Optimization of Virotherapy for Cancer

Biesecker, Matt; Kimn, Jung Han; Lu, Huitian; Dingli, David; Bajzer, Željko

doi:10.1007/s11538-009-9456-0

Cited by 42 publications

(34 citation statements)

References 37 publications

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“…Recent efforts have attempted to construct and validate mathematical models of measles oncolytic behavior [99–102]. The first such model developed by Dingli et al [99] explored the population dynamics of radiovirotherapy and the model parameters were fitted to prior experimental data of MV-NIS on multiple myeloma tumors.…”

Section: Mathematical Modeling and Predictive Test Systems Of Measlesmentioning

confidence: 99%

Attenuated Oncolytic Measles Virus Strains as Cancer Therapeutics

Msaouel¹,

Iankov²,

Dispenzieri³

et al. 2012

CPB

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Attenuated measles virus vaccine strains have emerged as a promising oncolytic vector platform, having shown significant anti-tumor activity against a broad range of malignant neoplasms. Measles virus strains derived from the attenuated Edmonston-B (MV-Edm) vaccine lineage have been shown to selectively infect, replicate in and lyse cancer cells while causing minimal cytopathic effect on normal tissues. This review summarizes the preclinical data that led to the rapid clinical translation of oncolytic measles vaccine strains and provides an overview of early clinical data using this oncolytic platform. Furthermore, novel approaches currently under development to further enhance the oncolytic efficacy of MV-Edm strains, including strategies to circumvent immunity or modulate immune system responses, combinatorial approaches with standard treatment modalities, virus retargeting as well as strategies for in vivo monitoring of viral replication are discussed.

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Section: Mathematical Modeling and Predictive Test Systems Of Measlesmentioning

confidence: 99%

Attenuated Oncolytic Measles Virus Strains as Cancer Therapeutics

Msaouel¹,

Iankov²,

Dispenzieri³

et al. 2012

CPB

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“…The lytic activity of viral particles is also a fundamental component. Several models have been custom tailored to certain viruses for the purpose of developing optimal therapy schedules [2,8]. Findings of these studies have recapitulated that each candidate virus has unique oncolytic dynamics, leading to a unique treatment plan.…”

Section: Introductionmentioning

confidence: 99%

A mathematical model for cell cycle-specific cancer virotherapy

Crivelli

Foldes

Kim

et al. 2012

Journal of Biological Dynamics

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Oncolytic viruses preferentially infect and replicate in cancerous cells, leading to elimination of tumour populations, while sparing most healthy cells. Here, we study the cell cycle-specific activity of viruses such as vesicular stomatitis virus (VSV). In spite of its capacity as a robust cytolytic agent,VSV cannot effectively attack certain tumour cell types during the quiescent, or resting, phase of the cell cycle. In an effort to understand the interplay between the time course of the cell cycle and the specificity of VSV, we develop a mathematical model for cycle-specific virus therapeutics. We incorporate the minimum biologically required time spent in the non-quiescent cell cycle phases using systems of differential equations with incorporated time delays. Through analysis and simulation of the model, we describe how varying the minimum cycling time and the parameters that govern viral dynamics affect the stability of the cancer-free equilibrium, which represents therapeutic success.

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“…Bajzer and Dingli as well as Jacobsen and Pilyugin added a syncytia-forming fusion and budding mechanisms to lysis for a mathematical model (Figure 4) that may be tailored to a particular viral mode of action [126][127][128][129]. These models only allowed budding as a mechanism for viral particle production from syncytia, assuming that no apoptosis occurs from fused cells [130].…”

Section: Modeling Specific Mechanisms Of Actionmentioning

confidence: 99%

Fighting Cancer with Mathematics and Viruses

Santiago¹,

Heidbuechel²,

Kandell³

et al. 2017

Preprint

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After decades of research, oncolytic virotherapy has recently advanced to clinical application, and currently a multitude of novel agents and combination treatments are being evaluated for cancer therapy. Oncolytic agents preferentially replicate in tumor cells, inducing tumor cell lysis and complex anti-tumor effects, such as innate and adaptive immune responses and the destruction of tumor vasculature. With the availability of different vector platforms and the potential of both genetic engineering and combination regimens to enhance particular aspects of safety and efficacy, the identification of optimal treatments for patient subpopulations or even individual patients becomes a top priority. Mathematical modeling can provide support in this arena by making use of experimental and clinical data to generate hypotheses about the mechanisms underlying complex biology and, ultimately, predict optimal treatment protocols. Increasingly complex models can be applied to account for therapeutically relevant parameters such as components of the immune system. In this review, we describe current developments in oncolytic virotherapy and mathematical modeling to discuss the benefit of integrating different modeling approaches into biological and clinical experimentation. Conclusively, we propose a mutual combination of these fields of research for more efficient development and effective treatments.

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Optimization of Virotherapy for Cancer

Cited by 42 publications

References 37 publications

Attenuated Oncolytic Measles Virus Strains as Cancer Therapeutics

Attenuated Oncolytic Measles Virus Strains as Cancer Therapeutics

A mathematical model for cell cycle-specific cancer virotherapy

Fighting Cancer with Mathematics and Viruses

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