Case Studies on Enabling Data-Rich, Parallelized Kinetic Analysis for Process Optimization with the ReactALL Platform

Abstract

Automated, scale-predictive reaction profiling is often constrained by material limitations, sampling fidelity, and/or reactor mixing artifacts. We report the application of ReactALL, a five-reactor automated platform with a 5 mL working volume that integrates overhead agitation with built-in sampling, to generate reproducible, data-rich time-course reaction profiles. The performance of ReactALL was validated through three industry-relevant case studies: (1) the rapid, material-efficient development of an amidation reaction (Sanofi), (2) variable time normalization analysis (VTNA) of a Pd-catalyzed Suzuki–Miyaura coupling reaction (MSD), and (3) kinetic modeling of an S_NAr reaction (Pfizer). In each case, data sets obtained from ReactALL closely matched those obtained from established laboratory reactors while offering >10-fold savings in material consumption compared to conventional automated laboratory reactors. The analysis of the generated data provided valuable and actionable insights into reaction behavior, accelerating effective process optimization and predictive modeling across a range of reaction types. Collectively, these results demonstrate that ReactALL enables material-efficient, parallelized generation of high-quality kinetic data, supporting mechanistic analysis, predictive modeling, and scalable process development.