A model based approach to an adaptive design space in chromatography

Abstract

Abstract We demonstrate an approach to construct an adaptive design space in the face of process variability, for an industrial hydrophobic interaction chromatography (HIC) with resin lot-to-lot variability. The step has a complex mixture of impurities in the feed stream and a multi component product. The step must deliver a specific distribution of product forms in the elution peak whilst maintaining product recovery and impurity removal. In our approach, a mechanistic model is used which gives a good representation of the system, and has been validated experimentally. The model is used to quickly and efficiently explore the impact of process parameters on process performance, utilizing stochastic simulation to generate probabilistic design spaces for different resin lots. The results indicate that significant increases in process robustness can be made by adapting the design space based on the resin lot in use, rather than fixing the design space for all resin lots. An adaptive design space enables operation further away from high risk regions, increases the size of operating regions and improves flexibility to variations in process inputs.