Macrolides May Prevent Severe Acute Respiratory Syndrome Coronavirus 2 Entry into Cells: A Quantitative Structure Activity Relationship Study and Experimental Validation.
J Chem Inf Model
; 61(4): 2016-2025, 2021 04 26.
Article
in English
| MEDLINE | ID: covidwho-1139703
ABSTRACT
The global pandemic caused by the emerging severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is threatening the health and economic systems worldwide. Despite the enormous efforts of scientists and clinicians around the world, there is still no drug or vaccine available worldwide for the treatment and prevention of the infection. A rapid strategy for the identification of new treatments is based on repurposing existing clinically approved drugs that show antiviral activity against SARS-CoV-2 infection. In this study, after developing a quantitative structure activity relationship analysis based on molecular topology, several macrolide antibiotics are identified as promising SARS-CoV-2 spike protein inhibitors. To confirm the in silico results, the best candidates were tested against two human coronaviruses (i.e., 229E-GFP and SARS-CoV-2) in cell culture. Time-of-addition experiments and a surrogate model of viral cell entry were used to identify the steps in the virus life cycle inhibited by the compounds. Infection experiments demonstrated that azithromycin, clarithromycin, and lexithromycin reduce the intracellular accumulation of viral RNA and virus spread as well as prevent virus-induced cell death, by inhibiting the SARS-CoV-2 entry into cells. Even though the three macrolide antibiotics display a narrow antiviral activity window against SARS-CoV-2, it may be of interest to further investigate their effect on the viral spike protein and their potential in combination therapies for the coronavirus disease 19 early stage of infection.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Pharmaceutical Preparations
/
COVID-19
Type of study:
Prognostic study
Topics:
Vaccines
Limits:
Humans
Language:
English
Journal:
J Chem Inf Model
Journal subject:
Medical Informatics
/
Chemistry
Year:
2021
Document Type:
Article
Affiliation country:
Acs.jcim.0c01394
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