Asymptotic analysis and optimal control of an integro-differential system modelling healthy and cancer cells exposed to chemotherapy

TL;DR

This paper models healthy and cancer cell populations with resistance levels using integro-differential equations, analyzing their long-term behavior under chemotherapy and devising near-optimal treatment strategies.

Contribution

It introduces a novel integro-differential model with phenotypic structure and develops an optimal control approach for chemotherapy scheduling.

Findings

Both cell populations converge to resistant phenotypes over time.

A quasi-optimal infusion protocol effectively minimizes cancer cells.

Periodic treatment schedules are compared with the optimal strategy.

Abstract

We consider a system of two coupled integro-differential equations modelling populations of healthy and cancer cells under therapy. Both populations are structured by a phenotypic variable, representing their level of resistance to the treatment. We analyse the asymptotic behaviour of the model under constant infusion of drugs. By designing an appropriate Lyapunov function, we prove that both densities converge to Dirac masses. We then define an optimal control problem, by considering all possible infusion protocols and minimising the number of cancer cells over a prescribed time frame. We provide a quasi-optimal strategy and prove that it solves this problem for large final times. For this modelling framework, we illustrate our results with numerical simulations, and compare our optimal strategy with periodic treatment schedules.