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.

Neither the influence of high shear rates nor the impact of cavitation on protein aggregation is fully understood. The effect of cavitation bubble collapse‐derived hydroxyl radicals on the aggregation behavior of human serum albumin (HSA) was investigated. Radicals were generated by pumping through a micro‐orifice, ultra‐sonication, or chemically by Fenton's reaction. The amount of radicals produced by the two mechanical methods (0.12 and 11.25 nmol/(L min)) was not enough to change the protein integrity. In contrast, Fenton's reaction resulted in 382 nmol/(L min) of radicals, inducing protein aggregation. However, the micro‐orifice promoted the formation of soluble dimeric HSA aggregates. A validated computational fluid dynamic model of the orifice revealed a maximum and average shear rate on the order of 10⁸ s⁻¹ and 1.2 × 10⁶ s⁻¹, respectively. Although these values are among the highest ever reported in the literature, dimer formation did not occur when we used the same flow rate but suppressed cavitation. Therefore, aggregation is most likely caused by the increased surface area due to cavitation‐mediated bubble growth, not by hydroxyl radical release or shear stress as often reported.

Process analytical technology combines understanding and control of the process with real‐time monitoring of critical quality and performance attributes. The goal is to ensure the quality of the final product. Currently, chromatographic processes in biopharmaceutical production are predominantly monitored with UV/Vis absorbance and a direct correlation with purity and quantity is limited. In this study, a chromatographic workstation was equipped with additional online sensors, such as multi‐angle light scattering, refractive index, attenuated total reflection Fourier‐transform infrared, and fluorescence spectroscopy. Models to predict quantity, host cell proteins (HCP), and double‐stranded DNA (dsDNA) content simultaneously were developed and exemplified by a cation exchange capture step for fibroblast growth factor 2 expressed in Escherichia coliOnline data and corresponding offline data for product quantity and co‐eluting impurities, such as dsDNA and HCP, were analyzed using boosted structured additive regression. Different sensor combinations were used to achieve the best prediction performance for each quality attribute. Quantity can be adequately predicted by applying a small predictor set of the typical chromatographic workstation sensor signals with a test error of 0.85 mg/ml (range in training data: 0.1–28 mg/ml). For HCP and dsDNA additional fluorescence and/or attenuated total reflection Fourier‐transform infrared spectral information was important to achieve prediction errors of 200 (2–6579 ppm) and 340 ppm (8–3773 ppm), respectively.

Regulatory recommendations for quality by design instead of quality by testing raise increasing interest in new sensor technologies. An online monitoring system for downstream processes is developed, which is based on an array of online detectors. Besides standard detectors (UV, pH, and conductivity), our chromatographic workstation is equipped with a fluorescence and a mid‐infrared spectrometer, a light scattering, and a refractive index detector. The combination of these sensors enables the prediction of specific protein concentration and various purity attributes, such as high molecular weight impurities, DNA and host cell protein content during the elution phase of a chromatographic antibody capture process. Prediction models solely based on online signals are set up providing real‐time predictions. No mechanistic models or information about the chromatographic runs is used. These predictions allow online pooling decisions replacing time‐ and labor‐intensive laboratory measurements. Different process variations, such as changes in the column load or elution buffer, are introduced to test the predictive power of the models. Extrapolation of the models worked well when the column load is changed, whereas model adjustment is necessary when the elution conditions are changed considerably.