Dendritic Immunotherapy Improvement for an Optimal Control Murine Model

Abstract

Therapeutic protocols in immunotherapy are usually proposed following the intuition and experience of the therapist. In order to deduce such protocols mathematical modeling, optimal control and simulations are used instead of the therapist's experience. Clinical efficacy of dendritic cell (DC) vaccines to cancer treatment is still unclear, since dendritic cells face several obstacles in the host environment, such as immunosuppression and poor transference to the lymph nodes reducing the vaccine effect. In view of that, we have created a mathematical murine model to measure the effects of dendritic cell injections admitting such obstacles. In addition, the model considers a therapy given by bolus injections of small duration as opposed to a continual dose. Doses timing defines the therapeutic protocols, which in turn are improved to minimize the tumor mass by an optimal control algorithm. We intend to supplement therapist's experience and intuition in the protocol's implementation. Experimental results made on mice infected with melanoma with and without therapy agree with the model. It is shown that the dendritic cells' percentage that manages to reach the lymph nodes has a crucial impact on the therapy outcome. This suggests that efforts in finding better methods to deliver DC vaccines should be pursued.

Delayed Effect of Dendritic Cells Vaccination on Survival in Glioblastoma: A Systematic Review and Meta-Analysis

“…Interestingly, Rangel-Reyes et al have shown this delay in a mathematical model for dendritic cell treatment. They evaluated common obstacles, such as immunosuppression and poor transfer to lymph nodes, that reduce the effect of the DCV and entered them into a mathematical model, and showed that time can be considered in the model as the gestation time or transport delay of the DCV [ 46 ]. In addition, the DCV may have less effect on more invasive glioblastomas.…”

Dose optimization of an adjuvanted peptide-based personalized neoantigen melanoma vaccine

“…In recent years, mechanistic and quantitative systems pharmacology (QSP) models have proved to be useful for understanding the complex interactions among the immune system, tumors, and therapeutic interventions [9][10][11][12][13]. These mathematical tools allow for modeling specific cell populations such as dendritic, memory T, helper T, cytotoxic T, or natural killer cells, as well as the tumor microenvironment [14][15][16][17][18][19][20], and have been used to better understand and improve multiple cancer treatments/vaccine regimens [21][22][23][24][25][26], as well as a tool to quantitatively measure immunotherapy responses of certain human immune cell functions such as tumor antigen-specific T cell responses that may lead to tumor reduction [4].…”

Optimal Control of Tumor Growth Model with Dendritic Cells as Immunotherapy

“…Sharma and Samanta also applied the model in optimal control problem in order to minimize the amount of drugs to be used in reducing the number of tumor growth. Furthermore, in 2017, Rangel-Reyes et al conducted an optimal control study of the murine model to measure the effectiveness of dendritic cells [14]. The model consists of tumor cells, CD4 + T cells as helper cells, CD8 + T or CTL cells, dendritic cells, IL-2, TGF-β + T as inhibitory cells, IFN-γ which increases regulation of MHC class 1, and the number of MHC class 1 for each melanoma cell [14].…”