The replicability of oncolytic virus: Defining conditions in tumor virotherapy

Tian, Jianjun Paul

doi:10.3934/mbe.2011.8.841

Cited by 54 publications

(58 citation statements)

References 14 publications

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“…• The tumour-virus interaction is one to one, that is, one virus infects one tumour cell which later multiplies into infected tumour cells [57].…”

Section: Tumour Density U(r T) and I(r T)mentioning

confidence: 99%

“…Virus production is dependent on its burst size, that is, the larger the burst size, the higher the number of viruses produced [57]. Virus production is taken to be bδI, where b is the virus burst size measured per day and δ is the infected tumour cell death rate measured per day [57].…”

Section: Virus Density V(r T)mentioning

confidence: 99%

“…Virus production is taken to be bδI, where b is the virus burst size measured per day and δ is the infected tumour cell death rate measured per day [57]. The virus gets deactivated in body tissue at a rate γ per day.…”

Section: Virus Density V(r T)mentioning

confidence: 99%

“…In this paper, we extend the growing literature on tumour virotherapy models by presenting a mathematical model of chemovirotherapy which builds upon those presented by Tian [57] and Malinzi et al [36]. To this end, we develop and analyse a mathematical model which combines chemotherapy and virotherapy treatments.…”

Section: Introductionmentioning

confidence: 95%

“…Tian [57] presented a basic mathematical model in the form of ordinary differential equations (ODEs), for virotherapy treatment which incorporated virus burst size. This mathematical model was based on an earlier basic virotherapy models of Wodarz and Komarova [70].…”

Section: Introductionmentioning

confidence: 99%

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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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“…• The tumour-virus interaction is one to one, that is, one virus infects one tumour cell which later multiplies into infected tumour cells [57].…”

Section: Tumour Density U(r T) and I(r T)mentioning

confidence: 99%

Section: Virus Density V(r T)mentioning

confidence: 99%

Section: Virus Density V(r T)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Modelling the spatiotemporal dynamics of chemovirotherapy cancer treatment

Malinzi

Eladdadi

Sibanda

2017

Journal of Biological Dynamics

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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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Dynamics of immunotherapy antitumor models with impulsive control strategy

Wang

Zhang

2021

Math Methods in App Sciences

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In this paper, using the methods of killing tumors and impulsive differential equations, two immunotherapy antitumor models for describing therapies of general tumors and advanced solid tumors are established. By using the theories of impulsive equations, small amplitude perturbation techniques, and the comparison technique, we obtain the conditions which guarantee the global asymptotical stability of the tumor‐eliminated periodic solution and system permanence, when immunotherapy alone is performed. The numerical results of the influences of the impulsive perturbation on the inherent oscillation show rich dynamics, such as period‐doubling bifurcation and chaos. Moreover, the effects of the combination of radiotherapy with immunotherapy on antitumor are obtained, including the threshold value of stability conditions of tumor‐eradication periodic solution when the mixed combination treatment of immunotherapy and radiotherapy is performed. Some numerical simulations for the effects of the timing of radiotherapy application and the timing of injection T cells on the threshold value are performed. Finally, we present some theoretical methods for suppressing the growth of tumors.

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The replicability of oncolytic virus: Defining conditions in tumor virotherapy

Cited by 54 publications

References 14 publications

Modelling the spatiotemporal dynamics of chemovirotherapy cancer treatment

Modelling the spatiotemporal dynamics of chemovirotherapy cancer treatment

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

Dynamics of immunotherapy antitumor models with impulsive control strategy

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