Potency and timing of antiviral therapy as determinants of duration of SARS CoV-2 shedding and intensity of inflammatory response

Abstract

Treatments are desperately needed to lower the hospitalization and case fatality rates of SARS CoV-2 infection. In order to meaningfully impact the COVID-19 pandemic, promising antiviral therapies must be identified within the next several months. However, the number of clinical trials that can be performed in this timeframe is limited. We therefore developed a mathematical model which allows projection of all possible therapeutic approaches. Our model recapitulates off-treatment viral dynamics and predicts a three-phase immune response. Addition of treatment with remdesivir, hydroxychloroquine, neutralizing antibodies or cellular immunotherapy demonstrates that if in vivo drug potency is high, then rapid elimination of virus is possible. Potent therapies dosed soon after peak viral load when infected people typically develop symptoms, are predicted to decrease shedding duration and intensity of the effector immune response, but to have little effect on viral area under the curve, which is driven by high levels of early SARS CoV-2 replication. Potent therapy dosed prior to peak viral load, when infection is usually pre-symptomatic, is predicted to be the only option to lower viral area under the curve. We also identify that clinically meaningful drug resistance is less likely to emerge with a highly potent agent that is dosed after peak viral load. Our results support an early test and treat approach for COVID-19, but also demonstrate the need to identify early viral shedding kinetic features that are the most predictive surrogates of clinical severity and transmission risk.

Mathematical modeling explains differential SARS CoV-2 kinetics in lung and nasal passages in remdesivir treated rhesus macaques

“…modeling of human infection that antiviral treatment with moderate potency would not clear viral 173 infection in the nasal passage (or sputum) if dosed prior to the peak viral load but would clear infection if 174 dose several days later. 8 Our simulations of the rhesus macaque data arrive at a similar conclusion in the 175 nasal passage, that, paradoxically, later treatment with a moderate potency drug results in lower viral 176 loads, whereas treatment started before peak results in increased late viral loads (Fig 11a). In contrast, in 177 the lungs, later treatment at days 2 or 4 leads to a subsequent viral load trajectory similar to that of the 178 earlier treated animals during the later stages of infection (Fig 11b).…”

“…Infected cell death rates were generally higher while viral replication rates were uniformly lower 104 in the nasal passages relative to lungs in the untreated animals ( Table 4). The density-dependent exponent 105 had a similar value in both compartments (k=0.09), was similar to that predicted in humans, 8 and led to a 106 2-2.5-fold increase in the overall death rate of infected cells at peak viral load. suppressed viral replication varied somewhat across animals.…”

“…clinically meaningful endpoints such as death or need for hospitalization. In the case of SARS-CoV- [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] infected people viral load can be routinely measured in nasal samples or saliva. 2 However, the primary 21 site of disease is lung tissue.…”

“…for an intensifying innate response to a higher burden of infection, proliferation of susceptible epithelial 85 cells, the conversion of susceptible and/or infected cells to an infection refractory state and the movement 86 of the virus between nasal passages and the lung. 8 We fit different versions of this model to the viral loads 87 in nasal passages and lung from 6 infected, remdesivir-treated animals, 5 and 14 infected and untreated 88 animals, including the 6 vehicle animals in the remdesivir protocol (RM7-12), 5 and 8 other animals 89 infected using the same protocol (4 of which had extended nasal viral load measures through 21 days after 90 infection). 9 Each model explored was a version of the full model in Using model selection theory, we found that the model with minimal complexity necessary to 93 explain the observed data was the one in Fig.…”

Mechanistic modeling of the SARS-CoV-2 and immune system interplay unravels design principles for diverse clinicopathological outcomes

“…However mechanistic underpinnings of such dysregulation remain largely underexplored. Mathematical modeling efforts for COVID-19, similar to the case for other infectious diseases (51), have mainly focused on epidemiological dynamics (52,53), relative to those focusing on within-host dynamics (54,55). Such intra-host dynamics models have been extensively studied for HIV, HCV and cancer (56)(57)(58)(59).…”