Mathematical and computer models (MCMs) are now commonly used to further analyse and explain viral kinetics from experimental data of in vitro and in vivo infections. Analyses by the better MCMs are even featured in experimental journals in virology, epidemiology, and immunology.
MCMs can play a very important role in optimizing antiviral therapy. For example, MCMs are used to simulate and predict the outcome of scenarios that cannot be performed in a human subject or an animal for obvious ethical reasons (e.g., withholding treatment to study the course of a deadly virus). But MCMs can also be used to interpolate and extrapolate the course and outcome of therapies from a limited but carefully designed set of experiments (e.g., estimating the impact of various doses or dose combinations based on studies conducted at two or three different concentrations only).
In this talk, I will present MCMs that describe the course of an influenza infection in vitro. I will show how we have used these MCMs to determine the mode of action and efficacy of antiviral compounds from in vitro experiments, and to evaluate optimal combinations for antiviral cocktails to avoid resistance and maximize efficacy. I will also use the model to demonstrate the flaws in current experimental procedures to determine antiviral efficacy in vitro and in the definitions of synergy and antagony in the context of drug combinations.