Sustainable Pharmaceutical Manufacturing: Autonomous Development of Telescoped Catalytic Reactions

可持续药物制造：伸缩催化反应的自主开发

• 批准号: 2882495
• 金额: --
• 依托单位: University of Leeds
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2882495
• 关键词: Sustainable; Pharmaceutical; Manufacturing; Autonomous; Development

Pharmaceutical manufacturing typically uses batch processing at multiple locations, requiring several energy-intensive purification and transportation steps, resulting in a large carbon footprint. Combination of reaction steps in uninterrupted reaction networks ('telescoping') has the potential to greatly improve the sustainability of manufacturing processes. Of particular interest, but also significant challenge, is the integration of different types of catalysis (chemical, biological, photo) within a synthetic pathway. This interdisciplinary project, based across the Schools of Chemistry and Chemical Engineering at the University of Leeds, will investigate flow chemistry and machine learning approaches for the development of telescoped catalytic reactions, which will enable the design of novel and sustainable pathways for pharmaceutical synthesis. This project has two key objectives, each with its own significant academic research challenge: Develop novel procedures for the self-optimisation of multi-step continuous flow processes. Machine learning techniques will be coupled with online multipoint analysis to rapidly explore complex interactions between catalytic steps. Design new multi-step reactor configurations for the telescoping of catalytic steps. Catalytic reactions will be investigated under continuous flow conditions, and different types of catalysts compartmentalised to enable operation at divergent reaction conditions. Research areas (i) and (ii) will be coupled to enable integration of different types of catalysts within a synthetic pathway, and the technology demonstrated towards the synthesis of pharmaceutically relevant compounds. This work will be conducted within the Institute of Process Research and Development.

制药生产通常在多个地点使用批量处理，需要几个能源密集型的纯化和运输步骤，导致大量的碳足迹。不间断反应网络中反应步骤的组合（“伸缩”）有可能大大提高制造工艺的可持续性。特别令人感兴趣的，但也是重大的挑战，是不同类型的催化（化学，生物，光）的合成途径内的整合。这个跨学科的项目，基于整个化学和化学工程学院在利兹大学，将调查流动化学和机器学习方法的发展，望远镜催化反应，这将使设计新颖的和可持续的药物合成途径。该项目有两个关键目标，每个目标都有自己的重大学术研究挑战：开发多步连续流过程自优化的新程序。机器学习技术将与在线多点分析相结合，以快速探索催化步骤之间复杂的相互作用。设计新的多步反应器配置，用于催化步骤的伸缩。将在连续流动条件下研究催化反应，并将不同类型的催化剂划分为不同的反应条件。研究领域（i）和（ii）将被耦合，以使不同类型的催化剂在合成途径中的整合，以及对药物相关化合物的合成所展示的技术。这项工作将在工艺研究和发展研究所内进行。