Multi-Dimensional Electron Diffraction: New Technology and Data Analytics for Improved Pharmaceutical Understanding and Performance

This project will develop new methods in the area of multi-dimensional electron diffraction with the principal aim being to determine atomic and nanoscale structural information, hitherto unobtainable with other methods, on pharmaceutical molecules of interest in different but highly relevant environments. The project is an industrial CASE award in collaboration with GSK. In order to deliver future impact across the GSK portfolio, there will be a focus on developing automated acquisition and analysis pipelines harnessing machine learning and materials informatics.
The focus of work will be primarily on two electron diffraction techniques: three-dimensional electron diffraction (3D-ED) and scanning electron diffraction (SED). Both techniques are based on the acquisition of a series of two-dimensional diffraction patterns. These diffraction patterns encode information such as crystalline structure and orientation. 3D-ED enables the structure of a molecule to be determined, whilst SED allows for the microstructure of the compound to be probed. 3D-ED offers a promising alternative to X-ray diffraction (XRD) because it can use much smaller crystallite sizes to determine structural information. In comparison, XRD methods require larger single crystals which can be hard to grow for many pharmaceutical compounds. SED holds promising potential for direct probing of drug product which will be beneficial to manufacturing. In drugs manufacturing, compounds are produced en masse in varied product forms which may affect the compound's properties. For example, the manufacturing processes may involve mechanical milling which could bring about mechanochemical phase transformations, altering the structure of the drug product and therefore its performance. Both techniques of 3D-ED and SED require further development for use with more complex pharmaceutical ingredients. The project will aim to use big data methods, such as machine learning, to increase automation across the data analysis pipeline. This research is key to increasing the feasibility of more widespread adoption of electron diffraction as a technique for crystallographic and microstructural investigation of active pharmaceutical ingredients.