Identification of potential SARS-CoV-2 inhibitors among well-tolerated drugs using drug repurposing and in vitro approaches
The 3C-like protease (3CL<sup>pro</sup>) is essential in the SARS-CoV-2 life cycle and a promising target for antiviral drug discovery, as no similar proteases exist in humans. This study aimed to identify effective SARS-CoV-2 inhibitors among FDA-approved drugs. Previous computational analysis revealed several drugs with high binding affinity to the 3CL<sup>pro</sup> active site. In vitro enzymatic assays confirmed that ten of these drugs effectively inhibited the enzyme. To evaluate their impact on viral replication, we used non-infectious SARS-CoV-2 sub-genomic replicons in lung and intestinal cells. Amcinonide, eltrombopag, lumacaftor, candesartan, and nelfinavir inhibited replication at low micromolar concentrations. Lumacaftor showed IC50 values of 964 nM in Caco-2 cells and 458 nM in Calu-3 cells, while candesartan had IC50 values of 714 nM and 1.05 µM, respectively. Furthermore, dual combination experiments revealed that amcinonide, pimozide, lumacaftor, and eltrombopag acted as potent inhibitors at nanomolar concentrations when combined with candesartan. This study highlights lumacaftor, candesartan, and nelfinavir as effective inhibitors of SARS-CoV-2 replication in vitro and emphasizes their potential for repurposing as antiviral treatments. These findings support future clinical trials and may lead to breakthroughs in COVID-19 treatment strategies.
Authors
- Mahmut Çalışkan (ORCID: https://orcid.org/0000-0003-3321-0111)
- Mehmet Topçul (ORCID: https://orcid.org/0000-0002-9685-9404)
- Alper Okyar (ORCID: https://orcid.org/0000-0002-9891-5588)
- Cem Şimşek (ORCID: https://orcid.org/0000-0002-7037-5233)
- İbrahim Halil Kavaklı (ORCID: https://orcid.org/0000-0001-6624-3505)
- İdi̇l Çeti̇n (ORCID: https://orcid.org/0000-0002-3961-6422)
- Şeref Gül (ORCID: https://orcid.org/0000-0002-5613-1339)
- Cihan Aydın (ORCID: https://orcid.org/0000-0002-1401-5727)
- Betül Oruçoğlu
Institutions
- Koç University (TR)
- Bezmiâlem Vakıf Üniversitesi (TR)
- Türkisch-Deutsche Universität (TR)
- Institute of Life Sciences (IN)
- Istanbul Medeniyet University (TR)
- Istanbul University (TR)
Publication Details
- Journal
- Scientific Reports
- Published
- 2025-04-22
- DOI
- https://doi.org/10.1038/s41598-025-88388-4
- Citations
- 2
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 3.01
Funders
- Bilimsel Araştirma Projeleri Birimi, Istanbul Üniversitesi
- Türkiye Bilimsel ve Teknolojik Araştırma Kurumu