Bioprocessing of Human Mesenchymal Stem Cells: From Planar Culture to Microcarrier-Based Bioreactors

Human mesenchymal stem cells (hMSCs) have demonstrated great potential to be used as therapies for many types of diseases. Due to their immunoprivileged status, allogeneic hMSCs therapies are particularly attractive options and methodologies to improve their scaling and manufacturing are needed. Microcarrier-based bioreactor systems provide higher volumetric hMSC production in automated closed systems than conventional planar cultures. However, more sophisticated bioprocesses are necessary to successfully convert from planar culture to microcarriers. This article summarizes key steps involved in the planar culture to microcarrier hMSC manufacturing scheme, from seed train, inoculation, expansion and harvest. Important bioreactor parameters, such as temperature, pH, dissolved oxygen (DO), mixing, feeding strategies and cell counting techniques, are also discussed.

Seed train process intensification strategy offers potential for rapid, cost-effective scale-up of biosimilars manufacturing

A perfusion approach at N-1, where cells stay in the exponential growth phase throughout the entire culture duration, is becoming more common as a strategy for process intensification. This is because the higher cell densities it generates allows manufacturers to skip seed stages and reduce process transfer time through multiple bioreactor sizes, thus providing more cost-effective biologics production in smaller facilities. However, this N-1 perfusion approach requires optimization. In this article, we describe the development and proof-of-concept studies with single-use rocking motion perfusion bioreactors in which we have achieved a ten-fold increase in viable cell count in N-1 seed stage, compared to the fed-batch control process, in just 6–8 days. We also mention in detail how we inoculated a 50 L bioreactor production run using this intensified seed train and show comparable growth kinetics and yield with a control process, also at 50 L scale. Using this intensification approach in the future will help our manufacturing facility, the Biopharma Division of Intas Pharmaceuticals Ltd., reach 4000 L production-scale volumes with fewer process transfer steps, and without changing the feeding strategy or production bioreactors of our biologics’ portfolio.

Dynamic parameter estimation and prediction over consecutive scales, based on moving horizon estimation: applied to an industrial cell culture seed train

Bioprocess modeling has become a useful tool for prediction of the process future with the aim to deduce operating decisions (e.g. transfer or feeds). Due to variabilities, which often occur between and within batches, updating (re-estimation) of model parameters is required at certain time intervals (dynamic parameter estimation) to obtain reliable predictions. This can be challenging in the presence of low sampling frequencies (e.g. every 24 h), different consecutive scales and large measurement errors, as in the case of cell culture seed trains. This contribution presents an iterative learning workflow which generates and incorporates knowledge concerning cell growth during the process by using a moving horizon estimation (MHE) approach for updating of model parameters. This estimation technique is compared to a classical weighted least squares estimation (WLSE) approach in the context of model updating over three consecutive cultivation scales (40–2160 L) of an industrial cell culture seed train. Both techniques were investigated regarding robustness concerning the aforementioned challenges and the required amount of experimental data (estimation horizon). It is shown how the proposed MHE can deal with the aforementioned difficulties by the integration of prior knowledge, even if only data at two sampling points are available, outperforming the classical WLSE approach. This workflow allows to adequately integrate current process behavior into the model and can therefore be a suitable component of a digital twin.