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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 link between hydrodynamics and biological process behavior of antibody-producing mammalian cell cultures is still not fully understood. Common methods to describe dependencies refer mostly to averaged hydrodynamic parameters obtained for individual cultivation systems. In this study, cellular effects and locally resolved hydrodynamics were investigated for impellers with different spatial hydrodynamics. Therefore, the hydrodynamics, mainly flow velocity, shear rate and power input, in a single- and a three-impeller bioreactor setup were analyzed by means of CFD simulations, and cultivation experiments with antibody-producing Chinese hamster ovary (CHO) cells were performed at various agitation rates in both reactor setups. Within the three-impeller bioreactor setup, cells could be cultivated successfully at much higher agitation rates as in the single-impeller bioreactor, probably due to a more uniform flow pattern. It could be shown that this different behavior cannot be linked to parameters commonly used to describe shear effects on cells such as the mean energy dissipation rate or the Kolmogorov length scale, even if this concept is extended by locally resolved hydrodynamic parameters. Alternatively, the hydrodynamic heterogeneity was statistically quantified by means of variance coefficients of the hydrodynamic parameters fluid velocity, shear rate, and energy dissipation rate. The calculated variance coefficients of all hydrodynamic parameters were higher in the setup with three impellers than in the single impeller setup, which might explain the rather stable process behavior in multiple impeller systems due to the reduced hydrodynamic heterogeneity. Such comprehensive insights lead to a deeper understanding of the bioprocess. The link between hydrodynamics and biological process behavior of antibody-producing mammalian cell cultures is still not fully understood. Common methods to describe dependencies refer mostly to averaged hydrodynamic parameters obtained for individual cultivation systems. In this study, cellular effects and locally resolved hydrodynamics were investigated for impellers with different spatial hydrodynamics. Therefore, the hydrodynamics, mainly flow velocity, shear rate and power input, in a single- and a three-impeller bioreactor setup were analyzed by means of CFD simulations, and cultivation experiments with antibody-producing Chinese hamster ovary (CHO) cells were performed at various agitation rates in both reactor setups. Within the three-impeller bioreactor setup, cells could be cultivated successfully at much higher agitation rates as in the single-impeller bioreactor, probably due to a more uniform flow pattern. It could be shown that this different behavior cannot be linked to parameters commonly used to describe shear effects on cells such as the mean energy dissipation rate or the Kolmogorov length scale, even if this concept is extended by locally resolved hydrodynamic parameters. Alternatively, the hydrodynamic heterogeneity was statistically quantified by means of variance coefficients of the hydrodynamic parameters fluid velocity, shear rate, and energy dissipation rate. The calculated variance coefficients of all hydrodynamic parameters were higher in the setup with three impellers than in the single impeller setup, which might explain the rather stable process behavior in multiple impeller systems due to the reduced hydrodynamic heterogeneity. Such comprehensive insights lead to a deeper understanding of the bioprocess.

Techniques for tissue culture have seen significant advances during the last decades and novel 3D cell culture systems have become available. To control their high complexity, experimental techniques and their Digital Twins (modelling and computational tools) are combined to link different variables to process conditions and critical process parameters. This allows a rapid evaluation of the expected product quality. However, the use of mathematical simulation and Digital Twins is critically dependent on the precise description of the problem and correct input parameters. Errors here can lead to dramatically wrong conclusions. The intention of this review is to provide an overview of the state-of-the-art and remaining challenges with respect to generating input values for computational analysis of mass and momentum transport processes within tissue cultures. It gives an overview on relevant aspects of transport processes in tissue cultures as well as modelling and computational tools to tackle these problems. Further focus is on techniques used for the determination of cell-specific parameters and characterization of culture systems, including sensors for on-line determination of relevant parameters. In conclusion, tissue culture techniques are well-established, and modelling tools are technically mature. New sensor technologies are on the way, especially for organ chips. The greatest remaining challenge seems to be the proper addressing and handling of input parameters required for mathematical models. Following Good Modelling Practice approaches when setting up and validating computational models is, therefore, essential to get to better estimations of the interesting complex processes inside organotypic tissue cultures in the future.

Design of Experiments methods offer systematic tools for bioprocess development in Quality by Design, but their major drawback is the user-defined choice of factor boundary values. This can lead to several iterative rounds of time-consuming and costly experiments. In this study, a model-assisted Design of Experiments concept is introduced for the knowledge-based reduction of boundary values. First, the parameters of a mathematical process model are estimated. Second, the investigated factor combinations are simulated instead of experimentally derived and a constraint-based evaluation and optimization of the experimental space can be performed. The concept is discussed for the optimization of an antibody-producing Chinese hamster ovary batch and bolus fed-batch process. The same optimal process strategies were found if comparing the model-assisted Design of Experiments (4 experiments each) and traditional Design of Experiments (16 experiments for batch and 29 experiments for fed-batch). This approach significantly reduces the number of experiments needed for knowledge-based bioprocess development.

Currently applied methods for characterization and optimization of bio-pharmaceutical processes are still strongly empirical. This often involves Design of Experiment (DoE) methods that require a large number of time-consuming experiments and can hardly fulfill the requirements of ‘Quality by Design’ (QbD). Linking mathematical models with experimental methods is seen as an efficient strategy that significantly reduces development time and costs. Furthermore, if combined with advanced Process Analytical Technology (PAT), higher automation and more efficient workflows can be established along the product lifecycle. This contribution presents the state of the art of model-based tools for experimental design and gives an outlook on future trends in the field of bio-process engineering.

DOI: 10.1002/elsc.202000037 The cover feature visualizes our recent article about the investigation of the regulation of the Pyruvate Dehydrogenase Complex (PDC) during the lactate switch in batch cultures of Chinese Hamster Ovary cells. The relevance of this work to bioprocess engineering is highlighted in the background and the central cellular metabolic regulations are shown symbolically on the right‐hand side. The regulation of PDC through phosphorylation was quantified at three regulating sites using a novel indirect flow cytometry protocol, shown as “glowing” antibodies. For details see article DOI 10.1002/elsc.202000037 on page 99. [Image: see text]

Miniaturized bioreactor (MBR) systems are routinely used in the development of mammalian cell culture processes. However, scale-up of process strategies obtained in MBR- to larger scale is challenging due to mainly non-holistic scale-up approaches. In this study, a model-based workflow is introduced to quantify differences in the process dynamics between bioreactor scales and thus enable a more knowledge-driven scale-up. The workflow is applied to two case studies with antibody-producing Chinese hamster ovary cell lines. With the workflow, model parameter distributions are estimated first under consideration of experimental variability for different scales. Second, the obtained individual model parameter distributions are tested for statistical differences. In case of significant differences, model parametric distributions are transferred between the scales. In case study I, a fed-batch process in a microtiter plate (4 ml working volume) and lab-scale bioreactor (3750 ml working volume) was mathematically modeled and evaluated. No significant differences were identified for model parameter distributions reflecting process dynamics. Therefore, the microtiter plate can be applied as scale-down tool for the lab-scale bioreactor. In case study II, a fed-batch process in a 24-Deep-Well-Plate (2 ml working volume) and shake flask (40 ml working volume) with two feed media was investigated. Model parameter distributions showed significant differences. Thus, process strategies were mathematically transferred, and model predictions were simulated for a new shake flask culture setup and confirmed in validation experiments. Overall, the workflow enables a knowledge-driven evaluation of scale-up for a more efficient bioprocess design and optimization.

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.

No matter the scale, stirred tank bioreactors are the most commonly used systems in biotechnological production processes. Single-use and reusable systems are supplied by several manufacturers. The type, size, and number of impellers used in these systems have a significant influence on the characteristics and designs of bioreactors. Depending on the desired application, classic shaft-driven systems, bearing-mounted drives, or stirring elements that levitate freely in the vessel may be employed. In systems with drive shafts, process hygiene requirements also affect the type of seal used. For sensitive processes with high hygienic requirements, magnetic-driven stirring systems, which have been the focus of much research in recent years, are recommended. This review provides the reader with an overview of the most common agitation and seal types implemented in stirred bioreactor systems, highlights their advantages and disadvantages, and explains their possible fields of application. Special attention is paid to the development of magnetically driven agitators, which are widely used in reusable systems and are also becoming more and more important in their single-use counterparts.Key Points• Basic design of the most frequently used bioreactor type: the stirred tank bioreactor• Differences in most common seal types in stirred systems and fields of application• Comprehensive overview of commercially available bioreactor seal types• Increased use of magnetically driven agitation systems in single-use bioreactors

Rising demands for biopharmaceuticals and the need to reduce manufacturing costs increase the pressure to develop productive and efficient bioprocesses. Among others, a major hurdle during process development and optimization studies is the huge experimental effort in conventional design of experiments (DoE) methods. As being an explorative approach, DoE requires extensive expert knowledge about the investigated factors and their boundary values and often leads to multiple rounds of time-consuming and costly experiments. The combination of DoE with a virtual representation of the bioprocess, called digital twin, in model-assisted DoE (mDoE) can be used as an alternative to decrease the number of experiments significantly. mDoE enables a knowledge-driven bioprocess development including the definition of a mathematical process model in the early development stages. In this chapter, digital twins and their role in mDoE are discussed. First, statistical DoE methods are introduced as the basis of mDoE. Second, the combination of a mathematical process model and DoE into mDoE is examined. This includes mathematical model structures and a selection scheme for the choice of DoE designs. Finally, the application of mDoE is discussed in a case study for the medium optimization in an antibody-producing Chinese hamster ovary cell culture process.

Rising demands for biopharmaceuticals and the need to reduce manufacturing costs increase the pressure to develop productive and efficient bioprocesses. Among others, a major hurdle during process development and optimization studies is the huge experimental effort in conventional design of experiments (DoE) methods. As being an explorative approach, DoE requires extensive expert knowledge about the investigated factors and their boundary values and often leads to multiple rounds of time-consuming and costly experiments. The combination of DoE with a virtual representation of the bioprocess, called digital twin, in model-assisted DoE (mDoE) can be used as an alternative to decrease the number of experiments significantly. mDoE enables a knowledge-driven bioprocess development including the definition of a mathematical process model in the early development stages. In this chapter, digital twins and their role in mDoE are discussed. First, statistical DoE methods are introduced as the basis of mDoE. Second, the combination of a mathematical process model and DoE into mDoE is examined. This includes mathematical model structures and a selection scheme for the choice of DoE designs. Finally, the application of mDoE is discussed in a case study for the medium optimization in an antibody-producing Chinese hamster ovary cell culture process.

Conventional design of experiments (DoE) methods require expert knowledge about the investigated factors and their boundary values and mostly lead to multiple rounds of time-consuming and costly experiments. The combination of DoE with mathematical process modeling in model-assisted DoE (mDoE) can be used to increase the mechanistic understanding of the process. Furthermore, it is aimed to optimize the processes with respect to a target (e.g., amount of cells, product titer), which also provides new insights into the process. In this chapter, the workflow of mDoE is explained stepwise including corresponding protocols. Firstly, a mathematical process model is adapted to cultivation data of first experimental data or existing knowledge. Secondly, model-assisted simulations are treated in the same way as experimentally derived data and included as responses in statistical DoEs. The DoEs are then evaluated based on the simulated data, and a constrained-based optimization of the experimental space can be conducted. This loop can be repeated several times and significantly reduces the number of experiments in process development.

The bioreactor conditions and cell diversity in mammalian cell cultures are often regarded as homogeneous. Recently, the influence of various kinds of heterogeneities on production rates receives increasing attention. Besides spatial gradients within the cultivation system, the variation between cell populations and the progress of the cells through the cell cycle can affect the dynamics of the cultivation process. Strong metabolic up- and down-regulations leading to variable productivities, even in exponentially growing cell cultures, have been identified in CHO cell cultivations. Consequently, scientific studies of cell cycle-related effects and metabolic regulations require experiments utilizing cell cycle-enriched subpopulations. Importantly, the enrichment procedure itself must not strongly interfere with the cell culture under investigation. Such subpopulations can be generated by near-physiological countercurrent centrifugal elutriation, which is described in the following chapter. At first, a brief overview regarding the cell cycle, currently identified effects and commonly used methods, and their applicability is outlined. Then, the experimental setup and the synchronization itself are explained.

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.

The bioreactor conditions and cell diversity in mammalian cell cultures are often regarded as homogeneous. Recently, the influence of various kinds of heterogeneities on production rates receives increasing attention. Besides spatial gradients within the cultivation system, the variation between cell populations and the progress of the cells through the cell cycle can affect the dynamics of the cultivation process. Strong metabolic up- and down-regulations leading to variable productivities, even in exponentially growing cell cultures, have been identified in CHO cell cultivations. Consequently, scientific studies of cell cycle-related effects and metabolic regulations require experiments utilizing cell cycle-enriched subpopulations. Importantly, the enrichment procedure itself must not strongly interfere with the cell culture under investigation. Such subpopulations can be generated by near-physiological countercurrent centrifugal elutriation, which is described in the following chapter. At first, a brief overview regarding the cell cycle, currently identified effects and commonly used methods, and their applicability is outlined. Then, the experimental setup and the synchronization itself are explained.

SU-8 100 is a negative-tone photoresist which is used for the manufacturing of structures with high thickness and aspect ratio. The use of SU-8 100 systems in cell culture is difficult due to its cytotoxicity resulting from leachables. In this article, the production process of non-cytotoxic SU-8 100 foils is described. The aim is to create a polymer scaffold with integrated sensors in order to enable online measurements of cell growth and viability within a three-dimensional cell culture. This scaffold could be a useful tool for testing biological effects of pharmaceutical substances. Therefore, the scaffold material must not have a negative influence on the cell growth and viability. Among the biocompatibility tests described in DIN EN ISO 10993-5, the neutral red uptake method was chosen to evaluate the cytotoxicity of polymer foils. The production process of SU-8 100 was altered in such a way that non-cytotoxic SU-8 100 foils can be manufactured.

An in 2016 published DECHEMA guideline concerning process engineering characterization and a new Escherichia coli model process were utilized for the qualification of two geometrically similar stirred stainless steel bioreactors (30 L and 100 L working volume). The achieved results demonstrate that performing an additional biological model process is a valuable complement to the process engineering characterization. Optical densities of 27 (100 L) and 39 (30 L) were reached in the batch cultivation process.

Process models can be used for model-based control strategies, but model development is a time-consuming and laborious task. To reduce the modeling effort, a new structured compartment model was developed, which may easily be adapted to different cultivation processes. The proposed six-compartment model was used to describe the time courses of cultivations of bacteria, yeast, fungi, and mammalian cell lines, namely, Escherichia coli, Lactobacillus delbrueckii, Saccharomyces cerevisiae, Cyathus striatus, and a hybridoma mammalian cell line. The model can describe the time courses of important state variables and can be adapted to the cultivation processes by parameterization. This reduces the modeling effort for a new process significantly.

Cell population heterogeneities and their changes in mammalian cell culture processes are still not well characterized. In this study, the formation and dynamics of cell population heterogeneities were investigated with flow cytometry and stably integrated fluorescent markers based on the lentiviral gene ontology (LeGO) vector system. To achieve this, antibody-producing CHO cells were transduced with different LeGO vectors to stably express single or multiple fluorescent proteins. This enables the tracking of the transduced populations and is discussed in two case studies from the field of bioprocess engineering: In case study I, cells were co-transduced to express red, green, and blue fluorescent proteins and the development of sub-populations and expression heterogeneities were investigated in high passage cultivations (total 130 days). The formation of a fast-growing and more productive population was observed with a simultaneous increase in cell density and product titer. In case study II, different preculture growth phases and their influence on the population dynamics were investigated in mixed batch cultures with flow cytometry (offline and automated). Four cell line derivatives, each expressing a different fluorescent protein, were generated and cultivated for different time intervals, corresponding to different growth phases. Mixed cultures were inoculated from them, and changes in the composition of the cell populations were observed during the first 48 h of cultivation with reduced process productivity. In summary, we showed how the dynamics of population heterogeneities can be characterized. This represents a novel approach to investigate the dynamics of cell population heterogeneities under near-physiological conditions with changing productivity in mammalian cell culture processes. Electronic supplementary material The online version of this article (10.1007/s00216-020-02401-5) contains supplementary material, which is available to authorized users.

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.

Techniques to provide in vitro tissue culture have undergone significant changes during the last decades, and current applications involve interactions of cells and organoids, three-dimensional cell co-cultures, and organ/body-on-chip tools. Efficient computer-aided and mathematical model-based methods are required for efficient and knowledge-driven characterization, optimization, and routine manufacturing of tissue culture systems. As an alternative to purely experimental-driven research, the usage of comprehensive mathematical models as a virtual in silico representation of the tissue culture, namely a digital twin, can be advantageous. Digital twins include the mechanistic of the biological system in the form of diverse mathematical models, which describe the interaction between tissue culture techniques and cell growth, metabolism, and the quality of the tissue. In this review, current concepts, expectations, and the state of the art of digital twins for tissue culture concepts will be highlighted. In general, DT’s can be applied along the full process chain and along the product life cycle. Due to the complexity, the focus of this review will be especially on the design, characterization, and operation of the tissue culture techniques.

Stirred tank bioreactors are still the predominant cultivation systems in large scale biopharmaceutical production. Today, several manufacturers provide both reusable and single-use systems, whereas the broad variety of designs and properties lead to deviations in biological performance. Although the methods for bioreactor characterization are well established, varying experimental conditions and procedures can result in significantly different outcomes. In order to guarantee a reliable comparison and evaluation of different single-use and reusable bioreactor types, standardized methods for their characterization are needed. Equally important is the biological capability of bioreactors, which must be accessed by standardized cultivation procedures of industrially relevant organisms (bacteria, yeasts as well as mammalian and animal cell cultures). In addition, the implementation of well-defined uniform procedures for biological and engineering characterization during the development phase can support a fast assessment of the suitability of a bioreactor system. Based on stirred bioreactors, we describe the aspects of the engineering characterization in order to discuss further the biological characterization as a valuable complement. Finally, a case study is presented.

The human skin is involved in protecting the inner body from constant exposure to outer environmental stimuli. There is an evident need to screen for toxicity and the efficacy of drugs and cosmetics applied to the skin. To date, animal studies are still the standard method for substance testing, although they are currently controversially discussed Therefore, the multi-organ chip is an attractive alternative to replace animal testing. The two-organ chip is designed to hold 96-well cell culture inserts (CCIs). Small-sized skin equivalents are needed for this. In this study, full-thickness skin equivalents (ftSEs) were generated successfully inside 96-well CCIs. These skin equivalents developed with in vivo-like histological architecture, with normal differentiation marker expressions and proliferation rates. The 96-well CCI-based ftSEs were successfully integrated into the two-organ chip. The permeation of fluorescein sodium salt through the ftSEs was monitored during the culture. The results show a decreasing value for the permeation over time, which seems a promising method to track the development of the ftSEs. Additionally, the permeation was implemented in a computational fluid dynamics simulation, as a tool to predict results in long-term experiments. The advantage of these ftSEs is the reduced need for cells and substances, which makes them more suitable for high throughput assays. The human skin is involved in protecting the inner body from constant exposure to outer environmental stimuli. There is an evident need to screen for toxicity and the efficacy of drugs and cosmetics applied to the skin. To date, animal studies are still the standard method for substance testing, although they are currently controversially discussed Therefore, the multi-organ chip is an attractive alternative to replace animal testing. The two-organ chip is designed to hold 96-well cell culture inserts (CCIs). Small-sized skin equivalents are needed for this. In this study, full-thickness skin equivalents (ftSEs) were generated successfully inside 96-well CCIs. These skin equivalents developed with in vivo-like histological architecture, with normal differentiation marker expressions and proliferation rates. The 96-well CCI-based ftSEs were successfully integrated into the two-organ chip. The permeation of fluorescein sodium salt through the ftSEs was monitored during the culture. The results show a decreasing value for the permeation over time, which seems a promising method to track the development of the ftSEs. Additionally, the permeation was implemented in a computational fluid dynamics simulation, as a tool to predict results in long-term experiments. The advantage of these ftSEs is the reduced need for cells and substances, which makes them more suitable for high throughput assays.

The understanding of cell-cycle-dependent population heterogeneities in mammalian cell culture and their influence on production rates is still limited. Furthermore, metabolic regulations arising from self-expressed signaling factors (autocrine/autoinhibitory factors) have been postulated in the past, but no determination of such effects have been made so far for fast-growing production Chinese hamster ovary (CHO) cells in chemically defined media. In this study, a novel approach combining near-physiological treatment of cells (including synchronization), population resolved mechanistic modeling and statistical analysis was developed to identify population inhomogeneities. Cell-cycle-dependent population dynamics and metabolic regulations due to a putative autocrine factor were examined and their impact on the metabolic rates and antibody production of near-physiologically synchronized CHO DP-12 cell cultures was determined. To achieve this, a population resolved model was extended to describe putative autocrine-dependentt and cell-cycle-related metabolic rates for glucose, glutamine, lactate, ammonia, and antibody production. The model parameters were estimated based on data of two repeated batch cultivations (three batches each), with main substrates in excess and potentially inhibiting waste products (lactate and ammonium) controlled within narrow ranges. Significant changes, due to a putative autocrine factor, were identified for lactate and ammonia formation and antibody production. The cell growth and the uptake of glucose and glutamine were only partially affected by the putative autocrine under the given conditions. The results indicate the presence of a self-expressed autocrine factor and its strong impact on the metabolism of CHO DP-12 cells. Furthermore, glucose and glutamine uptake, as well as the formation of ammonium and antibody were found to be significantly cell-cycle-dependent. The combined approach has a strong potential to improve the understanding of the interplay of population heterogeneities and signal factors in mammalian cell culture.

In vitro cultivated skin models have become increasingly relevant for pharmaceutical and cosmetic applications, and are also used in drug development as well as substance testing. These models are mostly cultivated in membrane-insert systems, their permeability toward different substances being an essential factor. Typically, applied methods for determination of these parameters usually require large sample sizes (e.g., Franz diffusion cell) or laborious equipment (e.g., fluorescence recovery after photobleaching (FRAP)). This study presents a method for determining permeability coefficients directly in membrane-insert systems with diameter sizes of 4.26 mm and 12.2 mm (cultivation area). The method was validated with agarose and collagen gels as well as a collagen cell model representing skin models. The permeation processes of substances with different molecular sizes and permeation through different cell models (consisting of collagen gel, fibroblast, and HaCaT) were accurately described. Moreover, to support the above experimental method, a simulation was established. The simulation fits the experimental data well for substances with small molecular size, up to 14 x 10-10 m Stokes radius (4,000 MW), and is therefore a promising tool to describe the system. Furthermore, the simulation can considerably reduce experimental efforts and is robust enough to be extended or adapted to more complex setups.

Measles virus (MV) with attenuated pathogenicity has potential as oncolytic agent. However, the clinical translation of this therapy concept has one major hurdle: the production of sufficient amounts of infectious oncolytic MV particles. The current study describes oncolytic MV production in Vero cells grown on microcarrier using serum-free medium. The impact of the number of harvests, cell concentration at infection (CCI), multiplicity of infection (MOI), and temperature on MV production was determined in different production scales/systems (static T-flasks, dynamic spinner, and bioreactor system) and modes (batch, repeated-batch, and perfusion). Cell growth, metabolic, and production kinetics were analyzed. It was found that the number of harvests had the strongest positive impact on MV yield in each production scale, and that high temperatures affected MV yield adversely. Moderate MV titers were produced in T- and spinner flasks at 37°C (∼107 TCID50 mL−1, where TCID50 is tissue culture infective doses 50%), but stirred tank reactor (STR) MV production at 37°C yielded up to 10 000-fold lower MV titers. In contrast, at lower temperatures (32°C, 27°C), 1.4 × 107 TCID50 mL−1 were achieved in the STR. Variations in MOI and CCI had almost no influence on MV production yield. The current study improves oncolytic MV production process understanding and identifies process bottlenecks for large-scale production.