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Advanced Real‐Time Process Analytics for Multistep Synthesis in Continuous Flow **
Author(s) -
Sagmeister Peter,
Lebl René,
Castillo Ismael,
Rehrl Jakob,
Kruisz Julia,
Sipek Martin,
Horn Martin,
Sacher Stephan,
Cantillo David,
Williams Jason D.,
Kappe C. Oliver
Publication year - 2021
Publication title -
angewandte chemie international edition
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.831
H-Index - 550
eISSN - 1521-3773
pISSN - 1433-7851
DOI - 10.1002/anie.202016007
Subject(s) - flow chemistry , process analytical technology , computer science , process engineering , continuous flow , analytics , process (computing) , chemistry , active ingredient , combinatorial chemistry , biological system , biochemical engineering , work in process , data mining , engineering , bioinformatics , operations management , biology , operating system
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.