The reported impact of shear stress on protein aggregation has been contradictory. At high shear rates, the occurrence of cavitation or entrapment of air is reasonable and their effects possibly misattributed to shear stress. Nine different proteins (α‐lactalbumin, two antibodies, fibroblast growth factor 2, granulocyte colony stimulating factor [GCSF], green fluorescence protein [GFP], hemoglobin, human serum albumin, and lysozyme) are tested for their aggregation behavior on vapor/liquid interfaces generated by cavitation and compared it to the isolated effects of high shear stress and air/liquid interfaces generated by foaming. Cavitation induced the aggregation of GCSF by +68.9%, hemoglobin +4%, and human serum albumin +2.9%, compared to a control, whereas the other proteins do not aggregate. The protein aggregation behaviors of the different proteins at air/liquid interfaces are similar to cavitation, but the effect is more pronounced. Air‐liquid interface induced the aggregation of GCSF by +94.5%, hemoglobin +35.5%, and human serum albumin (HSA) +31.1%. The results indicate that the sensitivity of a certain protein toward cavitation is very similar to air/liquid‐induced aggregation. Hence, hydroxyl radicals cannot be seen as the driving force for protein aggregation when cavitation occurs. Further, high shear rates of up to 10⁸ s⁻¹ do not affect any of the tested proteins. Therefore, also within this study generated extremely high isolated shear rates cannot be considered to harm structural integrity when processing proteins.

Process characterization is necessary in the biopharmaceutical industry, leading to concepts such as design of experiments (DoE) in combination with process modeling. However, these methods still have shortcomings, including large numbers of required experiments. The concept of intensified design of experiments (iDoE) is proposed, that is, intra‐experimental shifts of critical process parameters (CPP) that combine with hybrid modeling to more rapidly screen a particular design space. To demonstrate these advantages, a comprehensive experimental design of Escherichia coli (E. coli) fed‐batch cultivations (20 L) producing recombinant human superoxide dismutase is presented. The accuracy of hybrid models trained on iDoE and on a fractional‐factorial design is evaluated, without intra‐experimental shifts, to simultaneously predict the biomass concentration and product titer of the full‐factorial design. The hybrid model trained on data from the iDoE describes the biomass and product at each time point for the full‐factorial design with high and adequate accuracy. The fractional‐factorial hybrid model demonstrates inferior accuracy and precision compared to the intensified approach. Moreover, the intensified hybrid model only required one‐third of the data for model training compared to the full‐factorial description, resulting in a reduced experimental effort of >66%. Thus, this combinatorial approach has the potential to accelerate bioprocess characterization.

Cross-flow ultrafiltration is a powerful tool used in bioprocesses to concentrate and separate biopharmaceuticals. However, there is a major challenge for its effective use: the fouling rate and decrease in the permeate flux vary substantially depending on the concentration and characteristics of the incoming feed, which causes variations in the process durations. We developed a hybrid model for use in predicting the flux evolution and duration of cross-flow ultrafiltration processes for various proteins, membrane types, input parameters, and filtration modes. The trained hybrid model is able to determine the process duration with an average normalized root-mean-square error of less than 2.5. It has superior adaptation to varying filtration characteristics compared to the mechanistic film theory model and can handle both batch- and fed-batch ultrafiltration using the same training set. In addition, the presented hybrid model can be used as a digital twin to simulate virtual processes with varying input parameters and different batch modes, which is a valuable tool for efficient process development or optimization.

The fast exploration of a design space and identification of the best process conditions facilitating the highest space-time yield are of great interest for manufacturers. To obtain this information, depending on the design space, a large number of practical experiments must be performed, analyzed, and evaluated. To reduce this experimental effort and increase the process understanding, we evaluated a model-based design of experiments to rapidly identify the optimum process conditions in a design space maximizing space-time yield. From a small initial dataset, hybrid models were implemented and used as digital bioprocess twins, thus obtaining the recommended optimal experiment. In cases where these optimum conditions were not covered by existing data, the experiment was carried out and added to the initial data set, re-training the hybrid model. The procedure was repeated until the model gained certainty about the best process conditions, i.e., no new recommendations. To evaluate this workflow, we utilized different initial data sets and assessed their respective performances. The fastest approach for optimizing the space-time yield in a three-dimensional design space was found with five initial experiments. The digital twin gained certainty after four recommendations, leading to a significantly reduced experimental effort compared to other state-of-the-art approaches. This highlights the benefits of in silico design space exploration for accelerating knowledge-based bioprocess development, and reducing the number of hands-on experiments, time, energy, and raw materials.

Despite the advantages of mathematical bioprocess modeling, successful model implementation already starts with experimental planning and accordingly can fail at this early stage. For this study, two different modeling approaches (mechanistic and hybrid) based on a four‐dimensional antibody‐producing CHO fed‐batch process are compared. Overall, 33 experiments are performed in the fractional factorial four‐dimensional design space and separated into four different complex data partitions subsequently used for model comparison and evaluation. The mechanistic model demonstrates the advantage of prior knowledge (i.e., known equations) to get informative value relatively independently of the utilized data partition. The hybrid approach displayes a higher data dependency but simultaneously yielded a higher accuracy on all data partitions. Furthermore, our results demonstrate that independent of the chosen modeling framework, a smart selection of only four initial experiments can already yield a very good representation of a full design space independent of the chosen modeling structure. Academic and industry researchers are recommended to pay more attention to experimental planning to maximize the process understanding obtained from mathematical modeling.

Upstream bioprocess characterization and optimization are time and resource-intensive tasks. Regularly in the biopharmaceutical industry, statistical design of experiments (DoE) in combination with response surface models (RSMs) are used, neglecting the process trajectories and dynamics. Generating process understanding with time-resolved, dynamic process models allows to understand the impact of temporal deviations, production dynamics, and provides a better understanding of the process variations that stem from the biological subsystem. The authors propose to use DoE studies in combination with hybrid modeling for process characterization. This approach is showcased on Escherichia coli fed-batch cultivations at the 20L scale, evaluating the impact of three critical process parameters. The performance of a hybrid model is compared to a pure data-driven model and the widely adopted RSM of the process endpoints. Further, the performance of the time-resolved models to simultaneously predict biomass and titer is evaluated. The superior behavior of the hybrid model compared to the pure black-box approaches for process characterization is presented. The evaluation considers important criteria, such as the prediction accuracy of the biomass and titer endpoints as well as the time-resolved trajectories. This showcases the high potential of hybrid models for soft-sensing and model predictive control. The evaluation of a three-dimensional Escherichia coli design space, utilizing different modeling techniques, provides a comprehensive data basis to compare the advantages and limitations of different modeling approaches. Hybrid modeling has been proven to be superior compared to solely endpoint evaluation using design of experiments or black-box modeling, highlighting the potential of hybrid models for soft-sensing and model predictive control.