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Generalizing Model To Analyze The Behavior Of Cancer Cells In Cancer…
Generalizing Model To Analyze The Behavior Of Cancer Cells In Cancer Patients
Problem
Cancer is a complex disease, with mechanisms involving variables that are difficult to predict and standardize.
There is no cure
There is treatment
It usually consists of trying to eliminate cancer cells while preserving healthy ones.
Problems?
It usually makes the patient very fragile
May end up selecting treatment-resistant cancer cells
Solution
Adopt treatment periodization strategies so as not to allow the proliferation of resistant cells and increase the patient's quality of life
Reference
Clinical Data Validated Mathematical Model for Intermittent Abiraterone Response in Castration-Resistant Prostate Cancer Patients
Authors
Justin Bennett1
Xixu Hu2
Karissa Gund3
Jingteng Liu1
Anya Porter
Explore the response of intermittent androgen deprivation therapy to metastasized prostate cancer by employing a previously clinical data validated mathematical model
The model uses a system of ordinary differential equations constructed using Droop’s nutrient limiting theory assuming castration-sensitive cells and castration-resistant cells and studying how they vary according to some parameters
Intracellular Androgen
Serum Androgen
Castration-Resistant Cells (CR)
PSA level
Castration-Sensitive Cells (CS)
Link
https://www.siam.org/Portals/0/Documents/S130057FINAL.pdf?ver=2021-02-25-095809-417
Proposal
Our project aims, based on the reference model and studying mechanisms of other cancers, to generalize the proposed system of equations in order to be able to expand it to other types of diseases.
For this, we will have to identify other types of cancer with mechanisms that are sufficiently similar to prostate cancer in order to be able to trace equivalences and adapt the differential equations to maintain a pattern.
With these data, we'll bring the context closer to models that we already know as the SIR and, in this way, draw parallels in order to define a model that works for more than one type of cancer, changing only specific terms for each one.
We plan to validate and calibrate our model with information taken from a database containing information about patients with the cancers that we decide upon after the evaluation. However, initially we will use the database used in the reference article that takes into account 41 patients at Phoenix, Arizona’s Mayo Clinic
We hope to build a valid model that can have a positive influence on the treatment of people with cancer.
