Author Identifier

Seshadri Vasan: https://orcid.org/0000-0002-7326-3210

Document Type

Journal Article

Publication Title

Journal of Molecular Graphics and Modelling

Volume

136

Publisher

Elsevier

School

School of Medical and Health Sciences

RAS ID

77386

Funders

Birla Institute of Technology and Science, Pilani / University of York / Royal College of Physicians and Surgeons of Glasgow

Comments

Muzaffar-Ur-Rehman, M., Chougule, K. S., Chandu, A., Kuthe, P. V., Garg, M., Sankaranarayanan, M., & Vasan, S. S. (2025). In silico evaluation of bisphosphonates identifies leading candidates for SARS-CoV-2 RdRp inhibition. Journal of Molecular Graphics and Modelling, 136, 108939. https://doi.org/10.1016/j.jmgm.2024.108939

Abstract

The novel coronavirus disease (COVID-19) pandemic has resulted in 777 million confirmed cases and over 7 million deaths worldwide, with insufficient treatment options. Innumerable efforts are being made around the world for faster identification of therapeutic agents to treat the deadly disease. Post Acute Sequelae of SARS-CoV-2 infection or COVID-19 (PASC), also called Long COVID, is still being understood and lacks treatment options as well. A growing list of drugs are being suggested by various in silico, in vitro and ex vivo models, however currently only two treatment options are widely used: the RNA-dependent RNA polymerase (RdRp) inhibitor remdesivir, and the main protease inhibitor nirmatrelvir in combination with ritonavir. Computational drug development tools and in silico studies involving molecular docking, molecular dynamics, entropy calculations and pharmacokinetics can be useful to identify new targets to treat COVID-19 and PASC, as shown in this work and our recent paper that identified alendronate as a promising candidate. In this study, we have investigated all bisphosphonates (BPs) on the ChEMBL database which can bind competitively to nidovirus RdRp-associated nucleotidyl (NiRAN) transferase domain, and systematically down selected seven candidates (CHEMBL608526, CHEMBL196676, CHEMBL164344, CHEMBL4291724, CHEMBL4569308, CHEMBL387132, CHEMBL98211), two of which closely resemble the approved drugs minodronate and zoledronate. This work and our recent paper together provide an in silico mechanistic explanation for alendronate and zoledronate users having dramatically reduced odds of SARS-CoV-2 testing, COVID-19 diagnosis, and COVID-19-related hospitalizations, and indicate that similar observational studies in Japan with minodronate could be valuable.

DOI

10.1016/j.jmgm.2024.108939

Creative Commons License

Creative Commons Attribution-Noncommercial 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License

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