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

该项目将在多维电子衍射领域开发新的方法，主要目的是确定在不同但高度相关的环境中有关感兴趣的药物分子的原子和纳米级结构信息，这是其他方法迄今无法获得的。该项目是与葛兰素史克合作的工业案例奖。为了在整个葛兰素史克产品组合中产生未来的影响，将专注于开发利用机器学习和材料信息学的自动化采集和分析管道。工作重点将主要集中在两种电子衍射技术上：三维电子衍射(3D-ED)和扫描电子衍射(SED)。这两种技术都是基于获取一系列二维衍射图。这些衍射图编码了晶体结构和取向等信息。3D-ED可以确定分子结构，而SED可以探测化合物的微观结构。3D-ED为X射线衍射法提供了一种很有前途的替代方法，因为它可以使用更小的微晶尺寸来确定结构信息。相比之下，X射线衍射法需要更大的单晶，这对许多药物化合物来说可能很难生长。SED在直接探测药物产品方面具有很大的潜力，这将有利于生产。在药品生产中，化合物以不同的产品形式大量生产，这可能会影响化合物的性能。例如，制造过程可能涉及机械研磨，这可能会导致机械力化学相变，从而改变药物产品的结构，从而改变其性能。3D-ED和SED技术都需要进一步开发，以便与更复杂的药物成分一起使用。该项目的目标是使用大数据方法，如机器学习，提高整个数据分析管道的自动化程度。这项研究是增加更广泛地采用电子衍射法作为有效药物成分的结晶学和微观结构研究技术的可行性的关键。

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.