PhD Studentship: Predictive Design of Drug Co-Crystals Through Correlative Studies of Inter-Molecular Interactions

Closing Date: 29 April 2024 at 23:59

Eligibility: UK Applicants only

Funding

EPSRC Doctoral Training Partnership Studentship offering the award of fees, together with a tax-free maintenance grant of £19,237 per year for 3.5 years. 

Lead Supervisor’s full name & email address

Dr Anuradha Pallipurath - a.r.pallipurath@leeds.ac.uk

Co-supervisor name: To be confirmed

Project summary

This interdisciplinary project presents an exciting opportunity for an ambitious scientist or engineer to work across the boundaries of chemistry, physics and engineering, with opportunities to develop a broad portfolio of skills. A combination of Raman spectroscopy and total X-ray scattering techniques will be used to study the crystallisation of drug molecules and molecular analogues, to determine the influence of functional groups on their crystallisation behaviour. Predictive control of industrial crystallisation requires an understanding of how drugs behave in solution, and getting experimental structural information in the solution state has not been possible until recently. With advances in computing facilities and in X-ray total scattering and Raman instrumentation, we can now realistically hope to establish the details of the intermolecular interactions between the drug and the surrounding chemical environment and the structural dynamics during crystallisation. The information gained in this project will enable improvements in process control and predictive modelling. The project will combine experimental work with researchers at Leeds and at the UK's national synchrotron radiation facility, Diamond Light Source, and will involve some development of computational data analysis code and molecular modelling. You will also have an opportunity to learn machine learning methods for the analysis of structural information together with the development of correlative analysis techniques. You will be funded by the Royal Society and EPSRC DTP.

Control of crystallisation requires the understanding of structural dynamics of the molecules in the phase from which they form. Most industrial methods involve the use of solvents to control crystallisation. While there are methods to predict how molecular interactions affect batch processing, they are limited in how well the solvent system can be represented through known chemical and physical parameters of individual components. 

Synchrotron science has progressed in leaps and bounds recently and this allows for X-ray total scattering studies from non-crystalline materials such as solution phases as well as amorphous states. Further, Raman spectroscopy will provide information about molecular conformations for large molecules with flexible bonds. Together, these allow for more accurate molecular models to be generated and refined against experimental data. The resulting understanding of inter-molecular interactions will provide a wealth of new inforamtion, which can be used to improve predictive design and control of crystallisation using machine-learning methods. 

This studentship will entail the development of correlative techniques using X-ray pair distribution function analysis and Raman spectroscopy together with molecular modelling of the experimental data. There will also be oppotunities to explore the use of machine learning to mine the wealth of information generated from these techniques for the various systems studied.

Entry requirements

First or Upper Second Class UK Bachelor (Honours) or equivalent

Subject Area: Analytical Chemistry, Chemical Engineering, Materials Science, Applied Physics

Keywords: X-ray Scattering, Crystallisation, Machine Learning, Raman Spectroscopy

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