Sustainable Pharmaceutical Manufacturing: Autonomous Development of Telescoped Catalytic Reactions

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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.