title: Modeling of tumor growth in dendritic cell-based immunotherapy using artificial neural networks.
abstract: Exposure-response modeling and simulation is especially useful in oncology as it permits to predict and design un-experimented clinical trials as well as dose selection. Dendritic cells (DC) are the most effective immune cells in the regulation of immune system. To activate immune system, DCs may be matured by many factors like bacterial CpG-DNA, Lipopolysaccharaide (LPS) and other microbial products. 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. Also, tumor lysate was added to DCs followed by addition of maturation factors. Simulations show that the proposed model can interpret the important features of empirical data. Owing to the nonlinearity properties, 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 to be effective in suppressing the tumor growth.
authors.affiliation: Laboratory of Signals and Electronic Systems, Electrical and Computer Engineering Faculty, K.N. Toosi University of Technology, Tehran, Iran. Electronic address: m_mehrian@ee.kntu.ac.ir.
authors.forename: Mohammad
authors.initials: M
authors.lastname: Mehrian
authors.affiliation: Laboratory of Signals and Electronic Systems, Electrical and Computer Engineering Faculty, K.N. Toosi University of Technology, Tehran, Iran. Electronic address: Asemani@eetd.kntu.ac.ir.
authors.forename: Davud
authors.initials: D
authors.lastname: Asemani
authors.affiliation: Laboratory of Signals and Electronic Systems, Electrical and Computer Engineering Faculty, K.N. Toosi University of Technology, Tehran, Iran. Electronic address: abazar_arabameri@ee.kntu.ac.ir.
authors.forename: Abazar
authors.initials: A
authors.lastname: Arabameri
authors.affiliation: Department of Immunology, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran. Electronic address: a.pourgholaminejad@modares.ac.ir.
authors.forename: Arash
authors.initials: A
authors.lastname: Pourgholaminejad
authors.affiliation: Department of Immunology, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran. Electronic address: hajatij@tums.ac.ir.
authors.forename: Jamshid
authors.initials: J
authors.lastname: Hadjati
mesh_terms: D000818:Animals; D001854:Bone Marrow Cells / Q000166:cytology; D019496:Cancer Vaccines / Q000494:pharmacology / Q000627:therapeutic use*; D045744:Cell Line, Tumor; D018899:CpG Islands; D003713:Dendritic Cells / Q000276:immunology*; D005260:Female; D007167:Immunotherapy; D051379:Mice; D008807:Mice, Inbred BALB C; D008954:Models, Biological; D009369:Neoplasms / Q000276:immunology / Q000473:pathology / Q000628:therapy*; D016571:Neural Networks, Computer; D047368:Tumor Burden
chemical_list: D019496:Cancer Vaccines
keywords: Artificial neural network; Dendritic cells; Immunotherapy; Tumor growth rate