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Downstream process development for recombinant glycoproteins from yeast is cumbersome due to hyperglycosylation of target proteins. In a previous study, we purified three recombinant glycoproteins from Pichia pastoris using a simple two-step flowthrough mode approach using monolithic columns. In this study, we investigated a novel automated data science approach for identifying purification conditions for such glycoproteins using monolithic columns. We performed three sets of design of experiments in analytical scale to determine the separation efficiency of monolithic columns for three different recombinant horseradish peroxidase (HRP) isoenzymes. For ease of calculation, we introduced an arbitrary term, the relative impurity removal (IR), which is representative of the amount of impurities cleared. Both, the experimental part and the data analysis were automated and took less than 40 min for each HRP isoenzyme. We tested the identified purification conditions in laboratory scale and performed respective offline analyses to verify results from analytical scale. We found a clear correlation between the IR estimated online through our novel data-driven approach and the IR determined offline. Summarizing, we present a novel methodology, applying analytical scale advantages which can be used for fast and efficient DSP development for recombinant glycoproteins from yeast without offline analyses.

Ultrafiltration is a powerful method used in virtually every pharmaceutical bioprocess. Depending on the process stage, the product-to-impurity ratio differs. The impact of impurities on the process depends on various factors. Solely mechanistic models are currently not sufficient to entirely describe these complex interactions. We have established two hybrid models for predicting the flux evolution, the protein rejection factor and two components’ concentration during crossflow ultrafiltration. The hybrid models were compared to the standard mechanistic modeling approach based on the stagnant film theory. The hybrid models accurately predicted the flux and concentration over a wide range of process parameters and product-to-impurity ratios based on a minimum set of training experiments. Incorporating both components into the modeling approach was essential to yielding precise results. The stagnant film model exhibited larger errors and no predictions regarding the impurity could be made, since it is based on the main product only. Further, the developed hybrid models exhibit excellent interpolation properties and enable both multi-step ahead flux predictions as well as time-resolved impurity forecasts, which is considered to be a critical quality attribute in many bioprocesses. Therefore, the developed hybrid models present the basis for next generation bioprocessing when implemented as soft sensors for real-time monitoring of processes.