Optimal Treatment for Drug-Induced Cancer Persisters Involves Release Periods and Intermediate Drug Doses

Targeted cancer therapies often induce a reversible drug-tolerant state in subpopulations of cells, akin to bacterial persistence. Precise characterization of these “cancer persisters” may inform the design of more effective treatment strategies. A previous investigation into the transition to persistence of colorectal cancer cell lines has revealed a distinct dependence on drug presence and concentration, not typical of bacterial systems. Leveraging these findings, this study uses mathematical modeling to explore intermittent treatment protocols aimed at diminishing the long-term fitness of the treated population, thereby enhancing therapeutic efficacy. We adapt a mathematical model originally designed for bacteria to describe colorectal cancer population dynamics in response to a series of treatment and release cycles. The model predicts the long-term increase or reduction of a treated population, as well as its asymptotic composition, leading us to identify success and failure regions within a clinically accessible parameter space, also in combination with hypothetical drugs that act on persisters. Strikingly, our analysis suggests, perhaps counterintuitively, that optimal treatment outcomes may be achieved in correspondence of nonzero recovery periods and lower than currently administered in the clinics drug concentrations. Furthermore, by incorporating patient drug pharmacokinetics in the model, we demonstrate that intermittent dosing strategies currently explored in clinical trials can be optimized to potentially rival the efficacy of continuous dosing regimens. These findings underscore the potential of mathematical models in guiding the design of optimal treatment protocols by fine-tuning nontrivial decisional trade-offs.