{"database":"BioModels","file_versions":[{"headers":{"Content-Type":["application/json"]},"body":{"files":{"Txt":["https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000850?filename=curation_notes.txt"],"Owl":["https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000850?filename=Jenner2019-biopax2.owl","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000850?filename=Jenner2019-biopax3.owl"],"Xml":["https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000850?filename=Jenner2019.xml","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000850?filename=manifest.xml"],"Other":["https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000850?filename=metadata.rdf","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000850?filename=Jenner2019.cps","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000850?filename=Jenner2019.sedml","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000850?filename=Jenner2019-matlab.m","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000850?filename=curation_image.png","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000850?filename=Jenner2019.ode","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000850?filename=Jenner2019-octave.m"]},"type":"primary"},"statusCode":"OK","statusCodeValue":200}],"scores":null,"additional":{"submitter":["Johannes Meyer"],"curationStatus":["Manually curated"],"modellingApproach":["ordinary differential equation model"],"levelVersion":["L2V4"],"submitter_keywords":["Oncology"],"full_dataset_link":["https://www.ebi.ac.uk/biomodels/BIOMD0000000850"],"publication_pubmed":["31400344"],"isPrivate":["false"],"repository":["BioModels"],"omics_type":["Models"],"modelFormat":["SBML"],"tokenised_name":["Jenner2019   Oncolytic virotherapy for tumours following a Gompertz growth law"],"publication_year":["2019"],"submissionId":["MODEL1911120002"],"first_author":["Adrianne L Jenner"],"publication_authors":["Adrianne L Jenner, Peter S Kim, Federico Frascoli"],"publication":["31400344,\n                            Oncolytic viruses are genetically engineered to treat growing tumours and represent a very promising therapeutic strategy. Using a Gompertz growth law, we discuss a model that captures the in vivo dynamics of a cancer under treatment with an oncolytic virus. With the aid of local stability analysis and bifurcation plots, the typical interactions between virus and tumour are investigated. The system shows a singular equilibrium and a number of nonlinear behaviours that have interesting biological consequences, such as long-period oscillations and bistable states where two different outcomes can occur depending on the initial conditions. Complete tumour eradication appears to be possible only for parameter combinations where viral characteristics match well with the tumour growth rate. Interestingly, the model shows that therapies with a high initial injection or involving a highly effective virus do not universally result in successful strategies for eradication. Further, the use of additional, \"boosting\" injection schedules does not always lead to complete eradication. Our framework, instead, suggests that low viral loads can be in some cases more effective than high loads, and that a less resilient virus can help avoid high amplitude oscillations between tumours and virus. Finally, the model points to a number of interesting findings regarding the role of oscillations and bistable states between a tumour and an oncolytic virus. Strategies for the elimination of such fluctuations depend strongly on the initial viral load and the combination of parameters describing the features of the tumour and virus.. null, 480.\n                            School of Mathematics and Statistics, University of Sydney, Sydney, NSW, Australia. Electronic address: a.jenner@maths.usyd.edu.au."],"submitter_mail":["johannes.p.meyer@gmail.com"],"submitter_affiliation":["EMBL-EBI"],"publicationId":["BIOMD0000000850"],"pubmed_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 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 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 are explored.","Oncolytic viruses are genetically engineered to treat growing tumours and represent a very promising therapeutic strategy. Using a Gompertz growth law, we discuss a model that captures the in vivo dynamics of a cancer under treatment with an oncolytic virus. With the aid of local stability analysis and bifurcation plots, the typical interactions between virus and tumour are investigated. The system shows a singular equilibrium and a number of nonlinear behaviours that have interesting biological consequences, such as long-period oscillations and bistable states where two different outcomes can occur depending on the initial conditions. Complete tumour eradication appears to be possible only for parameter combinations where viral characteristics match well with the tumour growth rate. Interestingly, the model shows that therapies with a high initial injection or involving a highly effective virus do not universally result in successful strategies for eradication. Further, the use of additional, \"boosting\" injection schedules does not always lead to complete eradication. Our framework, instead, suggests that low viral loads can be in some cases more effective than high loads, and that a less resilient virus can help avoid high amplitude oscillations between tumours and virus. Finally, the model points to a number of interesting findings regarding the role of oscillations and bistable states between a tumour and an oncolytic virus. Strategies for the elimination of such fluctuations depend strongly on the initial viral load and the combination of parameters describing the features of the tumour and virus."],"pubmed_title":["Mathematical Modelling of the Interaction Between Cancer Cells and an Oncolytic Virus: Insights into the Effects of Treatment Protocols.","Oncolytic virotherapy for tumours following a Gompertz growth law."],"pubmed_authors":["Jenner Adrianne L AL, Kim Peter S PS, Frascoli Federico F","Jenner Adrianne L AL, Yun Chae-Ok CO, Kim Peter S PS, Coster Adelle C F ACF"],"additional_accession":[]},"is_claimable":false,"name":"Jenner2019 - Oncolytic virotherapy for tumours following a Gompertz growth law","description":"\n      \n        This is a mathematical model using a Gompertz growth law to describe the in vivo dynamics of a cancer under treatment with an oncolytic virus.\n      \n    ","dates":{"last_modification":"2024-08-22","publication":"2024-09-02","submission":"2019-11-12"},"accession":"BIOMD0000000850","cross_references":{"sbo":["SBO:0000179"],"pubmed":["31400344","29644518"],"ncit":["C62713","C717","C36294","C3439"],"biomodels__db":["BIOMD0000000850","MODEL1911120002"],"go":["GO:0008219","GO:0044659"],"cl":["CL:0001063"],"bto":["BTO:0000152"],"efo":["0001460"]}}