Digital Seed Train Twins and Statistical Methods

Rodríguez, Tanja Hernández; Frahm, Björn

doi:10.1007/10_2020_137

Cited by 5 publications

(4 citation statements)

References 45 publications

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“…concerning passaging intervals, substrate/nutrient concentrations, initial viable cell densities and viable cell densities before transferring cells into the next cultivation vessel as well as the amount of cell suspension and fresh medium), which have to be considered in the simulation workflow. A detailed description of the required components and calculation routines are described in [13,14].…”

Section: Upstream Simulationmentioning

confidence: 99%

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Designing Robust Biotechnological Processes Regarding Variabilities Using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design

et al. 2022

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Development and optimization of biopharmaceutical production processes with cell cultures is cost- and time-consuming and often performed rather empirically. Efficient optimization of multiple objectives such as process time, viable cell density, number of operating steps & cultivation scales, required medium, amount of product as well as product quality depicts a promising approach. This contribution presents a workflow which couples uncertainty-based upstream simulation and Bayes optimization using Gaussian processes. Its application is demonstrated in a simulation case study for a relevant industrial task in process development, the design of a robust cell culture expansion process (seed train), meaning that despite uncertainties and variabilities concerning cell growth, low variations of viable cell density during the seed train are obtained. Compared to a non-optimized reference seed train, the optimized process showed much lower deviation rates regarding viable cell densities (<10% instead of 41.7%) using five or four shake flask scales and seed train duration could be reduced by 56 h from 576 h to 520 h. Overall, it is shown that applying Bayes optimization allows for optimization of a multi-objective optimization function with several optimizable input variables and under a considerable amount of constraints with a low computational effort. This approach provides the potential to be used in the form of a decision tool, e.g., for the choice of an optimal and robust seed train design or for further optimization tasks within process development.

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Section: Upstream Simulationmentioning

confidence: 99%

“…This calculation has to be performed for every cultivation scale and passaging step. For more details refer to [16,14,13].…”

Section: Upstream Simulationmentioning

confidence: 99%

Designing Robust Biotechnological Processes Regarding Variabilities Using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design

et al. 2022

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“…Mathematical process models appear to be suitable tools for analysis, for the generation of process understanding and for simulation and prediction. Several examples of using such process models addressing biopharmaceutical manufacturing can be found in the literature [10][11][12][13][14][15][16]. Within this field, uncertainty-based methods gained attention because model uncertainty, uncertainty in measurements and batch-to-batch variability can be taken into consideration in this way.…”

Section: Introductionmentioning

confidence: 99%

Considerations of the Impacts of Cell-Specific Growth and Production Rate on Clone Selection—A Simulation Study

Rodríguez

Morerod²,

Pörtner

et al. 2021

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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.

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“…Однозначной трактовки понятия ЦМ нет: исследователи объясняют его через природу трансформации структурных аспектов бизнеса (целей, бизнес-моделей, бизнес-процессов) [15]; активизацию инноваций [13]; роль информационной поддержки бизнеса [8]; необходимость интеграции ИТ-технологий и бизнес-стратегий [6].…”

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Научная Визуализация Как Основа Цифрового Менеджмента

Крупина

2023

Вестник Тверского Государственного Университета. Серия: Экономика И Управление

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Digital Seed Train Twins and Statistical Methods

Cited by 5 publications

References 45 publications

Designing Robust Biotechnological Processes Regarding Variabilities Using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design

Designing Robust Biotechnological Processes Regarding Variabilities Using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design

Considerations of the Impacts of Cell-Specific Growth and Production Rate on Clone Selection—A Simulation Study

Научная Визуализация Как Основа Цифрового Менеджмента

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