PLoS Pathogens (Sep 2024)

A comprehensive study of SARS-CoV-2 main protease (Mpro) inhibitor-resistant mutants selected in a VSV-based system.

  • Francesco Costacurta,
  • Andrea Dodaro,
  • David Bante,
  • Helge Schöppe,
  • Ju-Yi Peng,
  • Bernhard Sprenger,
  • Xi He,
  • Seyed Arad Moghadasi,
  • Lisa Maria Egger,
  • Jakob Fleischmann,
  • Matteo Pavan,
  • Davide Bassani,
  • Silvia Menin,
  • Stefanie Rauch,
  • Laura Krismer,
  • Anna Sauerwein,
  • Anne Heberle,
  • Toni Rabensteiner,
  • Joses Ho,
  • Reuben S Harris,
  • Eduard Stefan,
  • Rainer Schneider,
  • Theresia Dunzendorfer-Matt,
  • Andreas Naschberger,
  • Dai Wang,
  • Teresa Kaserer,
  • Stefano Moro,
  • Dorothee von Laer,
  • Emmanuel Heilmann

DOI
https://doi.org/10.1371/journal.ppat.1012522
Journal volume & issue
Vol. 20, no. 9
p. e1012522

Abstract

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Nirmatrelvir was the first protease inhibitor specifically developed against the SARS-CoV-2 main protease (3CLpro/Mpro) and licensed for clinical use. As SARS-CoV-2 continues to spread, variants resistant to nirmatrelvir and other currently available treatments are likely to arise. This study aimed to identify and characterize mutations that confer resistance to nirmatrelvir. To safely generate Mpro resistance mutations, we passaged a previously developed, chimeric vesicular stomatitis virus (VSV-Mpro) with increasing, yet suboptimal concentrations of nirmatrelvir. Using Wuhan-1 and Omicron Mpro variants, we selected a large set of mutants. Some mutations are frequently present in GISAID, suggesting their relevance in SARS-CoV-2. The resistance phenotype of a subset of mutations was characterized against clinically available protease inhibitors (nirmatrelvir and ensitrelvir) with cell-based, biochemical and SARS-CoV-2 replicon assays. Moreover, we showed the putative molecular mechanism of resistance based on in silico molecular modelling. These findings have implications on the development of future generation Mpro inhibitors, will help to understand SARS-CoV-2 protease inhibitor resistance mechanisms and show the relevance of specific mutations, thereby informing treatment decisions.