Fundamental insights in early-stage inclusion body formation.

Early-stage inclusion body formation is still mysterious. Literature is ambiguous about the existence of rod-shaped protein aggregates, a potential sponge-like inclusion body scaffold as well as the number of inclusion bodies per Escherichia coli cell. In this study, we verified the existence of rod-shaped inclusion bodies, confirmed their porous morphology, the presence of multiple protein aggregates per cell and modelled inclusion body formation as function of the number of generations.

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.

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.

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.

Injection of the diaphragmatic crus with methylene blue for coloration of the canine thoracic duct.

OBJECTIVE: To describe and compare onset and intensity of thoracic duct (TD) coloration after injection of methylene blue into the diaphragmatic crus and mesenteric lymph node. STUDY DESIGN: Experimental study. ANIMALS: Adult dogs (n = 18). METHODS: Methylene blue (≤0.5 mg/kg 1% solution) was injected into the left (n = 9) or right (n = 9) diaphragmatic crus via right 10th intercostal thoracotomy. TD coloration was graded over 10 minutes. A right paracostal laparotomy was then performed in all dogs, and an equal volume of methylene blue injected into a mesenteric lymph node (n = 18). TD color grading was repeated. Statistical analysis was performed on subject weight, volume of contrast agent injected between left and right crus, and number of successful outcomes between diaphragmatic crus injection and mesenteric lymph node injection. RESULTS: TD coloration occurred in 6 dogs with left crus injection and 4 dogs with right crus injection with obvious staining present in 2 and 3 dogs, respectively. Successful outcome was noted in all dogs with mesenteric lymph node injection. The number of successful outcomes was significantly greater after mesenteric lymph node injection compared with diaphragmatic crus injection (P < .001). CONCLUSIONS: Methylene blue injected into the diaphragmatic crura and mesenteric lymph node was successful in coloring the TD; however, mean thoracic duct color grade and number of successful outcomes were significantly higher after mesenteric injection.