Mathematical models to inform the treatment and prevention of Alzheimer's disease.

Background: The recent success of amyloid-targeting monoclonal antibodies (mAbs) signifies a turning point in the treatment of Alzheimer's disease (AD) and gives credence to the much debated 'amyloid hypothesis'. However, tackling a disease as complex in its pathology and biology as AD will require combination therapies, such as those that target both the amyloid and tau pathways at multiple stages, or that combine preventative and acute therapies. QSP models have been developed for AD treatments to explore the incidence of adverse effects and to compare the mechanisms of action of various drugs, including mAbs. However, to date, such models have not been used to investigate the potential efficacy of vaccines, such as those targeting amyloid or tau. A key difference between mAbs and vaccination is the diversity of antibodies produced during the polyclonal immune response. While a given mAb has a single, constant binding profile across the various target species, the polyclonal antibody mixture evoked by vaccination will target a more diverse range of species with a wide range of affinities, and, through affinity maturation, will evolve over time. As such, vaccination is expected to provide longer lasting protection, and, due to affinity maturation, is also inherently personalised. Understanding the factors that impact the clinical effect of vaccination is important for designing and assessing clinical trials, as well as considering how best to combine vaccination with other therapies.

Aims & Methodology: This DPhil, in collaboration with AC Immune, will seek to develop scientific software comprising mathematical and computational models to investigate vaccination as a strategy for treating and preventing AD. Initially, the project will involve the extension of ordinary differential equation models developed for mAbs to simulate the effects of a polyclonal mixture of antibodies. In addition, the project will seek to develop an improved mathematical representation of the amyloid aggregation pathway. A successful representation of these two aspects should allow for amyloid aggregates of varying size and capture the process of nucleation and growth, with different antibodies binding to different aggregates with varying affinity.

Given the complexity and limitations of ordinary differential equation models, a number of techniques to extend the modelling framework will be considered. Agent based models (ABMs) can be used on smaller scales to understand the fundamental dynamics. Linking ABMs to theoretical frameworks at larger scales is a fundamental multiscale problem in mathematical biology and will be pursued in the current context, examining the extent to which larger scale, continuum models of amyloid plaque formation systematically and faithfully incorporate smaller scale dynamics.

Multiscale model development is of particular importance when considering the temporal and spatial features of AD development and progression. Temporal factors are relevant to questions surrounding preventative measures, for which vaccination is an attractive strategy, especially in at risk patient groups who experience accelerated disease onset. Spatial features include patterns of amyloid plaque deposition and clearance, and patterns of inflammation. This latter concept is of interest given the accumulating evidence that inflammation is a central mechanism in AD development, though the underlying mechanisms are relatively under-explored. Integrating spatial information into multiscale models will require the integration of imaging and clinical data; an appropriate strategy for this will also need to be developed. While multiscale models will constitute an important area of investigation in this project, the proposed DPhil is not restricted to multiscale model development and will also consider model validation and interpretation in depth.

EPSRC Research Area: This project falls within the EPSRC 'Mathematical Biology' research area.

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.