Advanced Crystal Shape Descriptors for Precision Particulate Design, Characterisation and Processing (Shape4PPD)

用于精密颗粒设计、表征和加工的高级晶体形状描述符 (Shape4PPD)

• 批准号: EP/W003678/1
• 负责人: Kevin Roberts
• 金额: $ 123.37万
• 依托单位: University of Leeds
• 依托单位国家: 英国
• 项目类别: Research Grant
• 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=EP%2FW003678%2F1
• 关键词: Advanced; Crystal; Shape; Descriptors; Precision

Developing and improving our R&D and manufacturing capabilities to prepare greater numbers of higher quality crystalline materials has become a growing societal and hence industrial need. This requires higher levels of precision and speed throughout the R&D development cycle to meet the evolving needs for precision crystals in fine chemical's sector such as for pharmaceuticals, agrochemicals and additives. For example, a more differentiated product range is expected to be produced with a significantly faster molecule to patient journey, in much smaller volumes and at significantly lower costs. For pharmaceuticals, this will provide a wider range of more targeted medicines and dosage forms, ensuring the delivery of patient-targeted dosage forms with much improved safety and efficacy, hence enormously benefiting economy, environment and society. Such an increase in the multiplicity of crystalline products demands the implementation of digitally-enabled and AI technologies as highlighted in UK government policy and global initiatives.The surface properties of crystals are very important for the digital design and manufacture of precision particles via solution crystallisation. Control of the surfaces expressed on crystalline particles represents a critical objective for the fine chemical industry which manufactures ca. 70% of their ingredients in solid (crystalline) form. These crystals have their unique shapes and surface chemistry which, when variable, can impact adversely upon product quality and performance. Specifically, the effective digital design of such products and the associated processes for their manufacture demands a detailed knowledge of surface properties of the product's formulation ingredients. Currently there exists a critical gap to relate the measurable properties at the molecular and single crystal levels to the behaviour and performance of the same material when it is manufactured or used in particulate form. This perspective demands the development of a digitally-enabled platform which is able to characterise, monitor and control crystal size and shape. However, existing crystal shape descriptors available with current commercial particle measurement systems have limited capabilities and the corresponding algorithms tend, unrealistically, to be based upon the assumption that non-spherical crystals can be treated as spherical ones. Therefore, the development of advanced process-inspired analytical tools, particularly of AI-based approach, combining with first-principle, shape-based models are clearly needed. Such approaches are important in order to ensure that the UK's research-led fine chemical and pharmaceutical industry continues to provide outstanding international leadership in product development and manufacture so maintaining and enhancing its global competitiveness.The proposed research will apply machine learning based upon crystal morphology prediction (forward engineering) to map from 2D in-process microscopy data back to a description of a crystal's 3D shape (reverse engineering) and, through this, to its functional surface properties. This will enable the design and control of more efficient and agile manufacturing processes for crystalline fine chemicals, delivering precision crystals with a much tighter specification in terms of their size and shape than is currently feasible, hence resulting in products having more consistency, less variability, higher quality. The outcomes will be a digital platform of crystal shape characterisation and process dynamics control for precision particle manufacture. The approach developed will be shared through academic collaboration (such as the CMAC Hub, INFORM2020, Cambridge Crystallographic Data Centre, Imperial College etc.) and with industry (Infineum, Keyence, Pfizer, Roche, Syngenta etc.) and also extended in due course more widely, expecting potentially enormous economic and societal impact.

发展和提高我们的研发和制造能力，以制备更多更高质量的晶体材料已成为日益增长的社会需求，因此也是工业需求。这就要求在整个研发周期中实现更高的精度和速度，以满足精细化工领域（如制药、农用化学品和添加剂）对精密晶体不断变化的需求。例如，预计将以更快的分子到患者的旅程、更小的体积和更低的成本生产更差异化的产品系列。对于药品而言，这将提供更广泛的更有针对性的药物和剂型，确保以更高的安全性和有效性提供针对患者的剂型，从而极大地造福于经济、环境和社会。晶体产品的多样性如此增加，需要实施数字化和人工智能技术，这在英国政府政策和全球倡议中得到了强调。晶体的表面性质对于通过溶液结晶进行精密颗粒的数字化设计和制造非常重要。控制结晶颗粒的表面是生产钙的精细化工行业的一个关键目标。70%的成分是固体（晶体）形式。这些晶体具有其独特的形状和表面化学性质，当变化时，会对产品质量和性能产生不利影响。具体而言，此类产品的有效数字化设计及其制造相关工艺需要详细了解产品配方成分的表面特性。目前存在一个关键的差距，将分子和单晶水平上的可测量性质与以颗粒形式制造或使用的相同材料的行为和性能联系起来。这种观点要求开发一种数字化平台，能够测量，监测和控制晶体尺寸和形状。然而，与当前商业颗粒测量系统一起可用的现有晶体形状描述符具有有限的能力，并且相应的算法不切实际地倾向于基于非球形晶体可以被视为球形晶体的假设。因此，显然需要开发先进的过程启发分析工具，特别是基于AI的方法，结合第一原理，基于形状的模型。这些方法对于确保英国以研究为主导的精细化工和制药行业继续在产品开发和制造方面提供卓越的国际领导地位，从而保持和提高其全球竞争力非常重要。（正向工程）从2D过程中显微镜数据映射回晶体的3D形状描述（逆向工程），并通过此映射回其功能表面特性。这将使设计和控制更有效和灵活的结晶精细化学品制造工艺成为可能，提供比目前可行的尺寸和形状规格更严格的精密晶体，从而使产品具有更高的一致性，更少的可变性，更高的质量。其成果将是一个晶体形状表征和过程动态控制的数字平台，用于精密颗粒制造。开发的方法将通过学术合作（如CMAC Hub，INFORM2020，剑桥晶体数据中心，帝国理工学院等）共享。和行业（Infineum，Keyence，Pfizer，Roche，Syngenta等）并在适当的时候扩大到更广泛的范围，预计可能产生巨大的经济和社会影响。