On‐Line Control of Glucose Concentration in High‐Yielding Mammalian Cell Cultures Enabled Through Oxygen Transfer Rate Measurements

Abstract

Glucose control is vital to ensure consistent growth and protein production in mammalian cell cultures. The typical fed-batch glucose control strategy involving bolus glucose additions based on infrequent off-line daily samples results in cells experiencing significant glucose concentration fluctuations that can influence product quality and growth. This study proposes an online method to control and manipulate glucose utilizing readily available process measurements. The method generates a correlation between the cumulative oxygen transfer rate and the cumulative glucose consumed. This correlation generates an on-line prediction of glucose that has been successfully incorporated into a control algorithm manipulating the glucose feed-rate. This advanced process control (APC) strategy enables the glucose concentration to be maintained at an adjustable set-point and has been found to significantly reduce the deviation in glucose concentration in comparison to conventional operation. This method has been validated to produce various therapeutic proteins across cell lines with different glucose consumption demands and is successfully demonstrated on micro (15 mL), laboratory (7 L), and pilot (50 L) scale systems. This novel APC strategy is simple to implement and offers the potential to significantly enhance the glucose control strategy for scales spanning micro-scale systems through to full scale industrial bioreactors.

Multivariate Data Analysis Methodology to Solve Data Challenges Related to Scale‐Up Model Validation and Missing Data on a Micro‐Bioreactor System

“…The strength of these correlations enable exploitation for soft‐sensor development or advanced control applications. This was demonstrated by Goldrick et al . to predict the glucose concentration on‐line on a mammalian cell culture based off a strong correlation between the cumulative oxygen transfer rate and the cumulative glucose consumed.…”

“…Typically, data sets are enriched using feature generation leveraging additional information through the generation of meaningful feature vectors. These can include the cumulative sum, specific productivity, or calculated variables such as oxygen transfer rate (OTR) or oxygen mass transfer rate ( k L a ) . Off‐line data recorded at slightly different times each day could be categorized into daily off‐line measurements to simplify subsequent analysis.…”

Lactate-Based Model Predictive Control Strategy of Cell Growth for Cell Therapy Applications

“…Previous studies have therefore investigated the benefits of controlling the environment of the cell culture vessels using standard physicochemical process parameters [ 15 ]. In addition, other studies developed potential soft sensors using the metabolic responses of the cells to control the process, mostly glucose concentration [ 33 , 34 ]. This work used this metabolic soft sensor concept by measuring the lactate concentration off-line and used it as an indication of the cell growth, which can otherwise only be measured at the end of the bioprocess.…”

Recent Developments in Bioprocessing of Recombinant Proteins: Expression Hosts and Process Development

“…Different approaches for the determination of critical timepoints for product stability in an E. coli IB bioprocess were studied, and an empirical value was found that can be utilized as a process analytical tool (Slouka et al, 2019). An on-line method to control and manipulate glucose was studied and was validated to produce various recombinant therapeutic proteins across cell lines with different glucose consumption demands; it was then successfully demonstrated on micro (15 ml)-, laboratory (7 l)-, and pilot (50 l)-scale systems (Goldrick et al, 2018). For a P. pastoris fermentation to produce human interferon alpha 2b, a PAT platform was developed to monitor and control µ using capacitance ( C) during the induction phase (Katla et al, 2019).…”