An increasing need toward a more efficient expansion of adherent progenitor cell types arises with the advancements of cell therapy. The use of a dynamic expansion instead of a static planar expansion could be one way to tackle the challenges of expanding adherent cells at a large scale. Microcarriers are often reported as a biomaterial for culturing cells in suspension. However, the type of microcarrier has an effect on the cell expansion. In order to find an efficient expansion process for a specific adherent progenitor cell type, it is important to investigate the effect of the type of microcarrier on the cell expansion. Human periosteum-derived progenitor cells are extensively used in skeletal tissue engineering for the regeneration of bone defects. Therefore, we evaluated the use of different microcarriers on human periosteum-derived progenitor cells. In order to assess the potency, identity and viability of these cells after being cultured in the spinner flasks, this study performed several in vitro and in vivo analyses. The novelty of this work lies in the combination of screening different microcarriers for human periosteum-derived progenitor cells with in vivo assessments of the cells’ potency using the microcarrier that was selected as the most promising one. The results showed that expanding human periosteum-derived progenitor cells in spinner flasks using xeno-free medium and Star-Plus microcarriers, does not affect the potency, identity or viability of the cells. The potency of the cells was assured with an in vivo evaluation, where bone formation was achieved. In summary, this expansion method has the potential to be used for large scale cell expansion with clinical relevance.
Implementing a personalised feeding strategy for each individual batch of a bioprocess could significantly reduce the unnecessary costs of overfeeding the cells. This paper uses lactate measurements during the cell culture process as an indication of cell growth to adapt the feeding strategy accordingly. For this purpose, a model predictive control is used to follow this a priori determined reference trajectory of cumulative lactate. Human progenitor cells from three different donors, which were cultivated in 12-well plates for five days using six different feeding strategies, are used as references. Each experimental set-up is performed in triplicate and for each run an individualised model-based predictive control (MPC) controller is developed. All process models exhibit an accuracy of 99.80% ± 0.02%, and all simulations to reproduce each experimental run, using the data as a reference trajectory, reached their target with a 98.64% ± 0.10% accuracy on average. This work represents a promising framework to control the cell growth through adapting the feeding strategy based on lactate measurements.
Providing a cost-efficient feeding strategy for cell expansion processes remains a challenging task due to, among other factors, donor variability. The current method to use a fixed medium replacement strategy for all cell batches results often in either over- or underfeeding these cells. In order to take into account the individual needs of the cells, a model predictive controller was developed in this work. Reference experiments were performed by expanding human periosteum derived progenitor cells (hPDCs) in tissue flasks to acquire reference data. With these data, a time-variant prediction model was identified to describe the relation between the accumulated medium replaced as the control input and the accumulated lactate produced as the process output. Several forecast methods to predict the cell growth process were designed using multiple collected datasets by applying transfer function models or machine learning. The first controller experiment was performed using the accumulated lactate values from the reference experiment as a static target function over time, resulting in over- or underfeeding the cells. The second controller experiment used a time-adaptive target function by combining reference data as well as current measured real-time data, without over- or underfeeding the cells.
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