The Role of the Innate Immune System in Oncolytic Virotherapy

Phan, Tuan Anh; Tian, Jianjun Paul

doi:10.1155/2017/6587258

Cited by 29 publications

(31 citation statements)

References 18 publications

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“…The state variables of the models in [8,9] are uninfected tumor cells, infected tumor cells, and immune cells. Phan and Tian [10] added another state variable to them, which describes the virus-free population. They examined the impact of innate immune response on infected cancer cells and the virus population.…”

Section: Introductionmentioning

confidence: 99%

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Modeling dynamics of cancer virotherapy with immune response

2020

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Virotherapy is a therapeutic treatment for cancer. It uses genetically engineered viruses to selectively infect, replicate in, and destroy cancer cells without damaging normal cells. In this paper, we present a modified model to include, within the dynamics of virotherapy, the interaction between uninfected tumor cells and immune response. The model is analyzed qualitatively to produce five equilibrium points. One of these equilibriums demonstrates the effect observed in virotherapy, where the immune system demolishes infected cells as well as viruses. Moreover, the existence and stability of the equilibrium points are established under certain criteria. Numerical simulations are performed to display the agreement with the analytical results. Finally, parameter analysis is carried out to illustrate which parameters in the model affect the outcome of virotherapy.

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Section: Introductionmentioning

confidence: 99%

“…In this paper, we modify the mathematical model in [10] by introducing a more realistic interaction between the innate immune system and uninfected tumor cells. This is based on the fact that both tumor and virus-infected cells are recognized by natural killer cells that are part of the innate immune system [16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Modeling dynamics of cancer virotherapy with immune response

2020

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“…Let x(t), y(t), v(t), and z(t) represent the number of tumour cells, infected tumour cells, free virus and innate immune cells, respectively. Based on our models [20,31], the mathematical model for oncolytic viral therapy with the infection delay and the innate immune response is proposed in the following.…”

Section: Mathematical Model and Positivenessmentioning

confidence: 99%

“…However, this model did not include the free virus population, and it may not give a complete the dynamic picture of viral therapy with innate immune response. There also are some other mathematical models that describe the dynamics of oncolytic viral therapy [20,24].…”

Section: Introductionmentioning

confidence: 99%

“…However, these models either have two equations or three equations for tumour cells and infected tumour cells or/and viruses, and do not incorporate immune responses. To obtain insights, we have built a mathematical model for oncolytic virotherapy with the innate immune system [20], and a computational model [29]. In the current study, we further build the infection time and innate immune response into our basic model as the system of delay differential equations that models the oncolytic virotherapy on solid tumours with the delay of viral infection in the presence of the innate immune response.…”

Section: Introductionmentioning

confidence: 99%

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Backward Hopf bifurcation in a mathematical model for oncolytic virotherapy with the infection delay and innate immune effects

Guo

Niu

Tian

2019

Journal of Biological Dynamics

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In this paper, we consider a system of delay differential equations that models the oncolytic virotherapy on solid tumours with the delay of viral infection in the presence of the innate immune response. We conduct qualitative and numerical analysis, and provide possible medical implications for our results. The system has four equilibrium solutions. Fixed point analysis indicates that increasing the burst size and infection rate of the viruses has positive contribution to the therapy. However, increasing the immune killing infection rate, the immune stimulation rate, or the immune killing virus rate may lead the treatment failed. The viral infection time delay induces backward Hopf bifurcations, which means that the therapy may fail before time delay increases passing through a Hopf bifurcation. The parameter analysis also shows how saddle-node and Hopf bifurcations occur as viral burst size and other parameters vary, which yields further insights into the dynamics of the virotherapy.

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Quantitative Systems Pharmacology Approaches for Immuno‐Oncology: Adding Virtual Patients to the Development Paradigm

Chelliah¹,

Lazarou²,

Bhatnagar

et al. 2020

Clin Pharma and Therapeutics

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Drug development in oncology commonly exploits the tools of molecular biology to gain therapeutic benefit through reprograming of cellular responses. In immuno‐oncology (IO) the aim is to direct the patient’s own immune system to fight cancer. After remarkable successes of antibodies targeting PD1/PD‐L1 and CTLA4 receptors in targeted patient populations, the focus of further development has shifted toward combination therapies. However, the current drug‐development approach of exploiting a vast number of possible combination targets and dosing regimens has proven to be challenging and is arguably inefficient. In particular, the unprecedented number of clinical trials testing different combinations may no longer be sustainable by the population of available patients. Further development in IO requires a step change in selection and validation of candidate therapies to decrease development attrition rate and limit the number of clinical trials. Quantitative systems pharmacology (QSP) proposes to tackle this challenge through mechanistic modeling and simulation. Compounds’ pharmacokinetics, target binding, and mechanisms of action as well as existing knowledge on the underlying tumor and immune system biology are described by quantitative, dynamic models aiming to predict clinical results for novel combinations. Here, we review the current QSP approaches, the legacy of mathematical models available to quantitative clinical pharmacologists describing interaction between tumor and immune system, and the recent development of IO QSP platform models. We argue that QSP and virtual patients can be integrated as a new tool in existing IO drug development approaches to increase the efficiency and effectiveness of the search for novel combination therapies.

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The Role of the Innate Immune System in Oncolytic Virotherapy

Cited by 29 publications

References 18 publications

Modeling dynamics of cancer virotherapy with immune response

Modeling dynamics of cancer virotherapy with immune response

Backward Hopf bifurcation in a mathematical model for oncolytic virotherapy with the infection delay and innate immune effects

Quantitative Systems Pharmacology Approaches for Immuno‐Oncology: Adding Virtual Patients to the Development Paradigm

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