Theoretical understanding of evolutionary dosing following tumor dynamics

M.A. Masud and Eunjung Kim

Chaos, Solitons & Fractals, 2024, vol. 179, issue C

Abstract: Recent preclinical and clinical studies have suggested that evolution-based cancer therapies exploiting intratumor competition can significantly delay resistance. Various mathematical and computational models have been used to explain the effectiveness of particular treatment strategies. However, this evolutionary approach often assumes only preexisting resistance, although acquired resistance and cancer phenotype plasticity can dramatically affect treatment outcomes. We theoretically investigate whether an evolutionary therapy that capitalizes on plasticity could delay resistance. In particular, we propose a time-dependent evolutionary dose that can modulate the evolving competition among drug-sensitive, drug-resistant, and plastic cells. We assume a Lotka–Volterra type competition model of cancer cell populations with constant carrying capacity to analyze the dynamics of tumor growth to show that some tumors can be controlled with proper dosing, which balances the competition between different cell species so that they coexist in a stable tumor below a tolerable burden. The emergence of a new resistant species may further increase the tumor volume, which can be nullified by lowering the dose. In contrast, increasing the tumor volume due to new metastatic formation requires a higher dose. Additionally, we propose tumor-dynamics-driven adaptive scheduling consisting of subsequent treatment holidays and low- and high-dose periods. This approach can contain the tumor at variable volumes below the tolerable burden, which may facilitates reiteration of the treatment strategy in the case of any evolutionary changes. Overall, this study provides a theoretical understanding of the evolutionary dosing that can delay resistance.

Keywords: Evolutionary therapy; Acquired resistance; Phenotype plasticity; Adaptive therapy; Multiple resistant species; Metastasis (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:179:y:2024:i:c:s096007792400002x

DOI: 10.1016/j.chaos.2024.114451

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