(208b) Automated Reaction Optimization Under Dynamic Flow Conditions

Automated optimization in flow reactors is a technology that continues to gain interest in academic and industrial research. For drug substance applications, where limited material is available for extensive studies, it is imperative that the automated optimization procedure identify ideal conditions for manufacturing in a resource sparing manner. It is equally as important that these investigations provide data-rich results so that the information can be used for process understanding. Achieving these two objectives in parallel is challenging with traditional automated optimization systems that rely on steady-state data. Dynamic flow systems, which adjust reaction inputs in a controlled manner to collect transient reaction results, maximize reaction information content. In this work, the benefits of automated optimization flow reactors and dynamic flow systems are combined to demonstrate the synergistic gains in knowledge collection and reaction information using a nucleophilic aromatic substitution as a case study. Using a gradient-based search algorithm, the reaction is optimized using a multi-faceted objective function that accounts for yield, material input, and productivity. Results from the automated dynamic optimization were used to establish a reaction model to provide great insight in the reaction kinetics and selectivity.