Comparison of mechanistic and hybrid modeling approaches for characterization of a CHO cultivation process: Requirements, pitfalls and solution paths.

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.

Increasing test specificity without impairing sensitivity: lessons learned from SARS-CoV-2 serology.

BACKGROUND: Serological tests are widely used in various medical disciplines for diagnostic and monitoring purposes. Unfortunately, the sensitivity and specificity of test systems are often poor, leaving room for false-positive and false-negative results. However, conventional methods were used to increase specificity and decrease sensitivity and vice versa. Using SARS-CoV-2 serology as an example, we propose here a novel testing strategy: the 'sensitivity improved two-test' or 'SIT²' algorithm. METHODS: SIT² involves confirmatory retesting of samples with results falling in a predefined retesting zone of an initial screening test, with adjusted cut-offs to increase sensitivity. We verified and compared the performance of SIT² to single tests and orthogonal testing (OTA) in an Austrian cohort (1117 negative, 64 post-COVID-positive samples) and validated the algorithm in an independent British cohort (976 negatives and 536 positives). RESULTS: The specificity of SIT² was superior to single tests and non-inferior to OTA. The sensitivity was maintained or even improved using SIT² when compared with single tests or OTA. SIT² allowed correct identification of infected individuals even when a live virus neutralisation assay could not detect antibodies. Compared with single testing or OTA, SIT² significantly reduced total test errors to 0.46% (0.24-0.65) or 1.60% (0.94-2.38) at both 5% or 20% seroprevalence. CONCLUSION: For SARS-CoV-2 serology, SIT² proved to be the best diagnostic choice at both 5% and 20% seroprevalence in all tested scenarios. It is an easy to apply algorithm and can potentially be helpful for the serology of other infectious diseases.

SARS-CoV-2 RBD-specific and NP-specific antibody response of healthcare workers in the westernmost Austrian state Vorarlberg: a prospective cohort study.

OBJECTIVES: Austria, and particularly its westernmost federal state Vorarlberg, developed an extremely high incidence rate during the COVID-19 pandemic. Healthcare workers (HCWs) worldwide are known to have an increased risk of contracting the disease within the working environment and, therefore, the seroprevalence in this population is of particular interest. We thus aimed to analyse SARS-CoV-2-specific antibody dynamics in Vorarlberg HCWs. DESIGN: Prospective cohort study of HCWs including testing at three different time points for the prevalence of anti-SARS-CoV-2 IgG antibodies specific for nucleocapsid protein (NP) and receptor-binding domain (RBD). SETTING: All five state hospitals of Vorarlberg. PARTICIPANTS: A total of 395 HCWs, enrolled in June 2020 (time point 1 (t1)), 2 months after the end of the first wave, retested between October and November at the beginning of the second wave (time point 2 (t2)) and again at the downturn of the second wave in January 2021 (time point 3 (t3)). MAIN OUTCOMES: We assessed weak and strong seropositivity and associated factors, including demographic and clinical characteristics, symptoms consistent with COVID-19 infection, infections verified by reverse transcription PCR (RT-PCR) and vaccinations. RESULTS: At t1, 3% of HCWs showed strong IgG-specific responses to either NP or RBD. At t2, the rate had increased to 4%, and at t3 to 14%. A strong response was found to be stable for up to 10 months. Overall, only 55% of seropositive specimen had antibodies against both antigens RBD and NP; 29% had only RBD-specific and 16% only NP-specific antibodies. Compared with the number of infections found by RT-PCR, the number of HCWs being seropositive was 38% higher. CONCLUSION AND RELEVANCE: Serological testing based on only one antigen implicates the risk of missing infections; thus, the set of antigens should be broadened in the future. The seroprevalence among participating HCWs was comparable to the general population in Austria. Nevertheless, in view of undetected infections, monitoring and surveillance should be reconsidered.

Proton-transfer-reaction mass spectrometry (PTR-MS) for online monitoring of glucose depletion and cell concentrations in HEK 293 gene therapy processes.

OBJECTIVES: The applicability of proton-transfer-reaction mass spectrometry (PTR-MS) as a versatile online monitoring tool to increase consistency and robustness for recombinant adeno-associated virus (rAAV) producing HEK 293 bioprocesses was evaluated. We present a structured workflow to extract process relevant information from PTR-MS data. RESULTS: Reproducibility of volatile organic compound (VOC) measurements was demonstrated with spiking experiments and the process data sets used for applicability evaluation consisted of HEK 293 cell culture triplicates with and without transfection. The developed data workflow enabled the identification of six VOCs, of which two were used to develop a soft sensor providing better real-time estimates than the conventional capacitance sensor. Acetaldehyde, another VOC, provides online process information about glucose depletion that can directly be used for process control purposes. CONCLUSIONS: The potential of PTR-MS for HEK 293 cell culture monitoring has been shown. VOC data derived information can be used to develop soft sensors and to directly set up new process control strategies.

High shear resistance of insect cells: the basis for substantial improvements in cell culture process design.

Multicellular organisms cultivated in continuous stirred tank reactors (CSTRs) are more sensitive to environmental conditions in the suspension culture than microbial cells. The hypothesis, that stirring induced shear stress is the main problem, persists, although it has been shown that these cells are not so sensitive to shear. As these results are largely based on Chinese Hamster Ovary (CHO) cell experiments the question remains if similar behavior is valid for insect cells with a higher specific oxygen demand. The requirement of higher oxygen transfer rates is associated with higher shear forces in the process. Consequently, we focused on the shear resistance of insect cells, using CHO cells as reference system. We applied a microfluidic device that allowed defined variations in shear rates. Both cell lines displayed high resistance to shear rates up to 8.73 × 105 s-1. Based on these results we used microbial CSTRs, operated at high revolution speeds and low aeration rates and found no negative impact on cell viability. Further, this cultivation approach led to substantially reduced gas flow rates, gas bubble and foam formation, while addition of pure oxygen was no longer necessary. Therefore, this study contributes to the development of more robust insect cell culture processes.

A comprehensive antigen production and characterisation study for easy-to-implement, specific and quantitative SARS-CoV-2 serotests.

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. FUNDING: WWTF, Project No. COV20-016; BOKU, LBI/LBG.

Model Transferability and Reduced Experimental Burden in Cell Culture Process Development Facilitated by Hybrid Modeling and Intensified Design of Experiments.

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.

Soft sensor based on 2D-fluorescence and process data enabling real-time estimation of biomass in Escherichia coli cultivations.

In bioprocesses, specific process responses such as the biomass cannot typically be measured directly on-line, since analytical sampling is associated with unavoidable time delays. Accessing those responses in real-time is essential for Quality by Design and process analytical technology concepts. Soft sensors overcome these limitations by indirectly measuring the variables of interest using a previously derived model and actual process data in real time. In this study, a biomass soft sensor based on 2D-fluorescence data and process data, was developed for a comprehensive study with a 20-L experimental design, for Escherichia coli fed-batch cultivations. A multivariate adaptive regression splines algorithm was applied to 2D-fluorescence spectra and process data, to estimate the biomass concentration at any time during the process. Prediction errors of 4.9% (0.99 g/L) for validation and 3.8% (0.69 g/L) for new data (external validation), were obtained. Using principal component and parallel factor analyses on the 2D-fluorescence data, two potential chemical compounds were identified and directly linked to cell metabolism. The same wavelength pairs were also important predictors for the regression-model performance. Overall, the proposed soft sensor is a valuable tool for monitoring the process performance on-line, enabling Quality by Design.

Hybrid Modeling and Intensified DoE: An Approach to Accelerate Upstream Process Characterization.

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.

Comparison of Modeling Methods for DoE-Based Holistic Upstream Process 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.

Influence of cavitation and high shear stress on HSA aggregation behavior.

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.

Impact of Cavitation, High Shear Stress and Air/Liquid Interfaces on Protein Aggregation.

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.