.1073/pnas.2025581118