The growing biopharmaceutical industry has reached a level of maturity that allows for the monitoring of numerous key variables for both process characterization and outcome predictions. Sensors were historically used in order to maintain an optimal environment within the reactor to optimize process performance. However, technological innovation has pushed towards on-line in situ continuous monitoring of quality attributes that could previously only be estimated off-line. These new sensing technologies when coupled with software models have shown promise for unique fingerprinting, smart process control, outcome improvement, and prediction. All this can be done without requiring invasive sampling or intervention on the system. In this paper, the state-of-the-art sensing technologies and their applications in the context of cell culture monitoring are reviewed with emphasis on the coming push towards industry 4.0 and smart manufacturing within the biopharmaceutical sector. Additionally, perspectives as to how this can be leveraged to improve both understanding and outcomes of cell culture processes are discussed.

Shake flasks are a foundational tool in early process development by allowing high throughput exploration of the design space. However, lack of online data at this scale can hamper rapid decision making. Oxygen transfer rate (OTR) monitoring has been readily applied as an online process characterization tool at the benchtop bioreactor scale. Recent advances in modern sensing technology have allowed OTR monitoring to be available at the shake flask level. It is now possible to multiplex time-of-action (e.g., Induction, temperature shift, pH shift, feeding initiation, point of harvest) characterization studies by relying on careful analysis of OTR profile kinetics. As a result, there is potential to save time and capital expenditures while exploring process intensification studies though accurate and physiologically relevant online data. In this article, we detail the application of OTR monitoring to characterize the impact that recombinant protein production has on an inducible CHO cell line expressing Palivizumab. We then test out time-of-action studies to intensify protein production outcomes. We observe that recombinant protein expression causes a metabolic load that diminishes potential biomass growth. As a result, when compared to a control standard process, delaying induction and temperature shift has the potential to improve viable cell densities (VCD) by 2-fold thus increasing recombinant protein yield by over 30 %. The study also demonstrates that OTR can serve as a useful tool to detect cessation of exponential growth. Consequently, time-of-action points that are characteristic of inducible systems can be formulated accurately and reliably to maximize production performance.

Background: Chinese hamster ovary (CHO) cells are highly important host cells in recombinant DNA technology. The rapid ability to conduct experiments in silico, using mathematical models of growing CHO cells, has helped advance the optimization of bioreactor conditions towards higher viable cell concentrations and mAb production. Several kinetic and system level models have been proposed for CHO cells. Results: In this work we have provided a new kinetic model of CHO cell metabolism and have shown its application for the simulation of novel high-throughput metabolomics data for fed-batch production in bioreactors with CHO cells grown using two feeding strategies. RNASeq data from CHO cells was used to guide selection of major metabolic pathways that were included in the kinetic model. The kinetic model includes 37 completely described reactions processing 45 species (metabolites). This model is based on previously published kinetic characteristics for this system and was evaluated against metabolomics data for CHO cells grown under two different feeding regimes. Conclusions: This work provides kinetic model for energy metabolism of CHO cells. Application of the model offers insights into possible causes of different performance of two feeding strategies, thus suggesting possible problems and future optimization routes.