Multiscale modeling of a self-renewing, self-deploying antiviral for SARS-CoV-2

While vaccine deployment is reducing the risk of infection and transmission of SARS-CoV-2, effective antiviral therapies are still needed. Antivirals with a mechanism of action distinct from current vaccines are especially key in the event that vaccine-resistant variants evolve. One such class of antivirals consists of non-pathogenic viral mutants that compete with wild-type virus, and conditionally replicate in its presence. In principle, these biologically-derived antivirals prey upon SARS-CoV-2 to reduce viral load, self-renew as long as the infection is sustained, and could be transmitted from one individual to another. This class of antivirals are known as ‘Therapeutic Interfering Particles’ (TIPs) and have been theoretically and experimentally characterized for HIV-1. Here, we develop multiscale models for the efficacy of a TIP against SARS-CoV-2 in reducing patient viral load via targeted administration, and in reducing population-level COVID-19 mortality via self-deployment. Specifically, we modeled within-host replication, between-host transmission, and epidemiological spread. Our models are parameterized using in vitro data for a TIP against SARS-CoV-2, and by in vitro, in vivo, and epidemiological data for wild-type SARS-CoV-2 replication and transmission. We make predictions for the promising efficacy of TIPs for individual patients, propose key considerations for delivery of TIPs to individuals, and identify barriers to self-deployment.