Abstract
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 (4ml 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 (2ml 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.
Highlights
In the initial phase of bioprocess design and optimization, the identification of an appropriate process strategy can be demanding due to the large number of influencing factors.1-3 To accelerate the optimization procedure, miniaturized bioreactors (MBRs) are typically applied in the field of mammalian cell culture processes with Chinese hamster ovary (CHO) cells
In CS1, the glutamine synthetase CHO (GS-CHO) culture was modeled in the micro-matrix system (MM) and 5 L bioreactor (5 L BR), model-parametric uncertainties were derived and statistically compared
A model-based workflow for the scale-up of a process strategy developed in MBR- to larger scale as well as evaluation and comparison of the process dynamics was introduced
Summary
In the initial phase of bioprocess design and optimization, the identification of an appropriate process strategy (e.g., feeding design) can be demanding due to the large number of influencing factors. To accelerate the optimization procedure, miniaturized bioreactors (MBRs) are typically applied in the field of mammalian cell culture processes with Chinese hamster ovary (CHO) cells. The scale-up and scale-down of process understanding (i.e., knowledge) between MBR systems and benchtop- or production-scale can be challenging due to varying process performance and cellular changes.. To enable a more knowledge-driven decision making, mathematical process models can be combined with statistical tools to further reduce the experimental effort and to understand the complexity and dynamics of an existing bioprocess in silico.. Individual model parameter distributions can be statistically compared to identify changes in the process dynamics between the modeled scales, as shown in Möller et al.. Individual model parameter distributions can be statistically compared to identify changes in the process dynamics between the modeled scales, as shown in Möller et al.15 This approach was successfully applied for the model uncertainty-based evaluation of an antibody-producing CHO process from shake flask cultures up to pilot scale.
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