Novel chemometric strategy based on the application of artificial neural networks to crossed mixture design for the improvement of recombinant protein production in continuous culture

Establishment of a design space for biopharmaceutical purification processes using DoE

Recent trends in the pharmaceutical sector are changing the way protein purification processes are designed and executed, moving from operating the process in a fixed point to allowing a permissible region in the operating space known as design space. This trend is driving product development to design quality into the manufacturing process (Quality by Design) and not to rely exclusively on testing quality in the product. A typical purification step has numerous operating parameters that can impact its performance. Therefore, optimization and robustness analysis in purification processes can be time-consuming since they are mainly grounded on experimental work. A valuable approach consists in the combination of an adequate risk analysis technique for selecting the relevant factors influencing process performance and the design of experiment methodology. The latter allows for many process variables which can be studied at the same time; thus, the number of tests will be reduced in comparison with the conventional approach based on trial and error. These multivariate studies permit a detailed exploration in the experimental range and lay the foundation of Quality by Design principles application. This article outlines a recommended sequence of activities toward the establishment of an expanded design space for a purification process.