Process Analytical Technology and Quality-by-Design for Animal Cell Culture

Comparison of partial least square, artificial neural network, and support vector regressions for real-time monitoring of CHO cell culture processes using in situ near-infrared spectroscopy

The biopharmaceutical industry must guarantee the efficiency and biosafety of biological medicines, which are quite sensitive to cell culture process variability. Real-time monitoring procedures based on vibrational spectroscopy such as near-infrared (NIR) spectroscopy, are then emerging to support innovative strategies for retro-control of key parameters as substrates and by-product concentration. Whereas monitoring models are mainly constructed using partial least squares regression (PLSR), spectroscopic models based on artificial neural networks (ANNR) and support vector regression (SVR) are emerging with promising results. Unfortunately, analysis of their performance in cell culture monitoring has been limited. This study was then focused to assess their performance and suitability for the cell culture process challenges. PLSR had inferior values of the determination coefficient (R² ) for all the monitored parameters (i.e., 0.85, 0.93, and 0.98, respectively for the PLSR, SVR, and ANNR models for glucose). In general, PLSR had a limited performance while models based on ANNR and SVR have been shown superior due to better management of inter-batch heterogeneity and enhanced specificity. Overall, the use of SVR and ANNR for the generation of calibration models enhanced the potential of NIR spectroscopy as a monitoring tool.

Generic Workflow for the Setup of Mechanistic Process Models

Process models, consisting of transferable and applicable knowledge, can be used for various tasks such as process development and optimization, and for predicting and controlling critical process variables. In this regard, mechanistic process models, describing the mechanism of a system with a distinct model structure and characteristic parameters, are very promising.The development of a reliable and applicable model is usually the critical step, before model simulation and application show beneficial effects. In this chapter, a workflow for the generation of mechanistic process models is presented and applied on a typical cell culture process. The workflow includes the definition of critical reactions and the identification of their kinetics. By an iterative approach different reactions and kinetics are tested and model quality is assessed, leading to a final, target-oriented model.

Low glucose concentrations within typical industrial operating conditions have minimal effect on the transcriptome of recombinant CHO cells

Typically, mammalian cell culture medium contains high glucose concentrations that are analogous to diabetic levels in humans, suggesting that mammalian cells are cultivated in excessive glucose. Using RNA-Seq, this study characterized the Chinese hamster ovary (CHO) cell transcriptome under two glucose concentrations to assess the genetic effects associated with metabolic pathways, in addition to other global responses. The initial extracellular glucose concentrations used represented high (30 mM) and low (10 mM) glucose conditions, where at the time the transcriptomes were compared, the glucose concentrations were approximately 24 and 4.4 mM for the mid-exponential cultures, where 4.4 mM represents a common target concentration in the biopharmaceutical industry for controlled fed-batch cultures. A recombinant CHO cell line producing a monoclonal antibody was used, such that the impact on glycosylation genes could be evaluated. Relatively few genes were identified as being significantly different (FDR ≤ 0.01) between the high and low glucose conditions, for example, only 575 genes, and only 40 of these genes had 2-fold or greater differences. Gene expression differences for glycolysis, TCA cycle, and glycosylation-related reactions were minimal and unlikely to have biological significance. This transcriptome study indicates that low glucose concentrations in the culture medium are unlikely to cause any biologically significant or detrimental changes to CHO cells at the transcriptome level. Furthermore, it is well-known that maintaining low glucose concentrations in fed-batch cultures can reduce lactate production, which in turn improves process outcomes. Taken together, the transcriptome data supports the continued development of low glucose-based processes to control lactate. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 33:771-785, 2017.