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 þ68.9%, hemoglobin þ4%, and human serum albumin þ2.9%, 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 þ94.5%, hemoglobin þ35.5%, and human serum albumin (HSA) þ31.1%.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 10 8 s À1 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.

Background: Antibody tests are essential tools to investigate humoral immunity following SARS-CoV-2 infection or vaccination. While first-generation antibody tests have primarily provided qualitative results, accurate seroprevalence studies and tracking of antibody levels over time require highly specific, sensitive and quantitative test setups. Methods: We have developed two quantitative, easy-to-implement SARS-CoV-2 antibody tests, based on the spike receptor binding domain and the nucleocapsid protein. Comprehensive evaluation of antigens from several biotechnological platforms enabled the identification of superior antigen designs for reliable serodiagnostic. Cut-off modelling based on unprecedented large and heterogeneous multicentric validation cohorts allowed us to define optimal thresholds for the tests' broad applications in different aspects of clinical use, such as seroprevalence studies and convalescent plasma donor qualification. Findings: Both developed serotests individually performed similarly-well as fully-automated CE-marked test systems. Our described sensitivity-improved orthogonal test approach assures highest specificity (99.8%); thereby enabling robust serodiagnosis in low-prevalence settings with simple test formats. The inclusion of a calibrator permits accurate quantitative monitoring of antibody concentrations in samples collected at different time points during the acute and convalescent phase of COVID-19 and disclosed antibody level thresholds that correlate well with robust neutralization of authentic SARS-CoV-2 virus. Interpretation: We demonstrate that antigen source and purity strongly impact serotest performance. Comprehensive biotechnology-assisted selection of antigens and in-depth characterisation of the assays allowed us to overcome limitations of simple ELISA-based antibody test formats based on chromometric reporters, to yield comparable assay performance as fully-automated platforms.

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.

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.

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.

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Background SARS-CoV-2 antibody tests have undergone a remarkable improvement in performance. However, due to the low seroprevalence in several areas, very high demands are made on their specificity. Furthermore, the low antibody-response in some individuals requires high test sensitivity to avoid underestimating true seroprevalence. Optimization of testing has been reported through lowering manufacturer cut-offs to improve SARS-CoV-2 assay sensitivity or by combining two tests to improve specificity at the cost of sensitivity. However, these strategies have thus far been used in isolation of each other. Methods To increase sensitivity, cut-offs of three commercially available SARS-CoV-2 automated assays (Roche, Abbott, and DiaSorin) were reduced according to published values in a pre-pandemic specificity cohort (n=1117) and a SARS-CoV-2 positive cohort (n=64). All three testing systems were combined in an orthogonal approach with a confirmatory test, which was one of the remaining automated assays or one of two commercial ELISAs directed against the spike protein receptor binding-domain (RBD) or the nucleocapsid antigen (NP). Results The modified orthogonal test strategy resulted in an improved specificity of at least 99.8%, often even 100%, in all 12 tested combinations with no significant decline in sensitivity. In our cohort, regardless of whether the assays were used for screening or confirmation, combining Roche and Abbott delivered the best overall performance (+~10% sensitivity compared to the single tests and 100% specificity). Conclusion Here we propose a novel orthogonal assay strategy that approaches 100% specificity while maintaining or even significantly improving the screening test's sensitivity.

Reliable process development is accompanied by intense experimental effort. The utilization of an intensified design of experiments (iDoE) (intra-experimental critical process parameter (CPP) shifts combined) with hybrid modeling potentially reduces process development burden. The iDoE can provide more process response information in less overall process time, whereas hybrid modeling serves as a commodity to describe this behavior the best way. Therefore, a combination of both approaches appears beneficial for faster design screening and is especially of interest at larger scales where the costs per experiment rise significantly. Ideally, profound process knowledge is gathered at a small scale and only complemented with few validation experiments on a larger scale, saving valuable resources. In this work, the transferability of hybrid modeling for Chinese hamster ovary cell bioprocess development along process scales was investigated. A two-dimensional DoE was fully characterized in shake flask duplicates (300 ml), containing three different levels for the cultivation temperature and the glucose concentration in the feed. Based on these data, a hybrid model was developed, and its performance was assessed by estimating the viable cell concentration and product titer in 15 L bioprocesses with the same DoE settings. To challenge the modeling approach, 15 L bioprocesses also comprised iDoE runs with intra-experimental CPP shifts, impacting specific cell rates such as growth, consumption, and formation. Subsequently, the applicability of the iDoE cultivations to estimate static cultivations was also investigated. The shaker-scale hybrid model proved suitable for application to a 15 L scale (1:50), estimating the viable cell concentration and the product titer with an NRMSE of 10.92% and 17.79%, respectively. Additionally, the iDoE hybrid model performed comparably, displaying NRMSE values of 13.75% and 21.13%. The low errors when transferring the models from shaker to reactor and between the DoE and the iDoE approach highlight the suitability of hybrid modeling for mammalian cell culture bioprocess development and the potential of iDoE to accelerate process characterization and to improve process understanding.