@article{7604d565aef040e29a58431a2cf06dc4,
title = "Advanced Real-Time Process Analytics for Multistep Synthesis in Continuous Flow**",
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.",
keywords = "flow chemistry, multistep synthesis, process analytical technologies, process control, real-time analysis",
author = "Peter Sagmeister and Ren{\'e} Lebl and Ismael Castillo and Jakob Rehrl and Julia Kruisz and Martin Sipek and Martin Horn and Stephan Sacher and David Cantillo and Williams, {Jason D.} and Kappe, {C. Oliver}",
note = "Funding Information: This work was funded by the Austrian Research Promotion Agency FFG No. 871458, within the program ?Produktion der Zukunft?. The INFRA FLOW project (Zukunftsfonds Steiermark No. 9003) is funded by the State of Styria (Styrian Funding Agency SFG). The CCFLOW Project (Austrian Research Promotion Agency FFG No. 862766) is funded through the Austrian COMET Program by the Austrian Federal Ministry of Transport, Innovation and Technology (BMVIT), the Austrian Federal Ministry for Digital and Economic Affairs (BMDW), and by the State of Styria (Styrian Funding Agency SFG). The authors would like to thank Dr. Clemens Minnich and Dr. Simon Kern (S-PACT) for software and chemometric model support, Dr. Stefan Kowarik (University of Graz) for assistance with neural networks and Mr Bernd Stein (HiTec Zang) for LabManager connectivity support. Funding Information: This work was funded by the Austrian Research Promotion Agency FFG No. 871458, within the program “Produktion der Zukunft”. The INFRA FLOW project (Zukunftsfonds Steiermark No. 9003) is funded by the State of Styria (Styrian Funding Agency SFG). The CCFLOW Project (Austrian Research Promotion Agency FFG No. 862766) is funded through the Austrian COMET Program by the Austrian Federal Ministry of Transport, Innovation and Technology (BMVIT), the Austrian Federal Ministry for Digital and Economic Affairs (BMDW), and by the State of Styria (Styrian Funding Agency SFG). The authors would like to thank Dr. Clemens Minnich and Dr. Simon Kern (S‐PACT) for software and chemometric model support, Dr. Stefan Kowarik (University of Graz) for assistance with neural networks and Mr Bernd Stein (HiTec Zang) for LabManager connectivity support. Publisher Copyright: {\textcopyright} 2021 The Authors. Angewandte Chemie International Edition published by Wiley-VCH GmbH",
year = "2021",
month = apr,
day = "6",
doi = "10.1002/anie.202016007",
language = "English",
volume = "60",
pages = "8139--8148",
journal = "Angewandte Chemie - International Edition",
issn = "1433-7851",
publisher = "Wiley-VCH ",
number = "15",
}