Repurposing Thiazole/Benzothiazole-Based Thiazolidinone Derivatives as SARS-CoV-2 Main Protease (Mpro) Inhibitors: An Integrated Computational and Experimental Study
Background/Objectives: SARS-CoV-2, the causative agent of COVID-19, relies on its main protease (Mpro) for viral replication, making this enzyme an attractive target for antiviral drug development. In the present study, we investigated whether a library of previously synthesized thiazole/benzothiazole-based thiazolidinone derivatives, originally developed as anti-HIV agents, could be repurposed as SARS-CoV-2 Mpro inhibitors. Methods: A total of 85 thiazole/benzothiazole-based thiazolidinone derivatives were computationally screened against SARS-CoV-2 Mpro using molecular docking. Based on their predicted binding affinities, fifteen were selected for experimental evaluation. Enzymatic assays were used to quantify Mpro inhibition, and the compounds with the clearest enzymatic activity were then examined for their ability to limit viral replication in cell culture models challenged with the Delta and Omicron variants. Results: Molecular docking identified several derivatives with favorable predicted interactions within the Mpro active site. Experimental evaluation revealed five compounds with measurable inhibitory activity against Mpro, exhibiting IC50 values spanning 0.19 to 13.15 μM. Further antiviral testing demonstrated that some of the active compounds reduced viral replication in cell-based assays against both Delta and Omicron variants. However, a direct correlation between enzymatic inhibition and antiviral efficacy was not observed, indicating that additional pharmacological and cellular factors may influence antiviral activity. Conclusions: These findings demonstrate that thiazole/benzothiazole-based thiazolidinone derivatives represent a promising chemical scaffold for SARS-CoV-2 Mpro inhibitor development. Further optimization and mechanistic studies are warranted to improve their therapeutic potential against COVID-19.
Authors
- Hathaichanok Chuntakaruk (ORCID: https://orcid.org/0000-0002-0636-8469)
- Dominique Schols (ORCID: https://orcid.org/0000-0003-3256-5850)
- Steven De Jonghe (ORCID: https://orcid.org/0000-0002-3872-6558)
- Thanyada Rungrotmongkol (ORCID: https://orcid.org/0000-0002-7402-3235)
- Athina A. Geronikaki (ORCID: https://orcid.org/0000-0001-9894-7777)
- Ioannis S. Vizirianakis (ORCID: https://orcid.org/0000-0003-1459-9774)
- Leentje Persoons (ORCID: https://orcid.org/0000-0002-7898-0868)
- Anthi Petrou (ORCID: https://orcid.org/0000-0001-8587-2710)
- Aliki Papadimitriou-Tsantarliotou
Institutions
- Chulalongkorn University (TH)
- University of Nicosia (CY)
- Aristotle University of Thessaloniki (GR)
- Rega Institute for Medical Research (BE)
Publication Details
- Journal
- BioChem
- Published
- 2026-09-24
- DOI
- https://doi.org/10.3390/biochem6040029
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 0.00