Stochastic resetting antiviral therapies prevents drug resistance development

TL;DR

This paper introduces a stochastic resetting model for antiviral therapy, revealing how optimal therapy schedules can prevent drug resistance development through phase transition behaviors.

Contribution

It develops a mean-field stochastic resetting framework for modeling viral resistance, providing analytical insights into therapy optimization and phase transition phenomena.

Findings

Optimal resetting rates minimize resistance development

Phase transitions occur in therapy efficacy as a function of parameters

Simulations confirm analytical predictions in HIV-1 model

Abstract

We study minimal mean-field models of viral drug resistance development in which the efficacy of a therapy is described by a one-dimensional stochastic resetting process with mixed reflecting-absorbing boundary conditions. We derive analytical expressions for the mean survival time for the virus to develop complete resistance to the drug. We show that the optimal therapy resetting rates that achieve a minimum and maximum mean survival times undergo a second and first-order phase transition-like behaviour as a function of the therapy efficacy drift. We illustrate our results with simulations of a population-dynamics model of HIV-1 infection.