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Al-Tuwairqi2020 - Dynamics of cancer virotherapy - Phase I treatment

ABSTRACT: This ordinary differential equation model of cancer virotherapy dynamics is described in the publication: Salma M. Al-Tuwairqi, Najwa O. Al-Johani, Eman A. Simbawa, "Modeling dynamics of cancer radiovirotherapy", Journal of Theoretical Biology, Volume 506, 2020, 110405, ISSN 0022-5193, DOI: 10.1016/j.jtbi.2020.110405. Comment: This model is represented by the equations in system (2) of the publication manuscript and describes the cancer-virus interactions for the Phase I virotherapy treatment. Abstract: Advances in genetic engineering have paved the way for a new therapy for cancer, which is called virotherapy. This treatment uses genetically engineered viruses which selectively infect, replicate in, and destroy cancer cells without damaging normal cells. Furthermore, current research and clinical trials have indicated that these viruses can be delivered as single agents or in combination with other therapies. In this paper, we propose systems of ordinary differential equations for modeling the dynamics of aggressive tumor growth under radiovirotherapy treatment. We divide the treatment period into two phases; consequently, we present two mathematical models. First, we formulate the virotherapy model as Phase I of the treatment. Then we extend the model to include radiotherapy in combination with virotherapy as Phase II of the treatment. Comprehensive qualitative analyses of both models are conducted. Furthermore, numerical experiments are performed in order to support the analytical results. An analysis of the parameters is also carried out to investigate their effects on the outcome of the treatment. Overall, the analytical results reveal that radiovirotherapy is more effective than, and a good alternative to, virotherapy, as it is capable of eradicating tumors completely.

DISEASE(S): Cancer

SUBMITTER: Rahuman Sheriff

PROVIDER: BIOMD0000001031 | BioModels | 2021-08-16

REPOSITORIES: BioModels

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Modeling dynamics of cancer radiovirotherapy.

Al-Tuwairqi Salma M SM Al-Johani Najwa O NO Simbawa Eman A EA

Journal of theoretical biology 20200729

Advances in genetic engineering have paved the way for a new therapy for cancer, which is called virotherapy. This treatment uses genetically engineered viruses which selectively infect, replicate in, and destroy cancer cells without damaging normal cells. Furthermore, current research and clinical trials have indicated that these viruses can be delivered as single agents or in combination with other therapies. In this paper, we propose systems of ordinary differential equations for modeling the ...[more]

PMID: 32738266

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Project description:The paper describes a model of oncolytic virotherapy. Created by COPASI 4.26 (Build 213) This model is described in the article: Mathematical Modelling of the Interaction Between Cancer Cells and an Oncolytic Virus: Insights into the Effects of Treatment Protocols Adrianne L. Jenner, Chae-Ok Yun, Peter S. Kim, Adelle C. F. Coster Bull Math Biol (2018) 80:1615–1629 Abstract: Oncolyticvirotherapyisanexperimentalcancertreatmentthatusesgenet- ically 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 Kim et al. (Biomaterials 32(9):2314–2326, 2011) modified virus particles with the polymer polyethylene glycol (PEG) and the monoclonal antibody herceptin. Whilst PEG mod- ification appeared to improve plasma retention and initial infectivity, it also increased the virus particle arrival time. We derive a mathematical model that describes the inter- action 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 are explored. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Dataset Information

Al-Tuwairqi2020 - Dynamics of cancer virotherapy - Phase I treatment

Publications

Modeling dynamics of cancer radiovirotherapy.

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