Design and Evaluation of Computer-Based Multiple Drug Dosing Control for Cancer Tumor Treatment
Year: 2026
Type: conference-paper
Abstract: Tailoring cancer treatment to individual patients is very difficult because drugs, tumor growth, and immune responses, and cytokine signals interact, all of which are complicated by the unique biology of each patient. To solve this, this work introduces a simulation tool using Simulink that models these interactions to help pick the best treatment strategy. At its core, is a 15-equation ordinary differential equation (ODE) model, which include various processes, including pharmacokinetics, immune activation, T-cell maturation delays, IL-2 signaling, cytokine signaling, T-cell exhaustion, and tumor-growth kill dynamics. To ensure the model truly translates to real-world clinical settings, its individual components were carefully calibrated using a mix of in vitro immune data, cytokine release assays, and preclinical datasets, subsequently refining the uncertain variables through Bayesian and non-linear mixed-effects (NLME) estimation. The simulations successfully mirrored key biological realities, such as drug decay profiles, the precise timing of immune responses, and the delayed regression of tumors. These findings highlight the model's strong capacity to accurately capture patient-to-patient differences in treatment response, as well as the phenomenon of immune-driven tumor relapse. Ultimately, this framework serves as a robust and adaptable in silico platform for evaluating multidrug cancer treatments, laying crucial groundwork for the future development of patient-specific computational models in precision medicine oncology.
Institution: Obuda University
Language: English
Subfield: Modeling and Simulation
Field: Mathematics
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7204165753