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UV chromatographic data in combination with multivariate data analysis (MVDA) has been extensively used for bioprocess monitoring. However, they are usually attributed to shifts along the retention time and require preprocessing. Misaligned UV chromatographic data result in inconsistent MVDA models. Numerous preprocessing techniques are available, each varying in the number of meta-parameters to optimize, complexity and computational time. Therefore, we aimed at developing a generic workflow to screen for preprocessing techniques. We chose four datasets with increasing complexity containing UV chromatographic data from reverse-phase and size exclusion chromatography HPLC. We aligned all four datasets using three preprocessing techniques, namely icoshift, PAFFT and RAFFT algorithms. We chose several statistical tools to validate the performance of the preprocessing techniques and to screen for meta-parameters. We validated the performance of the preprocessing techniques in terms of data preservation, complexity and computational time, and identified the optimal ranges of meta-parameters for each dataset. Finally, we established principal component analysis (PCA) models to evaluate the chosen alignment technique. Summarizing, in this study a generic workflow has been developed to validate alignment of chromatographic data using statistical tools.

Horseradish peroxidase (HRP), a versatile heme-containing glycoprotein frequently used in medical diagnostics, is primarily isolated from plant. This process involves filtration, salt precipitation and several chromatography steps, which is expensive, renders low yields and gives isoenzyme mixtures. Although single isoenzymes can be recombinantly produced in Pichia pastoris, they get hyper-glycosylated in the yeast rendering the downstream process cumbersome and thus not competitive. In this study, we analyzed the purification of three HRP isoenzymes differing in the number of N-glycosylation sites recombinantly produced in P. pastoris. We wanted to 1) determine potential correlations between the mode of protein production and the resulting product quality and downstream process, 2) investigate correlations between the number of N-glycosylation sites of HRP and the mode of purification, and 3) find the optimal purification strategy for the recombinantly produced HRP isoenzymes. Thus, we applied different cultivation strategies and tested downstream processes employing both particlebased resins as well as monolithic supports. We showed that the mode of cultivation affected product purity but not the subsequent downstream process. Purification of each of the three HRP isoenzymes using monolithic supports was successful and independent of the number of N-glycosylation sites, whereas purification by particle-based resins was highly affected by glycosylation. Summarizing, we demonstrated a novel feasibility of using monoliths operated in flow through mode for the purification of comparatively small biomolecules.