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Model-informed development and dose rationale for combination therapy in oncology

   School of Engineering

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  Prof Michael Chappell  No more applications being accepted  Funded PhD Project (UK Students Only)

About the Project

The use of combination therapy in oncology, such as checkpoint inhibitor-based immunotherapy has revolutionized the treatment of cancer patients. Yet, many of the drug combinations that are currently in clinical development, were selected in an empirical manner, with poor understanding of the underlying pharmacodynamic interaction. More robust approaches are needed to explore the mechanisms of action of the pharmacodynamic interaction and facilitate translation of pharmacological effect of drug combinations from nonclinical experiments to humans and explore optimisation of therapeutic index.

Aim and scope of the investigation: To develop a modelling framework to predict and translate the pharmacodynamic interaction in oncology for both tumour growth and safety (e.g. thrombocytopenia). The modelling will provide a better understanding and translation of the nature of the interaction of drug combinations in preclinical experiments. The proposed model-based approach will also inform more informative experimental protocols for the ranking of drug combinations based on the underlying pharmacodynamic interactions.

Methodology: Using clinical and nonclinical data from literature and GSK, a modelling framework will be developed to support the development of a decision algorithm for the selection of dose and drug combinations in oncology. This will entail the use of non-linear mixed-effects to capture biological and technical variability in in vivo cancer model studies. This will enable the parameterisation of ordinary differential equations based models of tumour growth and treatment effect. A range of methods to describe drug interactions will be explored. Scaling strategies to translate drug-specific properties (e.g. interaction mechanism) and system-specific properties (i.e. tumour growth dynamics) from nonclinical experiments to humans will be developed as well. Data from currently approved regimens (e.g. nivolumab/ipilimumab) and combinations that are currently in the clinic (e.g. pembrolizumab-based combinations) will be used as a reference for the purpose of the analysis.

  1. Contrast extent and variability of response to standard of care (SoC) in mouse xenografted cell lines and patients by developing models of longitudinal tumour size.

Outcome: Assess the extent of translatability of exposure-response relationship and whether testing in multiple xenograft models provide additional information

  1. Build a mathematical framework upon these models of nonclinical SoC efficacy that includes the effects of 2 or more treatments

Outcome: Proposed mathematical framework and recommendations of nonclinical combination study design

Impact of the research: This research programme will make use of experimental and clinical data to evaluate the pharmacodynamic interaction in a more systematic manner. It is envisaged that the results of this analysis will lead to more informative experimental protocols to explore drug combinations and better translation of anti-tumour activity from preclinical experiments to humans. The availability of the proposed modelling framework is anticipated to improve the rationale for the selection of dose and drug combinations in oncology.


The award will cover the tuition fees at the UK student rate, plus a stipend of £16,000 per annum for 3.5 years of full-time study. EU and international students are welcome to apply, however, due to funding restrictions on fees, only the home fees rate will be included.


Candidates should be eligible for home fees (typically UK national). We welcome applications from candidates with an Engineering or Physical Science degree (minimum level 2:1), or a Masters degree at merit level in a relevant field of study.

How to apply:

Candidates should submit a formal application, details of how to do so can be found here https://warwick.ac.uk/fac/sci/eng/postgraduate/applypgr/ 

In the application form funding section enter: SourceGSK Professor Mike Chappell - PhD Scholarship: Model-informed development and dose rationale for combination therapy in oncology

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