Computational model of the cancer necrotic core formation in a tumor-on-a-chip device

• Published in: Journal of theoretical biology 2024-09, Vol.592, p.111893, Article 111893
• Main Authors: Bonifácio, Elton Diêgo, Araújo, Cleudmar Amaral, Guimarães, Marcília Valéria, de Souza, Márcio Peres, Lima, Thiago Parente, de Avelar Freitas, Bethânia Alves, González-Torres, Libardo Andrés
• Format: Article
• Language: English
• Subjects: Finite element analysis; Microfluidic devices; Necrotic core; Tumor cells
• ISSN: 0022-5193; 1095-8541; 1095-8541
• DOI: 10.1016/j.jtbi.2024.111893

•Finite element simulation of necrotic core formation in a tumor-on-a-chip device.•Nutrient availability and local pH are crucial in governing necrotic core dynamics.•High concentration of living cells alters diffusivity by changing MEC morphology.•Warburg effect reproduces cancer metabolism, impacting pH and nutrient gradients. The mechanisms underlying the formation of necrotic regions within avascular tumors are complex and poorly understood. In this paper, we investigate the formation of a necrotic core in a 3D tumor cell culture within a microfluidic device, considering oxygen, nutrients, and the microenvironment acidification by means of a computational-mathematical model. Our objective is to simulate cell processes, including proliferation and death inside a microfluidic device, according to the microenvironmental conditions. We employed approximation utilizing finite element models taking into account glucose, oxygen, and hydrogen ions diffusion, consumption and production, as well as cell proliferation, migration and death, addressing how tumor cells evolve under different conditions. The resulting mathematical model was examined under different scenarios, being capable of reproducing cell death and proliferation under different cell concentrations, and the formation of a necrotic core, in good agreement with experimental data reported in the literature. This approach not only advances our fundamental understanding of necrotic core formation but also provides a robust computational platform to study personalized therapeutic strategies, offering an important tool in cancer research and treatment design.