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
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

2691-2698

Subventions

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.

Auteurs

Sorin Draghici (S)

Department of Computer Science and.
Department of Obstetrics and Gynecology, Wayne State University, Michigan 48202, USA.
Advaita Bioinformatics, Ann Arbor, MI 48105, USA.

Tuan-Minh Nguyen (TM)

Department of Computer Science and.

Larry A Sonna (LA)

Bon Air Consulting, Bon Air, Vancouver, WA 98683, USA.

Cordelia Ziraldo (C)

Department of Internal Medicine.

Radu Vanciu (R)

Department of Internal Medicine.

Raef Fadel (R)

Department of Internal Medicine.

Austin Morrison (A)

Department of Pharmacy.

Rachel M Kenney (RM)

Department of Pharmacy.

George Alangaden (G)

Division of Infectious Diseases, Henry Ford Health System, Detroit, MI 48202, USA.

Mayur Ramesh (M)

Division of Infectious Diseases, Henry Ford Health System, Detroit, MI 48202, USA.

Gil Mor (G)

Department of Obstetrics and Gynecology, Wayne State University, Michigan 48202, USA.

Classifications MeSH