Vaccines and drugs have helped reduce disease severity and blunt the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, ongoing virus transmission, continuous evolution, and increasing selective pressures have the potential to yield viral variants capable of resisting these interventions. Here, we investigate the susceptibility of natural variants of the main protease [M^pro; 3C-like protease (3CL^pro)] of SARS-CoV-2 to protease inhibitors. Multiple single amino acid changes in M^pro confer resistance to nirmatrelvir (the active component of Paxlovid). An additional clinical-stage inhibitor, ensitrelvir (Xocova), shows a different resistance mutation profile. Importantly, phylogenetic analyses indicate that several of these resistant variants have pre-existed the introduction of these drugs into the human population and are capable of spreading. These results encourage the monitoring of resistance variants and the development of additional protease inhibitors and other antiviral drugs with different mechanisms of action and resistance profiles for combinatorial therapy.

The development of SARS-CoV-2 main protease (M^pro) inhibitors for the treatment of COVID-19 has mostly benefitted from X-ray structures and preexisting knowledge of inhibitors; however, an efficient method to generate M^pro inhibitors, which circumvents such information would be advantageous. As an alternative approach, we show here that DNA-encoded chemistry technology (DEC-Tec) can be used to discover inhibitors of M^pro. An affinity selection of a 4-billion-membered DNA-encoded chemical library (DECL) using M^pro as bait produces novel non-covalent and non-peptide-based small molecule inhibitors of M^pro with low nanomolar K_i values. Furthermore, these compounds demonstrate efficacy against mutant forms of M^pro that have shown resistance to the standard-of-care drug nirmatrelvir. Overall, this work demonstrates that DEC-Tec can efficiently generate novel and potent inhibitors without preliminary chemical or structural information.