(358e) Quality by Design and Multivariate Process Representations
AIChE Annual Meeting
Tuesday, November 9, 2010 - 5:35pm to 6:10pm
Bioprocess design, development and manufacturing involve different goals and use different tools. At the design stage a cell-centric approach dominates. During development and especially later during manufacturing a process-centered perspective prevails as if biomanufacturing performance was dictated by process variables alone. At the end of downstream processing a product is isolated in required purity and only then a link is established to the desired targeted product profile.
A stronger link between process and product must be established throughout all those stages in order to bring biomanufacturing into the XXI century and significantly improve process performance, reduce process variability and moreover ensure consistent product quality, efficacy and safety at the end. Process analytical technology represents the combined use of different tools applicable through many of those stages. Though PAT is still mostly process-centered it can be used within the QbD (quality by design) context to link process to product.
A study of a bacterial cultivation process for the production of a secondary metabolite was made at several scales (lab to plant) using multivariate design space concepts. A multivariate process representation is more informative of process changes along batch processing time and also across different scales, than descriptions based on univariate formulations of culture or process parameters. Univariate classical approaches can thus be replaced by more straightforward, summarized and reliable process state representations based on whole process analysis.
In our talk we will show the equivalency of using both approaches by using in-situ FT-NIR spectroscopy to fingerprint the cultivation media and in doing so map the process trajectories throughout a batch. The proposed approach is fully in-line with the definition of a process design-space containing the information needed for accurate scale-up and process optimization.