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      A Cellular Automata Model of Oncolytic Virotherapy in Pancreatic Cancer

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          Abstract

          Oncolytic virotherapy is known as a new treatment to employ less virulent viruses to specifically target and damage cancer cells. This work presents a cellular automata model of oncolytic virotherapy with an application to pancreatic cancer. The fundamental biomedical processes (like cell proliferation, mutation, apoptosis) are modeled by the use of probabilistic principles. The migration of injected viruses (as therapy) is modeled by diffusion through the tissue. The resulting diffusion–reaction equation with smoothed point viral sources is discretized by the finite difference method and integrated by the IMEX approach. Furthermore, Monte Carlo simulations are done to quantitatively evaluate the correlations between various input parameters and numerical results. As we expected, our model is able to simulate the pancreatic cancer growth at early stages, which is calibrated with experimental results. In addition, the model can be used to predict and evaluate the therapeutic effect of oncolytic virotherapy.

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          Most cited references36

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          ONCOLYTIC VIROTHERAPY

          Oncolytic virotherapy is an emerging treatment modality which uses replication competent viruses to destroy cancers. Advances in the past two years include preclinical proof of feasibility for a single-shot virotherapy cure, identification of drugs that accelerate intratumoral virus propagation, new strategies to maximize the immunotherapeutic potential of oncolytic virotherapy, and clinical confirmation of a critical viremic thereshold for vascular delivery and intratumoral virus replication. The primary clinical milestone was completion of accrual in a phase III trial of intratumoral herpes simplex virus therapy using talimogene laherparepvec for metastatic melanoma. Challenges for the field are to select ‘winners’ from a burgeoning number of oncolytic platforms and engineered derivatives, to transiently suppress but then unleash the power of the immune system to maximize both virus spread and anticancer immunity, to develop more meaningful preclinical virotherapy models and to manufacture viruses with orders of magnitude higher yields compared to established vaccine manufacturing processes.
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            Integrating oncolytic viruses in combination cancer immunotherapy

            Oncolytic viruses can be usefully integrated into tumour immunotherapies, as they target multiple steps within the cancer-immunity cycle. Oncolytic viruses directly lyse tumour cells, leading to the release of soluble antigens, danger signals and type I interferons, which drive antitumour immunity. In addition, some oncolytic viruses can be engineered to express therapeutic genes or can functionally alter tumour-associated endothelial cells, further enhancing T cell recruitment into immune-excluded or immune-deserted tumour microenvironments. Oncolytic viruses can also utilize established tumours as an in situ source of neoantigen vaccination through cross-presentation, resulting in regression of distant, uninfected tumours. These features make oncolytic viruses attractive agents for combination strategies to optimize cancer immunotherapy.
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              Experimental therapy of human glioma by means of a genetically engineered virus mutant.

              Malignant gliomas are the most common malignant brain tumors and are almost always fatal. A thymidine kinase-negative mutant of herpes simplex virus-1 (dlsptk) that is attenuated for neurovirulence was tested as a possible treatment for gliomas. In cell culture, dlsptk killed two long-term human glioma lines and three short-term human glioma cell populations. In nude mice with implanted subcutaneous and subrenal U87 human gliomas, intraneoplastic inoculation of dlsptk caused growth inhibition. In nude mice with intracranial U87 gliomas, intraneoplastic inoculation of dlsptk prolonged survival. Genetically engineered viruses such as dlsptk merit further evaluation as novel antineoplastic agents.
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                Author and article information

                Contributors
                J.chen-6@tudelft.nl
                Journal
                Bull Math Biol
                Bull. Math. Biol
                Bulletin of Mathematical Biology
                Springer US (New York )
                0092-8240
                1522-9602
                31 July 2020
                31 July 2020
                2020
                : 82
                : 8
                : 103
                Affiliations
                [1 ]Department of Biomedical Engineering and Physics, Amsterdam UMC, Amsterdam, The Netherlands
                [2 ]GRID grid.5292.c, ISNI 0000 0001 2097 4740, Delft Institute of Applied Mathematics, , Delft University of Technology, ; Delft, The Netherlands
                [3 ]GRID grid.6451.6, ISNI 0000000121102151, Faculty of Biomedical Engineering, , Technion-Israel Institute of Technology, ; Haifa, Israel
                [4 ]GRID grid.12155.32, ISNI 0000 0001 0604 5662, Division of Mathematics and Statistics, Faculty of Sciences, , Hasselt University, ; Diepenbeek, Belgium
                Author information
                http://orcid.org/0000-0002-4033-5636
                Article
                780
                10.1007/s11538-020-00780-5
                7395005
                32737595
                e5bd8a6b-1c2a-49bb-810d-839249e87d89
                © The Author(s) 2020

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 11 March 2020
                : 16 July 2020
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100004543, China Scholarship Council;
                Award ID: 201607720043
                Award Recipient :
                Categories
                Original Paper
                Custom metadata
                © Society for Mathematical Biology 2020

                Quantitative & Systems biology
                cellular automata,computational modeling,cancer treatment,virotherapy,monte carlo simulations

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