Modelling heterogeneity in viral-tumour dynamics: The effects of gene-attenuation on viral characteristics. (7th October 2018)

• Record Type: Journal Article
• Authors: Jenner, Adrianne; Yun, Chae-Ok; Yoon, Arum; Kim, Peter S.; Coster, Adelle C.F.
• Is Part Of: Journal of theoretical biology. Volume 454(2018)
• Page Start: 41
• Page End: 52
• Publication Date: 2018-10-07
• Subjects: Oncolytic virus -- Tumour -- In vitro -- Mathematical modelling -- Integro-differential equation -- E1B 19 -- E1B 55; Biology -- Periodicals; Biological Science Disciplines -- Periodicals; Biology -- Periodicals; Biologie -- Périodiques; Theoretische biologie; Biology; Periodicals; 571.05
• DOI: 10.1016/j.jtbi.2018.05.030
• Languages: English
• ISSNs: 0022-5193

Abstract

Highlights: A model for a oncolytic virus-tumour interaction allowing for heterogeneity. Effective model representing experimental observations of the virus-tumour population. Hierarchical parameter optimisation identifies processes affected by viral modifications. Abstract: The use of viruses as a cancer treatment is becoming increasingly more robust; however, there is still a long way to go before a completely successful treatment is formulated. One major challenge in the field is to select which virus, out of a burgeoning number of oncolytic viruses and engineered derivatives, can maximise both treatment spread and anticancer cytotoxicity. To assist in solving this problem, an in-depth understanding of the virus-tumour interaction is crucial. In this article, we present a novel integro-differential system with distributed delays embodying the dynamics of an oncolytic adenovirus with a fixed population of tumour cells in vitro, allowing for heterogeneity to exist in the virus and cell populations. The parameters of the model are optimised in a hierarchical manner, the purpose of which is not to obtain a perfect representation of the data. Instead, we place our parameter values in the correct region of the parameter space. Due to the sparse nature of the data it is not possible to obtain the parameter values with any certainty, but rather we demonstrate the suitability of the model. Using our model we quantify how modifications to the viral genome alter the viral characteristics, specifically how the attenuation of the E1B 19 and E1B 55 gene affect the system performance, and identify the dominant processes altered by the mutations. From our analysis, we conclude that the deletion of the E1B 55 gene significantly reduces the replication rate of the virus in comparison to the deletion of the E1B 19 gene. We also found that the deletion of both the E1B 19 and E1B 55 genes resulted in a long delay in the average replication start time of the virus. This leads us to propose the use of E1B 19 gene-attenuated adenovirus for cancer therapy, as opposed to E1B 55 gene-attenuated adenoviruses.