Revisiting the paradigm of reaction optimization with an interdisciplinary approach

The 21st century faces an unprecedented environmental crisis associated with the
centralized, mass production of chemicals. While the research from the scientific community has helped to raise potential solutions to relieve the environmental burden associated with industrial chemistry, it usually requires time, money, and resources-consuming tasks starting at the R&D scale. New emerging process technologies including continuous flow micro and mesofluidic reactors have contributed to address these challenges in particular in conjunction with their integration with automated robots or coded scripts, artificial intelligence, and Process Analytical Technology (PAT). While these advanced technologies bear lots of promises, the transposition of existing batch or new processes under flow conditions remains essentially based on trials-and-errors or know-how from operators having ordinary skills in the art. There is still a
huge gap for accessing guided transposition under flow conditions since these miniaturized systems alter the commonly accepted operational time and temperature domain. Unguided optimizations are associated with the generation of large volumes of waste reactor effluent with often minimal relevant information.
Herein, we propose an innovative methodology for optimizing reactions with the minimal
but most efficient human intervention that proceeds with upstream quantum chemistry-guided assistance for assessing transposability under flow conditions. Our strategy relies on an assistant that extracts relevant kinetics information for predicting an ideal set of reagents, reaction time, or temperature for reaching a specific conversion at a given concentration. This versatile approach has been successfully applied to two case studies. The first case study concerns a large-scale sulfoxide thermolysis yielding a key intermediate towards estrogenic hormone estetrol. The second case study rather relies on the upstream process of discovery in medicinal chemistry with the generation of libraries of aminated compounds. This second example feeds upon the reactivity of nitrosoarenes, with potential concrete applications for the synthesis of fentanyl derivatives. The amount of experimental work has been reduced with the use of flow technology, automation, and IR in-line monitoring whereas kinetics data were extracted either experimentally, computationally, or in combination with machine learning.