Identification of the Optimal Pattern of the Injection and Dosage of DC Immunotherapy Using the Mathematical Models Based on Ordinary Differential Equations.

BACKGROUND: Mathematical modeling offers the possibility to select the optimal dose of a drug or vaccine. Considerable evidence show that many bacterial components can activate dendritic cells (DCs). Our previous report showed that multiple doses of DCs matured with Listeria monocytogenes led to tumor regression whereas multiple doses of CpG-matured DCs affected tumor reversely. OBJECTIVE: To assess a combined pattern of DC vaccination proposed by a mathematical model for tumor regression. METHOD: WEHI164 cells were inoculated subcutaneously in the right flank of BALB/c mice. Bone marrow-derived DCs were matured by Listeria monocytogenes and CpG motifs. DCs were injected using specific patterns and doses predicted by mathematical modeling. Effector cell-mediated cytotoxicity, gene expression of T cell-related transcription factors, as well as tumor growth and survival rate, were assessed in different groups. RESULTS: Our study indicated that the proposed mathematical model could simulate the tumor and immune system interaction, and it was verified by decreasing tumor size in the List+CpG group. However, comparing the effect of different treatment modalities on Th1/Treg transcription factor expression or cytotoxic responses revealed no advantage for combined therapy over other treatment modalities. CONCLUSIONS: These results suggest that finding new combinations of DC vaccines for the treatment of tumors will be promising in the future. The results of this study support the mathematical modelling for DC vaccine design. However, some parameters in this model must be modified to provide a more optimized therapy approach.

Optimized Dose of Dendritic Cell-based Vaccination in Experimental Model of Tumor Using Artificial Neural Network.

Previous studies have demonstrated that maturation of dendritic cells (DCs) by pathogenic components through pathogen-associated molecular patterns (PAMPs) such as Listeria monocytogenes lysate (LML) or CpG DNA can improve cancer vaccination in experimental models. In this study, a mathematical model based on an artificial neural network (ANN) was used to predict several patterns and dosage of matured DC administration for improved vaccination. The ANN model predicted that repeated co-injection of tumor antigen (TA)-loaded DCs matured with CpG (CpG-DC) and LML (List-DC) results in improved antitumor immune response as well as a reduction of immunosuppression in the tumor microenvironment. In the present study, we evaluated the ANN prediction accuracy about DC-based cancer vaccines pattern in the treatment of Wehi164 fibrosarcoma cancer-bearing mice. Our results showed that the administration of the DC vaccine according to ANN predicted pattern, leads to a decrease in the rate of tumor growth and size and augments CTL effector function. Furthermore, gene expression analysis confirmed an augmented immune response in the tumor microenvironment. Experimentations justified the validity of the ANN model forecast in the tumor growth and novel optimal dosage that led to more effective treatment.