Experimental design and inferential modeling in pharmaceutical crystallization

Abstract

Abstract A fractional factorial experimental design was used to investigate relative effects of operating conditions on the filtration resistance of a slurry produced in a pharmaceutical semicontinuous batch crystallizer. The six operating variables were seed type, seed amount, temperature, solvent ratio, addition time, and filtration resistance was used to define the factory operating procedure, which reduced filtration time 3.7‐fold. Several chemometric techniques were used to construct inferential models between the in‐process measurement of partcle chord‐length distribution and filtration resistance to help detect operational problems before completing the batch and decide when batch crystal lization runs should end. Depending on the model quality criterion, the most popular chemometric models of partial least squares and top‐down principal‐component regression can produce lower quality models. Another chemometric approach, confidence‐interval principal‐component regression, predicted 70% more accurately than the best OLS model. The main effects and inferential models serve different but complementary roles in developing and implementing high performance crystallization process opertions. A main‐effects model constructed from statistical experimental design data determined optimal operating conditions rapidly, while the inferential model can determine opertional problems and batch end times during batch end time during batch‐process operations.