PAT – Advancing innovation, efficiency and control in biopharma manufacturing

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WHAT IS PAT?
Process Analytical Technology (PAT) is a regulatory framework defined by the Food and Drug Administration (FDA) with the main intention to foster innovation and encourage the implementation of more efficient approaches in pharmaceutical development, manufacturing and quality assurance (1). The aim is to build quality into the products instead of testing the quality of the product at the end of the process, thus introducing the principle of Quality by Design (QbD) – a science-driven, risk-based approach supported also by the International Council for Harmonization (ICH) (2-6).

 

The application of PAT opens opportunities for a deeper process understanding and the development of dynamic manufacturing processes with real-time monitoring, real-time assurance, and real-time release. Key to PAT implementation is the identification of critical material attributes (CMAs) and critical process parameters (CPPs) that impact critical quality attributes (CQAs). The development of appropriate analytical methods to monitor sources of variability allows for real-time control of the process, maintaining the desired product quality within a defined design space.

OVERVIEW OF PAT TECHNOLOGIES
In order to derive the appropriate conclusions, it’s essential to select the right PAT technology, identify relevant CMAs, CPPs, and CQAs, and define suitable strategies for data analysis, control, and process optimization. Whether monitoring happens in-line (meaning the sample is analyzed directly in the process), on-line (the sample is diverted from the process, analyzed and returned to the process), at-line (the sample is removed from the process for analysis in a near-by lab), or off-line (the sample is removed from the process for analysis in a third-party lab) strongly depends on the process itself and the selected analysis method. However, there is a strong trend for in-line monitoring as it allows a more dynamic, real-time analysis and fastest response times, especially in comparison to off-line measurements that would result in a significantly delayed response.
A variety of in-line, non-destructive analytical technologies exist including infrared spectroscopy (IR), dynamic light scattering (DLS), and Raman spectroscopy (7).

Application of PAT in biopharmaceutical manufacturing
While PAT is implemented quite broadly in small molecule manufacturing processes, adoption in the biopharmaceutical sector is progressing, but at a much slower pace. A specific challenge is coping with the complexity of the overall process and understanding the very dynamic environment of biological reactions, genetic regulations, potential cell-cell interactions, as well as the wide variety of cell lines, media compositions, and intermediates. PAT implementation in bioprocesses offers significant value because measurements can take place more frequently, changes can be monitored in real time, and control through feedback loops can be much faster and more efficient than in traditional processes without PAT.
Currently, PAT is mostly used to monitor CPPs in upstream cell culture or microbial fermentation processes. More traditional sensor measurements in bioreactors include the measurement of pH, dissolved oxygen (DO), carbon dioxide, temperature, pressure, and capacitance. More advanced spectroscopic or chromatographic methods have been developed to perform in-line or at-line measurements of additional parameters such as metabolites or intermediates, host cell proteins (HCPs), nutrients, cell density, cell viability, aggregates, particulates, and product concentration or the simultaneous measurement of multiple analytes.
While used to monitor batch processes, PAT is also an increasingly important tool in process intensification of semi-continuous and continuous (perfusion) upstream processes.

Zooming in on Raman
Raman spectroscopy is an optical analytical technique utilizing the so-called Raman effect, an inelastic scattering of photons induced by irradiation with a laser (8). In essence, it is possible to identify molecules through a unique molecular fingerprint (7, 9).
Benefits of Raman include its high molecular specificity, high reproducibility, and suitability for continuous in-line measurements through the insertion of a probe into the process. In addition, Raman can be used to monitor multiple parameters simultaneously. In contrast to other analytical technologies such as IR, there is low interference of water as it is a weak Raman scatterer. These benefits show clearly why Raman is so well-suited as a PAT technology in biopharmaceutical manufacturing: It allows for a highly specific quantitative analysis of complex, aqueous bioprocess solutions in-line and in real time (9).
While the primary application of Raman technology in the biopharmaceutical sector today is in process development, there is a clear trend towards broader implementation in GMP manufacturing. This is why it is important to select models that are scalable and transferable between instruments as lab-scale instruments might not meet the requirements for a GMP, manufacturing-scale environment. Ideally, the use of a GMP-ready PAT Raman analyzer should be considered directly from the start, coupled with a software solution that includes characteristics such as an audit trail and record management, and that complies with GAMP 5 and GMP requirements.

Raman in upstream and downstream processes
In the biopharmaceutical industry, Raman is predominantly applied for monitoring and control of upstream processes, typically directly in-line in bioreactors. Raman-monitored parameters include viable cell density, lactate, amino acids, glucose, glutamine, glutamate, ammonium, pH or cell volume.(7, 10) The development of more sophisticated models allows for a more advanced use for automated and feedback-based process control and quantitative measurements, such as Raman-based automated feed through glucose and amino acid control, and the monitoring of product quality attributes in cell culture, such as glycosylation (11, 12). While Raman spectroscopy is not yet broadly applied in downstream processes today, it brings significant value for process optimization through measurement of e.g. product aggregation, membrane fouling, or precipitation (13).

Raman: Challenges and Opportunities
Raman offers tremendous advantages to monitor and control bioprocesses in real time. It can be applied throughout the entire process, from material identification to upstream and downstream process steps to release testing. Because Raman spectroscopy can be performed in-line and in real time, changes and outliers can be identified quickly, triggering appropriate process control strategies, and resulting in a timely and more efficient process management.
Key limiting factors of Raman are its sensitivity as a sufficient analyte concentration and variation of the concentration are required for quantitative analysis and the detection of changes over time via multivariate modeling. A reference database is mandatory to be able to match the analysis result with the corresponding molecular fingerprint. The initial effort to establish the right process and metrics, as well as a suitable model is relatively high – but this is true for other PAT technologies as well. Extraction of quantitative information is rather complex and, again, strongly dependent on a robust model.
However, for most applications in biopharmaceutical manufacturing, especially in the upstream space, Raman has proven its suitability – even for challenging tasks such as identifying specific metabolites or monitoring the glycoform distribution – and support exists in the form of specialized chemometric experts, reducing the initial effort for implementation.

 

PAT and the facility of the future
The use of PAT tools sets the foundation not only for a thorough understanding and control of the process, but also for a seamless transition to Biopharma 4.0 realizing fully automated, self-monitoring and autonomously regulated, continuous bioprocesses in a facility of the future. Such a set-up will collect real-time data and make appropriate adjustments across the entire process, e.g. by adjusting downstream methods according to the measured CPPs in the upstream process step. In short, implementation of the PAT framework is the basis of a dynamic, future-ready, and more efficient manufacturing process.

However, the adoption pace in the biopharma sector is relatively slow, primarily due to specific challenges that must be addressed on the path toward the facility of the future. Overcoming these challenges requires transitioning existing analytical technologies to in-line or on-line operations. This involves developing fit-for-purpose solutions that are compatible with the PAT framework and complex biopharmaceutical processes. For example, additional development in the area of real-time product release and biosafety analysis is particularly crucial to reduce the lag times associated with cell-based assays or bioburden, mycoplasma, and endotoxin testing.
Furthermore, establishing appropriate analytical techniques and suitable methods and models for data evaluation is key. Limited process understanding and a lack of expertise in continuous processes or PAT models can pose challenges during PAT implementation in biomanufacturing sites, leading to hesitation in relying on PAT for integrity or release testing.
In this context, suppliers of PAT analytical instruments become invaluable partners to biopharmaceutical manufacturers. Leveraging their profound understanding of process needs and analytical capabilities, these suppliers can offer support by providing or developing appropriate models, thereby guiding and expediting PAT adoption. Having access to chemometric experts makes PAT implementation significantly faster and more convenient, especially if the support extends to streamlining the entire process.

 

REFERENCES AND NOTES

1. Food and Drug Administration (FDA). Pharmaceutical Quality for the 21st Century – A Risk-Based Approach Progress Report. 2007.
2. International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use (ICH). ICH Harmonised Tripartite Guideline: Pharmaceutical Development Q8(R2). 2009.
3. International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use (ICH). ICH Harmonised Tripartite Guideline: Pharmaceutical Quality System Q10. 2008.
4. International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use (ICH). ICH Harmonised Guideline: Quality Risk Management Q9(R1). 2023.
5. International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use (ICH). ICH Harmonized Guideline: Continuous Manufacturing of Drug Substances and Drug Products Q13. 2022.
6. Food and Drug Administration (FDA). Guidance for Industry: PAT — A Framework for Innovative Pharmaceutical Development, Manufacturing, and Quality Assurance. September 2004.
7. Gerzon G, Sheng Y, Kirkitadze M. Process Analytical Technologies – Advances in bioprocess integration and future perspectives. Journal of Pharmaceutical and Biomedical Analysis. 2022;207:114379.
8. De Beer T, Burggraeve A, Fonteyne M, Saerens L, Remon JP, Vervaet C. Near infrared and Raman spectroscopy for the in-process monitoring of pharmaceutical production processes. International Journal of Pharmaceutics. 2011;417(1):32-47.
9. Esmonde-White KA, Cuellar M, Lewis IR. The role of Raman spectroscopy in biopharmaceuticals from development to manufacturing. Analytical and bioanalytical chemistry. 2022;414(2):969-91.
10. Esmonde-White KA, Cuellar M, Lewis IR. The role of Raman spectroscopy in biopharmaceuticals from development to manufacturing. Analytical and bioanalytical chemistry. 2022;414(2):969-91.
11. Schwarz H, Mäkinen ME, Castan A, Chotteau V. Monitoring of amino acids and antibody N-glycosylation in high cell density perfusion culture based on Raman spectroscopy. Biochemical Engineering Journal. 2022;182:108426.
12. Webster TA, Hadley BC, Dickson M, Busa JK, Jaques C, Mason C. Feedback control of two supplemental feeds during fed-batch culture on a platform process using inline Raman models for glucose and phenylalanine concentration. Bioprocess and Biosystems Engineering. 2021;44(1):127-40.
13. Lin YK, Leong HY, Ling TC, Lin D-Q, Yao S-J. Raman spectroscopy as process analytical tool in downstream processing of biotechnology. Chinese Journal of Chemical Engineering. 2021;30:204-11.