Impact of fed-batch process intensification on the productivity and product quality of two CHO cell lines expressing unique novel molecular format proteins.

While monospecific antibodies have long been the foundational offering of protein therapeutics, recent advancements in antibody engineering have allowed for the development of far more complex antibody structures. Novel molecular format (NMF) proteins, such as bispecific antibodies (BsAbs), are structures capable of multispecific binding, allowing for expanded therapeutic functionality. As demand for NMF proteins continues to rise, biomanufacturers face the challenge of increasing bioreactor process productivity while simultaneously maintaining consistent product quality. This challenge is exacerbated when producing structurally complex proteins with asymmetric modalities, as seen in NMFs. In this study, the impact of a high inoculation density (HID) fed-batch process on the productivity and product quality attributes of two CHO cell lines expressing unique NMFs, a monospecific antibody with an Fc-fusion protein and a bispecific antibody, compared to low inoculation density (LID) platform fed-batch processes was evaluated. It was observed that an intensified platform fed-batch process increased product concentrations by 33 and 109% for the two uniquely structured complex proteins in a shorter culture duration while maintaining similar product quality attributes to traditional fed-batch processes.

An automated high inoculation density fed-batch bioreactor, enabled through N-1 perfusion, accommodates clonal diversity and doubles titers.

An important consideration for biopharmaceutical processes is the cost of goods (CoGs) of biotherapeutics manufacturing. CoGs can be reduced by dramatically increasing the productivity of the bioreactor process. In this study, we demonstrate that an intensified process which couples a perfused N-1 seed reactor and a fully automated high inoculation density (HID) N stage reactor substantially increases the bioreactor productivity as compared to a low inoculation density (LID) control fed-batch process. A panel of six CHOK1SV GS-KO® CHO cell lines expressing three different monoclonal antibodies was evaluated in this intensified process, achieving an average 85% titer increase and 132% space-time yield (STY) increase was demonstrated when comparing the 12-day HID process to a 15-day LID control process. These productivity increases were enabled by automated nutrient feeding in both the N-1 and N stage bioreactors using in-line process analytical technologies (PAT) and feedback control. The N-1 bioreactor utilized in-line capacitance to automatically feed the bioreactor based on a capacitance-specific perfusion rate (CapSPR). The N-stage bioreactor utilized in-line Raman spectroscopy to estimate real-time concentrations of glucose, phenylalanine, and methionine, which are held to target set points using automatic feed additions. These automated feeding methodologies were shown to be generalizable across six cell lines with diverse feed requirements. We show this new process can accommodate clonal diversity and reproducibly achieve substantial titer uplifts compared to traditional cell culture processes, thereby establishing a baseline technology platform upon which further increases bioreactor productivity and CoGs reduction can be achieved.

Automated Raman feed-back control of multiple supplemental feeds to enable an intensified high inoculation density fed-batch platform process.

Biologics manufacturing is increasingly moving toward intensified processes that require novel control strategies in order to achieve higher titers in shorter periods of time compared to traditional fed-batch cultures. In order to implement these strategies for intensified processes, continuous process monitoring is often required. To this end, inline Raman spectroscopy was used to develop partial least squares models to monitor changes in residual concentrations of glucose, phenylalanine and methionine during the culture of five different glutamine synthetase piggyBac® Chinese hamster ovary clones cultured using an intensified high inoculation density fed-batch platform process. Continuous monitoring of residual metabolite concentrations facilitated automated feed-rate adjustment of three supplemental feeds to maintain glucose, phenylalanine, and methionine at desired setpoints, while maintaining other nutrient concentrations at acceptable levels across all clones cultured on the high inoculation density platform process. Furthermore, all clones cultured on this process achieved high viable cell concentrations over the course of culture, indicating no detrimental impacts from the proposed feeding strategy. Finally, the automated control strategy sustained cultures inoculated at high cell densities to achieve product concentrations between 5 and 8.3 g/L over the course of 12 days of culture.

Forecasting and control of lactate bifurcation in Chinese hamster ovary cell culture processes.

Biomanufacturing exhibits inherent variability that can lead to variation in performance attributes and batch failure. To help ensure process consistency and product quality the development of predictive models and integrated control strategies is a promising approach. In this study, a feedback controller was developed to limit excessive lactate production, a widespread metabolic phenomenon that is negatively associated with culture performance and product quality. The controller was developed by applying machine learning strategies to historical process development data, resulting in a forecast model that could identify whether a run would result in lactate consumption or accumulation. In addition, this exercise identified a correlation between increased amino acid consumption and low observed lactate production leading to the mechanistic hypothesis that there is a deficiency in the link between glycolysis and the tricarboxylic acid cycle. Using the correlative process parameters to build mechanistic insight and applying this to predictive models of lactate concentration, a dynamic model predictive controller (MPC) for lactate was designed. This MPC was implemented experimentally on a process known to exhibit high lactate accumulation and successfully drove the cell cultures towards a lactate consuming state. In addition, an increase in specific titer productivity was observed when compared with non-MPC controlled reactors.

A system identification approach for developing model predictive controllers of antibody quality attributes in cell culture processes.

As the biopharmaceutical industry evolves to include more diverse protein formats and processes, more robust control of Critical Quality Attributes (CQAs) is needed to maintain processing flexibility without compromising quality. Active control of CQAs has been demonstrated using model predictive control techniques, which allow development of processes which are robust against disturbances associated with raw material variability and other potentially flexible operating conditions. Wide adoption of model predictive control in biopharmaceutical cell culture processes has been hampered, however, in part due to the large amount of data and expertise required to make a predictive model of controlled CQAs, a requirement for model predictive control. Here we developed a highly automated, perfusion apparatus to systematically and efficiently generate predictive models using application of system identification approaches. We successfully created a predictive model of %galactosylation using data obtained by manipulating galactose concentration in the perfusion apparatus in serialized step change experiments. We then demonstrated the use of the model in a model predictive controller in a simulated control scenario to successfully achieve a %galactosylation set point in a simulated fed-batch culture. The automated model identification approach demonstrated here can potentially be generalized to many CQAs, and could be a more efficient, faster, and highly automated alternative to batch experiments for developing predictive models in cell culture processes, and allow the wider adoption of model predictive control in biopharmaceutical processes. © 2017 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers Biotechnol. Prog., 33:1647-1661, 2017.

A novel approach for using dielectric spectroscopy to predict viable cell volume (VCV) in early process development.

Online monitoring of viable cell volume (VCV) is essential to the development, monitoring, and control of bioprocesses. The commercial availability of steam-sterilizable dielectricspectroscopy probes has enabled successful adoption of this technology as a key noninvasive method to measure VCV for cell-culture processes. Technological challenges still exist, however. For some cell lines, the technique's accuracy in predicting the VCV from probepermittivity measurements declines as the viability of the cell culture decreases. To investigate the cause of this decrease in accuracy, divergences in predicted vs. actual VCV measurements were directly related to the shape of dielectric frequency scans collected during a cell culture. The changes in the shape of the beta dispersion, which are associated with changes in cell state, are quantified by applying a novel "area ratio" (AR) metric to frequency-scanning data from the dielectric-spectroscopy probes. The AR metric is then used to relate the shape of the beta dispersion to single-frequency permittivity measurements to accurately predict the offline VCV throughout an entire fed-batch run, regardless of cell state. This work demonstrates the possible feasibility of quantifying the shape of the beta dispersion, determined from frequency-scanning data, for enhanced measurement of VCV in mammalian cell cultures by applying a novel shape-characterization technique. In addition, this work demonstrates the utility of using changes in the shape of the beta dispersion to quantify cell health.