Modeling of Dendritic Cell-based vaccination Immunotherapy using Artificial Neural Networks

Exposure-response Modeling and Simulation is especially useful in oncology as it permits to predict and design un-experimented clinical trials and dose selection. Dendritic Cells (DC) are the most effective immune cells in the regulation of immune system. In this paper, a model based on Artificial Neural Network (ANN) is presented for analyzing the dynamics of antitumor vaccines using empirical data obtained from the experimentations of different groups of mice treated with DCs matured by bacterial CpG-DNA, LPS and whole lysate of a Gram-positive bacteria Listeria monocytogenes. Simulations show that the proposed model can interpret important features of empirical data. Owing to nonlinearity capability of ANN, the proposed ANN model has been able not only to describe the contradictory empirical results, but also to predict new vaccination patterns for controlling the tumor growth. For example, the proposed model predicts an exponentially-increasing pattern of CpG-matured DC which appears to be effective in suppressing the tumor growth. Keywords: Artificial Neural Network, Dendritic Cells, Immunotherapy, Tumor growth rate;