Back

Emergence of SARS-CoV-2 Resistance with Monoclonal Antibody Therapy

Choudhary, M. C.; Chew, K. W.; Deo, R.; Flynn, J. P.; Regan, J.; Crain, C. R.; Moser, C.; Hughes, M.; Ritz, J.; Ribeiro, R. M.; Ke, R.; Dragavon, J. A.; Javan, A. C.; Nirula, A.; Klekotka, P.; Greninger, A. L.; Fletcher, C. V.; Daar, E. S.; Wohl, D. A.; Eron, J. J.; Currier, J. S.; Parikh, U. M.; Sieg, S. F.; Perelson, A. S.; Coombs, R. W.; Smith, D. M.; Li, J. Z.

2021-09-15 infectious diseases
10.1101/2021.09.03.21263105 medRxiv
Show abstract

Resistance mutations to monoclonal antibody (mAb) therapy has been reported, but in the non-immunosuppressed population, it is unclear if in vivo emergence of SARS-CoV-2 resistance mutations alters either viral replication dynamics or therapeutic efficacy. In ACTIV-2/A5401, non-hospitalized participants with symptomatic SARS-CoV-2 infection were randomized to bamlanivimab (700mg or 7000mg) or placebo. Treatment-emergent resistance mutations were significantly more likely detected after bamlanivimab 700mg treatment than placebo (7% of 111 vs 0% of 112 participants, P=0.003). There were no treatment-emergent resistance mutations among the 48 participants who received bamlanivimab 7000mg. Participants with emerging mAb resistant virus had significantly higher pre-treatment nasopharyngeal and anterior nasal viral load. Intensive respiratory tract viral sampling revealed the dynamic nature of SARS-CoV-2 evolution, with evidence of rapid and sustained viral rebound after emergence of resistance mutations, and worsened symptom severity. Participants with emerging bamlanivimab resistance often accumulated additional polymorphisms found in current variants of concern/interest and associated with immune escape. These results highlight the potential for rapid emergence of resistance during mAb monotherapy treatment, resulting in prolonged high level respiratory tract viral loads and clinical worsening. Careful virologic assessment should be prioritized during the development and clinical implementation of antiviral treatments for COVID-19.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.