Computationally prioritized drugs inhibit SARS-CoV-2 infection and syncytia formation.
7-hydroxystaurosporine
COVID-19
SARS-CoV-2
bafetinib
delta variant
drug design
drug repositioning
kinase inhibitors
syncytia
virtual screening
Journal
Briefings in bioinformatics
ISSN: 1477-4054
Titre abrégé: Brief Bioinform
Pays: England
ID NLM: 100912837
Informations de publication
Date de publication:
17 01 2022
17 01 2022
Historique:
received:
22
06
2021
revised:
03
11
2021
accepted:
04
11
2021
pubmed:
29
12
2021
medline:
2
2
2022
entrez:
28
12
2021
Statut:
ppublish
Résumé
The pharmacological arsenal against the COVID-19 pandemic is largely based on generic anti-inflammatory strategies or poorly scalable solutions. Moreover, as the ongoing vaccination campaign is rolling slower than wished, affordable and effective therapeutics are needed. To this end, there is increasing attention toward computational methods for drug repositioning and de novo drug design. Here, multiple data-driven computational approaches are systematically integrated to perform a virtual screening and prioritize candidate drugs for the treatment of COVID-19. From the list of prioritized drugs, a subset of representative candidates to test in human cells is selected. Two compounds, 7-hydroxystaurosporine and bafetinib, show synergistic antiviral effects in vitro and strongly inhibit viral-induced syncytia formation. Moreover, since existing drug repositioning methods provide limited usable information for de novo drug design, the relevant chemical substructures of the identified drugs are extracted to provide a chemical vocabulary that may help to design new effective drugs.
Identifiants
pubmed: 34962256
pii: 6484515
doi: 10.1093/bib/bbab507
pmc: PMC8769897
pii:
doi:
Substances chimiques
Antiviral Agents
0
Pyrimidines
0
7-hydroxystaurosporine
7BU5H4V94A
Staurosporine
H88EPA0A3N
bafetinib
NVW4Z03I9B
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Novo Nordisk Foundation
ID : 0066176
Organisme : Horizon 2020
ID : 814572
Organisme : Academy of Finland
Organisme : Chan Zuckerberg Initiative
Organisme : European Regional Development Fund
ID : GINOP-2.3.2-15-2016-00037
Informations de copyright
© The Author(s) 2021. Published by Oxford University Press.
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