Process Analytical Technology for Upstream Bioprocessing

Summary
In commercial cell culture bioprocessing, consistent high quality protein is a fundamental goal that is typically accomplished during development through product and process engineering of bioreactor parameters. The FDA’s Center for Drug Evaluation and Research (CDER)’s Office of Biotechnology Products’ upstream bioprocessing laboratory, a part of the Office of Pharmaceutical Quality’s Center of Excellence (COE) in Manufacturing Science and Innovation, studies Process Analytical Technology (PAT) for upstream bioprocessing, focusing on the production of monoclonal antibodies. These capabilities are being leveraged to study continuous bioreactor cell culture production and compatible PAT tools. Case studies are presented that illustrate collaborative laboratory research being conducted on PAT tools for upstream bioprocessing to support regulatory decision making.

Introduction
Most biotechnology pharmaceuticals are glycosylated proteins produced in bioreactor cell culture that are subsequently purified into drug substance prior to formulation and fill/finish into a final dosage form. In the case of manufacturing of a therapeutic monoclonal antibody (mAb), post-translational modifications during cell culture can significantly affect stability and potency. To evaluate multiple critical quality attributes (CQAs), the final product is tested offline by advanced biochemical methods such as high performance liquid chromatography (HPLC), bioassays, and mass spectrometry (MS). Ideally, process conditions and CQAs would be monitored in real time during upstream bioprocessing to enable changes to the process that ensure resulting product quality.
PAT, initially developed in the petrochemical industry, involves real-time monitoring and control of product quality during manufacture. To apply this concept to biotechnology, we are using a model cell culture with a focused PAT strategy to demonstrate if culture manipulations, either as a control strategy or through unanticipated process conditions, can cause product variations that would not be detected in a timely manner by a lot release program that uses routine biochemical testing1, 2. In CDER’s Office of Biotechnology Products’ upstream bioprocessing laboratory, one of our goals is to facilitate PAT approaches to bioreactors through showcasing enhanced cell culture process knowledge, statistical modeling, and predictive linkage to biomolecule CQAs for an in-house model antibody bioprocess3. The recent establishment of the COE in Manufacturing Science and Innovation within FDA’s Office of Pharmaceutical Quality embodies CDER’s mission to ensure that regulatory decisions are founded on regulatory science4. The COE aims to advance science critical for drug product development, manufacturing, and regulatory drug evaluation. For our upstream bioprocessing team, this aim translates to the development of internal capabilities for identifying potential PAT failure modes to foster greater understanding of complex analytics, spectroscopic methods, and modeling, in order to support regulatory submissions and review.
Recently, we completed the integration and application of direct measurement analytics to our bioreactors, including a biochemical nutrient analyzer and a real-time glucose/lactate monitor, as well as dielectric spectroscopy probes, which require a routine regression model for analysis5, 6. These provide a platform for the generation of large orthogonal data sets in a single run due to the increased sample density and scope of measurements made. In order to set the framework for exerting PAT control in bioprocessing, we analyzed bioreactor engineering variables, cell culture parameters and product quality attributes7. In our initial attempts to implement PAT, a model IgG3-producing hybridoma cell line grown in a serum-free bioreactor culture was developed as a model system for study of fermentanomics8.
In order to further identify factors that can impact important glycoforms, we next performed a design of experiments (DoE) study to screen product quality impacts of eleven commercially relevant culture parameters9. These studies found that cell culture pH shifts can lower fucosylation, an antibody modification important for antibody-dependent cell-mediated cytotoxicity (ADCC) effector function. In a follow-up study, we observed linkages between cell culture process parameters, downstream capture chromatography performance, and subsequent antibody attributes, providing insights into upstream impacts on downstream antibody quality10.
To more closely model the biopharmaceutical industry and increase our overall IgG production yield, we have now switched to an improved, more commercially relevant model system that uses a Chinese hamster ovary (CHO) cell line. Our advanced high-throughput micro-bioreactor system was used to optimize cell growth conditions and IgG titer11. The new CHO cell line produces high quality antibody in greater quantities to support more comprehensive characterization of CQAs through cell culture utilizing PAT tools. Additionally, we aim to further understand the gaps and limitations of our PAT tools with regard to drift of the results over time and plan to study the use of multiple orthogonal tools to potentially create a multi-layered control strategy. Traditionally, sensors provide communication from the bioreactor, and information flows between the sensor and a control unit. As we move forward, the use of redundant and orthogonal sensors will become important for communication among themselves, thereby creating automated external calibrations and providing redundancy in case of probe failures or drift. This is especially important for probes that monitor electrochemical reactions (i.e. pH, dissolved oxygen, etc.)12. Evaluation of multiple tools is important for developing chemometric models quickly and for understanding the potential for greater automation and control. Despite cellular biology being a significant source of process variability, the characterization of cells’ internal metabolic state requires measurement of nutrient and metabolic byproduct concentrations that are traditionally obtained offline. Bringing these measurements into real time should reduce this variability and allow for greater process control. Here, we present two case studies on PAT tools for upstream bioprocessing that aim to further our understanding of real-time process monitoring and control in support of regulatory assessment.

Case Study 1: Automated real-time nutrient analysis
The concentrations of key nutrients in bioreactors affect not only the cell growth rate and viability, but also mAb yield and CQAs like glycosylation and glycation. Thus, optimized, well-controlled feeding strategies can be used to maintain quality and improve yield. The standard approach to analyzing media composition involves several types of offline measurements: ion-specific electrodes for ammonia, sodium and potassium concentrations, amperometric electrodes with immobilized enzymes for glucose, lactate, glutamine and glutamate levels, and trypan blue and digital imaging for viable cell density, viability, and cell size. While the sample preparation and operator expertise necessary for these approaches are minimal, they provide a limited scope of analyte information and depend on external calibrations.
Monitoring nutrients in real-time creates the potential for feed-forward control such that key nutrient ranges and feeds identified as critical process parameters (CPPs) can be maintained for the entirety of the process. Additionally, as instruments are internally calibrated and can be run in dialysis mode, no bioreactor volume is lost to sampling.
We compared glucose measurements using our standard offline approach to data from automated, real-time glucose analysis for three bioreactors with different feeding strategies (Table 1).

As expected, the fed-batch cultures performed better, reaching higher, more productive cell densities. The trends of the online glucose analyses closely followed the offline analyses (Figure 1).

However, the online analyses provided real-time data that could be used as a tool for feeding decisions. For example, at 96 hours post-inoculation, the fed-batch bioreactor being supplemented with glutamine (Gln) and non-essential amino acids (NEAA) reached approximately 1 g/L of glucose. Had a feed-forward control system been in place, glucose could have been supplemented or fresh media could have been perfused to maintain the glucose concentration at 1 g/L. Instead, the glucose was depleted, and cell viability plummeted over the subsequent 24 hours.

Case Study 2: Dielectric spectroscopy for inline monitoring of viable cell density
Traditionally, culture samples are removed aseptically from the culture on a regular basis to measure cell density and viability using trypan blue dye exclusion and microscopy or an automated cell counter. Even when using sophisticated cell counting equipment such as an at-line biochemical and cell viability analyzer, the software may not reliably decipher individual cells if the cells clump during high density culturing. Inline monitoring of viable cell density using a spectroscopy approach provides a real-time data stream and eliminates the issues that come along with trypan blue dye exclusion-based cell counting. In dielectric spectroscopy (DS), the ability of cells to store electrical charge is measured as a function of the frequency of the applied electrical field using a sterile probe inserted into the bioreactor. Live cells have intact membranes which act as a dielectric material when subjected to an electric field, causing the cells to behave as tiny capacitors. Thus, the measured capacitance is proportional to the density of viable cells in the bioreactor. Real-time cell growth data are crucial for moving towards feed-forward control in order to maintain cell densities at desired high concentrations. This type of tight control around a cell density set point is important when a bioreactor process is being used with perfusion technologies for continuous manufacturing.
In order to compare DS to offline strategies for monitoring cell growth, we ran three bioreactors connected to DS probes in batch, fed-batch, and perfusion modes. Samples were removed twice a day for offline measurements of cell density on a cell culture analyzer. While the overall trend was similar between the real-time DS measurements and the offline cell counts, the DS data revealed subtle changes and potential cellular growth signals that were not readily captured using offline measurements alone (Figure 2).

Conclusion
PAT tools are growing in their utilization for upstream bioprocessing and are being expanded to include more complex analytical tools that can assist in protein quality assessment. Here, we described two case studies of scale down model bioprocesses demonstrating the use of multiple tools for real-time monitoring of bioreactor parameters that have the potential for feedback/feedforward control of nutrient additions, media perfusion, and cell density. Future studies will include development of spectroscopy models that require offline data and orthogonal PAT tools providing high sample density in order to build advanced chemometric model development-based control strategies for real-time monitoring and control of bioreactor performance.

Acknowledgements
The authors would like to thank Brittany Chavez, Nicholas Trunfio, and Sai-Rashmika Velugula for the data and input they provided, and Kurt Brorson, Timothy Wadkins and Ramesh Venna for critical reading of the work. Partial internal funding and support for this work was provided by the CDER Critical Path Program (CA #16-13). This project was supported in part by an appointment to the Internship/Research Participation Program at the Office of Biotechnology Products, U.S. Food and Drug Administration, administered by the Oak Ridge Institute for Science and Education through an interagency agreement between the U.S. Department of Energy and FDA.

References
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9. Agarabi, C.D., Chavez, B.K., Lute, S.C., Read, E.K., Rogstad, S., Awotwe-Otoo, D., Brown, M.R., Boyne, M.T. 2nd, Brorson, K.A.   Exploring the linkage between cell culture process parameters and downstream processing utilizing a plackett-burman design for a model monoclonal antibody. Biotechnology Progress. 2017; 33(1): 163-170.
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Disclaimer: This publication reflects the views of the author and should not be construed to represent FDA’s views or policies.

 

Author Biographies

Erica Fratz-Berilla leads the cell culture team of upstream bioprocessing within FDA CDER’s Office of Biotechnology Products, Division of Biotechnology Review and Research II. Previously, she was an ORISE fellow in FDA CDER’s Office of Testing and Research working in upstream and downstream bioprocessing. She has experience in cell culture, small-scale bioreactor operation, perfusion technologies, and protein purification. Erica has a B.S. in biochemistry from Lehigh University and a Ph.D. in Medical Sciences from University of South Florida.

LCDR Agarabi is a Regulatory Research Officer and Principal Investigator in CDER’s Division of Biotechnology Review and Research II in the Office of Biotechnology. He received is PharmD, M.S. in Manufacturing Systems Engineering, MS & PhD in Pharmaceutical Sciences from the University of Rhode Island and an MBA from Georgetown University. Prior to joining the FDA, he worked in industrial biotechnology and small molecule pharmaceutical manufacturing. At the FDA, he is responsible for leading an applied laboratory based research program and conducting regulatory review of biotechnology submissions. His work supports of the agency’s regulatory review, policy development and compliance activities. He has received internal funding for multiple competitive grant proposals and won several awards for bioprocessing. LCDR Agarabi has authored over 30 peer reviewed journal articles, a book chapter, over 20 poster presentations, and given numerous domestic and international presentations on bioprocessing and manufacturing.

 



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