The value of monitoring to control evolving populations

Abstract

Populations can evolve to adapt to external changes. The capacity to evolve and adapt makes successful treatment of infectious diseases and cancer difficult. Indeed, therapy resistance has become a key challenge for global health. Therefore, ideas of how to control evolving populations to overcome this threat are valuable. Here we use the mathematical concepts of stochastic optimal control to study what is needed to control evolving populations. Following established routes to calculate control strategies, we first study how a polymorphism can be maintained in a finite population by adaptively tuning selection. We then introduce a minimal model of drug resistance in a stochastically evolving cancer cell population and compute adaptive therapies. When decisions are in this manner based on monitoring the response of the tumor, this can outperform established therapy paradigms. For both case studies, we demonstrate the importance of high-resolution monitoring of the target population to achieve a given control objective, thus quantifying the intuition that to control, one must monitor.stochastic optimal control | adaptive cancer therapy | drug resistance

The potential impact of coinfection on antimicrobial chemotherapy and drug resistance

“…One key challenge is to predict population-level consequences from mechanistic interactions in individual hosts [58,59]; or, in the opposite direction, to interpret ecological patterns of co-occurrence as evidence for within-host mechanisms. An example for these challenges is provided by the HIV–TB interactions.…”

Modeling Tumor Clonal Evolution for Drug Combinations Design

“…In recent studies, a linear quadratic model was used to derive optimal radiotherapy scheduling for glioblastoma treatment, with demonstrated improved survival on mice 102 . The concept of ‘adaptive therapy’ was also recently proposed based on mathematical modeling to show potential efficacy in a modulated drug scheduling that controls a stable tumor burden, allowing a sensitive tumor subpopulation to suppress the outgrowth of the resistant subpopulation 103,104 . The differential fitness differences between resistant and sensitive subpopulations have been studied in other tumor models that provide rationales for drug holiday schedules.…”

Modelling bistable tumour population dynamics to design effective treatment strategies

“…Both mechanisms are supported by numerous experimental evidence (e.g., for clonal evolution, [10]; for differential plasticity of tumour cells [8,[11][12][13][14]). However, the latter process may account for drug resistance that can be reverted when the therapy is lifted [15,16].…”