Oncolytic potency and reduced virus tumor-specificity in oncolytic virotherapy. A mathematical modelling approach

Abstract

In the present paper, we address by means of mathematical modeling the following main question: How can oncolytic virus infection of some normal cells in the vicinity of tumor cells enhance oncolytic virotherapy? We formulate a mathematical model describing the interactions between the oncolytic virus, the tumor cells, the normal cells, and the antitumoral and antiviral immune responses. The model consists of a system of delay differential equations with one (discrete) delay. We derive the model’s basic reproductive number within tumor and normal cell populations and use their ratio as a metric for virus tumor-specificity. Numerical simulations are performed for different values of the basic reproduction numbers and their ratios to investigate potential trade-offs between tumor reduction and normal cells losses. A fundamental feature unravelled by the model simulations is its great sensitivity to parameters that account for most variation in the early or late stages of oncolytic virotherapy. From a clinical point of view, our findings indicate that designing an oncolytic virus that is not 100% tumor-specific can increase virus particles, which in turn, can further infect tumor cells. Moreover, our findings indicate that when infected tissues can be regenerated, oncolytic viral infection of normal cells could improve cancer treatment.

Modeling Oncolytic Viral Therapy, Immune Checkpoint Inhibition, and the Complex Dynamics of Innate and Adaptive Immunity in Glioblastoma Treatment

“…The parameter h T represents the population of T s at which the immune cells lyse tumor cells at half of their maximum killing rate. We use a baseline value of h T = 2.7 × 10 4 cells from Banerjee et al (2015), but we allow for a feasible range that includes much smaller values, as seen in Mahasa et al (2017).…”

“…Term (5b) represents the proliferation of adaptive T cells due to the presence of tumor antigens on both susceptible and infected tumor cells. We assume a baseline value for the tumor cell-mediated proliferation rate of tumor-specific adaptive immune cells, a AT , of 0.0016 h −1 , converted from the rate in Mahasa et al (2017). We again use Michaelis-Menten kinetics with half-saturation constant h T to model the saturation of T cell activity, due to the restrictive tumor architecture.…”

“…Term (5d) represents natural death of antitumor T cells. We use δ YT = 3.75 × 10 −4 h −1 from Mahasa et al (2017), corresponding to a half-life of about 77 days.…”

A mathematical model of viral oncology as an immuno-oncology instigator

“…Mathematical models have been used extensively to understand and predict tumour growth and tumourimmune interactions (see Santiago et al [2017]; Walker and Enderling [2016]; Wodarz [2016] for reviews). Existing models range from formulations as ordinary dierential equations (ODEs) [Idema et al, 2010;Kim et al, 2015;Kirschner and Panetta, 1998;MacNamara and Eftimie, 2015], to partial dierential equations [Hillen et al, 2013;Malinzi et al, 2017] and discrete DDEs [Liu et al, 2007;Mahasa et al, 2017;Villasana and Radunskaya, 2003]. Crivelli et al [2012] developed and analysed a discrete DDE model of tumour growth and viral oncology.…”

Chandipura Virus Utilizes the Prosurvival Function of RelA NF-κB for Its Propagation

“…Mathematical studies revealing a quantitative constraint underlying RelAdependent viral propagation. Mathematical analyses of viral growth kinetics often offer valuable mechanistic insight into RNA virus pathogenesis (36,37). Using mathematical formalism, we set out to probe quantitatively the dependence of CHPV growth on RelA-sensitive cell death events.…”