Modeling the emergence of viral resistance in SARS-CoV-2 patients treated with an anti-spike monoclonal antibody
The COVID-19 pandemic has led to over 670 million cases and 6.8 million deaths worldwide. To mitigate the loss of lives, emergency use authorization was given to several monoclonal antibody therapies for the treatment of mild-to-moderate SARS-CoV-2 patients with a high risk of progressing to severe disease. Monoclonal antibodies used to treat SARS-CoV-2 target the spike protein of the virus and block its ability to enter and infect target cells. Monoclonal antibody therapy can thus accelerate the decline in viral load, which results in a lower hospitalization rate among high-risk patients. However, viral resistance has been observed, in some cases leading to a transient viral rebound that can be as large as 3-4 logs. Indeed, resistance has compromised the use of all previously authorized monoclonal antibodies. Although resistance can be expected, the large rebounds observed are much more difficult to explain. We hypothesize replenishment of target cells is necessary to generate the high transient viral rebound. Thus, we formulated two models with different mechanisms for target cell replenishment and fit them to data from SARS-CoV-2 infected individuals treated with a monoclonal antibody. We showed that both models can explain the emergence of resistant virus associated with high transient viral rebounds. We found that variations in the target cell supply rate and adaptive immunity parameters have a strong impact on the magnitude or observability of the viral rebound associated with the emergence of resistant virus, which may explain why only some individuals develop observable transient resistant viral rebound. Our study highlights the conditions that can lead to resistance and subsequent viral rebound in monoclonal antibody treatments of acute infection and have broader application in explaining the rebound of virus.