Use of Split‐Plot Optimal Design for Optimization of a Constrained Two‐Step Chemical Reaction Process: A Case Study Example

ABSTRACT Process development is a requirement in chemical pharmaceutical manufacturing. Design of experiments (DOE) is an essential tool for an empirical approach to process development. Most real‐world processes have design constraints that prevent the use of classical experimental designs. Algorithmically‐generated optimal design (OD) is particularly useful to accommodate such constraints with a minimal number of experimental runs. DOE application to the optimization of a two‐step chemical reaction process with logistical and chemistry constraints is exemplified in this paper. A 5‐factor split‐plot OD is used, requiring mixed‐effects model fitting of the product and impurity responses. The complexities in the analysis introduced in accommodating the constraints are considered. Statistical models generated from the DOE predicted results of confirmatory runs, demonstrating the adequacy of the models. The DOE study successfully identified the optimal process condition in a systematic and efficient manner. The synergy achieved when chemists and statisticians partner together is key to this success. Case studies like this paper aim to educate practitioners on the use of OD to promote its more widespread use.