A Computational Framework for Exploring SARS-CoV-2 Pharmacodynamic Dose and Timing Regimes

Abstract

Emerging diseases—and none as recently or devastatingly impactful toward humans as COVID-19—pose an immense challenge to researchers concerned with infectious disease. This study is tasked with expanding the computational probe of treatment regimes in a differential equations-based model of the SARS-CoV-2 host–virus interaction. Parameters within the model are tweaked to simulate dose specifications. Further, parametric variations are introduced in a timed manner to infer the importance of dose timing. Arming in silico testing, and eventually, clinical testing, with abundant information on simulated therapeutic regimes is the overall contribution of this pharmacodynamic model; thus, a wide range of dose and timing combinations are examined. Therapeutic interventions that block viral replication inhibit viral entry into host cells, and vaccination-induced antibodies are all studied alone and in combination. Especially during early detection, exhaustive parameter sweeps of well-suited within-host models are often the first step in the clinical response to a novel disease.

Keywords

• COVID-19
• differential equation-based modeling
• host–virus immune response
• pharmacodynamic model