View on a mechanistic model of Chlorella vulgaris in incubated shake flasks.

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.

Model-assisted DoE software: optimization of growth and biocatalysis in Saccharomyces cerevisiae bioprocesses.

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, https://doi.org/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.

Dynamic parameter estimation and prediction over consecutive scales, based on moving horizon estimation: applied to an industrial cell culture seed train.

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.

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.

Estimation of Process Model Parameters.

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.

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.

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.

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.

Seed train optimization for cell culture.

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.

Improving bioreactor cultivation conditions for sensitive cell lines by dynamic membrane aeration.

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.

Evaluation of selected control strategies for fed-batch cultures of a hybridoma cell line.

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.

Adaptive, model-based control by the Open-Loop-Feedback-Optimal (OLFO) controller for the effective fed-batch cultivation of hybridoma cells.

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.

Determination of dissolved CO(2) concentration and CO(2) production rate of mammalian cell suspension culture based on off-gas measurement.

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.

Early glomerulopathy is present in young, type 1 (insulin-dependent) diabetic patients with microalbuminuria.

Increased urinary albumin excretion, microalbuminuria, may be the first sign of early diabetic nephropathy. We examined glomeruli by morphometric methods in 17 patients with Type 1 (insulin-dependent) diabetes mellitus and microalbuminuria. The median age was 19 (range 18-29) years, duration of diabetes 12 (8-15) years, mean blood pressure 93 (87-115) mm Hg, glomerular filtration rate 132 (101-209) ml.min-1.1.73 m2 -2, albumin excretion rate (mean over 1 year) 32 (15-194) micrograms/min. Reference data were obtained from 11 healthy kidney donors. Mesangial volume estimates were obtained by serial sectioning in three total profiles in each of three glomeruli in diabetic patients. Basement membrane thickness and matrix volume fraction were estimated from one level per glomerulus. Two matrix parameters, matrix star volume and matrix thickness, were estimated. Interstitial volume fraction in cortex was measured by light microscopy. The morphological parameters were significantly increased in the diabetic group compared to the control group, basement membrane thickness (mean with 95% confidence intervals) was 595 nm (549-641 nm) vs 305 nm (287-325 nm), p = 0.0001; mesangial volume fraction 0.22 (0.21-0.23) vs 0.19 (0.18-0.21), p = 0.04, and matrix volume fraction 0.13 (0.12-0.13 vs 0.09 (0.08-0.10), p = 0.001. Also matrix star volume and thickness, interstitial volume fraction and mean capillary diameter were significantly increased. The intra-individual variation among glomeruli expressed as coefficient of variation was 7.4% vs 9% (basement membrane thickness) and 11.7% vs 25% (mesangial volume fraction) in the diabetic and the control group, respectively.(ABSTRACT TRUNCATED AT 250 WORDS)