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

多维电子衍射：提高药物理解和性能的新技术和数据分析

• 批准号: 2597620
• 金额: --
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2597620
• 关键词: Multi; Dimensional; Electron; Diffraction; New

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.

该项目将开发多维电子衍射领域的新方法，其主要目的是确定原子和纳米级结构信息，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药物分子感兴趣。该项目是与GSK合作的工业案例奖。为了在GSK产品组合中发挥未来的影响力，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。工作重点将主要放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一系列二维衍射图案的采集。这些衍射图案编码信息，如晶体结构和取向。3D-ED可以确定分子的结构，而SED可以探测化合物的微观结构。3D-ED为X射线衍射（XRD）提供了一种有前途的替代方案，因为它可以使用更小的晶粒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可能很难生长。SED具有直接探测药物产品的潜力，这将有利于生产。在药物制造中，化合物以不同的产品形式被生产，这可能影响化合物的性质。例如，生产工艺可能涉及机械研磨，这可能导致机械化学相变，改变制剂的结构，从而改变其性能。3D-ED和SED这两种技术都需要进一步开发，以用于更复杂的药物成分。该项目旨在使用机器学习等大数据方法来提高数据分析管道的自动化程度。这项研究是增加更广泛采用电子衍射作为活性药物成分的晶体学和微观结构研究技术的可行性的关键。