Mathematical modelling of the interaction between cancer cells and an oncolytic virus: insights into the effects of treatment protocols

TL;DR

This paper develops a mathematical model to analyze how different treatment protocols and virus modifications influence the effectiveness of oncolytic virotherapy in cancer treatment.

Contribution

It introduces a new mathematical model that incorporates virus modifications and treatment protocols, enabling predictions of cancer response beyond experimental data.

Findings

Treatment protocol significantly impacts therapy outcomes.

PEG modification improves virus retention but delays tumor infection.

Model predicts optimal dosage strategies based on tumor growth parameters.

Abstract

Oncolytic virotherapy is an experimental cancer treatment that uses genetically engineered viruses to target and kill cancer cells. One major limitation of this treatment is that virus particles are rapidly cleared by the immune system, preventing them from arriving at the tumour site. To improve virus survival and infectivity modified virus particles with the polymer polyethylene glycol (PEG) and the monoclonal antibody herceptin. While PEG modification appeared to improve plasma retention and initial infectivity it also increased the virus particle arrival time. We derive a mathematical model that describes the interaction between tumour cells and an oncolytic virus. We tune our model to represent the experimental data by Kim et al. (2011) and obtain optimised parameters. Our model provides a platform from which predictions may be made about the response of cancer growth to other treatment protocols beyond those in the experiments. Through model simulations we find that the treatment protocol affects the outcome dramatically. We quantify the effects of dosage strategy as a function of tumour cell replication and tumour carrying capacity on the outcome of oncolytic virotherapy as a treatment. The relative significance of the modification of the virus and the crucial role it plays in optimising treatment efficacy is explored.