Glioblastoma multiforme (GBM) is an aggressive brain cancer with no curative treatment and poor prognosis. One of the biggest challenges with treating GBM is the inability of treatment to cross the blood-brain barrier resulting in poor drug distribution in the brain. Fortunately, scientists have recently developed a novel nose-to-brain delivery system that uses nanoparticles loaded with a chemotherapy drug called paclitaxel. Initial treatment investigations in vivo are showing significant promise in reducing and controlling the tumour burden. While exciting, before the treatment can move forward to clinical trial it needs to undergo a robust investigation for how effective it would be in a cohort of heterogeneous human patients and whether there is an optimal dosage protocol that should be used in a clinical trial.
To aid the progression of this treatment to clinical trial, we will develop a mathematical model for the intranasal injection of paclitaxel loaded nanoparticles and the effect of this treatment on GBM tumour cells. The parameters in the model will be optimised to data for the concentration of paclitaxel in the brain and the tumour volume over time under variations in the treatment. Using the model, and current human data for GBM tumour growth, we will create a virtual population of human patients for which we can simulate a clinical trial with this novel treatment. From this, we will be able to predict robust clinically relevant dosage protocols. In addition, we will be able to inform experimentalists of the robustness of the treatment and assist in moving this therapy forward and hopefully towards an effective cancer treatment for human patients.
In this project we will:
- develop a system of ODEs to describe the kinetics of paclitaxel loaded nanoparticle delivery and impact on GBM tumour cells
- parameterise the model using a range of different experimental data sets
- leverage known GBM tumour growth in humans, then create a virtual population of heterogeneous individuals
- use our virtual cohort to optimise the dosage protocol for the treatment and predict the robustness of treatment efficacy in a human cohort.
The research activities include:
- analysing the literature
- designing and prototyping a mathematical model
- fitting data from the literature
- evaluation of the model predictors
- creating a virtual population.
The project will involve working with experimental collaborators that are not mathematicians and potentially working with human data. The project can be developed for a VRES or Masters student.
We expect to develop a mathematical model for this novel treatment of brain cancer that is a biologically reliable model and can be used to investigate the robustness of this therapy and assist it in moving to a clinical trial.
Skills and experience
- Programming experience (in Matlab or otherwise).
- An understanding of ODEs, PDEs.
- Some understanding of biology would be good but is not required.
Contact the supervisor for more information.