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Developing mechanistic models of immune response to checkpoint inhibitors and cell death that address inter-subject heterogeneity

Project Description

Understanding the response of the immune system to perturbations is challenging because of the significant inter-subject variability in the response. This is likely due to the baseline immune status in each individual, which is a function of genetic and environmental factors. Further quantitative characterisation of immune response, including breaking of tolerance, would of course have implications for the understanding of normal immune biology as well as immune processes associated with aging, and immunological diseases such as arthritis and cancer. However, studies of the immune system in the mouse are challenging due to the significant number of animals required for such analyses as pairwise comparisons of groups.

The impact of PKPD and other systems modelling approaches has been to quantify concentration-response relationships which enable quantitative extrapolation of dose and schedule to the clinic. Mathematical modelling has also provided biological insights, generating hypotheses that can be tested in focussed in vivo experiments. Thus modelling is a useful tool for biological investigation, refining and reducing animal experiments and numbers required to come to robust conclusions. We believe such approaches have the potential for providing further insight into immunology. However, to further develop these data-driven modelling approaches, heterogeneity, and variability need to be reflected in mathematical models as well. Nonlinear Mixed effects (NLME, systematic predictable effects plus random variations), allows one to develop a biologically and pharmacologically relevant mathematical model for this system. The aims of this project are

1. Development of mechanistic models of the response of the murine immune system to check-point inhibitors that reflect the biological relationships between biomarkers and the pharmacology of the treatment
2. Use NLME to model the data, not only to obtain parameter estimates but to understand sources of variability
3. Understand the impact of study design and optimise study design
4. Develop models of immune check point inhibitors combined with DDR inhibitors that will incorporate both increased antigen presentation / immune priming and perturbation of immune cell proliferation / survival.

Entry Requirements:
Applicants are expected to hold or about to obtain, a minimum upper second class undergraduate degree (or equivalent) in pharmacy, pharmacology, mathematics, statistics, biological sciences, engineering or a related biological/physical science area. A strong mathematical background and/or a Masters degree in relevant subject area is desirable. Previous experience of data analysis and mathematical/computational modelling would be an advantage.

For information on how to apply for this project, please visit the Faculty of Biology, Medicine and Health Doctoral Academy website ( On the online application form please select PhD Pharmacy and Pharmaceutical Sciences. Informal enquiries may be made directly to the primary supervisor.

Funding Notes

This project is to be funded under the BBSRC Collaborative Training Partnership with AstraZeneca. Studentship funding is for a duration of 4 years to commence in September 2019 and covers UK/EU tuition fees, stipend (£17,509 per annum 2019/20). Due to the nature of the funding this studentship is only open to UK/EU nationals.

As an equal opportunities institution we welcome applicants from all sections of the community regardless of gender, ethnicity, disability, sexual orientation and transgender status. All appointments are made on merit.


1 Martin EC, Aarons L, Yates JW. Accounting for dropout in xenografted tumour efficacy studies: integrated endpoint analysis, reduced bias and better use of animals. Cancer Chemother Pharmacol (2016a), 78(1):131-41
2 Martin EC, Aarons L, Yates JW. Designing More Efficient Preclinical Experiments: A Simulation Study in Chemotherapy-Induced Myelosupression. Toxicol Sci (2016b), 150(1):109-16.
3 Martin, E. C., Yates, J. W. T., Ogungbenro, K., & Aarons, L. (2017). Choosing an optimal input for an intravenous glucose tolerance test to aid parameter identification. Journal of Pharmacy and Pharmacology, 1–9.
4 Martin EC, Aarons L, Yates JWT. Pharmacodynamic modelling of resistance to epidermal growth factor receptor inhibition in brain metastasis mouse models. Cancer Chemother Pharmacol (2018), 82(4):669-675.
5 Wendling T, Mistry H, Ogungbenro K, Aarons L. Predicting survival of pancreatic cancer patients treated with gemcitabine using longitudinal tumour size data. Cancer Chemother Pharmacol (2016), 77(5):927-38.

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