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Multiple pathways for SARS-CoV-2 resistance to nirmatrelvir
Open accessAbstract
Nirmatrelvir, an oral antiviral targeting the 3CL protease of SARS-CoV-2, has been demonstrated to be clinically useful against COVID-19 (refs. 1,2 ). However, because SARS-CoV-2 has evolved to become resistant to other therapeutic modalities 3–9 , there is a concern that the same could occur for nirmatrelvir. Here we examined this possibility by in vitro passaging of SARS-CoV-2 in nirmatrelvir using two independent approaches, including one on a large scale. Indeed, highly resistant viruses emerged from both and their sequences showed a multitude of 3CL protease mutations. In the experiment peformed with many replicates, 53 independent viral lineages were selected with mutations observed at 23 different residues of the enzyme. Nevertheless, several common mutational pathways to nirmatrelvir resistance were preferred, with a majority of the viruses descending from T21I, P252L or T304I as precursor mutations. Construction and analysis of 13 recombinant SARS-CoV-2 clones showed that these mutations mediated only low-level resistance, whereas greater resistance required accumulation of additional mutations. E166V mutation conferred the strongest resistance (around 100-fold), but this mutation resulted in a loss of viral replicative fitness that was restored by compensatory changes such as L50F and T21I. Our findings indicate that SARS-CoV-2 resistance to nirmatrelvir does readily arise via multiple pathways in vitro, and the specific mutations observed herein form a strong foundation from which to study the mechanism of resistance in detail and to inform the design of next-generation protease inhibitors.
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Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., et al. (2020) · Nature Methods · doi:10.1038/s41592-019-0686-2 · Cited by 38,430
Safety and Efficacy of the BNT162b2 mRNA Covid-19 Vaccine
Polack, F. P., Thomas, S. J., Kitchin, N., Absalon, J., Gurtman, A., Lockhart, S., Perez, J. L., Pérez Marc, G., Moreira, E. D., Zerbini, C., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2034577 · Cited by 14,592
Efficacy and Safety of the mRNA-1273 SARS-CoV-2 Vaccine
Baden, L. R., El Sahly, H. M., Essink, B., Kotloff, K., Frey, S., Novak, R., Diemert, D., Spector, S. A., Rouphael, N., Creech, C. B., et al. (2021) · New England Journal of Medicine · doi:10.1056/nejmoa2035389 · Cited by 10,119
seaborn: statistical data visualization
Waskom, M. (2021) · Journal of Open Source Software · doi:10.21105/joss.03021 · Cited by 7,310
Remdesivir for the Treatment of Covid-19 — Final Report
Beigel, J. H., Tomashek, K. M., Dodd, L. E., Mehta, A. K., Zingman, B. S., Kalil, A. C., Hohmann, E., Chu, H. Y., Luetkemeyer, A., Kline, S., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2007764 · Cited by 7,267
Safety and Efficacy of Single-Dose Ad26.COV2.S Vaccine against Covid-19
Sadoff, J., Gray, G., Vandebosch, A., Cárdenas, V., Shukarev, G., Grinsztejn, B., Goepfert, P. A., Truyers, C., Fennema, H., Spiessens, B., et al. (2021) · New England Journal of Medicine · doi:10.1056/nejmoa2101544 · Cited by 2,397
Oral Nirmatrelvir for High-Risk, Nonhospitalized Adults with Covid-19
Hammond, J., Leister-Tebbe, H., Gardner, A., Abreu, P., Bao, W., Wisemandle, W., Baniecki, M., Hendrick, V. M., Damle, B., Simón-Campos, A., et al. (2022) · New England Journal of Medicine · doi:10.1056/nejmoa2118542 · Cited by 2,323
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Wang, P., Nair, M. S., Liu, L., Iketani, S., Luo, Y., Guo, Y., Wang, M., Yu, J., Zhang, B., Kwong, P. D., et al. (2021) · Nature · doi:10.1038/s41586-021-03398-2 · Cited by 2,312
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Planas, D., Veyer, D., Baidaliuk, A., Staropoli, I., Guivel-Benhassine, F., Rajah, M. M., Planchais, C., Porrot, F., Robillard, N., Puech, J., et al. (2021) · Nature · doi:10.1038/s41586-021-03777-9 · Cited by 1,992 · cited 2×
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Metadata
- Authors
- Iketani, S., Mohri, H., Culbertson, B., Hong, S. J., Duan, Y., Luck, M. I., Annavajhala, M. K., Guo, Y., Sheng, Z., Uhlemann, A.-C., Goff, S. P., Sabo, Y., Yang, H., Chavez, A., & Ho, D. D.
- Venue
- Nature
- Published
- 2022-11-09
- Volume
- 613
- Issue
- 7944
- Pages
- 558-564
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W4308683905
- Cited by
- 480
- References
- 16
- FWCI
- 45.02
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Iketani, S., Mohri, H., Culbertson, B., Hong, S. J., Duan, Y., Luck, M. I., Annavajhala, M. K., Guo, Y., Sheng, Z., Uhlemann, A.-C., Goff, S. P., Sabo, Y., Yang, H., Chavez, A., & Ho, D. D. (2023). Multiple pathways for SARS-CoV-2 resistance to nirmatrelvir. Nature. https://doi.org/10.1038/s41586-022-05514-2
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