Computational modeling approaches to the dynamics of oncolytic viruses

Wodarz, Dominik

doi:10.1002/wsbm.1332

Cited by 35 publications

(46 citation statements)

References 41 publications

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“…Oscillations in tumour cell population size have been seen in vivo in several other viral dynamic models (see Bajzer et al, 2008;Dingli et al, 2009;Wodarz, 2016). Komarova and Wodarz (2010), show that using mass action to model the viral infectivity leads to strong oscillations in the population of viruses and cancer cells.…”

Section: Discussionmentioning

confidence: 94%

Treating cancerous cells with viruses: insights from a minimal model for oncolytic virotherapy

Jenner

Coster

Kim

et al. 2018

Letters in Biomathematics

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In recent years, interest in the capability of virus particles as a treatment for cancer has increased. In this work, we present a mathematical model embodying the interaction between tumour cells and virus particles engineered to infect and destroy cancerous tissue. To quantify the effectiveness of oncolytic virotherapy, we conduct a local stability analysis and bifurcation analysis of our model. In the absence of tumour growth or viral decay, the model predicts that oncolytic virotherapy will successfully eliminate the tumour cell population for a large proportion of initial conditions. In comparison, for growing tumours and decaying viral particles there are no stable equilibria in the model; however, oscillations emerge for certain regions in our parameter space. We investigate how the period and amplitude of oscillations depend on tumour growth and viral decay. We find that higher tumour replication rates result in longer periods between oscillations and lower amplitudes for uninfected tumour cells. From our analysis, we conclude that oncolytic viruses can reduce growing tumours into a stable oscillatory state, but are insufficient to completely eradicate them. We propose that it is only with the addition of other anti-cancer agents that tumour eradication may be achieved by oncolytic virus. ARTICLE HISTORY

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Section: Discussionmentioning

confidence: 94%

Treating cancerous cells with viruses: insights from a minimal model for oncolytic virotherapy

Jenner

Coster

Kim

et al. 2018

Letters in Biomathematics

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“…In this study we consider a mathematical modelling and computational approach to help us improve our understanding of the physical barriers that limit virus spread. The use of mathematical models to understand the temporal and spatio-temporal dynamics of viruses (including oncolytic viruses) has seen great developments over the last three decades [7,8,9,10,11,12,13]. While the majority of these models focused on the temporal dynamics of oncolytic viruses (mainly due to the availability of temporal data) [14,15,16,17,18,19,20,21,22,23], more recent advances in tumour imaging generated data on the spatial spread of tumours and viruses, which then led to the development of different mathematical models investigating the spatial spread of these viruses [21,23,24,25,26,27].…”

Section: Introductionmentioning

confidence: 99%

Multiscale modelling of cancer response to oncolytic viral therapy

Alzahrani

Eftimie

Trucu

2019

Mathematical Biosciences

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Oncolytic viruses (OV) are viruses that can replicate selectively within cancer cells and destroy them. While the past few decades have seen significant progress related to the use of these viruses in clinical contexts, the success of oncolytic therapies is dampened by the complex spatial tumour-OV interactions. In this work, we present a novel multiscale moving boundary modelling for the tumour-OV interactions, which is based on coupled systems of partial differential equations both at macro-scale (tissue-scale) and at micro-scale (cell-scale) that are connected through a double feedback link. At the macro-scale, we account for the coupled dynamics of uninfected cancer cells, OV-infected cancer cells, extracellular matrix (ECM) and oncolytic viruses. At the same time, at the micro scale, we focus on essential dynamics of urokinase plasminogen activator (uPA) system which is one of the important proteolytic systems responsible for the degradation of the ECM, with notable influence in cancer invasion. While sourced by the cancer cells that arrive during their macro-dynamics within the outer proliferating rim of the tumour, the uPA micro-dynamics is crucial in determining the movement of the macro-scale tumour boundary (both in terms of direction and displacement magnitude). In this investigation, we consider three scenarios for the macro-scale tumour-OV interactions. While assuming the usual context of reaction-diffusion-taxis coupled PDEs, the three macro-dynamics scenarios gradually explore the influence of the ECM taxis over the tumour-OV interaction, in the form of haptotaxis of both uninfected and infected cells populations as well as the indirect ECM taxis for the oncolytic virus. Finally, the complex tumour-OV interactions is investigated numerically through the development a new multiscale moving boundary computational framework. While further investigation is needed to validate the findings of our modelling, for the parameter regimes that we considered, our numerical simulations indicate that the viral therapy leads to control and decrease of the overall cancer expansion and in certain cases this can result even in the elimination of the tumour.

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“…Indeed, various yet limited, mathematical models of oncolytic virus therapies have been developed to examine the emerging properties of these dynamics, suggest optimal treatment strategies as well as to inform the design of new experiments. For a comprehensive review on mathematical models of virotherapy, the reader is referred to excellent papers, reviews, and the references therein in [14,20,25,26,28,[66][67][68][69]. Here, we restrict our literature review to few key papers in modelling virotherapy treatments that have motivated the formulation of our model.…”

Section: Introductionmentioning

confidence: 99%

Modelling the spatiotemporal dynamics of chemovirotherapy cancer treatment

Malinzi

Eladdadi

Sibanda

2017

Journal of Biological Dynamics

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Chemovirotherapy is a combination therapy with chemotherapy and oncolytic viruses. It is gaining more interest and attracting more attention in the clinical setting due to its effective therapy and potential synergistic interactions against cancer. In this paper, we develop and analyse a mathematical model in the form of parabolic nonlinear partial differential equations to investigate the spatiotemporal dynamics of tumour cells under chemovirotherapy treatment. The proposed model consists of uninfected and infected tumour cells, a free virus, and a chemotherapeutic drug. The analysis of the model is carried out for both the temporal and spatiotemporal cases. Travelling wave solutions to the spatiotemporal model are used to determine the minimum wave speed of tumour invasion. A sensitivity analysis is performed on the model parameters to establish the key parameters that promote cancer remission during chemovirotherapy treatment. Model analysis of the temporal model suggests that virus burst size and virus infection rate determine the success of the virotherapy treatment, whereas travelling wave solutions to the spatiotemporal model show that tumour diffusivity and growth rate are critical during chemovirotherapy. Simulation results reveal that chemovirotherapy is more effective and a good alternative to either chemotherapy or virotherapy, which is in agreement with the recent experimental studies. ARTICLE HISTORY

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Computational modeling approaches to the dynamics of oncolytic viruses

Cited by 35 publications

References 41 publications

Treating cancerous cells with viruses: insights from a minimal model for oncolytic virotherapy

Treating cancerous cells with viruses: insights from a minimal model for oncolytic virotherapy

Multiscale modelling of cancer response to oncolytic viral therapy

Modelling the spatiotemporal dynamics of chemovirotherapy cancer treatment

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