Uniting laboratory automation, DoE data, and modeling techniques to accelerate chemical process development

Abstract

The combination of parallel, automation technologies with increasingly sophisticated modeling and statistical techniques has greatly accelerated the ability of pharmaceutical process research and development groups to understand and develop robust, sustainable chemical processes. In this work, we present some insights into the practical application of automated design of experiments, robotic execution, and modeling of datasets. This starts with planning of experiments, trial applications to ensure proper installation of the chemistry on the automated platforms including analytical considerations, automated DoE execution, collation of structured data tables, and the automated statistical modeling of the results. This automated DoE workflow is further illustrated by drawing upon an example from a recent BMS program.