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Modeling of tumor growth in dendritic cell-based immunotherapy using artificial neural networks

Paper ID Volume ID Publish Year Pages File Format Full-Text
15138 1381 2014 8 PDF Available
Title
Modeling of tumor growth in dendritic cell-based immunotherapy using artificial neural networks
Abstract

•Four groups of DCs matured by CpG-DNA, LPS and whole lysate injected around tumors.•Tumor growth for each vaccine modeled by artificial neural networks.•ANN model is able to interpret the contradictory effects of DCs matured by CpG-DNA.•Proposed model predicts new efficient vaccination profiles.

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.

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Keywords
Artificial neural network; Dendritic cells; Immunotherapy; Tumor growth rate
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Modeling of tumor growth in dendritic cell-based immunotherapy using artificial neural networks
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Publisher
Database: Elsevier - ScienceDirect
Journal: Computational Biology and Chemistry - Volume 48, February 2014, Pages 21–28
Authors
, , , , ,
Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering
Get Full-Text Now
Don't Miss Today's Special Offer
Price was $35.95
You save - $31
Price after discount Only $4.95
100% Money Back Guarantee
Full-text PDF Download
Online Support
Any Questions? feel free to contact us