Pharmacokinetics and Pharmacodynamics

This project falls within the EPSRC Clinical technologies (excluding imaging) research area.
Modelling and simulation is becoming more widely used along the drug development journey. Pharmacokinetic modelling is used to predict the concentration of the drug over time after treatment and pharmacodynamic modelling is used to predict the efficacy and toxicity of a drug at a certain concentration. These two models when combined can help find the optimal dosing regimen that will streamline the drug development process and reduce simple trial and error methods in expensive clinical studies. However not every individual is identical, and everyone has slightly different reactions to a drug. This means a population based modelling approach should be taken, where there is a general model shared by everyone but the parameters of that model differ between individuals. And, these individual parameters are governed by a probability distribution of parameter values over the population.
Within this DPhil I aim to co-produce a new piece of open-source software that can help clinicians model the Pharmacokinetics and Pharmacodynamics of a drug. It will be usable by both modellers, who can input and explore the various models; and clinicians with low modelling skill, who can use the implemented models to predict the effect of a drug at a certain dose and input data to improve these predictions. It may also be used by Doctors and GPs to find a personalised dose for an individual patient that maximizes efficacy while minimizing toxicity.
While developing this software, I will be finding a way to standardise population based modelling and parameter inference so that it can be automated and used by non-modellers. Along with standardising the process of model development, comparison and selection. I will compare the methods used in other similar software, such as NONMEM and Monolix, and see how the features they provide can be implemented and improved upon in our software. I will also attempt to reproduce the results from previous studies with both old and new data. This will help test and develop the software and modelling process, as well as validate the results of the previous studies.
This DPhil is in collaboration with Roche who are providing guidance on what the stakeholders will require from this software; information on what current practices there are; help in the development of the software and techniques; and the data to reproduce previous studies. It is also in collaboration with Elsevier who are supporting the reproducibility of computational modelling and providing a platform for publishing open-source software.

The UK's world-leading position in biomedical research is critically dependent upon training scientists with the cutting-edge research skills and technological know-how needed to drive future scientific advances. Since 2009, the EPSRC and MRC CDT in Systems Approaches to Biomedical Science (SABS) has been working with its consortium of 22 industrial and institutional partners to meet this training need.

Over this period, our partners have identified a growing training need caused by the increasing reliance on computational approaches and research software. The new EPSRC CDT in Sustainable Approaches to Biomedical Science: Responsible and Reproducible Research - SABS:R^3 will address this need. By embedding a sustainable approach to software and computational model development into all aspects of the existing SABS training programme, we aim to foster a culture change in how the computational tools and research software that now underpin much of biomedical research are developed, and hence how quantitative and predictive translational biomedical research is undertaken.

As with all CDT Programmes, the future impact of SABS:R^3 will be through its alumni, and by the culture change that its training engenders. By these measures, our existing SABS CDT is already proving remarkably successful. Our alumni have gone on to a wide range of successful careers, 21 in academic research, 19 in industry (including 5 in SABS partner companies) and the other 10 working in organisations from the Office of National Statistics to the EPSRC. SABS' unique Open Innovation framework has facilitated new company connections and a high level of operational freedom, facilitating 14 multi-company, pre-competitive, collaborative doctoral research projects between 11 companies, each focused on a SABS student.

The impact of sustainable and open computational approaches on biomedical research is clear from existing SABS' student projects. Examples include SAbDab which resulted from the first-ever co-sponsored doctorate in SABS, by UCB and Roche. It was released as open source software, is embedded in the pipelines of several pharmaceutical companies (including UCB, Medimmune, GSK, and Lonza) and has resulted in 13 papers. The SABS student who developed SAbDab was initially seconded to MedImmune, sponsored by EPSRC IAA funding; he went on to work at Roche, and is now at BenevolentAI. Similarly, PanDDA, multi-dataset X-ray crystallographic software to detect ligand-bound states in protein complexes is in CCP4 and is an integral part of Diamond Light Source's XChem Pipeline. The SABS student who developed PanDDA was awarded an EMBO Fellowship.

Future SABS:R^3 students will undertake research supported by both our industrial partners and academic supervisors. These supervisors have a strong track record of high impact research through the release of open source software, computational tools, and databases, and through commercialisation and licensing of their research. All of this research has been undertaken in collaboration with industrial partners, with many examples of these tools now in routine use within partner companies.

The newly focused SABS:R^3 will permit new industrial collaborations. Six new partners have joined the consortium to support this new bid, ranging from major multinationals (e.g. Unilever) to SMEs (e.g. Lhasa). SABS:R^3 will continue to make all of its research and teaching resources publicly available and will continue to help to create other centres with similar aims. To promote a wider cultural change, the SABS:R^3 will also engage with the academic publishing industry (Elsevier, OUP, and Taylor & Francis). We will explore novel ways of disseminating the outputs of computational biomedical research, to engender trust in the released tools and software, facilitate more uptake and re-use.