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MEDIC: Mathematical Explorations of Drug and Ionising radiation
combinations in Cancer therapy
Cancer is one of the top two healthcare challenges: 1 in 3 people will
have cancer in their lifetime. To translate that to the ideas factory
that generated this proposal, about 30 people attended the ideas factory
in various capacities, therefore about 10 of them will have cancer
during their lifetime.
While many advances have been made in cancer treatment, there are still
ways in which therapy can be improved. It is, for example, usually
treated by combinations of surgery, radiotherapy, and/or chemotherapy,
but the precise interaction of these treatments and the ways in which
different people react to them is poorly understood. This makes it
virtually impossible to prescribe the best therapeutic strategy for an
individual patient.
The challenge addressed in this project is to build a framework in which
to view disease through a personalised lens of predictive modelling, in
order to improve future combination therapy planning. We propose to do
this through an unprecedented multidisciplinary project:
mathematics-led, but drawing on our expertise in biology, physics, and
computer science. Our project reflects the structure of life through a
stratified, multi-scale description which deals with the important
parts, e.g. the organ containing the tumour, in great detail, whilst
describing the remainder of the whole in a more chunked way, able to
efficiently capture the essence of the necessary detail. Our longer term
goal is for our modelling framework to be generic, and adaptable to a
range of diseases and combined therapies. In this project, a generally
adaptable framework and the associated interconnected mathematical and
computational models and methods will be created. Having been validated
by biological experiments, these models will be refined and populated
with data to provide clinically useful predictions for our exemplar,
combined chemo/radiotherapy of glioma (a type of brain tumour).
Each component of the project draws on specific expertise provided by
the investigators:
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three-dimensional, spatially-resolved mathematical
models of drug delivery and tumour growth, coupling mass transport
with cell response, and simulated using fast computational algorithms,
provides a detailed, patient-specific, representation of response to
therapy;
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radiation interaction modelling, with associated algorithms to
speed-up accurate radiation therapy planning, provides details of the
influence of radiotherapy;
- experimental cell biology work, delivers data with which to validate
the models;
- mathematical modelling and supporting synergy experimentation
integrates whole-body effects with disease- and person-specific models;
- process algebra modelling of signalling inside and between cells,
bystander effects, and metastasis, provides models of cell response.
These will all have scientific outputs, but where the project really
reaps the benefits of multi-disciplinarity is at the interfaces of these
work packages, and through the combination of our joint approaches to
problems.
Through this project we will lay the foundations for our 20-year goal of
a generic framework for combined therapies by addressing a specific and
important example: combined radiation/drug therapies for glioma.
The project is very amenable to becoming an outreach vehicle capable of
demonstrating the public benefit of mathematics in a visual way. This
will be exploited through a variety of social media (e.g. animations
showing the spatio-temporal variation of drug and radiation delivery at
the local and patient scales, delivered via YouTube) and more
traditional forms of engagement (e.g. web presence, presentations to
local schools and at Science festivals).
The workpackages for the project are shown below:
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