Global prevalence of SARS-CoV-2 3CL protease mutations associated with nirmatrelvir or ensitrelvir resistance
Jonathan Daniel Ip, Allen Wing‐Ho Chu, Wan-Mui Chan, R Leung, Syed Muhammad Abdullah, Yanni Sun, Kelvin Kai‐Wang To
Abstract
Background Nirmatrelvir-ritonavir (Paxlovid) and ensitrelvir are 3-chymotrypsin-like cysteine protease (3CL pro ) inhibitors which have been approved for the treatment of COVID-19 in 2021 and 2022, respectively. Previous studies have identified 3CL pro mutations that are associated with reduced susceptibility to these antivirals. The aim of the current study was to estimate the global prevalence of 3CL pro inhibitor-resistant SARS-CoV-2 strains. Methods We compiled a list of 3CL pro mutations which have been associated with nirmatrelvir or ensitrelvir resistance based on either viral replication or 3CL pro activity assays, and determined their prevalence among 13.4 million sequences deposited in GISAID as of December 14, 2022, about 1 year after the approval of nirmatrelvir-ritonavir. We analyzed the prevalence for different time periods, SARS-CoV-2 lineages and geographical locations. Findings Overall, 0.5% (67,095/13,446,588) of the sequences contained at least one mutation that was shown to affect the inhibitory activity of nirmatrelvir or ensitrelvir on viral replication or 3CL pro activity. We did not observe any increasing trend of resistance after the widespread clinical use of nirmatrelvir-ritonavir. G15S (2070 per million) and T21I (1386 per million) were the most prevalent mutations, and these mutations were dominant in some SARS-CoV-2 lineages. E166V and S144E, previously shown to affect the inhibitory activity of nirmatrelvir on viral replication or protease activity by > 100-folds, were found in <1 per million sequences. Interpretation Our data suggest that 3CL pro inhibitor resistance is currently rare. However, continuous global genotypic and phenotypic surveillance would be crucial in the early detection of resistant mutants. Funding Richard and Carol Yu, May Tam Mak Mei Yin, The Shaw Foundation Hong Kong, Michael Tong, Marina Lee, Government Consultancy Service, the Emergency Key Program of Guangzhou Laboratory (See acknowledgements for full list).