4.5 Article

On the treatment of melanoma: A mathematical model of oncolytic virotherapy

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MATHEMATICAL BIOSCIENCES
卷 365, 期 -, 页码 -

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ELSEVIER SCIENCE INC
DOI: 10.1016/j.mbs.2023.109073

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Mathematical modelling; Oncolytic virotherapy; Disease dynamics; Differential equation modelling

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In this study, a mathematical model of oncolytic virotherapy for melanoma treatment is developed and analyzed. The model considers the impact of hypoxia on tumor dynamics and the engineering design of oncolytic viruses. The results show that oxygen-rich environments favor the use of adenoviruses as oncolytic agents and emphasize the importance of balancing viral infection and oncolytic capabilities in the engineering of these viruses.
We develop and analyze a mathematical model of oncolytic virotherapy in the treatment of melanoma. We begin with a special, local case of the model, in which we consider the dynamics of the tumour cells in the presence of an oncolytic virus at the primary tumour site. We then consider the more general regional model, in which we incorporate a linear network of lymph nodes through which the tumour cells and the oncolytic virus may spread. The modelling also considers the impact of hypoxia on the disease dynamics. The modelling takes into account both the effects of hypoxia on tumour growth and spreading, as well as the impact of hypoxia on oncolytic virotherapy as a treatment modality. We find that oxygen-rich environments are favourable for the use of adenoviruses as oncolytic agents, potentially suggesting the use of complementary external oxygenation as a key aspect of treatment. Furthermore, the delicate balance between a virus' infection capabilities and its oncolytic capabilities should be considered when engineering an oncolytic virus. If the virus is too potent at killing tumour cells while not being sufficiently effective at infecting them, the infected tumour cells are destroyed faster than they are able to infect additional tumour cells, leading less favourable clinical results. Numerical simulations are performed in order to support the analytic results and to further investigate the impact of various parameters on the outcomes of treatment. Our modelling provides further evidence indicating the importance of three key factors in treatment outcomes: tumour microenvironment oxygen concentration, viral infection rates, and viral oncolysis rates. The numerical results also provide some estimates on these key model parameters which may be useful in the engineering of oncolytic adenoviruses.

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