Intensified design of experiments for upstream bioreactors

Stosch, Moritz von; Willis, M.J.

doi:10.1002/elsc.201600037

Cited by 46 publications

(33 citation statements)

References 29 publications

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“…These shifts were done in compliance with already published constraints. [ 25 ] Since the inducer was not consumed by the cells, a shift toward lower inducer concentrations was not feasible without heavy and impractical dilution of the fermentation broth. Therefore, the 3D design space was subdivided into three 2D induction planes for the iDoE approach, and shifts were only performed for the temperature and the specific growth rate in the respective induction plane.…”

Section: Methodsmentioning

confidence: 99%

“…[ 24 ] One of the main challenges is to describe the process dynamics in response to intra‐experimental changes and to estimate the behavior of the cells under constant conditions. Therefore, a time‐resolved hybrid model can be built on iDoE data to describe the occurring process dynamics, [ 25 ] because it captures the whole process. This emphasizes a combinatorial approach, using hybrid modeling and iDoE, to generate process knowledge and simultaneously accelerate process characterization.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Modeling and Intensified DoE: An Approach to Accelerate Upstream Process Characterization

Bayer

Striedner

Duerkop

2020

Biotechnology Journal

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Process characterization is necessary in the biopharmaceutical industry, leading to concepts such as design of experiments (DoE) in combination with process modeling. However, these methods still have shortcomings, including large numbers of required experiments. The concept of intensified design of experiments (iDoE) is proposed, that is, intra‐experimental shifts of critical process parameters (CPP) that combine with hybrid modeling to more rapidly screen a particular design space. To demonstrate these advantages, a comprehensive experimental design of Escherichia coli (E. coli) fed‐batch cultivations (20 L) producing recombinant human superoxide dismutase is presented. The accuracy of hybrid models trained on iDoE and on a fractional‐factorial design is evaluated, without intra‐experimental shifts, to simultaneously predict the biomass concentration and product titer of the full‐factorial design. The hybrid model trained on data from the iDoE describes the biomass and product at each time point for the full‐factorial design with high and adequate accuracy. The fractional‐factorial hybrid model demonstrates inferior accuracy and precision compared to the intensified approach. Moreover, the intensified hybrid model only required one‐third of the data for model training compared to the full‐factorial description, resulting in a reduced experimental effort of >66%. Thus, this combinatorial approach has the potential to accelerate bioprocess characterization.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hybrid Modeling and Intensified DoE: An Approach to Accelerate Upstream Process Characterization

Bayer

Striedner

Duerkop

2020

Biotechnology Journal

View full text Add to dashboard Cite

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“…In particular, decision‐making based on models is a more rational approach regarding critical factors and responses of the process under investigation. One option is the use of design of experiments (DoE) approaches, where experiments are planned in a statistically optimal way to reduce the number of cultivations to be performed and to investigate the impact of parameters on product yield and product quality (Brendel & Marquardt, 2008; Kreutz & Timmer, 2009; von Stosch & Willis, 2017). A drawback of these methods, however, is their inability to handle more complex systems dynamics, for example, changes in critical cell properties or medium composition with process time, the release of inhibitory compounds into the cultivation broth, or the decrease in specific precursor concentrations required for product synthesis.…”

Section: Introductionmentioning

confidence: 99%

A dynamic model linking cell growth to intracellular metabolism and extracellular by‐product accumulation

Ramos

Rath

Genzel

et al. 2020

Biotech & Bioengineering

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Mathematical modeling of animal cell growth and metabolism is essential for the understanding and improvement of the production of biopharmaceuticals. Models can explain the dynamic behavior of cell growth and product formation, support the identification of the most relevant parameters for process design, and significantly reduce the number of experiments to be performed for process optimization. Few dynamic models have been established that describe both extracellular and intracellular dynamics of growth and metabolism of animal cells. In this study, a model was developed, which comprises a set of 33 ordinary differential equations to describe batch cultivations of suspension AGE1.HN.AAT cells considered for the production of α1‐antitrypsin. This model combines a segregated cell growth model with a structured model of intracellular metabolism. Overall, it considers the viable cell concentration, mean cell diameter, viable cell volume, concentration of extracellular substrates, and intracellular concentrations of key metabolites from the central carbon metabolism. Furthermore, the release of metabolic by‐products such as lactate and ammonium was estimated directly from the intracellular reactions. Based on the same set of parameters, this model simulates well the dynamics of four independent batch cultivations. Analysis of the simulated intracellular rates revealed at least two distinct cellular physiological states. The first physiological state was characterized by a high glycolytic rate and high lactate production. Whereas the second state was characterized by efficient adenosine triphosphate production, a low glycolytic rate, and reactions of the TCA cycle running in the reverse direction from α‐ketoglutarate to citrate. Finally, we show possible applications of the model for cell line engineering and media optimization with two case studies.

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“…Herein, by the utilization of intra‐experimental shifts more than one CPP combination setting is addressed per cultivation run, reducing the total number of experiments. [ 40,41 ] This promising approach in combination with the herein developed bootstrap‐aggregated hybrid modeling strategy will be investigated in future publications.…”

Section: Discussionmentioning

confidence: 99%

Comparison of Modeling Methods for DoE‐Based Holistic Upstream Process Characterization

Bayer

Stosch

Striedner

et al. 2020

Biotechnology Journal

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Upstream bioprocess characterization and optimization are time and resource-intensive tasks. Regularly in the biopharmaceutical industry, statistical design of experiments (DoE) in combination with response surface models (RSMs) are used, neglecting the process trajectories and dynamics. Generating process understanding with time-resolved, dynamic process models allows to understand the impact of temporal deviations, production dynamics, and provides a better understanding of the process variations that stem from the biological subsystem. The authors propose to use DoE studies in combination with hybrid modeling for process characterization. This approach is showcased on Escherichia coli fed-batch cultivations at the 20L scale, evaluating the impact of three critical process parameters. The performance of a hybrid model is compared to a pure data-driven model and the widely adopted RSM of the process endpoints. Further, the performance of the time-resolved models to simultaneously predict biomass and titer is evaluated. The superior behavior of the hybrid model compared to the pure black-box approaches for process characterization is presented. The evaluation considers important criteria, such as the prediction accuracy of the biomass and titer endpoints as well as the time-resolved trajectories. This showcases the high potential of hybrid models for soft-sensing and model predictive control.

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Intensified design of experiments for upstream bioreactors

Cited by 46 publications

References 29 publications

Hybrid Modeling and Intensified DoE: An Approach to Accelerate Upstream Process Characterization

Hybrid Modeling and Intensified DoE: An Approach to Accelerate Upstream Process Characterization

A dynamic model linking cell growth to intracellular metabolism and extracellular by‐product accumulation

Comparison of Modeling Methods for DoE‐Based Holistic Upstream Process Characterization

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