Abordagens matemática e estatística aplicadas a um modelo de dinâmica tumoral com células CAR-T

Abstract

Cancer represents a significant challenge for contemporary medicine, being one of the leading causes of death worldwide. In recent years, advances in research have brought new therapeutic perspectives, including CAR-T cell immunotherapy, which utilizes genetically modified T lymphocytes to recognize and attack tumor cells expressing specific antigens. Despite promising results, challenges such as defining optimal treatment protocols and overcoming resistance to immunotherapy still persist. This work aims to contribute to the effectiveness of this therapy by analyzing a mathematical model that describes the tumor response to CAR-T cell immunotherapy in immunodeficient mice. Using Bayesian inference techniques and optimal control theory, the model was partially calibrated with experimental data available in the literature and optimized to minimize both the number of tumor cells and the required dose of CAR-T cells, seeking greater treatment efficiency. The results indicated that adjustments in the initial doses of administered CAR-T cells can significantly reduce tumor burden; however, depending on the dose level, the formation of CAR-T memory cells may be compromised, affecting the long-term response. The proposed integrated approach may contribute to the development of more effective and personalized therapeutic protocols for CAR-T cell cancer treatment.