Informing antiviral effectiveness for influenza A and SARS-CoV-2 by quantifying within-host interaction between transmission and immunity

Antiviral therapies are among the most effective pharmaceutical interventions in treatment of a variety of viral pathogens. To optimize the antiviral effectiveness it is crucial to characterize the relationship between multiple cellular modes of antiviral action and the complex response of the host’s innate immune system relative to the within host dynamics of a proliferating virus. Since their introduction in 1968 and 2019, Influenza A virus (IAV) H3N2 and Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), respectively, have caused unprecedented damage on the public health infrastructure globally. In addition to the substantial burden of morbidity and mortality around the world, both viruses have the potential of undergoing evolution leading to antigenic escape from the prevailing interventions. These biological characteristics advocate for urgent development of effective antivirals for the treatment of IAV and SARS-CoV-2.

In this multi-stage study, we develop a suite of within-host models encompassing a number of hypotheses regarding virus-specific innate host functional responses and their impacts on the proliferation of IAV H3N2 and SARS-CoV-2 viruses. We use likelihood-based statistical inference to confront these hypotheses with infection data on IAV H3N2 and SARS-CoV-2 from infection experiments in ferrets. Upon identifying the best-fitting model of within-host dynamics, we can quantify the potential impact of antiviral drug therapy as a function of effectiveness and timing of initiation.

We find significant mechanistic differences between the infection dynamics of H3N2 IAV and SARS-CoV-2 and associated model parameters. The treatment consequences of these differences are that SARS-CoV-2 is harder to control with antivirals, requiring earlier initiation and a more effective drug.