Mathematical Modeling of Oncolytic Virus Therapy Reveals Role of the Immune Response

Abstract

Oncolytic adenoviruses (OAds) present a promising path for cancer treatment due to their selectivity in infecting and lysing tumor cells and their ability to stimulate the immune response. In this study, we use an ordinary differential equation (ODE) model of tumor growth inhibited by oncolytic virus activity to parameterize previous research on the effect of genetically re-engineered OAds in A549 lung cancer tumors in murine models. We find that the data are best fit by a model that accounts for an immune response, and that the immune response provides a mechanism for elimination of the tumor. We also find that parameter estimates for the most effective OAds share characteristics, most notably a high infection rate and low viral clearance rate, that might be potential reasons for these viruses’ efficacy in delaying tumor growth. Further studies observing E1A and P19 recombined viruses in different tumor environments may further illuminate the extent of the effects of these genetic modifications.

Incorporating Intracellular Processes in Virus Dynamics Models

“…Model Equation (1) with source and death terms (λ > 0 and d > 0) holds the implicit assumption that the timescale of the viral infection is long, predicting that an established infection results in persistent (chronic) disease. Short-lived (acute) viral infections, where an established infection is eventually cleared, have been modeled by setting λ = d = 0; examples include influenza [29,47], dengue [48][49][50][51], Zika [52], oncolytic adenoviruses [53], and SARS-CoV-2 [32][33][34][35][36]. For reviews of in-host mathematical models of acute and chronic viral infection, see [30,42,54,55].…”

Bistability and chaotic behaviors in a 4D cancer oncolytic Virotherapy mathematical model: Pspice and FPGA implementations

“…Various mathematical approaches allow the analysis of biological systems. For example, to infer gene expression patterns together with the cell fate trajectory in cancer therapies, and with the objectives to dimensionally reduce the patterns' spaces, pseudotemporal ordering methods are useful [54][55][56][57]. In the dynamical systems point of view, there are some weakness to construct cancer networks, because of lack of enough time series measurements on some cell expressions such as protein oscillations, gene dynamics etc...…”

“…Some investigations were considered on the way immune response act on tumor-virus system and provide the literature mathematical models where delay-immune responses are present. Some of these models said simpler, consider perfect integration of the virus with tumor cells and others which are more complex that take into account the antiviral immune responses effects [57].…”