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Article

Optimising Hydrogel Release Profiles for Viro-Immunotherapy Using Oncolytic Adenovirus Expressing IL-12 and GM-CSF with Immature Dendritic Cells

1
Départment de Mathématiques et de Statistique, Université de Montréal, Montréal, QC H3T IJ4, Canda
2
Department of Mathematics, Faculty of Science, Engineering and Technology, Swinburne University of Technology, Hawthorn, VIC 3122, Australia
3
Department of Bioengineering, Hanyang University, Seongdong-gu, Seoul 04763, Korea
4
School of Mathematics and Statistics, University of Sydney, Sydney, NSW 2006, Australia
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(8), 2872; https://doi.org/10.3390/app10082872
Received: 20 March 2020 / Revised: 10 April 2020 / Accepted: 13 April 2020 / Published: 21 April 2020
(This article belongs to the Special Issue Dynamic Models of Biology and Medicine, Volume II)
Sustained-release delivery systems, such as hydrogels, significantly improve cancer therapies by extending the treatment efficacy and avoiding excess wash-out. Combined virotherapy and immunotherapy (viro-immunotherapy) is naturally improved by these sustained-release systems, as it relies on the continual stimulation of the antitumour immune response. In this article, we consider a previously developed viro-immunotherapy treatment where oncolytic viruses that are genetically engineered to infect and lyse cancer cells are loaded onto hydrogels with immature dendritic cells (DCs). The time-dependent release of virus and immune cells results in a prolonged cancer cell killing from both the virus and activated immune cells. Although effective, a major challenge is optimising the release profile of the virus and immature DCs from the gel so as to obtain a minimum tumour size. Using a system of ordinary differential equations calibrated to experimental results, we undertake a novel numerical investigation of different gel-release profiles to determine the optimal release profile for this viro-immunotherapy. Using a data-calibrated mathematical model, we show that if the virus is released rapidly within the first few days and the DCs are released for two weeks, the tumour burden can be significantly decreased. We then find the true optimal gel-release kinetics using a genetic algorithm and suggest that complex profiles present unnecessary risk and that a simple linear-release model is optimal. In this work, insight is provided into a fundamental problem in the growing field of sustained-delivery systems using mathematical modelling and analysis. View Full-Text
Keywords: sustained-release therapy; hydrogel; oncolytic virotherapy; immunotherapy; IL-12; GM-CSF; optimal control; data fitting; genetic algorithm; dendritic cells sustained-release therapy; hydrogel; oncolytic virotherapy; immunotherapy; IL-12; GM-CSF; optimal control; data fitting; genetic algorithm; dendritic cells
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MDPI and ACS Style

Jenner, A.L.; Frascoli, F.; Yun, C.-O.; Kim, P.S. Optimising Hydrogel Release Profiles for Viro-Immunotherapy Using Oncolytic Adenovirus Expressing IL-12 and GM-CSF with Immature Dendritic Cells. Appl. Sci. 2020, 10, 2872. https://doi.org/10.3390/app10082872

AMA Style

Jenner AL, Frascoli F, Yun C-O, Kim PS. Optimising Hydrogel Release Profiles for Viro-Immunotherapy Using Oncolytic Adenovirus Expressing IL-12 and GM-CSF with Immature Dendritic Cells. Applied Sciences. 2020; 10(8):2872. https://doi.org/10.3390/app10082872

Chicago/Turabian Style

Jenner, Adrianne L., Federico Frascoli, Chae-Ok Yun, and Peter S. Kim. 2020. "Optimising Hydrogel Release Profiles for Viro-Immunotherapy Using Oncolytic Adenovirus Expressing IL-12 and GM-CSF with Immature Dendritic Cells" Applied Sciences 10, no. 8: 2872. https://doi.org/10.3390/app10082872

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