Analysis of a delayed and diffusive oncolytic M1 virotherapy model with immune response
Introduction
Cancer is a group of diseases caused by uncontrolled growth of abnormal cells. It can spread to other parts of the body and lead to death. In 2018, the total number of new cancer cases were estimated to be 18.1 million and the number of deaths 9.6 million [1]. The number of new cases is expected to rise to 19.3 million by 2025 [2]. The most common conventional therapies used to treat cancer include radiotherapy and chemotherapy [2], [3]. However, these drugs are not selective and can damage normal healthy cells and cause many side effects like hair loss, fatigue and mouth sores [2]. Oncolytic virotherapy (OVT) is an experimental cancer treatment which has occupied a large area in experimental and theoretical research over the last few years [4], [5]. It uses oncolytic viruses which selectively target tumor cells while avoiding normal cells. Oncolytic viruses replicate inside tumor cells, which eventually leads to cell death. Thereupon, the new released virus particles proceed to kill other tumor cells [6]. The ability of oncolytic viruses to reduce tumor size or remove the tumor depends on the efficacy of OVT.
Many oncolytic viruses have been used in clinical trials such as reovirus, measles virus, vesicular stomatitis virus, herpes virus and adenovirus [7], [8], and great results have been achieved [5], [8], [9], [10]. However, there are many obstacles to this treatment approach which can affect the ability of oncolytic viruses to eradicate or control the tumor [5], [6], [8], [11], [12]. One of the main obstacles is caused by the immune system [6], [7], [11], [12]. The anti-tumor immune response can destroy the tumor cell before the viruses get the opportunity to replicate. This may influence the size of virus population and thus weaken the efficacy of treatment. Therefore, oncolytic viruses should be engineered for rapid removal of tumor cells before the accumulation of immune responses [6], [8], [11]. Although the immune system can have a negative impact on OVT, it can enhance and facilitate the success of the therapy [3], [7], [11], [13]. In fact, the relation between OVT and the immune system is poorly understood and it is an active area of research. Another important factor that can affect the success of OVT is the time period of some processes during viral infection [5], [14], [15].
Mathematical models have been considered as an important tool to understand the complicated dynamics of OVT and to determine the best treatment strategies. Many of these models are similar to HIV and HBV viral infection models [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29]. Some of these models were formulated using ordinary differential equations (ODEs). For example, Ratajczyk et al. [30] studied a model for glioma treatment that combines OVT with TNF- (tumor necrosis factor alpha) inhibitors. TNF-, which is produced by macrophages, induces apoptosis of infected tumor cells and thus constrains the replication of oncolytic viruses. The inhibition of TNF- can increase the efficiency of OVT. In [12], the same model was analyzed as an optimal control problem. Malinzi et al. [4] proposed a model to study the effect of combining oncolytic virotherapy with chemotherapy. The model describes the interaction between tumor cells, OVT, chemotherapy and immune response. They showed that OVT can enhance the ability of chemotherapy to eradicate the tumor if the correct dosage combination is used. Okamoto et al. [11] developed a model to show that reducing the selectivity of oncolytic viruses can facilitate tumor eradication before the start of adaptive immune response. Viral therapy models were also formulated using delay differential equations (DDEs). For instance, Wang et al. [5] extended the basic OVT model [31] and studied a system of nonlinear DDEs. They demonstrated that the virus burst size, which represents the number of new viruses formed inside the tumor cell, and the period of viral lytic cycle are two important factors that can influence the outcome of OVT. Hence, these two factors should be taken into account when developing new viruses for virotherapy. Kim et al. [14] considered a delayed model with cytotoxic T lymphocyte (CTL) immune response to study the impact of a time delay on OVT. They took the time delay as a bifurcation parameter and determined the existence conditions of Hopf bifurcation. Ashyani et al. [15] argued that cancer virotherapy fails in the second injection of the virus since CTLs suppress the infection before the virus removes the tumor. However, they showed using a delayed model that there is a narrow interval for the time delay where the therapy can be successful. Remarkably, the aforementioned models ignored the spatial variations in the distribution of cells and viruses. Nouni et al. [32] proposed a delayed model with saturated infection rate. They discussed the existence and local stability of all possible equilibria.
The diffusion of oncolytic viruses and cells within the tumor may play a crucial role in the delivery and success of cancer therapy [6], [13]. For this purpose, many mathematical models in the form of partial differential equations (PDEs) have been developed. For example, Tao and Guo [6] assumed that the immune response and viruses undergo diffusion and studied the spatiotemporal dynamics of uninfected tumor cells, infected tumor cells and immune response with a time delay. They found that the immune response may restrain the replication of oncolytic viruses and thus weaken the efficacy of OVT. Malinzi et al. [2] analyzed models that study the effect of the interaction between oncolytic viruses and CTL immune response on the outcome of OVT. In addition, they performed a stability analysis and obtained traveling wave solutions. Wang et al. [10] introduced a reaction–diffusion model with a time delay. They suggested different treatment strategies for OVT. In each strategy, they computed the optimal dosage required to completely eradicate the tumor depending on gene mutations in the cell.
In 2014, Lin et al. [33] identified a naturally occurring alphavirus M1 as a selective oncolytic virus which targets cancer cells that lack Zinc-finger antiviral protein (ZAP). The virus showed high efficacy in killing tumor cells without hurting normal cells. To get a better understanding of the role of M1 virus, Wang et al. [34] proposed an ODE model to study the effect of using oncolytic M1 virotherapy on the growth of normal and tumor cells. The normal and tumor cells compete on a limited nutrient source. The model takes the form where , , and denote the concentrations of nutrient, normal cells, tumor cells and free M1 virus particles, respectively. The parameter represents the nutrient recruitment rate, while is the minimum effective dosage of oncolytic M1 virus. The nutrient uptake rates of normal cells and tumor cells are given by and , respectively. The growth rate of normal cells as a consequence of consuming nutrient is given by , while the growth rate of tumor cells is given by . M1 virus kills tumor cells at rate , while it is generated by tumor cells at rate . The parameter is the washout rate constant of nutrient and bacteria. The parameters , and are the natural death rate constants of normal cells, tumor cells and M1 virus, respectively.
Our purpose in this paper is to extend model (1) by including three important effects that were not considered in [34]. First, the effect of anti-tumor immune response on the effectiveness of the therapy. Second, the effect of time lags during the process of consuming nutrient on tumor eradication. Third, the effect of diffusion on the spatio-temporal distributions of M1 virus and cells. The paper is organized as follows. In Section 2, we formulate the modified model. In Section 3, we prove the well-posedness of the model including the existence, non-negativity and boundedness of solutions. Also, we study all possible equilibrium points and determine the corresponding existence conditions. The last goal was not accomplished in [34] because they concentrated only on a tumor-free equilibrium point. In Section 4, we show that all equilibrium points are globally asymptotically stable and we confirm the local instability conditions. In Section 5, we perform some numerical simulations to support the theoretical results of the previous sections. Finally, Section 6 states the conclusion.
Section snippets
A delayed reaction–diffusion oncolytic M1 virotherapy model
In this section, we extend model (1) by adding two discrete time delays, diffusion and CTL immune response. All model’s components are assumed to diffuse freely in a connected and bounded domain with a smooth boundary . Thus, we study the following delayed partial differential equation model:
Basic properties
This section shows the existence, non-negativity and boundedness of solutions of model (2)–(4). Also, it determines all possible equilibrium points and the threshold conditions required for their existence.
Theorem 1 Assume that . Then, for any given initial conditions satisfying (3) there exists a unique non-negative and bounded solution defined on .
Proof Let be the Banach space of continuous functions from to with the norm , where is the Euclidean
Global properties
In this section, we show the global asymptotic stability of all equilibrium points mentioned in Theorem 2 by constructing suitable Lyapunov functionals. The construction of these functionals follows the method used in [39], [40], [41], where we first assume a Lyapunov functional with general coefficients and then solve a system of equations to find the values of the coefficients. Moreover, we verify the instability conditions of the equilibrium points.
Theorem 3 The competition-free equilibrium is
Numerical simulations
In this section, we conduct some numerical simulations in order to enhance the theoretical results presented in the previous sections. We take the spatial domain as with space step size and time step size . The parameters , , , , , , , and of model (2) are taken as free parameters. The rest of the parameters are fixed in Table 1. To verify the global stability of the six equilibria of model (2), we have the following cases:
- (i)
We take , ,
Conclusion
In this paper, we analyzed a delayed and diffusive oncolytic virotherapy model with immune response. We found that the model has six possible equilibrium points which are defined and stable under the following conditions:
- (i)
The competition-free equilibrium is defined and globally asymptotically stable if and . These threshold conditions determine when both the normal and tumor cell populations become extinct. Thus, this case does not show the effect of OVT or immune response on the
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgments
The authors are grateful to the handling editor and unknown referees for many constructive suggestions, which helped to improve the presentation of the paper.
Funding
This project was funded by the Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah, Saudi Arabia , under Grant No. (D-255-130-1440). The authors, therefore, gratefully acknowledge the DSR technical and financial support.
References (42)
- et al.
Analysis of virotherapy in solid tumor invasion
Math. Biosci.
(2015) - et al.
Lytic cycle: a defining process in oncolytic virotherapy
Appl. Math. Model.
(2013) - et al.
Oncolytic viruses and immunity
Curr. Opin. Immunol.
(2018) - et al.
Modeling oncolytic virotherapy: Is complete tumor-tropism too much of a good thing?
J. Theoret. Biol.
(2014) - et al.
Potentiating oncolytic viral therapy through an understanding of the initial immune responses to oncolytic viral infection
Curr. Opin. Virol.
(2015) - et al.
Hopf bifurcation analysis and optimal control of treatment in a delayed oncolytic virus dynamics
Math. Comput. Simulation
(2018) - et al.
A mathematical approach to effects of CTLs on cancer virotherapy in the second injection of virus
J. Theoret. Biol.
(2018) - et al.
Dynamics of a diffusive HBV model with delayed Beddington–DeAngelis response
Nonlinear Anal. RWA
(2014) Global properties of a class of HIV models
Nonlinear Anal. RWA
(2010)- et al.
Global stability of humoral immunity virus dynamics models with nonlinear infection rate and removal
Nonlinear Anal. RWA
(2015)
Dynamic analysis and optimal control for a model of hepatitis C with treatment
Commun. Nonlinear Sci. Numer. Simul.
Threshold dynamics of HCV model with cell-to-cell transmission and a non-cytolytic cure in the presence of humoral immunity
Commun. Nonlinear Sci. Numer. Simul.
Global dynamics of an HIV-1 infection model with distributed intracellular delays
Comput. Math. Appl.
Global stability for reaction–diffusion equations in biology
Comput. Math. Appl.
A mathematical model verifying potent oncolytic efficacy of M1 virus
Math. Biosci.
The GLOBOCAN 2018 Database
Oncolytic viral therapy and the immune system: a double-edged sword against cancer
Front. Immunol.
Enhancement of chemotherapy using oncolytic virotherapy: mathematical and optimal control analysis
Math. Biosci. Eng.
The competitive dynamics between tumor cells, a replication-competent virus and an immune response
J. Math. Biol.
Validation and analysis of a mathematical model of a replication-competent oncolytic virus for cancer treatment: implications for virus design and delivery
Cancer Res.
Optimal control model of tumor treatment with oncolytic virus and MEK inhibitor
Biomed. Res. Int.
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