Abstract
Antiviral therapies can yield different outcomes depending on their scheduling: a highly effective drug may produce treatment results ranging from successful to inconsequential, depending on therapeutic timing, dosing intervals, and dosage. The effectiveness of antiviral therapies can be assessed using mathematical models that describe viral spread within a host. In this work, we conduct a study based on the dynamic characterization of a target-cell model to address a multi-objective control problem aimed at designing highly effective and host-customizable antiviral therapies. These therapies involve finite-time antiviral treatments that minimize the viral load peak and the infection final size until infection clearance, while simultaneously reducing the total amount of drug intake as much as possible. Two optimization-based control strategies are proposed: a fixed-dose and a variable-dose approach. The variable-dose strategy achieves superior performance by explicitly considering the system dynamics in the design of the control. Simulation results, based on an identified model for COVID-19 patients treated with Paxlovid, illustrate the potential benefits of the proposed strategies.