Abstract: Modeling viral resistance to monotherapy with monoclonal antibody treatment for SARS-CoV-2
The COVID-19 pandemic has led to approximately 600 million cases and 6.5 million deaths. To mitigate the loss of lives, emergency authorization was given to several monoclonal antibody therapies for the treatments of mild-to-moderate SARS-CoV-2 patients with high risks of progressing to severe disease. Neutralizing monoclonal antibody treatments target the virusâs spike proteins to 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 can develop and lead to the occurrence of transient viral rebound in some patients. This raises an urgent concern regarding drug resistance that could compromise the efficacy of monoclonal antibody therapy. In this study, we develop mathematical models and fit them to data from SARS-CoV-2 patients. Our results demonstrate that a mechanism, which allows virus access to additional supply of target cells during the infection, is necessary to describe the transient viral rebound. In particular, incorporating the effect of innate and adaptive immune response in a target cell limited model with multiple viral populations paints the most accurate picture of the observed viral recrudescence.