Metabolic flux analysis linked to complex raw materials as tool for bioprocess improvement. (14th December 2018) - Hofer, Alexandra; Hauer, Stefan; Kroll, Paul; Herwig, Christoph
Graphical abstract: Highlights: Filling the gap between raw material characterization and process understanding. Combining MFA and sensitivity analysis for the detection of key material attributes. Application of the generated knowledge for process improvement. Introduction of a general workflow for the assessment of the impact of raw material. Abstract: Biopharmaceutical processes should be designed to maximize productivity and ensure product quality. What is underestimated in this context is the variability of the raw material. Especially complex raw materials are challenging to characterize, hence, the identification of crucial raw material attributes that influence productivity or product quality is troublesome. In this study metabolic flux analysis (MFA) is applied to fill the gap between raw material characterization and process understanding. The approach is demonstrated in corn steep liquor (CSL) and a Penicillium chrysogenum process. This bioprocess is already well understood and there exist various MFA models aiming at understanding the fungal metabolism and production pathways. However, MFA was as per our knowledge not used for the identification of critical raw material attributes. Hence, in this study an MFA model was adapted from literature including CSL related fluxes and CSL release kinetics. The application of a sensitivity analysis with respect to qPen and µ, revealed the potential of the model based approach: we identified methionine as a key attribute in CSL for penicillin production. As a consequence, an optimized process could be presented by reducing CSL in the media and pulsing methionine, which resulted in a duplication of product titer. In summary, the expansion of an MFA model with raw material characteristics featured by the application of sensitivity analysis is a promising approach for science-based decisions on crucial raw material attributes. It could facilitate the predictive design of complex raw materials along Quality by Design rationales as well as model-based process improvement with respect to raw material attributes. Additionally, the method allows the identification of raw material variability and the impact of these variances on the process.