Advanced Real‐Time Process Analytics for Multistep Synthesis in Continuous Flow. Issue 15 (24th February 2021)

• Record Type: Journal Article
• Authors: Sagmeister, Peter; Lebl, René; Castillo, Ismael; Rehrl, Jakob; Kruisz, Julia; Sipek, Martin; Horn, Martin; Sacher, Stephan; Cantillo, David; Williams, Jason D.; Kappe, C. Oliver
• Is Part Of: Angewandte Chemie international edition. Volume 60:Issue 15(2021)
• Page Start: 8139
• Page End: 8148
• Publication Date: 2021-02-24
• Subjects: flow chemistry -- multistep synthesis -- process analytical technologies -- process control -- real-time analysis; Chemistry -- Periodicals; 540
• DOI: 10.1002/anie.202016007
• Languages: English
• ISSNs: 1433-7851

Abstract

Abstract: In multistep continuous flow chemistry, studying complex reaction mixtures in real time is a significant challenge, but provides an opportunity to enhance reaction understanding and control. We report the integration of four complementary process analytical technology tools (NMR, UV/Vis, IR and UHPLC) in the multistep synthesis of an active pharmaceutical ingredient, mesalazine. This synthetic route exploits flow processing for nitration, high temperature hydrolysis and hydrogenation reactions, as well as three inline separations. Advanced data analysis models were developed (indirect hard modeling, deep learning and partial least squares regression), to quantify the desired products, intermediates and impurities in real time, at multiple points along the synthetic pathway. The capabilities of the system have been demonstrated by operating both steady state and dynamic experiments and represents a significant step forward in data‐driven continuous flow synthesis. Abstract : Process analytical technologies hold tremendous promise in multistep continuous flow synthesis. Here, four orthogonal real‐time analytical tools are employed in a three step synthesis, with phase separations. Advanced data processing techniques, such as indirect hard modeling and deep learning, facilitate quantification of up to 9 process components. These analytical capabilities are demonstrated for both steady state and dynamic experiments.