Model-informed drug development uses pharmacometric (PM) (quantitative pharmacology) approaches to inform trial design and optimise compound development strategies. This approach aims to reduce late-stage failure and improve the efficiency of drug development. We conceived and subsequently proved the concept of linking such an approach with pharmacoeconomic (PE) models to provide early estimates of cost-effectiveness, and inform future research. Leading pharmaceutical companies subsequently expressed a specific interest in the potential applications to inform their clinical drug development programmes. One application that has proven to be challenging to investigate via other means is the impact of non-adherence on treatment effect. Our earlier work indicated that PMPE methods could add value to trial methodology in this respect by identifying the value of different trial designs.
What the studentship will encompass:
In collaboration with the Universities of Otago and Sydney, and Pfizer, and utilizing the expertise within the TMRP, this PhD project will aim to develop then apply PMPE methods to drug treatments and adherence interventions in gout. A pharmacometric model will be developed, based on our previous experience and ongoing research at the University of Otago. This model will provide a basis for simulating the time course of drug effect on urate concentrations under conditions of varying dose implementation (adherence). A pharmacoeconomic model with take a health care payer perspective, to estimate the costs and consequences (expressed as quality-adjusted life years, QALYs) associated with urate lowering therapy. Data will come from an ongoing study of the effect of self-monitoring urate concentrations on adherence to allopurinol in people with gout. The integration of the PM and PE models will require additional, external evidence on the costs associated with the management of gout flares, and disutilities experienced by patients. This information will come from targeted reviews of the literature. The PMPE model will be fully probabilistic, and extended to a value of information analysis, to inform the design of a future trial of adherence interventions. Specifically, the Expected Net Benefit of Sampling (ENBS) will be estimated for different configurations of adherence services to examine the optimal trial design - a trial will be considered to be worth undertaking if the expected value of sample information is greater than the cost of the trial.
The lead supervisor will provide overall direction to the student. Associate Professor Wright will contribute pharmacometrics and clinical pharmacology expertise, and advise the student on the methods of PM modelling. Dr Stocker will provide clinical pharmacology expertise and guidance on the management of gout and adherence interventions. Drs Soto and Chan will provide an industry perspective and training in pharmacometrics.
The student will spend a period of time at the University of Otago and Pfizer to learn specific approaches to pharmacometric analyses.
PPI involvement will be via recruitment of patient(s) with experience of gout.
A degree in a mathematical discipline is required. A Master’s degree in a quantitative discipline is desirable.
HOW TO APPLY
You are applying for a PhD studentship from the MRC TMRP DTP. A list of potential projects and the application form is available online at:
Please complete the form fully. Incomplete forms will not be considered. CVs will not be accepted for this scheme.
Please apply giving details for your first choice project. You can provide details of up to two other TMRP DTP projects you may be interested in at section B of the application form.
Before making an application, applicants should contact the project primary supervisor to find out more about the project and to discuss their interests in the research.
The deadline for applications is 4pm (GMT) 18 February 2022. Late applications will not be considered.
Completed application forms must be returned to: [Email Address Removed]
Informal enquiries may be made to [Email Address Removed]