Abstract
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.
Highlights
In vitro and in vivo experiments are model systems of the real world, and given the understanding of the limitations of such approaches, study results are to be interpreted as simplifications that may Viruses 2017, 9, 239; doi:10.3390/v9090239 www.mdpi.com/journal/virusesViruses 2017, 9, 239 not hold truth in the real world, which for cancer biology is the patient
We aim to describe and discuss the contribution that integrated mathematical modeling could make to the advancement of oncolytic virotherapy (OVT)—with emphasis on the immunological aspects of OVT
Despite the many breakthroughs in oncolytic virus biology and mathematical modeling, further improvement in several arenas is essential for successful implementation of OVT in clinical reality
Summary
In vitro and in vivo experiments are model systems of the real world, and given the understanding of the limitations of such approaches, study results are to be interpreted as simplifications that may Viruses 2017, 9, 239; doi:10.3390/v9090239 www.mdpi.com/journal/viruses. If fitted to experimental data, mathematical models could identify appropriate parameters to estimate contributions of different mechanisms to simulated emergent dynamics. Sensitivity analysis may be deployed to investigate which mechanisms truly drive experimental outcome to identify promising treatment targets for example. For cancer biology, it may be cell migration rather than cell proliferation that drives an aggressive phenotype [2]. Alan Perelson and colleagues published a series of articles on optimal strategies in immunology [5,6,7] Such models helped identify the dynamics and time course of HIV infection [8,9,10] and response to treatment [11]. Having quantitative models in place that can simulate effective OVT could greatly reduce time and effort in the search for optimal OVT for subpopulations of cancer patients
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