Enhancing chemovirotherapy through a data-driven model with detailed consideration of immune system response using Unscented Kalman Filter

Recent advancements in mathematical modeling have enhanced the analysis of cancer responses to treatments, particularly in the promising field of chemovirotherapy. The present research introduces a novel data-driven mathematical model of chemovirotherapy that comprehensively incorporates the immune response. This thorough consideration of the immune system enables a comparative analysis of chemovirotherapy with immunotherapy, specifically CD8+T cells, CD4+T cells, and IL-2 cytokine therapies. By estimating a Michaelis-Menten constant from empirical therapy data for virotherapy, the computational efficiency of the dynamical system is enhanced while maintaining high accuracy in capturing virotherapy dynamics. Parameters are estimated using the Unscented Kalman Filter based on data from human melanoma cell lines. A stability analysis investigates parameter-dependent equilibrium shifts of the model, revealing that treatments such as chemotherapy destabilize the system at any dosage, which may inform treatment scenarios. Numerical simulations conducted on the model demonstrate that the combination of chemotherapy and virotherapy yields superior outcomes, particularly in cases of high tumor burden and weakened immune systems. This study presents a comprehensive framework for comparing immunotherapy, chemotherapy, and virotherapy, thereby advancing cancer therapeutic modeling and facilitating the optimization of comparative treatment strategies.