Toward an in silico viral kinetic model for Covid-19 with pharmacodynamic effects

Viral kinetics models have been successfully developed for infection diseases such as influenza A. The aim of this work is to adopt the rationale for the viral kinetics of SARS-CoV-2, while incorporating pharmacodynamic effects of Remdesivir, the only FDA-approved drug for Covid-19 at this stage. The model consists of a system of differential equations where parameters are not available but have to be determined through computational means. The published data source from hospitals in a variety of countries (Singapore, Germany, Korea, and France). For each patient there is a viral titre record of days to weeks after a positive COVID test. The model itself was adopted from an influenza A model developed by Baccam et al (2006),  which defines the relationship between rates of change in target cells, the infected cells and the viral titer. We adopted this framework and recalibrated the parameters using built-in routines in Matlab e.g. non-linear-multiple-regression and the genetic algorithm. While Type I interferon was used as the drug for treating influenza A in the Baccam et al (2006) model,  we incorporate the effect of Remdesivir on the viral kinetics to reduce viral shedding. In this talk I will describe the progress so far in the model development, the data we have collected, the limitations of the model and future directions.