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Item type:Publication, Access status: without files , Digitale Zwillinge und ihre Rolle im modellgestützten Design von ExperimentenSteigende Nachfrage nach Biopharmazeutika und die Notwendigkeit, die Herstellungskosten zu senken, erhöhen den Druck, produktive und effiziente Bioprozesse zu entwickeln. Unter anderem ist ein großes Hindernis bei der Prozessentwicklung und Optimierungsstudien der enorme experimentelle Aufwand bei herkömmlichen Methoden des Design of Experiments (DoE). Da DoE ein explorativer Ansatz ist, erfordert es umfangreiches Expertenwissen über die untersuchten Faktoren und deren Grenzwerte und führt oft zu mehreren Runden zeitaufwändiger und kostspieliger Experimente. Die Kombination von DoE mit einer virtuellen Darstellung des Bioprozesses, genannt digitaler Zwilling, in modellgestütztem DoE (mDoE) kann als Alternative verwendet werden, um die Anzahl der Experimente erheblich zu reduzieren. mDoE ermöglicht eine wissensbasierte Bioprozessentwicklung, einschließlich der Definition eines mathematischen Prozessmodells in den frühen Entwicklungsstadien. In diesem Kapitel werden digitale Zwillinge und ihre Rolle in mDoE diskutiert. Zuerst werden statistische DoE-Methoden als Grundlage von mDoE eingeführt. Zweitens wird die Kombination eines mathematischen Prozessmodells und DoE zu mDoE untersucht. Dies umfasst mathematische Modellstrukturen und ein Auswahlverfahren für die Wahl der DoE-Designs. Schließlich wird die Anwendung von mDoE in einer Fallstudie zur Medienoptimierung in einem antikörperproduzierenden Zellkulturprozess von chinesischen Hamster-Ovarien diskutiert.Publicationtype: Book Part23 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Bioprocess intensification with model-assisted DoE-strategies for the production of biopharmaceuticalsThe demand for highly effective biopharmaceuticals and the need to reduce manufacturing costs are increasing the pressure to develop productive and efficient bioprocesses. For this purpose, model-based process design concepts have been developed. Although first approaches were proposed, model-based process designs are still not state-of-the-art for cell culture processes during development or manufacturing. This highlights a need for improved methods and tools for optimal experimental design, optimal and robust process design and process optimization for the purposes of monitoring and control during manufacturing. In this review, an overview of the state of the art of model-based methods, their applications, further challenges, possible solutions and specific case studies for intensification of process development for production of biopharmaceuticals is presented. As a special focus, problems related to data generation (culture systems, process mode, specifically designed experiments) will be addressed.Publicationtype: Review Article42 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Implementation of mDoE-methods to a microcarrier-based expansion processes for mesenchymal stem cellsThe need for advanced therapy medicinal products (ATMPs) has gained increased attention in recent years. In this respect, a well-designed cell expansion process is needed to efficiently manufacture the required number of cells with the desired product quality. This step is challenging due to the biological complexity of the respective primary cell (e.g., mesenchymal stem cells (MSC)) and the usage of microcarrier-based expansion systems. One accelerating approach for process design is model-assisted Design of Experiments (mDoE) combining mathematical process models and statistical tools. In this study, the mDoE workflow was used for the development of an expansion processes with human immortalized mesenchymal stem cells (hMSC-TERT) and the aim of maximizing cell yield assuming only a limited amount of prior knowledge at a very early stage of development. First, suitable microcarriers for expansion in shake flasks were screened and the differentiation of the cells was proven. Second, initial experiments were performed to generate prior knowledge, which was then used to set up the mathematical model and to estimate the model parameters. Finally, the mDoE was used to determine and evaluate the design space to be performed experimentally. Overall, a cell expansion process using microcarriers in a shake flask culture was successfully implemented and a significant increase in cell yield (up to 6,2-fold) was achieved compared to literature.Publicationtype: Journal Article16 23 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Bioprocess intensification with modelassisted DoE-strategies for the production of biopharmaceuticalsThe demand for highly effective biopharmaceuticals and the need to reduce manufacturing costs are increasing the pressure to develop productive and efficient bioprocesses. For this purpose, model-based process design concepts have been developed. Although first approaches were proposed, model-based process designs are still not state-of-the-art for cell culture processes during development or manufacturing. This highlights a need for improved methods and tools for optimal experimental design, optimal and robust process design and process optimization for the purposes of monitoring and control during manufacturing. In this review, an overview of the state of the art of model-based methods, their applications, further challenges, possible solutions and specific case studies for intensification of process development for production of biopharmaceuticals is presented. As a special focus, problems related to data generation (culture systems, process mode, specifically designed experiments) will be addressed.Publicationtype: Book part11 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Design of cell expansion processes for adherent-growing cells with mDoE-workflowAdherent cells, mammalian or human, are ubiquitous for production of viral vaccines, in gene therapy and in immuno-oncology. The development of a cell-expansion process with adherent cells is challenging as scale-up requires the expansion of the cell culture surface. Microcarrier (MC)-based cultures are still predominate. However, the development of MC processes from scratch possesses particular challenges due to their complexity. A novel approach for the reduction of development times and costs of cell propagation processes is the combination of mathematical process models with statistical optimization methods, called model-assisted Design of Experiments (mDoE). In this study, an mDoE workflow was evaluated successfully for the design of a MC-based expansion process of adherent L929 cells at a very early stage of development with limited prior knowledge. At the start, the analytical methods and the screening of appropriate MCs were evaluated. Then, cause-effect relationships (e.g., cell growth related to medium conditions) were worked out, and a mathematical process model was set-up and adapted to experimental data for modeling purposes. The model was subsequently used in mDoE to identify optimized process conditions, which were proven experimentally. An eight-fold increase in cell yield was achieved basically by reducing the initial MC concentration.Publicationtype: Journal ArticleScopus© Citations 2 54 116 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Model-assisted DoE applied to microalgae processes(Elsevier, 2022-02-02); ; ; ; This study assesses the performance of the model-assisted Design of Experiment (mDoE) software toolbox for the design of two microalgae bioprocesses. The mDoE-toolbox was applied to maximize biomass growth for Desmodesmus pseudocommunis in a photobioreactor by varying the light intensity and pH and for Chlorella vulgaris in shake flasks, by varying the light intensity and duration. For both case studies, a mathematical mechanistic model was applied. In the first study only one experiment was necessary to adapt the mathematical model and identify a combination of light intensity and pH that improved biomass yield, as confirmed experimentally. In the second study, no well-established model was available for the specific experimental arrangement. On the basis of the literature, a mathematical model was constructed and a first cycle of mDoE was performed, thus identifying the desired factor combinations. Experiments confirmed the high biomass yield but revealed shortcomings of the model. The model was improved and a second cycle of mDoE was performed. The recommended factor combinations from both cycles were comparable. The mDoE was found to be a time-saving, cost-effective and useful method enabling the identification of factor combinations leading to high biomass production for the design of two different microalgae bioprocesses with low experimental effort.Publicationtype: Journal ArticleScopus© Citations 1 91 506 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Evaluation modellgestützter Design of Experiments-Methoden zur Auslegung biopharmazeutischer ProzesseThe growing demand for biopharmaceuticals and the need to reduce manufacturing costs are increasing the pressure to develop productive and efficient bioprocesses. Conventional Design of Experiments (DoE) methods are mostly used in process development and optimization. Being an exploratory approach, DoE requires extensive expert knowledge of the investigated factors and their limits, and often results in several time-consuming and costly series of experiments. In contrast to conventional DoE, in model-assisted DoE (mDoE) the recommended experiments are simulated using mathematical process models to evaluate the experimental space. This enables a well-defined experimental space, since changes can be made without additional experimental effort. Recommended experiments are thus implemented in a significantly reduced number. In addition to the significant reduction in the number of experiments, the use of mathematical process models is now seen as a sustainable part of a knowledge-driven bioprocess development strategy. However, mDoE methods have so far only been applied in the biopharmaceutical field for process design in the presence of broad available data. These processes are classified to late-stage process development, although it is currently unclear to what extent mDoE concepts can be applied to the early-stage of process development. Nevertheless, especially in early-stage processes there is a need for suitable methods for knowledge-driven process design. Therefore, the basic hypothesis of this dissertation is that mDoE methods are applicable to these very processes. For this purpose, early-stage processes for different cell lines were investigated and evaluatetd. Based on a broad data set for CHO DP12 cells, the components for the successful implementation of an mDoE were defined and integrated into an mDoE workflow. For this purpose, experimental designs were programmed and implemented in an mDoE toolbox. Using adherent-growing cells (mesenchymal stem cells, hMSC-TERT and mouse fibroblasts, L929), the utility of the mDoE workflow was investigated using different developmental stages. The cell behavior of adherent-growing cells was model-assisted as well as uncertainty-based analyzed, and a mathematical process model was established. Thereby, the cultivation conditions of L929 cells and hMSC-TERT were successfully adapted for cultivation in shake flasks. Thus, cultivations as well as bead-to-bead transfer in shake flasks were successfully implemented for the first time. For L929 cells, an 8-fold increase in cell yield was achieved by reducing the initial microcarrier concentration. When adjusting the bead-to-bead transfer of hMSC-TERTs, a 6.3-fold increase in cell concentration was achieved. Finally, correlations were detected at different scales, allowing scale transfer. Overall, the mDoE workflow was successfully applied to the expansion of various cell lines, analyzing cell behavior, increasing cell yields, and optimizing boundary conditions for mDoE-methods. The knowledge-based application of mDoE methods thus offers also in early-stage processes the possibility to design expansion processes and produce clinically relevant cell numbers.Publicationtype: Doctoral Thesis156 124 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Model-assisted DoE software: optimization of growth and biocatalysis in Saccharomyces cerevisiae bioprocesses(Springer, 2021-01-20); ; ; ; 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.Publicationtype: Journal ArticleScopus© Citations 10 113 320 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Model-based workflow for scale-up of process strategies developed in miniaturized bioreactor systems(Wiley, 2021-01-13); ; ; ; 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.Publicationtype: Journal ArticleScopus© Citations 8 142 541 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Digital Twins and Their Role in Model-Assisted Design of ExperimentsRising 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.Publicationtype: Book PartScopus© Citations 7 76