Multi-objective control to schedule therapies for acute viral infections

Area of Science:

• Mathematical modeling
• Virology
• Pharmacology

Background:

• Antiviral therapy effectiveness is highly dependent on precise scheduling, including timing, dosage, and intervals.
• Mathematical models are crucial for assessing viral dynamics and optimizing treatment strategies within a host.

Purpose of the Study:

• To design highly effective and customizable antiviral therapies using a target-cell model.
• To address the multi-objective control problem of minimizing viral load and drug intake simultaneously.

Main Methods:

• Dynamic characterization of a target-cell model.
• Development of two optimization-based control strategies: fixed-dose and variable-dose.
• Simulation of identified COVID-19 patient models treated with Paxlovid.

Main Results:

• The variable-dose strategy demonstrated superior performance compared to the fixed-dose approach.
• Proposed strategies effectively minimize viral load peak and final infection size.
• Reduced total drug intake was achieved through optimized dosing regimens.

Conclusions:

• Optimization-based control strategies can significantly enhance antiviral therapy efficacy.
• Variable-dose strategies, by incorporating system dynamics, offer improved outcomes.
• The findings have potential applications for personalized COVID-19 treatment with drugs like Paxlovid.