PKPD Modelling - Towards Personalised Treatment

This project falls within the EPSRC "Clinical technologies (excluding imaging)", "Mathematical Biology", "Non-linear systems", "Statistics and applied probability" and "Software Engineering" research areas.
Quantitative modelling has grown to be an integral part of R&D in the pharmaceutical industry. The main reasons for the recent shift towards model-informed drug development are arguably the high attrition rates in the drug development process, and an increased recognition of modelling results by the regulatory authorities. Less than one in ten drug candidates in the past decade has succeeded from the early drug development stages to the market. Such a low success rate provides a strong incentive for more efficient and informed drug development.
The dominant cause for low success rates of drug candidates has been reported to be a lack of efficacy or safety. This is related to either preclinically uncaptured safety risks, non-translational efficacy markers, or inadequate dose and regimen selection. The reasons why dosing regimens are often suboptimal are manifold, but the main contributing factor is that the continuous spectrum of dosing strategies cannot be sufficiently explored experimentally. The most commonly used method for defining dose and regimen of a compound is the pairwise comparison of a range of dosing strategies with a common control. However, limited resources constrain the number of different dosing regimens that can be tested with statistical significance. In addition, the time-dependent, and typically non-linear relationship between dose and response makes an informed prioritisation of potential dosing regimens almost impossible by traditional means. As a result, dosing strategies often remain poorly explored.
To address the need for a more informed dose and regimen selection, quantitative modelling approaches have been largely implemented in pharmaceutical R&D. In particular, pharmacokinetic and pharmacodynamic (PKPD) modelling is ideally suited to prioritise dosing strategies for clinical confirmation. PKPD models formalise the understanding of a compound's mode of action, and predict a time-resolved dose-response relationship. This allows a theoretical safety and efficacy assessment of dosing regimens prior to clinical trials. Promising dosing strategies can then be further investigated with the standard pairwise comparison method, alleviating the problem of poor dosing regimen exploration.
While the potential of PKPD modelling for predicting safe and efficacious dosing regimens has been largely recognised in the pharmaceutical industry, little emphasis has been put on the robustness and reproducibility of the modelling process itself. PKPD models are non-linear ODE models which are often complemented by probabilistic models for the measurement noise and/or the dose-response differences between individuals. As a result, PKPD models introduce a number of biologically meaningful parameters which need to be learned from preclinical or clinical data. The challenge of PKPD modelling is thus not only to find an appropriate model structure, but also to infer model parameters in high dimensional space in a robust and reproducible way.
In my DPhil, I will be working on a robust and reproducible PKPD modelling workflow, addressing the stability of numerical optimisation and sampling, as well as model identifiability and model selection. This project is kindly supported, and co-supervised by Hoffmann-La Roche and Elsevier. The software I will develop in context of this project will be made openly available on license terms which do not include copyleft feature, for example by further developing the python modelling packages "myokit" and "pints". Once a robust PKPD modelling workflow has been established, I hope to explore the potential of PKPD models to suggest personalised dosing regimens in clinical practice.

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.