Modeling the emergence of viral resistance for SARS-CoV-2 during treatment with an anti-spike monoclonal antibody

Abstract

The COVID-19 pandemic has led to over 760 million cases and 6.9 million deaths worldwide. To mitigate the loss of lives, emergency use authorization was given to several anti-SARS-CoV-2 monoclonal antibody (mAb) therapies for the treatment of mild-to-moderate COVID-19 in 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 and lower hospitalization rates among high-risk patients with susceptible variants. However, viral resistance has been observed, in some cases leading to a transient viral rebound that can be as large as 3-4 orders of magnitude. As mAbs represent a proven treatment choice for SARS-CoV-2 and other viral infections, evaluation of treatment-emergent mAb resistance can help uncover underlying pathobiology of SARS-CoV-2 infection and may also help in the development of the next generation of mAb therapies. 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 (homeostatic proliferation and return from an innate immune response anti-viral state) and fit them to data from persons with SARS-CoV-2 treated with a mAb. 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. Both variations in target cell supply rate and adaptive immunity parameters 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 mAb treatments during acute infection.

Within-host dynamics of antiviral treatment for SARS-CoV-2 infection

“…Yet, the low overall effectiveness of the antiviral treatment with remdesivir and the lack of difference between its effectiveness in survived and deceased individuals strongly points to the timing of hospitalization as the driving factor (Figure 5G). In contrast to previous studies highlighting the importance of early antiviral administration [18][19][20], we did not find significant correlations between the period from symptom onset to hospitalization/start of treatment and the effectiveness of remdesivir, underscoring the potential influence of other factors in determining treatment outcomes. Taken together, these findings suggest that early hospitalization post-symptom onset may bear greater significance for survival than antiviral treatment with remdesivir or than the timing of its initiation.…”

“…Future work might focus on using similar procedures on evaluating the clinical effectivenesses of other antiviral drugs, such as nirmatrelvir or molnupiravir, and in particular combination drug therapeutic approaches, such as paxlovid [17,18]. With minimal need for adaptation, our modeling approach is amenable to future applications, with the important potential to provide insights into COVID-19 treatment strategies and the development of resistance that would not be possible to obtain using standard epidemiological approaches [17][18][19].…”

“…We were unable to validate the model predictions for the immune response dynamics due to the lack of data. The size and scope of our sample did not permit us to stratify the data by typical confounding factors, such as age, sex, comorbidities, or viral load at admission while maintaining sufficient power for statistical inference [19]. We took steps to minimize potential variation in sample quality collection, targeting three independent SARS-CoV-2 sub-genomic regions for RT-qPCR (N/ORF1ab/S), and then accounted for measurement noise at the modeling stage.…”