Adverse events associated with potential drugs for COVID-19: a case study from real-world data.


Journal

Briefings in bioinformatics
ISSN: 1477-4054
Titre abrégé: Brief Bioinform
Pays: England
ID NLM: 100912837

Informations de publication

Date de publication:
22 03 2021
Historique:
received: 29 06 2020
revised: 03 08 2020
accepted: 26 08 2020
pubmed: 2 10 2020
medline: 15 4 2021
entrez: 1 10 2020
Statut: ppublish

Résumé

The coronavirus disease 2019 (COVID-19) has resulted as a global pandemic. The World Health Organization announced the most promising drugs in SOLIDARITY for the global trial, and several other drugs are under investigation through ongoing clinical trials to prove the effectiveness and safety of potential therapeutics. Here, we depicted the safety profile of these drugs and investigated their associated adverse events (AEs). We observed the associated AEs in different organs/systems, especially in skin and subcutaneous tissue, immune system and musculoskeletal and connective tissue. Furthermore, we observed strong bias of AEs in different groups of sex and age. Our study provides knowledge of the toxicity of potential COVID-19 drugs. While these drugs hold promise to fight the global pandemic, healthcare providers should pay attention to AEs to maximize the treatment benefit while minimizing toxicity.

Identifiants

pubmed: 33003194
pii: 5917051
doi: 10.1093/bib/bbaa234
pmc: PMC7543289
doi:

Substances chimiques

Antiviral Agents 0

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

1232-1238

Subventions

Organisme : NIAID NIH HHS
ID : R21 AI150085
Pays : United States
Organisme : NIDDK NIH HHS
ID : R01 DK090570
Pays : United States

Informations de copyright

© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Auteurs

Ying Jing (Y)

Department of Biochemistry and Molecular Biology, The University of Texas Health Science Center at Houston McGovern Medical School, USA.

Lixia Diao (L)

Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, USA.

Leng Han (L)

Department of Biochemistry and Molecular Biology, The University of Texas Health Science Center at Houston McGovern Medical School, USA.

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Classifications MeSH