COVID-19: disease pathways and gene expression changes predict methylprednisolone can improve outcome in severe cases.
Bioinformatics
; 2021 Mar 09.
Article
in English
| MEDLINE | ID: covidwho-1123228
ABSTRACT
MOTIVATION 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. RESULTS:
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 vs. 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 data sets including Vero E6 cells, lung and intestinal organoids, as well as additional patient lung sample vs. 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% vs. 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.AVAILABILITY:
iPathwayGuide is available at https//ipathwayguide.advaitabio.com/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Type of study:
Experimental Studies
/
Observational study
/
Prognostic study
Language:
English
Journal subject:
Medical Informatics
Year:
2021
Document Type:
Article
Affiliation country:
Bioinformatics
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