Process analytical technology tools for perfusion cell culture

Online deployment of an O-PLS model for dielectric spectroscopy-based inline monitoring of viable cell concentrations in Chinese hamster ovary cell perfusion cultivations

Viable cell concentration (VCC) is an essential parameter that is required to support the efficient cultivation of mammalian cells. Although commonly determined using at-line or off-line analytics, in-line capacitance measurements represent a suitable alternative method for the determination of VCC. In addition, these latter efforts are complimentary with the Food and Drug Administration's initiative for process analytical technologies (PATs). However, current applications for online determination of the VCC often rely on single frequency measurements and corresponding linear regression models. It has been reported that this may be insufficient for application at all stages of a mammalian cell culture processes due to changes in multiple cell parameters over time. Alternatively, dielectric spectroscopy, measuring capacitance at multiple frequencies, in combination with multivariate mathematical models, has proven to be more robust. However, this has only been applied for retrospective data analysis. Here, we present the implementation of an O-PLS model for the online processing of multifrequency capacitance signals and the on-the-fly integration of the models' VCC results into a supervisory control and data acquisition (SCADA) system commonly used for cultivation observation and control. This system was evaluated using a Chinese hamster ovary (CHO) cell perfusion process.

Machine learning and metabolic modelling assisted implementation of a novel process analytical technology in cell and gene therapy manufacturing

Process analytical technology (PAT) has demonstrated huge potential to enable the development of improved biopharmaceutical manufacturing processes by ensuring the reliable provision of quality products. However, the complexities associated with the manufacture of advanced therapy medicinal products have resulted in a slow adoption of PAT tools into industrial bioprocessing operations, particularly in the manufacture of cell and gene therapy products. Here we describe the applicability of a novel refractometry-based PAT system (Ranger system), which was used to monitor the metabolic activity of HEK293T cell cultures during lentiviral vector (LVV) production processes in real time. The PAT system was able to rapidly identify a relationship between bioreactor pH and culture metabolic activity and this was used to devise a pH operating strategy that resulted in a 1.8-fold increase in metabolic activity compared to an unoptimised bioprocess in a minimal number of bioreactor experiments; this was achieved using both pre-programmed and autonomous pH control strategies. The increased metabolic activity of the cultures, achieved via the implementation of the PAT technology, was not associated with increased LVV production. We employed a metabolic modelling strategy to elucidate the relationship between these bioprocess level events and HEK293T cell metabolism. The modelling showed that culturing of HEK293T cells in a low pH (pH 6.40) environment directly impacted the intracellular maintenance of pH and the intracellular availability of oxygen. We provide evidence that the elevated metabolic activity was a response to cope with the stress associated with low pH to maintain the favourable intracellular conditions, rather than being indicative of a superior active state of the HEK293T cell culture resulting in enhanced LVV production. Forecasting strategies were used to construct data models which identified that the novel PAT system not only had a direct relationship with process pH but also with oxygen availability; the interaction and interdependencies between these two parameters had a direct effect on the responses observed at the bioprocess level. We present data which indicate that process control and intervention using this novel refractometry-based PAT system has the potential to facilitate the fine tuning and rapid optimisation of the production environment and enable adaptive process control for enhanced process performance and robustness.

Process intensification for the continuous production of an antimicrobial peptide in stably-transformed Sf-9 insect cells

The antibiotic resistance crisis has prompted research into alternative candidates such as antimicrobial peptides (AMPs). However, the demand for such molecules can only be met by continuous production processes, which achieve high product yields and offer compatibility with the Quality-by-Design initiative by implementing process analytical technologies such as turbidimetry and dielectric spectroscopy. We developed batch and perfusion processes at the 2-L scale for the production of BR033, a cecropin-like AMP from Lucilia sericata, in stably-transformed polyclonal Sf-9 cells. This is the first time that BR033 has been expressed as a recombinant peptide. Process analytical technology facilitated the online monitoring and control of cell growth, viability and concentration. The perfusion process increased productivity by ~ 180% compared to the batch process and achieved a viable cell concentration of 1.1 × 10⁷ cells/mL. Acoustic separation enabled the consistent retention of 98.5–100% of the cells, viability was > 90.5%. The recombinant AMP was recovered from the culture broth by immobilized metal affinity chromatography and gel filtration and was able to inhibit the growth of Escherichia coli K12. These results demonstrate a successful, integrated approach for the development and intensification of a process from cloning to activity testing for the production of new biopharmaceutical candidates.

Automation of high CHO cell density seed intensification via online control of the cell specific perfusion rate and its impact on the N-stage inoculum quality

Current CHO cell production processes require an optimized space-time-yield. Process intensification can support achieving this by enhancing the productivity and improving facility utilization. The use of perfusion at the last stage of the seed train (N-1) for high cell density inoculation of the fed-batch N-stage production culture is a relatively new approach with few industry applicable examples. Within this work, the impact of the cell-specific perfusion rate (CSPR) of the N-1 perfusion and the relevance of its control for the quality of generated inoculation cells was evaluated using an automated perfusion rate (PR) control based on online biomass measurements. Precise correlations (R² = 0.99) between permittivity and viable cell counts were found up to the high densities of 100⋅10⁶ c·mL^-1. Cells from N-1 perfusion were cultivated at a high and low CSPR with 50 and 20 pL·(c·d)^-1, respectively. Lowered cell growth and an increased apoptotic reaction was found as a consequence of the latter due to nutrient limitations and reduced uptake rates. Subsequently, batch cultivations (N-stage) from the different N-1 sources were inoculated to evaluate the physiological state of the inoculum. Successive responses resulting from the respective N-1 condition were uncovered. While cell growth and productivity of approaches inoculated from high CSPR and a conventional seed were comparable, low CSPR inoculation suffered significantly in terms of reduced initial cell growth and impaired viability. This study underlines the importance to determine the CSPR for the design and implementation of an N-1 perfusion process in order to achieve the desired performance at the crucial production stage.

Application of lectin immobilized on polyHIPE monoliths for bioprocess monitoring of glycosylated proteins

In-process monitoring of glycosylated protein concentration becomes very important with the introduction of perfusion bioprocesses. Affinity chromatography based on lectins allows selective monitoring when carbohydrates are accessible on the protein surface. In this work, we immobilized lectin on polyHIPE type of monoliths and implemented it for bioprocess monitoring. A spacer was introduced to lectin, which increased binding kinetics toward Fc-fusion protein, demonstrated by bio-layer interferometry. Furthermore, complete desorption using 0.25 M galactose was shown. Affinity column exhibited linearity in the range between 0.5 and 8 mg/ml and flow-unaffected binding for the flow-rates between 0.5 and 8 ml/min. Long-term stability over at least four months period was demonstrated. No unspecific binding of culture media components, including host cell proteins and DNA, was detected. Results obtained by affinity column matched concentration values obtained by a reference method.

Dielectric Spectroscopy to Improve the Production of rAAV Used in Gene Therapy

The insect cell-baculovirus expression vector system is an established method for large scale recombinant adeno-associated virus (rAAV) production, largely due to its scalability and high volumetric productivities. During rAAV production it is critical to monitor process parameters such as Spodoptera frugiperda (Sf9) cell concentration, infection timing, and cell harvest viabilities since they can have a significant influence on rAAV productivity and product quality. Herein we developed the use of dielectric spectroscopy as a process analytical technology (PAT) tool used to continuously monitor the production of rAAV in 2 L stirred tank bioreactors, achieving enhanced control over the production process. This study resulted in improved manufacturing robustness through continuous monitoring of cell culture parameters, eliminating sampling needs, increasing the accuracy of infection timing, and reliably estimating the time of harvest. To increase the accuracy of baculovirus infection timing, the cell growth/permittivity model was coupled to a feedback loop with real-time monitoring. This system was able to predict baculovirus infection timing up to 24 h in advance for greatly improved accuracy of infection and ensuring consistent high rAAV productivities. Furthermore, predictive models were developed based on the dielectric measurements of the culture. These multiple linear regression-based models resulted in correlation coefficients (Q2) of 0.89 for viable cell concentration, 0.97 for viability, and 0.92 for cell diameter. Finally, models were developed to predict rAAV titer providing the capability to distinguish in real time between high and low titer production batches.

Multivariate data analysis of capacitance frequency scanning for online monitoring of viable cell concentrations in small-scale bioreactors

Viable cell concentration (VCC) is one of the most important process attributes during mammalian cell cultivations. Current state-of-the-art measurements of VCC comprise offline methods which do not allow for continuous process data. According to the FDA's process analytical technology initiative, process monitoring and control should be applied to gain process understanding and to ensure high product quality. In this work, the use of an inline capacitance probe to monitor online VCCs of a mammalian CHO cell culture process in small-scale bioreactors (250 mL) was investigated. Capacitance sensors using single frequency are increasingly common for biomass monitoring. However, the single-frequency signal corresponds to the cell polarization that represents the viable cell volume. Therefore single-frequency measurements are dependent on cell diameter changes. Measuring the capacitance across various frequencies (frequency scanning) can provide information about the VCC and cope with changing cell diameter. Applying multivariate data analysis on the frequency scanning data successfully enabled direct online monitoring of VCCs in this study. The multivariate model was trained with data from 5 standard cultivations. The model provided a prediction of VCCs with relative errors from 5.5 to 11%, which is a good agreement with the acceptance criterion based on the offline reference method accuracy (approximately 10% relative error) and strongly improved compared with single-frequency results (16 to 23% relative error). Furthermore, robustness trials were conducted to demonstrate the model's predictive ability under challenging conditions. The process deviations in regard to dilution steps and feed variations were detected immediately in the online prediction of the VCC with relative errors between 6.7 and 13.2%. Thus in summary, the presented method on capacitance frequency scanning demonstrates its suitability for process monitoring and control that can save batches, time, and cost. Graphical abstract.

Monitoring online biomass with a capacitance sensor during scale-up of industrially relevant CHO cell culture fed-batch processes in single-use bioreactors

In 2004, the FDA published a guideline to implement process analytical technologies (PAT) in biopharmaceutical processes for process monitoring to gain process understanding and for the control of important process parameters. Viable cell concentration (VCC) is one of the most important key performance indicator (KPI) during mammalian cell cultivation processes. Commonly, this is measured offline. In this work, we demonstrated the comparability and scalability of linear regression models derived from online capacitance measurements. The linear regressions were used to predict the VCC and other familiar offline biomass indicators, like the viable cell volume (VCV) and the wet cell weight (WCW), in two different industrially relevant CHO cell culture processes (Process A and Process B). Therefore, different single-use bioreactor scales (50–2000 L) were used to prove feasibility and scalability of the in-line sensor integration. Coefficient of determinations of 0.79 for Process A and 0.99 for Process B for the WCW were achieved. The VCV was described with high coefficients of determination of 0.96 (Process A) and 0.98 (Process B), respectively. In agreement with other work from the literature, the VCC was only described within the exponential growth phase, but resulting in excellent coefficients of determination of 0.99 (Process A) and 0.96 (Process B), respectively. Monitoring these KPIs online using linear regression models appeared to be scale-independent, enabled deeper process understanding (e.g. here demonstrated in monitoring, the feeding profile) and showed the potential of this method for process control.

Decision Tree-PLS (DT-PLS) algorithm for the development of process: Specific local prediction models

This work presents a novel multivariate statistical algorithm, Decision Tree-PLS (DT-PLS), to improve the prediction and understanding of dynamic processes based on local partial least square regression (PLSR) models for characteristic process groups defined based on Decision Tree (DT) analysis. The DT-PLS algorithm is successfully applied to two different cell culture data sets, one obtained from bioreactors of 3.5 L lab scale and the other obtained from the 15 ml ambr microbioreactor system. Substantial improvement in the predictive capabilities of the model can be achieved based on the localization compared to the classical PLSR approach, which is implemented in the commercially available packages. Additionally, the differences in the model parameters of the local models suggest that the governing process variables vary for the different process regimes indicating the different states of the cell under different process conditions.

Addressing the Manufacturing Challenges of Cell-Based Therapies

Exciting developments in the cell therapy field over the last decades have led to an increasing number of clinical trials and the first cell products receiving marketing authorization. In spite of substantial progress in the field, manufacturing of cell-based therapies presents multiple challenges that need to be addressed in order to assure the development of safe, efficacious, and cost-effective cell therapies.The manufacturing process of cell-based therapies generally requires tissue collection, cell isolation, culture and expansion (upstream processing), cell harvest, separation and purification (downstream processing), and, finally, product formulation and storage. Each one of these stages presents significant challenges that have been the focus of study over the years, leading to innovative and groundbreaking technological advances, as discussed throughout this chapter.Delivery of cell-based therapies relies on defining product targets while controlling process variable impact on cellular features. Moreover, commercial viability is a critical issue that has had damaging consequences for some therapies. Implementation of cost-effectiveness measures facilitates healthy process development, potentially being able to influence end product pricing.Although cell-based therapies represent a new level in bioprocessing complexity in every manufacturing stage, they also show unprecedented levels of therapeutic potential, already radically changing the landscape of medical care.

Process System Engineering Methodologies Applied to Tissue Development and Regenerative Medicine

Tissue engineering and the manufacturing of regenerative medicine products demand strict control over the production process and product quality monitoring. In this chapter, the application of process systems engineering (PSE) approaches in the production of cell-based products has been discussed. Mechanistic, empirical, continuum and discrete models are compared and their use in describing cellular phenomena is reviewed. In addition, model-based optimization strategies employed in the field of tissue engineering and regenerative medicine are discussed. An introduction to process control theory is given and the main applications of classical and advanced methods in cellular production processes are described. Finally, new nondestructive and noninvasive monitoring techniques have been reviewed, focusing on large-scale manufacturing systems for cell-based constructs and therapeutic products. The application of the PSE methodologies presented here offers a promising alternative to overcome the main challenges in manufacturing engineered tissue and regeneration products.

Host Cell Proteins in Biologics Manufacturing: The Good, the Bad, and the Ugly

Significant progress in the manufacturing of biopharmaceuticals has been made by increasing the overall titers in the USP (upstream processing) titers without raising the cost of the USP. In addition, the development of platform processes led to a higher process robustness. Despite or even due to those achievements, novel challenges are in sight. The higher upstream titers created more complex impurity profiles, both in mass and composition, demanding higher separation capacities and selectivity in downstream processing (DSP). This creates a major shift of costs from USP to DSP. In order to solve this issue, USP and DSP integration approaches can be developed and used for overall process optimization. This study focuses on the characterization and classification of host cell proteins (HCPs) in each unit operation of the DSP (i.e., aqueous two-phase extraction, integrated countercurrent chromatography). The results create a data-driven feedback to the USP, which will serve for media and process optimizations in order to reduce, or even eliminate nascent critical HCPs. This will improve separation efficiency and may lead to a quantitative process understanding. Different HCP species were classified by stringent criteria with regard to DSP separation parameters into “The Good, the Bad, and the Ugly” in terms of pI and MW using 2D-PAGE analysis depending on their positions on the gels. Those spots were identified using LC-MS/MS analysis. HCPs, which are especially difficult to remove and persistent throughout the DSP (i.e., “Bad” or “Ugly”), have to be evaluated by their ability to be separated. In this approach, HCPs, considered “Ugly,” represent proteins with a MW larger than 15 kDa and a pI between 7.30 and 9.30. “Bad” HCPs can likewise be classified using MW (>15 kDa) and pI (4.75–7.30 and 9.30–10.00). HCPs with a MW smaller than 15 kDa and a pI lower than 4.75 and higher than 10.00 are classified as “Good” since their physicochemical properties differ significantly from the product. In order to evaluate this classification scheme, it is of utmost importance to use orthogonal analytical methods such as IEX, HIC, and SEC.

Enhanced process understanding and multivariate prediction of the relationship between cell culture process and monoclonal antibody quality

This work investigates the insights and understanding which can be deduced from predictive process models for the product quality of a monoclonal antibody based on designed high-throughput cell culture experiments performed at milliliter (ambr-15^® ) scale. The investigated process conditions include various media supplements as well as pH and temperature shifts applied during the process. First, principal component analysis (PCA) is used to show the strong correlation characteristics among the product quality attributes including aggregates, fragments, charge variants, and glycans. Then, partial least square regression (PLS1 and PLS2) is applied to predict the product quality variables based on process information (one by one or simultaneously). The comparison of those two modeling techniques shows that a single (PLS2) model is capable of revealing the interrelationship of the process characteristics to the large set product quality variables. In order to show the dynamic evolution of the process predictability separate models are defined at different time points showing that several product quality attributes are mainly driven by the media composition and, hence, can be decently predicted from early on in the process, while others are strongly affected by process parameter changes during the process. Finally, by coupling the PLS2 models with a genetic algorithm first the model performance can be further improved and, most importantly, the interpretation of the large-dimensioned process-product-interrelationship can be significantly simplified. The generally applicable toolset presented in this case study provides a solid basis for decision making and process optimization throughout process development. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 33:1368-1380, 2017.

Novel approach for the prediction of cell densities and viability in standardized translucent cell culture biochips with near infrared spectroscopy

Near infrared spectroscopy is a rapid and nondestructive method for compositional analysis of biological material. The technology is widely used within bioreactors and possesses potential as a standardized method for quality control in miniaturized microfluidic cell culture systems. Here, we established a method for quantification of cell density and viability of adherent HepaRG cells cultured in a translucent, miniaturized cell culture biochip. The newly developed statistical models for interpretation of near infrared spectroscopy from biochips are the basis for a novel method of fast, continuous, and contact-free analysis of cell viability and real-time monitoring of cell growth. The technique thus paves the way for a robust and reliable high-throughput analysis of biochip-embedded cell cultures.