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

Abstract

To mitigate the loss of lives during the COVID-19 pandemic, 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 variants susceptible to mAb therapy. 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 antiviral 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.

Fig 1. Fit of the logistic proliferation model to the data.

The circles represent viral load data which are filled green when the viral population was dominated by BAM sensitive virus and filled red when dominated by resistant virus. The unfilled circles are data below the limit of quantification (2 log₁₀ RNA copies/mL) or limit of detection (1.4 log₁₀ RNA copies/mL, indicated by horizontal lines). Black curves show the best-fit of the model to the total viral load. When plotting the model fit, V₁ (BAM sensitive) is represented by a dashed–green curve and V₂ (BAM resistant) by a red curve. The vertical black line indicates the time of treatment initiation. The vertical red line indicates the estimated time, t* when adaptive immunity begins to emerge.

Fig 2. Fit of the innate immune response model to the data.

The circles represent viral load data which are filled green when the viral population was dominated by BAM sensitive virus and filled red when dominated by resistant virus. The unfilled circles are data below the limit of quantification or limit of detection (indicated by horizontal lines). Black curves show the best-fit of the model to the total viral load. When plotting the model fit, V₁ (BAM sensitive) is represented by a dashed–green curve and V₂ (BAM resistant) by a red curve. The vertical black line indicates the time of treatment initiation. The vertical red line indicates the estimated time, t*, when adaptive immunity begins to emerge.

Fig 3. The rate of target cell replenishment has a crucial role in driving the amplitude of the viral rebound.

Baseline parameters for the simulation are taken from the best fit parameters of B2-8. The first vertical red (dashed dot) line indicates the time of treatment initiation. The second vertical red (dashed) line indicates when adaptive immunity begins to emerge. (A) Viral rebound is more likely to be observable (e.g., sufficiently high VL) with increasing intrinsic growth rate r. (B) Viral rebound is more likely to be observable with increasing rate of refractory cells returning to cells susceptible to infection ρ.

Fig 4. Target cell replenishment can explain the emergence of resistant virus associated with high transient viral rebound.

(A) Natural course of acute infection as described by a standard viral dynamic model. (B) Without target cell replenishment, the resistant virus becomes the dominant population but does not lead to observable transient viral rebound. (C) Target cell replenishment–either via new production or target cell returning from the refractory state ‐ can drive the resistant viral population to an observable transient viral rebound.