Gevertz Jana L, Greene James M, Prosperi Samantha, Comandante-Lou Natacha, Sontag Eduardo D
Department of Mathematics and Statistics, The College of New Jersey, Ewing, NJ, USA.
Department of Mathematics, Clarkson University, Potsdam, NY, USA.
NPJ Syst Biol Appl. 2025 Apr 10;11(1):30. doi: 10.1038/s41540-025-00511-3.
There is growing recognition that phenotypic plasticity enables cancer cells to adapt to various environmental conditions. An example of this adaptability is the ability of an initially sensitive population of cancer cells to acquire resistance and persist in the presence of therapeutic agents. Understanding the implications of this drug-induced resistance is essential for predicting transient and long-term tumor dynamics subject to treatment. This paper introduces a mathematical model of drug-induced resistance which provides excellent fits to time-resolved in vitro experimental data. From observational data of total numbers of cells, the model unravels the relative proportions of sensitive and resistance subpopulations and quantifies their dynamics as a function of drug dose. The predictions are then validated using data on drug doses that were not used when fitting parameters. Optimal control techniques are then utilized to discover dosing strategies that could lead to better outcomes as quantified by lower total cell volume.
越来越多的人认识到，表型可塑性使癌细胞能够适应各种环境条件。这种适应性的一个例子是，最初对治疗敏感的癌细胞群体能够获得耐药性，并在治疗药物存在的情况下持续存在。了解这种药物诱导的耐药性的影响对于预测接受治疗的肿瘤的短期和长期动态至关重要。本文介绍了一种药物诱导耐药性的数学模型，该模型与时间分辨的体外实验数据拟合得非常好。从细胞总数的观测数据中，该模型揭示了敏感和耐药亚群的相对比例，并将它们的动态变化量化为药物剂量的函数。然后使用拟合参数时未使用的药物剂量数据对预测结果进行验证。接着利用最优控制技术来发现给药策略，这些策略可能会带来更好的结果，以更低的总细胞体积来衡量。
