JOURNAL ARTICLE

Designing a Network Proximity-Based Drug Repurposing Strategy for COVID-19

Paola StolfiLuigi ManniMarzia SoligoDavide VergniPaolo Tieri

Year: 2020 Journal:   Frontiers in Cell and Developmental Biology Vol: 8 Pages: 545089-545089   Publisher: Frontiers Media

Abstract

The ongoing COVID-19 pandemic still requires fast and effective efforts from all fronts, including epidemiology, clinical practice, molecular medicine, and pharmacology. A comprehensive molecular framework of the disease is needed to better understand its pathological mechanisms, and to design successful treatments able to slow down and stop the impressive pace of the outbreak and harsh clinical symptomatology, possibly via the use of readily available, off-the-shelf drugs. This work engages in providing a wider picture of the human molecular landscape of the SARS-CoV-2 infection via a network medicine approach as the ground for a drug repurposing strategy. Grounding on prior knowledge such as experimentally validated host proteins known to be viral interactors, tissue-specific gene expression data, and using network analysis techniques such as network propagation and connectivity significance, the host molecular reaction network to the viral invasion is explored and exploited to infer and prioritize candidate target genes, and finally to propose drugs to be repurposed for the treatment of COVID-19. Ranks of potential target genes have been obtained for coherent groups of tissues/organs, potential and distinct sites of interaction between the virus and the organism. The normalization and the aggregation of the different scores allowed to define a preliminary, restricted list of genes candidates as pharmacological targets for drug repurposing, with the aim of contrasting different phases of the virus infection and viral replication cycle.

Keywords:
Drug repositioning Repurposing Computational biology Biology Interaction network Pandemic Drug development Drug discovery Gene Drug Coronavirus disease 2019 (COVID-19) Disease Bioinformatics Infectious disease (medical specialty) Genetics Medicine Pharmacology

Metrics

26
Cited By
3.09
FWCI (Field Weighted Citation Impact)
45
Refs
0.93
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Computational Drug Discovery Methods
Physical Sciences →  Computer Science →  Computational Theory and Mathematics
SARS-CoV-2 and COVID-19 Research
Health Sciences →  Medicine →  Infectious Diseases
COVID-19 Clinical Research Studies
Health Sciences →  Medicine →  Infectious Diseases

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