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

该项目将开发多维电子衍射领域的新方法，主要目的是确定原子和纳米级的结构信息，这些信息迄今为止无法用其他方法获得，在不同但高度相关的环境中对感兴趣的药物分子。该项目是与GSK合作的工业案例奖。为了在未来对GSK的产品组合产生影响，将重点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。工作的重点将主要集中在两种电子衍射技术：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。这些衍射图样编码了诸如晶体结构和取向之类的信息。3D-ED可以确定分子的结构，而SED则可以探测化合物的微观结构。3D-ED为x射线衍射（XRD）提供了一个很有前途的替代方案，因为它可以使用更小的晶体尺寸来确定结构信息。相比之下，XRD方法需要更大的单晶，这对于许多药物化合物来说很难生长。SED在药品的直接探测方面具有广阔的应用前景，有利于药品的生产。在药物制造中，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质。例如，制造过程可能涉及机械研磨，这可能带来机械化学相变，改变药品的结构，从而改变其性能。3D-ED和SED技术都需要进一步开发以用于更复杂的药物成分。该项目旨在使用大数据方法，如机器学习，以提高整个数据分析管道的自动化程度。本研究是提高电子衍射作为一种有效药物成分晶体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.