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Evaluation of a series of nucleoside analogs as effective anticoronaviral-2 drugs against the Omicron-B.1.1.529/BA.2 subvariant: A repurposing research study.

Amgad M RabieMohnad Abdalla
Published in: Medicinal chemistry research : an international journal for rapid communications on design and mechanisms of action of biologically active agents (2022)
Mysterious evolution of a new strain of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the Omicron variant, led to a new challenge in the persistent coronavirus disease 2019 (COVID-19) battle. Objecting the conserved SARS-CoV-2 enzymes RNA-dependent RNA polymerase (RdRp) and 3'-to-5' exoribonuclease (ExoN) together using one ligand is a successful new tactic to stop SARS-CoV-2 multiplication and COVID-19 progression. The current comprehensive study investigated most nucleoside analogs (NAs) libraries, searching for the most ideal drug candidates expectedly able to act through this double tactic. Gradual computational filtration afforded six different promising NAs, riboprine/forodesine/tecadenoson/nelarabine/vidarabine/maribavir. Further biological assessment proved that riboprine and forodesine are able to powerfully inhibit the replication of the new virulent strains of SARS-CoV-2 with extremely minute in vitro anti-RdRp and anti-SARS-CoV-2 EC 50 values of about 0.21 and 0.45 μM for riboprine and about 0.23 and 0.70 μM for forodesine, respectively, surpassing both remdesivir and the new anti-COVID-19 drug molnupiravir. These biochemical findings were supported by the prior in silico data. Additionally, the ideal pharmacophoric features of riboprine and forodesine molecules render them typical dual-action inhibitors of SARS-CoV-2 replication and proofreading. These findings suggest that riboprine and forodesine could serve as prospective lead compounds against COVID-19. Graphical abstract.
Keyphrases
  • sars cov
  • respiratory syndrome coronavirus
  • coronavirus disease
  • molecular docking
  • escherichia coli
  • machine learning
  • deep learning
  • big data
  • molecular dynamics simulations
  • atomic force microscopy