Complex Dynamics of Virus Spread from Low Infection Multiplicities: Implications for the Spread of Oncolytic Viruses

Oncolytic viruses (OVs) are an emergent and unique therapy for cancer patients. Similar to chemo- and radiation therapy, OV can lyse (kill) cancer cell directly. In general, the advantages of OVs over other treatments are primarily: a higher safety profile (as shown by less adverse effects), ability to replicate, transgene(s) delivery, and stimulation of a host’s immune system against cancer. The latter has prompted successful use of OVs with other immunotherapeutic strategies in a synergistic manner. In spite of extended testing in pre-clinical and clinical setting, using biologically derived therapeutics like virus always raises potential concerns about safety (replication at non-intended locations) and bio-availability of the product. Recent advent in in vivo imaging techniques dramatically improves the convenience of use, quality of pictures, and amount of information acquired. Easy assessing of safety/localization of the biotherapeutics like OVs became a new potential weapon in the physician’s arsenal to improve treatment outcome. Given that OVs are typically replicating, in vivo imaging can also track virus replication and persistence as well as precisely mapping tumor tissues presence. This review discusses the importance of imaging in vivo in evaluating OV efficacy, as well as currently available tools and techniques.

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“…Tittering of tumor organs is the gold standard to determine viral quantity yet lacks information on spread. 24 , 25 …”

Section: The Role Of Imaging Tools In Ov Engineeringmentioning

confidence: 99%

The importance of imaging strategies for pre-clinical and clinical in vivo distribution of oncolytic viruses

Pelin

Wang

Bell

et al. 2018

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“…Viruses are infectious agents that rely on a living host cell to replicate. Infectious disease modeling has an extensive history in mathematics to simulate viral spread and cytotoxic effects [ 90 ]. Host cells are divided into susceptible (uninfected, S) and infected (I) cells, where C (number of total tumor cells) = S + I.…”

Section: Mathematical Modeling Of Infection: Susceptible and Infecmentioning

confidence: 99%

“…Further complexities may be added to a mathematical model to explicitly account for the interplay between multiplicities of viral infection and the antiviral states mediated by interferon [ 148 , 151 , 152 ] as a cellular response to viral infection. Of course, the model would require additional distinctions of antiviral and non-antiviral states [ 151 ] for uninfected cells (for example, see [ 90 ]).…”

Section: Modeling Specific Mechanisms Of Actionmentioning

confidence: 99%

Fighting Cancer with Mathematics and Viruses

Santiago

Heidbuechel

Kandell

et al. 2017

Viruses

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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 antitumor 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 research fields to increase the value of the preclinical development and the therapeutic efficacy of the resulting treatments.

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“…Infectious disease modeling has an extensive history in mathematics to simulate viral spread and cytotoxic effects [73]. Host cells are divided into susceptible (uninfected, S) and infected (I) cells, where C (number of total tumor cells) = S + I.…”

Section: Mathematical Modeling Of Infection: Susceptible and Infectedmentioning

confidence: 99%

“…Further complexities may be added to a mathematical model to explicitly account for the interplay between multiplicities of viral infection and the antiviral states mediated by interferon [119,122,123] as a cellular response to viral infection. Of course, the model would require additional distinction of antiviral and non-antiviral states [122] for uninfected cells (See [73] for example).…”

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.

show abstract

Complex Dynamics of Virus Spread from Low Infection Multiplicities: Implications for the Spread of Oncolytic Viruses

Cited by 32 publications

References 48 publications

The importance of imaging strategies for pre-clinical and clinical in vivo distribution of oncolytic viruses

The importance of imaging strategies for pre-clinical and clinical in vivo distribution of oncolytic viruses

Fighting Cancer with Mathematics and Viruses

Fighting Cancer with Mathematics and Viruses

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