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

Abstract

To impact the COVID-19 pandemic, lifesaving antiviral therapies must be identified. The number of clinical trials that can be performed is limited. We developed mathematical models to project multiple therapeutic approaches. Our models recapitulate off-treatment viral dynamics and predict a three-phase immune response. Simulated treatment with remdesivir, selinexor, neutralizing antibodies or cellular immunotherapy demonstrates that rapid viral elimination is possible if in vivo potency is sufficiently high. Therapies dosed soon after peak viral load when symptoms develop, may decrease shedding duration and immune response intensity, but have little effect on viral area under the curve (AUC), which is driven by high early viral loads. Potent therapy dosed prior to viral peak during pre-symptomatic infection, could lower AUC. Drug resistance may emerge with a moderately potent agent dosed before viral peak. Our results support early treatment for COVID-19 if shedding duration and not AUC is most predictive of clinical severity.

Role of favipiravir in the treatment of COVID-19

“…The antiviral drugs administered shortly after the onset of symptoms can shorten the course of clinical illness and it can reduce the infectiousness to others by reducing viral shedding ( Saber-Ayad et al, 2020 , Mitjà and Clotet, 2020 ). Goyal et al (2020) has performed a mathematical modeling to predict the impact of promising antiviral treatment. It has been reported that if a patient receives antiviral therapy in the early phase of infection, there are high chances that the duration of shedding and intensity of the effector immune response may decrease; however, there may be a limited impact on viral area under the curve (AUC) possibly owing to higher levels of early SARS-CoV-2 replication.…”

Innate Immunity Plays a Key Role in Controlling Viral Load in COVID-19: Mechanistic Insights from a Whole-Body Infection Dynamics Model

“…However, early initiation of antiviral therapy led to a lesser total viral load and a faster reduction of the load, which is consistent with results of other studies. 19 …”

“…For example, Goyal et al developed a mathematical model to predict the therapeutic outcomes of various COVID-19 treatment strategies. 19 Their model is based on target cell-limited viral dynamics 20 and incorporates the immune response to infection in order to predict viral load dynamics in patients pre- and post-treatment with various antiviral drugs. This model was used to project viral dynamics under hypothetical clinical scenarios involving drugs with varying potencies, different treatment timings postinfection, and levels of drug resistance, and the results of this study suggest the application of potent antiviral drugs prior to the peak viral load stage, i.e., in the presymptomatic stage, as an effective means of controlling infection in the body.…”

A modular framework for multiscale, multicellular, spatiotemporal modeling of acute primary viral infection and immune response in epithelial tissues and its application to drug therapy timing and effectiveness

“…g ., the variability of individual cells or the complex time course of virus release by individual infected cells). We have developed formal spatializations of a number of interesting ODE models of COVID, such as [ 61 , 62 ], to explore the effects of stochasticity of outcomes, the effects of spatial mechanisms, and infection dynamics at a particular site of infection on the predictions of these models. An additional benefit of our approach is that we can easily and consistently combine and integrate ODE models which focus on different aspects of the complex process of infection, spread and clearance ( e .…”

“…the local spread of virus or cytokine or the limited speed of movement of immune cells), or to additional factors which are difficult to include in an ODE model (e.g., the variability of individual cells or the complex time course of virus release by individual infected cells). We have developed formal spatializations of a number of interesting ODE models of COVID, such as [61,62], to explore the effects of stochasticity of outcomes, the effects of spatial mechanisms, and infection dynamics at a particular site of infection on the predictions of these models. An additional benefit of our approach is that we can easily and consistently combine and integrate ODE models which focus on different aspects of the complex process of infection, spread and clearance (e.g., combining published models of intracellular INF-induced viral resistance with spatial models of plaque spread in vitro [20,63]).…”