Mathematical modelling and optimisation of organ-on-a-chip in vitro systems

An essential feature of adequate prediction of drug toxicity in preclinical pharmaceutical development is the use of cellular in vitro models that recapitulate the physiology of human tissues as closely as possible. 3D cellular systems that include physiologically realistic fluid flow are important in providing appropriate shear stresses required for mechanobiological responses and correct function of cells. They can also provide the transport and recirculation of drugs, nutrients and waste compounds, as well as signalling molecules such as cytokines and chemokines, which drive cell-cell communication that is critical for physiological functioning of cells in-vitro. The distribution of such solutes is relevant to understanding cellular function and can be used to enhance pharmacokinetic and cell-cell interaction and signalling models in toxicology studies.
A specific focus is organ-on-a-chip models of liver cells, since hepatotoxicity is a major cause of clinical failure in drug development. These can be extended to include multiple cell types or multi organ systems (e.g. immune cells, gut cells) to incorporate further interactions relevant to the drug's mechanism of action. Mathematical modelling of such systems not only helps to understand and improve the physiological relevance of in vitro models, but will also enable the optimisation of relevant experimental settings, and most importantly, enable quantitative predictions regarding toxicity in the drug development process.
Aims and Objectives
Construct mechanistic mathematical models for fluid flow and solute transport for a range of organ-on-a-chip systems, coupled to relevant models of cellular function (e.g. metabolism, immune mediated effector-target toxicity, cytokine release cell-cell communication).
Obtain quantitative predictions of fluid flow, shear stresses, and concentration distributions, and compare with results obtained experimentally.
Determine optimal design and operating conditions of the in vitro system (flow rates, scaffold properties, system geometry) in order to match the in vivo environment experienced by cells as closely as possible and/or optimise the performance of the systems as a tool for toxicity assessments.
Inform and optimise experimental design and pharmacokinetic modelling through understanding of fluid flow.
Understand impact of fluid dynamical load on cellular function.
Obtain a general mathematical framework that can be applied and adapted to a variety of microfluidic systems, where advanced understanding of fluid mechanics can provide fundamental insights through the interaction between complex fluid flows and cell function, informing the drug discovery process.
Novelty of Research Methodology
Research methodology will include mechanistic mathematical modelling, analysis and in silico computation, in combination with experimental studies performed at Roche.
The mathematical model will incorporate a combination of ideas from fluid dynamic modelling (Navier-Stokes, Darcy/Brinkman equations, multiphase flows, reaction-advection-diffusion equations) which apply to different components of the system.
The model will be investigated through a combination of numerical techniques (e.g. finite-difference, finite element and spectral methods, use of commercial software) and analytical approaches on reduced models obtained by exploiting different time/length scales (e.g. linear and nonlinear stability theory, regular and singular perturbation theory, multiple-scales analysis, limiting cases in parameter space).
Data will be obtained from experimental studies and imaging performed at Roche.

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.