Predicting industrial-scale cell culture seed trains-A Bayesian framework for model fitting and parameter estimation, dealing with uncertainty in measurements and model parameters, applied to a nonlinear kinetic cell culture model, using an MCMC method.

For production of biopharmaceuticals in suspension cell culture, seed trains are required to increase cell number from cell thawing up to production scale. Because cultivation conditions during the seed train have a significant impact on cell performance in production scale, seed train design, monitoring, and development of optimization strategies is important. This can be facilitated by model-assisted prediction methods, whereby the performance depends on the prediction accuracy, which can be improved by inclusion of prior process knowledge, especially when only few high-quality data is available, and description of inference uncertainty, providing, apart from a "best fit"-prediction, information about the probable deviation in form of a prediction interval. This contribution illustrates the application of Bayesian parameter estimation and Bayesian updating for seed train prediction to an industrial Chinese hamster ovarian cell culture process, coppled with a mechanistic model. It is shown in which way prior knowledge as well as input uncertainty (e.g., concerning measurements) can be included and be propagated to predictive uncertainty. The impact of available information on prediction accuracy was investigated. It has been shown that through integration of new data by the Bayesian updating method, process variability (i.e., batch-to-batch) could be considered. The implementation was realized using a Markov chain Monte Carlo method.

Matrix-free human pluripotent stem cell manufacturing by seed train approach and intermediate cryopreservation.

BACKGROUND: Human pluripotent stem cells (hPSCs) have an enormous therapeutic potential, but large quantities of cells will need to be supplied by reliable, economically viable production processes. The suspension culture (three-dimensional; 3D) of hPSCs in stirred tank bioreactors (STBRs) has enormous potential for fuelling these cell demands. In this study, the efficient long-term matrix-free suspension culture of hPSC aggregates is shown. METHODS AND RESULTS: STBR-controlled, chemical aggregate dissociation and optimized passage duration of 3 or 4 days promotes exponential hPSC proliferation, process efficiency and upscaling by a seed train approach. Intermediate high-density cryopreservation of suspension-derived hPSCs followed by direct STBR inoculation enabled complete omission of matrix-dependent 2D (two-dimensional) culture. Optimized 3D cultivation over 8 passages (32 days) cumulatively yielded ≈4.7 × 1015 cells, while maintaining hPSCs' pluripotency, differentiation potential and karyotype stability. Gene expression profiling reveals novel insights into the adaption of hPSCs to continuous 3D culture compared to conventional 2D controls. CONCLUSIONS: Together, an entirely matrix-free, highly efficient, flexible and automation-friendly hPSC expansion strategy is demonstrated, facilitating the development of good manufacturing practice-compliant closed-system manufacturing in large scale.

Analysis of the effects of bench-scale cell culture platforms and inoculum cell concentrations on PSC aggregate formation and culture.

Introduction: Human pluripotent stem cells (hPSCs) provide many opportunities for application in regenerative medicine due to their ability to differentiate into cells from all three germ layers, proliferate indefinitely, and replace damaged or dysfunctional cells. However, such cell replacement therapies require the economical generation of clinically relevant cell numbers. Whereas culturing hPSCs as a two-dimensional monolayer is widely used and relatively simple to perform, their culture as suspended three-dimensional aggregates may enable more economical production in large-scale stirred tank bioreactors. To be more relevant to this biomanufacturing, bench-scale differentiation studies should be initiated from aggregated hPSC cultures. Methods: We compared five available bench-scale platforms for generating undifferentiated cell aggregates of human embryonic stem cells (hESCs) using AggreWell™ plates, low attachment plates on an orbital shaker, roller bottles, spinner flasks, and vertical-wheel bioreactors (PBS-Minis). Thereafter, we demonstrated the incorporation of an hPSC aggregation step prior to directed differentiation to pancreatic progenitors and endocrine cells. Results and discussion: The AggreWell™ system had the highest aggregation yield. The initial cell concentrations had an impact on the size of aggregates generated when using AggreWell™ plates as well as in roller bottles. However, aggregates made with low attachment plates, spinner flasks and PBS-Minis were similar regardless of the initial cell number. Aggregate morphology was compact and relatively homogenously distributed in all platforms except for the roller bottles. The size of aggregates formed in PBS-Minis was modulated by the agitation rate during the aggregation. In all cell culture platforms, the net growth rate of cells in 3D aggregates was lower (range: -0.01-0.022 h-1) than cells growing as a monolayer (range: 0.039-0.045 h-1). Overall, this study describes operating ranges that yield high-quality undifferentiated hESC aggregates using several of the most commonly used bench-scale cell culture platforms. In all of these systems, methods were identified to obtain PSC aggregates with greater than 70% viability, and mean diameters between 60 and 260 mm. Finally, we showed the capacity of hPSC aggregates formed with PBS-Minis to differentiate into viable pancreatic progenitors and endocrine cell types.

Digital Seed Train Twins and Statistical Methods.

Model-based concepts and simulation techniques in combination with digital tools emerge as a key to explore the full potential of biopharmaceutical production processes, which contain several challenging development and process steps. One of these steps is the time- and cost-intensive cell proliferation process (also called seed train) to increase cell number from cell thawing up to production scale. Challenges like complex cell metabolism, batch-to-batch variation, variabilities in cell behavior, and influences of changes in cultivation conditions necessitate adequate digital solutions to provide information about the current and near future process state to derive correct process decisions.For this purpose digital seed train twins have proved to be efficient, which digitally display the time-dependent behavior of important process variables based on mathematical models, strategies, and adaption procedures.This chapter will outline the needs for digitalization of seed trains, the construction of a digital seed train twin, the role of parameter estimation, and different statistical methods within this context, which are applicable to several problems in the field of bioprocessing. The results of a case study are presented to illustrate a Bayesian approach for parameter estimation and prediction of an industrial cell culture seed train for seed train digitalization. This chapter outlines the needs for digitalization of cell proliferation processes (seed trains), the construction of a digital seed train twin as well as the role of parameter estimation and different statistical methods within this context, which are applicable to several problems in the field of bioprocessing. The results of a case study are presented to illustrate a Bayesian approach for parameter estimation and prediction of an industrial cell culture seed, as an example for seed train digitalization. It has been shown in which way prior knowledge and input uncertainty can be considered and be propagated to predictive uncertainty.

Design, Optimization, and Adaptive Control of Cell Culture Seed Trains.

For the production of biopharmaceuticals, a procedure called seed train or inoculum train is required to generate an adequate number of cells for the inoculation of the production bioreactor. This seed train is time- and cost-intensive but offers potential for optimization. A method and a protocol are described for seed train mapping, directed modeling, and simulation as well as its optimization regarding selected optimization criteria such as optimal points in time for cell passaging. Furthermore, the method can also be applied for the transfer of a seed train to a different production plant or the design of a new seed train, for example, for a new cell line. Another application is to support the selection of the optimal clone for a new process. Seed train prediction can be performed for different clones, and so it can be analyzed how the seed train protocol would look like and for which clones a suitable seed train protocol could be found.Although the chapter is directed toward suspension cell lines, the method is also generally applicable, e.g., for adherent cell lines.

Improvement of growth rates through nucleoside media supplementation of CHO clones.

Chinese Hamster Ovary (CHO) cells are used for the production of therapeutic proteins. This work examines improving passaging growth rate of two CHO clones. Growth rates were significantly improved for both clones with supplementation of the nucleosides cytidine, hypoxanthine, uridine, and thymidine to the culturing media at the optimal concentration of 100 µM of each nucleoside. We investigated supplementing the same combination of nucleosides to seed bioreactors and production fed batch bioreactors. In the seed bioreactors, growth rate and harvest density were improved. However, in the production fed batch bioreactors, no improvements in growth rate or peak viable cell density were observed. Cell cycle analysis of the passaging cells provides evidence that nucleosides can affect the cell cycle. It is not clear from our work how the nucleosides impact the cell cycle regulatory pathways. Overall, nucleoside supplementation in cell culture media is an effective approach for improving growth rate in passaging and seed bioreactors of certain CHO cells.

High Intracellular Seed Train BiP Levels Correlate With Poor Production Culture Performance in CHO Cells.

Consistent cell culture performance is a prerequisite to ensure product quality consistency and achieve productivity goals for the manufacture of recombinant protein therapeutics, including monoclonal antibodies. Here a peculiar observation is reported where high levels of intracellular BiP in seed train cultures are consistently predictive of poor cell culture performance in the subsequent inoculum and production cultures for a monoclonal antibody produced in CHO cells. This investigation suggests that in this cell line the high intracellular BiP levels in the seed train are triggered by a slightly lower culture pH, which interferes with proper antibody folding and secretion. While the seed train culture does not display any obvious signs of the problem at slightly lower culture pH, inoculum trains, and production cultures sourced from these low pH seed trains display significantly lower cell growth and cell size. High intracellular BiP levels may interfere with UPR signaling, thereby hampering a proper and timely UPR response in the production media. Studies of other problematic cell lines have shown a similar correlation between intracellular BiP accumulation and poor production performance. The authors believe intracellular BiP levels in seed train should hence be low in order to increase the success rate in production.

Model-based strategy for cell culture seed train layout verified at lab scale.

Cell culture seed trains-the generation of a sufficient viable cell number for the inoculation of the production scale bioreactor, starting from incubator scale-are time- and cost-intensive. Accordingly, a seed train offers potential for optimization regarding its layout and the corresponding proceedings. A tool has been developed to determine the optimal points in time for cell passaging from one scale into the next and it has been applied to two different cell lines at lab scale, AGE1.HN AAT and CHO-K1. For evaluation, experimental seed train realization has been evaluated in comparison to its layout. In case of the AGE1.HN AAT cell line, the results have also been compared to the formerly manually designed seed train. The tool provides the same seed train layout based on the data of only two batches.

Efficiency improvement of an antibody production process by increasing the inoculum density.

Increasing economic pressure is the main driving force to enhance the efficiency of existing processes. We developed a perfusion strategy for a seed train reactor to generate a higher inoculum density for a subsequent fed batch production culture. A higher inoculum density can reduce culture duration without compromising product titers. Hence, a better capacity utilization can be achieved. The perfusion strategy was planned to be implemented in an existing large scale antibody production process. Therefore, facility and process constraints had to be considered. This article describes the initial development steps. Using a proprietary medium and a Chinese hamster ovary cell line expressing an IgG antibody, four different cell retention devices were compared in regard to retention efficiency and reliability. Two devices were selected for further process refinement, a centrifuge and an inclined gravitational settler. A concentrated feed medium was developed to meet facility constraints regarding maximum accumulated perfundate volume. A 2-day batch phase followed by 5 days of perfusion resulted in cell densities of 1.6 × 10(10) cells L(-1) , a 3.5 fold increase compared to batch cultivations. Two reactor volumes of concentrated feed medium were needed to achieve this goal. Eleven cultivations were carried out in bench and 50 L reactors showing acceptable reproducibility and ease of scale up. In addition, it was shown that at least three perfusion phases can be combined within a repeated perfusion strategy.