COVID-19: disease pathways and gene expression changes predict methylprednisolone can improve outcome in severe cases.
Journal
Bioinformatics (Oxford, England)
ISSN: 1367-4811
Titre abrégé: Bioinformatics
Pays: England
ID NLM: 9808944
Informations de publication
Date de publication:
09 Sep 2021
09 Sep 2021
Historique:
received:
03
09
2020
revised:
15
02
2021
accepted:
04
03
2021
medline:
12
3
2021
pubmed:
12
3
2021
entrez:
11
3
2021
Statut:
ppublish
Résumé
COVID-19 has several distinct clinical phases: a viral replication phase, an inflammatory phase and in some patients, a hyper-inflammatory phase. High mortality is associated with patients developing cytokine storm syndrome. Treatment of hyper-inflammation in these patients using existing approved therapies with proven safety profiles could address the immediate need to reduce mortality. We analyzed the changes in the gene expression, pathways and putative mechanisms induced by SARS-CoV2 in NHBE, and A549 cells, as well as COVID-19 lung versus their respective controls. We used these changes to identify FDA approved drugs that could be repurposed to help COVID-19 patients with severe symptoms related to hyper-inflammation. We identified methylprednisolone (MP) as a potential leading therapy. The results were then confirmed in five independent validation datasets including Vero E6 cells, lung and intestinal organoids, as well as additional patient lung sample versus their respective controls. Finally, the efficacy of MP was validated in an independent clinical study. Thirty-day all-cause mortality occurred at a significantly lower rate in the MP-treated group compared to control group (29.6% versus 16.6%, P = 0.027). Clinical results confirmed the in silico prediction that MP could improve outcomes in severe cases of COVID-19. A low number needed to treat (NNT = 5) suggests MP may be more efficacious than dexamethasone or hydrocortisone. iPathwayGuide is available at https://advaitabio.com/ipathwayguide/. Supplementary data are available at Bioinformatics online.
Identifiants
pubmed: 33693506
pii: 6164952
doi: 10.1093/bioinformatics/btab163
pmc: PMC7989618
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
2691-2698Subventions
Organisme : NCATS NIH HHS
ID : UL1 TR001863
Pays : United States
Organisme : National Institutes of Health [NIAID
ID : 1R01AI145829-01
Organisme : National Science Foundation
ID : 2029572
Informations de copyright
© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.