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Bioprocess modeling has become a useful tool for prediction of the process future with the aim to deduce operating decisions (e.g. transfer or feeds). Due to variabilities, which often occur between and within batches, updating (re-estimation) of model parameters is required at certain time intervals (dynamic parameter estimation) to obtain reliable predictions. This can be challenging in the presence of low sampling frequencies (e.g. every 24 h), different consecutive scales and large measurement errors, as in the case of cell culture seed trains. This contribution presents an iterative learning workflow which generates and incorporates knowledge concerning cell growth during the process by using a moving horizon estimation (MHE) approach for updating of model parameters. This estimation technique is compared to a classical weighted least squares estimation (WLSE) approach in the context of model updating over three consecutive cultivation scales (40–2160 L) of an industrial cell culture seed train. Both techniques were investigated regarding robustness concerning the aforementioned challenges and the required amount of experimental data (estimation horizon). It is shown how the proposed MHE can deal with the aforementioned difficulties by the integration of prior knowledge, even if only data at two sampling points are available, outperforming the classical WLSE approach. This workflow allows to adequately integrate current process behavior into the model and can therefore be a suitable component of a digital twin. Bioprocess modeling has become a useful tool for prediction of the process future with the aim to deduce operating decisions (e.g. transfer or feeds). Due to variabilities, which often occur between and within batches, updating (re-estimation) of model parameters is required at certain time intervals (dynamic parameter estimation) to obtain reliable predictions. This can be challenging in the presence of low sampling frequencies (e.g. every 24 h), different consecutive scales and large measurement errors, as in the case of cell culture seed trains. This contribution presents an iterative learning workflow which generates and incorporates knowledge concerning cell growth during the process by using a moving horizon estimation (MHE) approach for updating of model parameters. This estimation technique is compared to a classical weighted least squares estimation (WLSE) approach in the context of model updating over three consecutive cultivation scales (40–2160 L) of an industrial cell culture seed train. Both techniques were investigated regarding robustness concerning the aforementioned challenges and the required amount of experimental data (estimation horizon). It is shown how the proposed MHE can deal with the aforementioned difficulties by the integration of prior knowledge, even if only data at two sampling points are available, outperforming the classical WLSE approach. This workflow allows to adequately integrate current process behavior into the model and can therefore be a suitable component of a digital twin.

The main purpose of bovine colostrum, being the milk secreted by a cow after giving birth, is to transfer passive immunity to the calf. The calves have an immature immune system as they lack immunoglobulins (Igs). Subsequently, the supply of good quality bovine colostrum is required. The quality of colostrum is classified by low bacterial counts and adequate Ig concentrations. Bacterial contamination can contain a variety of human pathogens or high counts of spoilage bacteria, which has become more challenging with the emerging use of bovine colostrum as food and food supplements. There is also a growing risk for the spread of zoonotic diseases originating from bovines. For this reason, processing based on heat treatment or other feasible techniques is required. This review provides an overview of literature on the microbial quality of bovine colostrum and processing methods to improve its microbial quality and keep its nutritional values as food. The highlights of this review are as follows: high quality colostrum is a valuable raw material in food products and supplements; the microbial safety of bovine colostrum is increased using an appropriate processing-suitable effective heat treatment which does not destroy the high nutrition value of colostrum; the heat treatment processes are cost-effective compared to other methods; and heat treatment can be performed in both small- and large-scale production.

Abstract Kinetic growth models are a useful tool for a better understanding of microalgal cultivation and for optimizing cultivation conditions. The evaluation of such models requires experimental data that is laborious to generate in bioreactor settings. The experimental shake flask setting used in this study allows to run 12 experiments at the same time, with 6 individual light intensities and light durations. This way, 54 biomass data sets were generated for the cultivation of the microalgae Chlorella vulgaris. To identify the model parameters, a stepwise parameter estimation procedure was applied. First, light-associated model parameters were estimated using additional measurements of local light intensities at differ heights within medium at different biomass concentrations. Next, substrate related model parameters were estimated, using experiments for which biomass and nitrate data were provided. Afterwards, growth-related model parameters were estimated by application of an extensive cross validation procedure. Graphic abstract

Bioprocess development and optimization are still cost- and time-intensive due to the enormous number of experiments involved. In this study, the recently introduced model-assisted Design of Experiments (mDoE) concept (Möller et al. in Bioproc Biosyst Eng 42(5):867, 10.1007/s00449-019-02089-7, 2019) was extended and implemented into a software (“mDoE-toolbox”) to significantly reduce the number of required cultivations. The application of the toolbox is exemplary shown in two case studies with Saccharomyces cerevisiae. In the first case study, a fed-batch process was optimized with respect to the pH value and linearly rising feeding rates of glucose and nitrogen source. Using the mDoE-toolbox, the biomass concentration was increased by 30% compared to previously performed experiments. The second case study was the whole-cell biocatalysis of ethyl acetoacetate (EAA) to (S)-ethyl-3-hydroxybutyrate (E3HB), for which the feeding rates of glucose, nitrogen source, and EAA were optimized. An increase of 80% compared to a previously performed experiment with similar initial conditions was achieved for the E3HB concentration.

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.

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.

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.

Cell culture technology has become a substantial domain of modern biotechnology, particularly in the pharmaceutical market. Today, products manufactured from cells itself dominate the biopharmaceutical industry. In addition, a limited number of products made of in vitro cultivated cells for regenerative medicine were launched to the market. Modeling of such processes is an important task since these systems are usually nonlinear and complex. In this chapter, a framework for the estimation of process model parameters and its implementation is shown. It is aimed to support the parameter estimation task, which increases the potential of implementation and improvement of mathematical process models into the novel and existing bioprocesses. Apart from the parameter estimation, evaluation of the estimated parameters plays an essential role in order to verify these parameters and subsequently the selected model. The workflow is outlined and shown specifically on the basis of a mathematical process model describing a mammalian cell culture batch process.

Cell culture technology has become a substantial domain of modern biotechnology, particularly in the pharmaceutical market. Today, products manufactured from cells itself dominate the biopharmaceutical industry. In addition, a limited number of products made of in vitro cultivated cells for regenerative medicine were launched to the market. Modeling of such processes is an important task since these systems are usually nonlinear and complex. In this chapter, a framework for the estimation of process model parameters and its implementation is shown. It is aimed to support the parameter estimation task, which increases the potential of implementation and improvement of mathematical process models into the novel and existing bioprocesses. Apart from the parameter estimation, evaluation of the estimated parameters plays an essential role in order to verify these parameters and subsequently the selected model. The workflow is outlined and shown specifically on the basis of a mathematical process model describing a mammalian cell culture batch process.

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.

Reliable scale-up of biopharmaceutical production processes is key in Quality by Design. In this study, a model-based workflow is described to evaluate the bioprocess dynamics during process transfer and scale-up computationally. First, a mathematical model describes the bioprocess dynamics of different state variables (e.g., cell density, titer). Second, the model parameter probability distributions are de- termined at different scales due to measurement uncertainty. Third, the quantified parameter distribu- tions are statistically compared to evaluate if the process dynamics have been changed. This workflow was tested for the scale-up of an antibody-producing CHO fed-batch process. Significant differences were identified between the process development (30 ml) and implementation (250 ml) scale, and the feeding strategy was validated using model-assisted Design of Experiments. Then, the validated process strategy was successfully scaled up to 2 l laboratory and 50 l pilot scale. In summary, the proposed workflow enables a knowledge-driven evaluation tool for bioprocess development. ©2020 Elsevier Ltd. All rights reserved.

Bioreactor processes have to provide an almost optimal environment to microorganisms or cells to promote growth and product formation. The design and operation of a bioreactor as the main element of fermentation is a complex task, not only with respect to a reactor's configuration and size but to the control and mode of operation. In this chapter, some fundamentals are discussed, including process and control strategies and concepts for process development. Next, examples for the use of model-based concepts for the design of experiments, feeding strategies, seed train layout, and control strategies, including simulation tools supporting biotechnological training and education, are shown.

The oxidation of lactose to lactobionic acid by a bi-enzymatic system in a membrane-aerated reactor was scaled up 100-fold from a miniaturized membrane contactor to a 20-L dynamic membrane aeration reactor. The conversion was catalyzed by an enzyme cascade consisting of cellobiose dehydrogenase as synthesizing enzyme and laccase as regenerating enzyme coupled by a redox mediator. A model of the process, combining mass-transfer and enzyme kinetics, was developed to predict optimal conversion conditions. The dynamic membrane aeration reactor was successfully operated in discontinuous and CSTR mode to achieve maximum productivity at very low power input (27.7 W m−3) and also greatly reduced enzyme inactivation by eliminating the high gas/liquid interfacial area of conventionally aerated stirred reactors or bubble columns. The reaction product, lactobionic acid was obtained with a space–time yield of 74.4 g L−1 d−1 and a degree of conversion higher than 97%. The dynamic membrane aeration reactor is well suited for the bubbleless oxygenation of laccase-regenerated dehydrogenase reactions and other oxidase-catalyzed reactions on large scale to perform sustainable enzymatic oxidation reactions employing enzymes sensitive to shear or the gas/liquid interface.

Zur Produktion pharmazeutischer Proteinwirkstoffe werden heute vielfach tierische Zelllinien eingesetzt. Wahrend robuste Zelllinien trotz fehlender Zellwand eine Sauerstoffversorgung uber Blasenbegasung tolerieren, ist zur Kultivierung von empfindlichen Zelllinien eine blasenfreie Sauerstoffversorgung erforderlich, z. B. per Membranbegasung. Ein wesentlicher Nachteil der Membranbegasung ist jedoch die begrenzte Stofftransportkapazitat, welche die Zelldichte und damit die Raum-Zeit-Ausbeute im Bioreaktor limitiert. Dies kann sich vor allem fur intensivierte Fermentationen mit hohen Zelldichten (Perfusion, ggf. Fed-Batch) nachteilig auswirken. Vorgestellt wird ein konstruktiv einfaches dynamisches Membranbegasungssystem, in dem die Funktionselemente „Rotor” und „Stator” in einem rotatorisch oszillierenden Membrantrager vereinigt sind.

Zellkultivierungen werden im industriellen Masstab vorwiegend im Batch- oder Fed-Batch-Betrieb durchgefuhrt. Aufgrund diverser Eigenschaften von Zellkulturen werden beim Fed-Batch hohe Anforderungen an die Prozessfuhrung gestellt, so dass sich durch eine optimierte Zudosierung von Nahrstoffen die Raum-Zeit-Ausbeute und damit die Wirtschaftlichkeit der Prozesse erheblich steigern lassen. Vorgestellt wird eine adaptive, modellgestutzte Strategie zur prozessbegleitenden Optimierung und Steuerung von Zellkultur-Fed-Batch-Prozessen.

Both conventional and genetic engineering techniques can significantly improve the performance of animal cell cultures for the large-scale production of pharmaceutical products. In this paper, the effect of such techniques on cell yield and antibody production of two NS0 cell lines is presented. On the one hand, the effect of fed-batch cultivation using dialysis is compared to cultivation without dialysis. Maximum cell density could be increased by a factor of approximately 5-7 by dialysis fed-batch cultivation. On the other hand, suppression of apoptosis in the NS0 cell line 6A1 bcl-2 resulted in a prolonged growth phase and a higher viability and maximum cell density in fed-batch cultivation in contrast to the control cell line 6A1 (100)3. These factors resulted in more product formation (by a factor of approximately 2). Finally, the adaptive model-based OLFO controller, developed as a general tool for cell culture fed-batch processes, was able to control the fed-batch and dialysis fed-batch cultivations of both cell lines.

Although fed-batch suspension culture of animal cells continues to be of industrial importance for the large scale production of pharmaceutical products, existing control concepts are still insufficient. Changes in cell metabolism during cultivation and between similar cultivations, the complexity of the cell metabolism, and the lack of on-line state variables restrict the transfer of available control strategies established in bioprocess engineering. A process control strategy designed to achieve optimized process control must account for all these difficulties and fit sophisticated requirements toward adaptability and flexibility. The combination of a fed-batch process and an Open-Loop-Feedback-Optimal (OLFO) control provides a new approach for cell culture process control that couples an efficient cultivation concept to a capable process control strategy. The application of an adaptive, model-based OLFO controller to a hybridoma cultivation and experimental results are presented.

The optimal control of hybridoma cell cultivations requires adaptive model based control strategies like the Open-Loop-Feedback-Optirnal-(OLFO) controller In this controller, the applied models must be able to predict the states of a cultivation reasonably well. Theoretical investigations show how the online-prediction performance of a model may be evaluated. Experimental results obtained by the application of a simple unstructured process model in a simplified OLFO-strategy illustrates the potential of this controlleL Experiments have been carried out, that show the value of oxygen-uptake-rate measurements for controlling the process under glutamine limiting conditions.

For the production of biopharmaceuticals a seed train is required to generate an adequate number of cells for 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 the seed train mapping, directed modeling without major effort, and its optimization regarding selected optimization criteria such as optimal points in time for cell passaging. Furthermore, the method can also be applied for the set-up of a new seed train, for example for a new cell line. Although the chapter is directed towards suspension cell lines, the method is also generally applicable, e.g. for adherent cell lines.

For the production of biopharmaceuticals a seed train is required to generate an adequate number of cells for 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 the seed train mapping, directed modeling without major effort, and its optimization regarding selected optimization criteria such as optimal points in time for cell passaging. Furthermore, the method can also be applied for the set-up of a new seed train, for example for a new cell line. Although the chapter is directed towards suspension cell lines, the method is also generally applicable, e.g. for adherent cell lines.

Although the importance of animal cell culture for the industrial (large scale) production of pharmaceutical products is continuously increasing, the sensibility of the cells towards their cultivation environment is still a challenging issue. In comparison to microbial cultures, cell cultures which are not protected by a cell wall are much more sensitive to shear stress and foam formation. Reactor design as well as the selection of ‘robust’ cell lines is particularly important for these circumstances. Nevertheless, even ‘sensitive’ cell lines are selected for certain pharmaceutical processes due to various reasons. These sensitive cell lines have even higher requirements regarding their cultivation environment. Important characteristics for the corresponding reactor design are a high (volumetric) gas mass transfer coefficient, low volumetric power input, low shear stress, low susceptibility to bio-fouling, the ability to cultivate sticky cells and sufficient mixing properties. Membrane aeration has been a long-known possibility to meet some of these requirements, but has not often been applied in recent years. The reasons lie mainly in low gas mass transfer rates, a limited installable volume-specific membrane surface area, restrictions in scalability and problems with membrane fouling. The dynamic membrane aeration bioreactor aeration is a simple concept for bubble-free oxygen supply of such sensitive cultures. It overcomes limitations and draw-backs of previous systems. Consisting of an oscillating, centrally arranged rotor (stirrer) that is wrapped with silicone membrane tubing, it enables doubling the gas mass transfer at the same shear stress in the investigated cultivation scales of 12, 20, 100, and 200 L. Continuous cultivation at these scales allows the same product output as fed-batch cultivation does at tremendously larger reactor volumes. Apart from introducing this novel technology, the presentation comprises selected cultivation results obtained for blood coagulation factor VIII in continuous mode and a therapeutic monoclonal antibody in fed-batch mode in comparison to reference trials.

Ralf Portner, Bjorn Frahm, Paul Lane, Axel Munack, Kathrin Kuhn, Volker C. Hass Technische Universitat Hamburg-Harburg, Bioprozessund Bioverfahrenstechnik, Denickestr. 15, 21071 Hamburg, Germany, Bayer Technology Services, Process Technology, 51368 Leverkusen, Germany, Bundesforschungsanstalt fur Landwirtschaft, Institut fur Technologie und Biosystemtechnik, Bundesallee 50, 38116 Braunschweig, Germany, Hochschule Bremen, Neustadtwall 30, 28199 Bremen, Germany

The determination of dissolved CO(2) and HCO(3)(-) concentrations as well as the carbon dioxide production rate in mammalian cell suspension culture is attracting more and more attention since the effects on major cell properties, such as cell growth rate, product quality/production rate, intracellular pH and apoptosis, have been revealed. But the determination of these parameters by gas analysis is complicated by the solution/dissolution of carbon dioxide in the culture medium. This means that the carbon dioxide transfer rate (CTR; which can easily be calculated from off-gas measurement) is not necessarily equal to carbon dioxide production rate (CPR). In this paper, a mathematical method to utilize off-gas measurement and culture pH for cell suspension culture is presented. The method takes pH changes, buffer and medium characteristics that effect CO(2) mass transfer into account. These calculations, based on a profound set of equations, allow the determination of the respiratory activity of the cells, as well as the determination of dissolved CO(2), HCO(3)(-) and total dissolved carbonate. The method is illustrated by application to experimental data. The calculated dissolved CO(2) concentrations are compared with measurements from an electrochemical CO(2) probe.

While fed-batch suspension culture of animal cells continues to be of industrial importance for the large-scale production of pharmaceutical products, existing control concepts are still insufficient. The present paper illustrates the advantages and disadvantages of different fed-batch strategies, including fixed-feed trajectories, control via OUR (oxygen uptake rate) (stoichiometric feeding), a priori determination of feed trajectories based on a kinetic model and the model-based adaptive OLFO (open-loop-feedback-optimal) control strategy. A recommendation as to which control strategy should be used for a specific process has to consider the respective process. For an established process with a well characterized and stable production cell line, probably the application of a fixed feed trajectory should be recommended. An adaptive, model-based control strategy could be the method of choice during cell-line development or for rapid production of small amounts of product for clinical trials, owing to its universal character and because it does not require intensive process development.

For the manufacturing of complex biopharmaceuticals using bioreactors with cultivated mammalian cells, high product concentration is an important objective. The phenotype of the cells in a reactor plays an important role. Are clonal cell populations showing high cell-specific growth rates more favorable than cell lines with higher cell-specific productivities or vice versa? Five clonal Chinese hamster ovary cell populations were analyzed based on the data of a 3-month-stability study. We adapted a mechanistic cell culture model to the experimental data of one such clonally derived cell population. Uncertainties and prior knowledge concerning model parameters were considered using Bayesian parameter estimations. This model was used then to define an inoculum train protocol. Based on this, we subsequently simulated the impacts of differences in growth rates (±10%) and production rates (±10% and ±50%) on the overall cultivation time, including making the inoculum train cultures; the final production phase, the volumetric titer in that bioreactor and the ratio of both, defined as overall process productivity. We showed thus unequivocally that growth rates have a higher impact (up to three times) on overall process productivity and for product output per year, whereas cells with higher productivity can potentially generate higher product concentrations in the production vessel.

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.HNAAT and CHO-K1. For evaluation, experimental seed train realization has been evaluated in comparison to its layout. In case of the AGE1.HNAAT 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.

The purpose of a seed train is the generation of an adequate number of cells for the inoculation of a production bioreactor. This is time- and cost-intensive: From volumes used for cell thawing or cell line maintenance the cell number has to be increased. The cells are usually run through many cultivation systems which become larger with each passage (e.g. T-flasks, roller bottles or shake flasks, small scale bioreactor systems and subsequently larger bioreactors. Single-use systems may be applied and systems which are inoculated at a partly filled state and culture volume is increased afterwards by medium addition). The production bioreactor is inoculated out of the largest seed train scale.

Concept Production of biopharmaceuticals for diagnostic and therapeutic applications with suspension cells in bioreactors requires a seed train up to production scale [1]. For the final process steps in pilot and production scale the scale-up steps are usually defined (e.g. a factor of 5 10). More difficult in this respect are the first steps, the transitions between T-flasks, spinner tubes, roller bottles, shake flasks, stirred bioreactors or single-use reactors, because here often scale-up steps are different. The experimental effort to lay these steps out is correspondingly high. At the same time it is known that the first cultivation steps have a significant impact on the success or failure on production scale. The concept for a model based design of the seed train consists of the following steps