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Using Statistical Machine Learning to Design Particles for Drug Delivery

   Department of Mathematical Sciences

   Applications accepted all year round  Self-Funded PhD Students Only

About the Project

Loughborough University has seen 94% of our research impact rated as ‘world-leading’ or ‘internationally excellent’, underlining the wide-ranging positive impacts that our research has on the world (REF, 2021). The Department of Mathematical Sciences saw 100% of its research impact rated as 'world-leading' or 'internationally excellent' (REF, 2021).

In choosing Loughborough for your research, you’ll work alongside academics who are leaders in their field. You will benefit from comprehensive support and guidance from our Doctoral College, including tailored careers advice, to help you succeed in your research and future career. Find out more.

Project Overview

Statistical Machine Learning (SML) is the process of gaining understanding by constructing models of observed data with the intention to use them for prediction. SML plays a key role in many areas of science, finance and industry. In this project, we will use SML to design drug delivery particles of right size and low cytotoxicity. Size and drug loading are key factors that significantly influence the stability, effectiveness and cytotoxicity of drug delivery particles. However, the formulation and manufacturing of particles of right size and drug loading for drug delivery, as with other complex processes, often fail to be fully modelled by classical statistical techniques due to the non-linear relationships existing between components and/or processing conditions.

This project aims to construct fast and robust generative models for learning the relationship between drug loading (encapsulation efficiency), particle size (and its distribution), processing conditions (temperature, flow rate, drying), reactant type as well as components and properties (surface tension, viscosity and density) and different particle generation systems manufactured by Loughborough spinout company Micropore Technology Ltd.

Uncertainty Analysis (UA) implies how likely the particle size and drug loading is when the information of some other manufacturing factors are unknown. UA also provides practitioners the useful information of model transferability and generalisability when using the models for new data. This PhD will aim to also apply UA to understand and reduce the uncertainty in the proposed models by combining the data on drug loading efficiency and particle size to predict best methods of manufacturing to manufacture the particles of low cytotoxicity.

The student will gain experience and skills in a range of SML methods include Bayesian Predictive models, Neural Network, Robust Statistics for data with outliers, variable selection and model assessment methods. The student will also have opportunity to learn image analysis methods and tools to extract the information about the particle size and their uniformity from image data collected using various imaging modalities (optical and SEM microscopy for micron sized particles and TEM microscopy for nanosized particles).

Entry requirements:

The successful candidate will have at least a 2:1 BSc (Hons) in a relevant mathematical/statistical or computer science discipline.

English language requirements:

Applicants must meet the minimum English language requirements. Further details are on the International website.

How to apply

All applications should be made online. Under programme name, select ‘Mathematical Sciences’. Please quote the advertised reference number MA/DZ-Un1/2022 in your application. To avoid delays in processing your application please ensure that you submit the minimum supporting documents.

Funding Notes

Fee band:
Band RB (UK: £4,500; international: £24,100)

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