Impact of variants of concern on SARS-CoV-2 viral dynamics in non-human primates

Abstract

The impact of variants of concern (VoC) on SARS-CoV-2 viral dynamics remains poorly understood and essentially relies on observational studies subject to various sorts of biases. In contrast, experimental models of infection constitute a powerful model to perform controlled comparisons of the viral dynamics observed with VoC and better quantify how VoC escape from the immune response. Here we used molecular and infectious viral load of 78 cynomolgus macaques to characterize in detail the effects of VoC on viral dynamics. We first developed a mathematical model that recapitulate the observed dynamics, and we found that the best model describing the data assumed a rapid antigen-dependent stimulation of the immune response leading to a rapid reduction of viral infectivity. When compared with the historical variant, all VoC except beta were associated with an escape from this immune response, and this effect was particularly sensitive for delta and omicron variant (p<10-6 for both). Interestingly, delta variant was associated with a 1.8-fold increased viral production rate (p=0.046), while conversely omicron variant was associated with a 14-fold reduction in viral production rate (p<10-6). During a natural infection, our models predict that delta variant is associated with a higher peak viral RNA than omicron variant (7.6 log10 copies/mL 95% CI 6.8 – 8 for delta; 5.6 log10 copies/mL 95% CI 4.8 – 6.3 for omicron) while having similar peak infectious titers (3.7 log10 PFU/mL 95% CI 2.4 – 4.6 for delta; 2.8 log10 PFU/mL 95% CI 1.9 – 3.8 for omicron). These results provide a detailed picture of the effects of VoC on total and infectious viral load and may help understand some differences observed in the patterns of viral transmission of these viruses.

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

“…This parameter combination indicates longer, more protracted infections in the hospitalized untreated individuals with otherwise similar viral load levels between individuals with severe and mild COVID-19. This finding aligns with previous reports [20] and reinforces the public health recommendation for prolonged isolation for individuals suffering moderate or severe COVID-19 [21].…”

“…This parameter combination indicates longer, more protracted infections in the hospitalized untreated individuals with otherwise similar viral load levels between individuals with severe and mild COVID-19. This finding aligns with previous reports [20] and reinforces the public health recommendation for prolonged isolation for individuals suffering moderate or severe COVID-19 [21]. Neither predicted cumulative viral loads, nor predicted mean viral loads were significantly different between treated individuals who died and those who survived, indicating that viral load dynamics are not a reliable predictor of mortality risk in this cohort (Figures 5D).…”

“…Upon infection, B increases with increasing viral load at rate p B and is limited to a maximal relative strength of 1, such that B (t) ∈ [0, 1]. We considered the activation of the immune response to lead to a reduced overall infectivity rate β 0 (1-B(t)), and hence, to fewer successful infections of susceptible cells [13,21]. According to how the immune response B impacts the overall infectivity rate β 0 (1- B ) in the model, an upper bound of B is necessary to avoid negative overall infectivity rates and, hence, to ensure biological meaningfulness.…”

Isolation may select for earlier and higher peak viral load but shorter duration in SARS-CoV-2 evolution

“…Altogether, these results imply that SARS-CoV-2 evolution toward an earlier peak viral load was maintained but slowed down. Interestingly, similar evolutionary trends were confirmed in a non-human primate model infected with different VOCs of SARS-CoV-2 42 , obviously without NPIs in these cases. We discuss a mechanism behind those convergent phenotypic changes in detail later (see Discussion ).…”

Temporal changes in SARS-CoV-2 clearance kinetics and the optimal design of antiviral pharmacodynamic studies: an individual patient data meta-analysis of a randomised, controlled, adaptive platform study (PLATCOV)

“…Some variants (e.g., BA.2.75) were clearly associated with higher baseline viral densities, which was not explained by differences in the interval from symptom onset. It is not possible to ascribe with confidence the underlying cause for these higher baseline viral loads, but it would be compatible with either differences in viral replication [28] or differences in tropism [29].…”