Currently, while conducting risk assessments, we are forced to assume that 100% of the pesticide particles and droplets that enter the respiratory tracts of operators, workers, bystanders and residents are absorbed into systemic circulation. For high crop, greenhouse and aerial uses, where inhalation exposures can contribute significantly to the overall exposure, refining the assumption of 100% absorption across the respiratory epithelium may represent an opportunity to use new science to develop/maintain our ability to register and sell competitive products and minimize use of mitigation measures (such as respiratory protection and closed cab tractors). Computational fluid dynamics models (CFD models) have been developed that can predict the region of the respiratory tract where particles would deposit as a function of their sizes. However, we do not understand the fate of these deposits well enough, including the extent to which they are metabolized, transported by the mucociliary escalator or absorbed in situ.
This project’s goal is to increase our understanding of inhalation absorption by marrying modelling and experimental efforts. The effectiveness of the key clearance mechanisms in selected regions of the respiratory tract as well as the level of in situ absorption will be modeled mathematically. Parameters for the models as well as validation targets will be provided by the experimental side of this project, performed through our collaboration with Kings College London.
The modelling studentship at UCL will focus on the development of a predictive model which will help inform regulatory risk assessments and provide refined positions regarding the systemic bioavailability of inhaled materials in the lung. The mathematical models will include both fluid mechanics frameworks to describe transport of particulates through the lung airways, as well as deposition/ absorption models (partial differential equation systems) for the respiratory epithelium. It will also be important to both parameterise and validate the robustness of the models developed, and this will be achieved through data acquired through the biological PhD project at Kings College London, and also through existing data sets provided by Syngenta.
This PhD Studentship offers an exciting opportunity to join a team of world-leading investigators and partners from UCL, Kings College London and Syngenta. All bring unique yet complementary expertise across mathematical and computational modelling, biology and in vitro models through to industrial application. Applicants should be interested in a career in biological engineering and healthcare technologies, and should be particularly committed to working with industry to ensure the application of their research outputs.
Applicants must have a UK-equivalent first degree in an appropriate mathematics, physics or engineering discipline. Proficiency in applied mathematics and programming (e.g. Matlab) experience are a distinct advantage. Students must have an aptitude for communicating their scientific research to different disciplines.
Application Procedure: See http://www.ucl.ac.uk/prospective-students/graduate/research/application
for information. A CV, full transcript of results (listing all subjects taken and their corresponding grades/marks) and a cover letter stating how the project meets your research interests must be included.
Contacts: Prospective candidates are strongly encouraged to email Dr Rebecca Shipley ([email protected]
) for an informal discussion before applying. Please attach your CV and full transcript of exam results when making an enquiry.
The project is funded through a BBSRC Case Studentship, with Syngenta as an industrial partner.
Full tuition fees and stipend for 4 years. The current BBSRC stipend is £16,290 for 2016/17 (this will be adjusted for inflation annually).
Candidates must be either UK residents, or EU residents who have been living in the UK for 3 years prior to the course commencing. EU residents who have not been living in the UK are eligible for fee only awards; see View Website