Near Infrared Spectroscopy as a Versatile PAT Tool for Continuous Downstream Bioprocessing

Process analytical technology (PAT) is an increasingly important aspect of biopharmaceutical manufacturing processes. FDA in 2004 described a regulatory framework for PAT and has been urging the industry to voluntarily develop innovative tools and techniques for quality control and assurance in biopharmaceutical manufacturing unit operations (1). The quality-by-design (QbD) framework encourages the use of PAT tools to “build quality into the process” rather than “testing quality into the product” (2). The importance of PAT tools is amplified in the case of continuous manufacturing processes, where individual batches are not well-defined and critical quality attributes (CQAs) of the therapeutic must be consistently maintained, monitored, and controlled over months of operation, rather than simply tested at the end of each unit operation or each batch (3–4). The need for real-time monitoring and control of CQAs and critical process parameters (CPPs) in continuous manufacturing has led to the adaptation of traditional end-of-batch quality testing techniques, such as analytical high-performance liquid chromatography (HPLC) and ultraviolet-280 (UV-280) spectroscopy, into in-line at-line modalities with periodic sampling from continuous unit operations (5–7). It has also spurred development of new analytical tools that provide information in near real-time and can be placed in-line in process flow streams.

In-line spectroscopic sensors using infrared, near-infrared, or Raman spectroscopy are one such new class of tools finding multiple PAT applications in bioprocessing (8). Spectroscopic PAT applications typically consist of in-line sensors combined with statistical or modeling methodology to monitor, control, and/or predict biotechnology processes. The key advantage of spectroscopy is the ability to gain information about the presence and quantity of multiple analytes in the process mixture simultaneously in a few seconds or less, overcoming a major analytical bottleneck and facilitating real-time control decisions (9). Spectroscopic techniques are also non-degradative and provide a wealth of spectral information that can be used in tracking process trajectories and flagging deviations (10,11). Thus, spectroscopic probes and flow cells are well-suited to PAT applications, particularly in tandem with multivariate data analytics (MVDA) tools, which are critical for reducing the dimensionality of large spectral datasets and extracting statistically significant quantitative information (12).

Table I summarizes the recent literature of the past five years on implementing spectroscopy-based analytical tools in downstream bioprocessing (13–29). The use of spectroscopic immersion probes as PAT tools in upstream microbial and mammalian bioreactors has been extensively reported in the recent literature for identification and quantification of proteins, by-products, and substrates (30–32). However, their use in downstream processing is a more recent development. Near infrared spectroscopy (NIRS), in particular, has been demonstrated to have a wide variety of potential applications in different downstream unit operations, including capture chromatography (24), protein PEGylation reactions (25), and tangential flow ultrafiltration (26). These three case studies demonstrate the versatility of NIRS as a PAT tool and showcase the common underlying framework of the different applications, namely the collection of real-time spectra followed by comparison with spectral calibration libraries using multivariate statistical techniques. NIRS is demonstrated to be a reliable tool for acquiring rapid quantitative information in a range of downstream bioprocesses, and a key enabler for real time control.

NIRS as a downstream PAT tool

Case study 1: NIRS for controlled loading. Protein A capture chromatography is typically the first downstream step in manufacturing of monoclonal antibodies (mAbs). One of the key difficulties in continuous Protein A chromatography is handling variability in the titer of the upstream material. The product titer is expected to change over time in the case of upstream perfusion cell culture systems (33). Even in the case of fed-batch processes, batch-to-batch variability necessitates flexibility in the downstream capture step (34). An increase or decrease in the protein concentration in the load stream affects the dynamic binding capacity of the Protein A resin, leading to changes in process performance (35). Over-loading the Protein A column with high-titer feed can lead to the loss of expensive mAb product, while under-loading decreases the resin utilization of the Protein A resin, one of the most expensive consumables that has been reported to contribute to up to 60% of the costs of the downstream processing train (36).

The NIRS-based PAT strategy shown in Figure 1 has been used to address this challenge of handling potential titer variability in continuous capture chromatography while maintaining resin utilization and preventing both under- and over-loading (24). NIRS flowthrough cells placed in the loading stream and the outlet of the load column were used to collect spectra of the harvest and column flowthrough every three seconds. These spectra were passed to online MVDA models calibrated with reference spectra to determine the concentration of mAb in the harvest and flowthrough to within ± 0.05 mg/mL. The real-time concentration data were used to calculate both the total mg of mAb loaded on the column as well as the percentage breakthrough from the column, and these were used to make control decisions to start and pause the loading in a three-column periodic counter current Protein A process on a continuous chromatography system (Cadence BioSMB, Pall). The normal operating range of the control system allowed for concentration variations in the harvest between 3–8 g/L, achieving optimal resin utilization as well as process scheduling within this range.

The NIRS-based PAT control strategy was tested by inducing a range of linear and non-linear deviations in the load titer in real time. The deviations were designed to closely simulate those which would potentially occur during a perfusion process. The NIR flow cells provided the advantage of monitoring not only loading but also changes in the column binding capacity in real time, which is useful for providing early warning of resin degradation or column quality issues and facilitating optimal loading without relying on adsorption isotherm models, which may not be valid after multiple cleaning cycles. The system allowed resin utilization to be maximized, lowering consumable costs by ensuring that the total protein processed per mg of resin was consistently maintained. The NIRS flow cells enabled online measurement of concentration and facilitated real-time control decisions for increased efficiency, flexibility, and agility of the continuous chromatography process in the face of unexpected deviations.

Case study 2: NIRS for monitoring/control. Protein modification with biocompatible polymers, such as polyethylene glycol (PEG), is often used to improve the pharmacological properties and stability of biotherapeutics (37). Manufacturing of PEGylated drugs requires a PEG conjugation reaction to be carried out on the purified drug substance at the end of the downstream train (38). Proper control of the PEGylation reaction is critical to maximize PEGylation efficiency while minimizing the presence of over-PEGylated variants. Various critical process parameters affect PEGylation quality, including pH, reaction time, and the concentration and order of addition of the reactants (39). In the present case study, the PEGylation reaction of recombinant human granulocyte colony stimulating factor (rh-GCSF) was considered. The challenge was to monitor the PEGylation process trajectory and reaction kinetics to flag potential deviations in real time, as well as to control the reaction quenching to optimize the production of the desired monoPEGylated variant before its further conversion into undesired diPEGylated and multiPEGylated forms.

A few different PAT tools have been explored in the literature for monitoring and control of PEGylation. Some researchers used at-line size exclusion chromatography to monitor the progress of the PEGylation reaction, though this was not suited for real-time control due to the long method run times of >30 minutes for an overall reaction time of one to two hours (40). Other researchers used at-line matrix assisted laser desorption ionization–time of flight (MALDI–TOF) mass spectrometry for tracking the kinetics of PEGylation, though the time scale of analysis was again too long at over one hour (41). The use of NIRS as a PAT tool in the present case study overcame the bottleneck of analysis time and was able to track the conversion of rhGCSF into its monoPEGylated and multiPEGylated forms on a time scale of a few seconds (25). The NIRS spectra were acquired every three seconds and compared against a calibrated MVDA regression model, built using control runs of the reaction with orthogonal HPLC-based quantification of the PEGylated variants. The spectra acquired during the control runs were also used to develop a multivariate batch evolution model of the ideal reaction trajectory. A flow chart of the NIRS-based PAT tool is shown in Figure 2.

The PAT tool was demonstrated in various control and deviated reaction runs in which online spectra were acquired by the NIRS immersion probe and used not only for quantification of the PEGylated variant, but also for comparison against the ideal reaction trajectory. This allowed process deviations to be identified and flagged in case a statistically significant difference was found between the trajectories of the current process versus the ideal one. A range of deviations were found to be identifiable by the PAT tool, including incorrect concentration of chemical additives, incorrect ratio of PEG to rhGCSF, incorrect order of addition of the reactants, and incorrect quenching time. The impact of each deviation on CQAs of the PEGylation reaction product was characterized, and each resulted in lower product purity and process yield. The NIRS probe was demonstrated to provide critical real-time information and form the basis of a robust PAT tool for monitoring and control of the PEGylation reaction of rhGCSF. The overall approach is generalizable to other downstream reaction steps, such as enzyme reactions or esterification.

Case study 3: NIRS for control of retentate concentration. Tangential flow ultrafiltration (UF) is a key unit operation in downstream processing of biotherapeutics, used for concentrating and buffer exchanging the in-process drug substance into the desired target concentration and formulation of the final drug product (42). This is typically the last unit operation prior to fill/finish and packaging. The concentration of the biotherapeutic in the final drug product is a CQA determined solely by this unit operation, necessitating robust control. In batch mode, concentration is achieved by recirculating the drug substance held in a large tank through the UF membrane until the desired volume reduction is achieved (43). However, batch-mode recirculation is not possible in the case of constantly incoming flow streams in continuous processing. The solution is to use single-pass UF in which the membrane module has a larger area and a long flow path, facilitating volume reduction of the feed stream in a single pass without the need for recirculation (44).

There are several operational challenges that must be overcome, however, to ensure that the retentate stream emerges from the single-pass tangential flow filtration (SPTFF) module consistently at the fixed target concentration. Reversible and irreversible membrane fouling as well as deviations in the concentration of the incoming flow stream from prior unit operations, such as polishing chromatography, lead to changes in the concentration factor achieved in a single pass (45,46). Therefore, a PAT strategy is needed to monitor the concentration of the incoming feed stream and control the flux across the membrane, to ensure that the retentate concentration does not vary over the course of the process. Researchers demonstrated a strategy leveraging in-line NIRS flow cells in the feed and retentate streams of an SPTFF module to make flux-based control decisions and ensure consistency in the retentate concentration, as shown in the schematic in Figure 3 (26).

The NIRS flow cells were able to measure the concentration of mAb in the range of 0.5–200 g/L, a significant improvement over UV-based quantification methods, using suitably calibrated spectral libraries. The control decisions were made on the basis of the real-time NIRS data as well as a pre-characterized design space for the limits of maximum flowrates and concentration factors achievable for a given feed concentration and flow rate within the pressure limits of the membrane module. The NIRS data were analyzed, and closed-loop control established with a permeate pump and retentate valve, allowing flux control on the time scale of <1 second. Control and scheduling decisions were made for integrating the SPTFF step with the rest of the continuous downstream train and for ensuring timely cleaning. The overall PAT strategy was demonstrated over two 12-hour case studies. The NIRS monitoring sensors enabled the development of a robust PAT control strategy to ensure that retentate concentration targets were consistently met over long continuous campaigns.

Summary

The need for rapid, non-degenerative, and robust analytical tools will continue to grow as the biotherapeutic industry embraces the PAT and QbD paradigms. Spectroscopic sensors are uniquely suited to fulfill this need as spectral information can be collected in a few seconds and analyzed in milliseconds using multivariate techniques. The spectra can yield a wealth of information about the in-process sample, including quantification of multiple analytes and the overall health of the process compared to an ideal trajectory. Spectroscopic applications in upstream microbial and mammalian cell cultures have grown exponentially in the past 10 years, and this can be expected to happen in the case of downstream unit operations in the near future. The rapidly growing interest in continuous downstream processes in both academia and industry will amplify this effect due to the unique real-time PAT control challenges that arise when batch-mode product quality testing is no longer an option. To truly bring spectroscopic applications into a manufacturing setting, however, the industry needs to gain confidence in the use of multivariate calibration models, which can only be done by having robust guidelines and frameworks for spectroscopic model calibration, validation, and periodic checking to account for shifts in spectroscopic sensor data over months or years. More fundamental studies are also needed to draw clear relationships between biotherapeutic CQAs and their effect on spectral features, as has been done in the case of generic pharmaceuticals. Finally, the ability of spectroscopic sensors to collect time-stamped spectral process signatures at high frequency over months or years is also a potential advantage that can be leveraged to create large-scale manufacturing datasets providing deep process history information, which can be of use to regulatory bodies.

References

1. FDA, Guidance for Industry, Q8 (R2) Pharmaceutical Development (CDER, CBER, November 2009).
2. A.S. Rathore and H. Winkle, Nat Biotechnol 27 (1) 26–34 (2009).
3. J.Glassey, et al., Biotechnology Journal 6 (4) 369–377 (2011).
4. A.S. Rathore, R. Bhambure, and V. Ghare, Anal Bioanal Chem 398 (1) 137–154 (2010).
5. A. Tiwari, et al., Anal Chem 90 (13) 7824–7829 (2018).
6. S. Huffman, K. Soni, and J. Ferraiolo, Bioprocess Int 12 (8) 66–72 (2014).
7. M. Jiang, et al., Biotechnol Bioeng 114 (11) 2445–2456 (2017).
8. K. A. Bakeev, Process Analytical Technology: Spectroscopic Tools and Implementation Strategies for the Chemical and Pharmaceutical Industries (John Wiley & Sons, West Sussex, UK, 2nd Ed. 2010).
9. M. Rüdt, T. Briskot, and J. Hubbuch, Journal of Chromatography A 1490, 2–9 (2017).
10. R.W Kessler, W. Kessler, and E. Zikulnig-Rusch, Chem Ing Tech 88 (6) 710–721 (2016).
11. J. ClaSSen, et al., Anal Bioanal Chem 409 (3) 651–666 (2017).
12. L. Eriksson, et al., Multi- and Megavariate Data Analysis: Basic Principles and Applications (MKS Umetrics AB, Malmö, Sweden, 2013).
13. N. Brestrich, et al., Biotechnology and Bioengineering 112 (7) 1406–1416 (2015).
14. S. Hansen, et al., “Mid-UV Protein Absorption Spectra and Partial Least Squares Regression as Screening and PAT Tool,” in Preparative Chromatography for Separation of Proteins, A. Staby, A.S. Rathore, and S. Ahuja, Eds. (John Wiley & Sons, 2017), pp. 501-536.
15. M. Rüdt, et al., Biotechnology and Bioengineering 114 (2) 368–373 (2017).
16. N. Brestrich, T. Hahn, and J. Hubbuch, Journal of Chromatography A 1437, 158–167 (2016).
17. M. Ghodbane, et al., “Analytical and Data Strategy for Continuous Downstream Manufacturing,” in Integrated Continuous Biomanufacturing IV, V. Warikoo, et al., Eds, ECI Symposium Series, 2019.
18. L. Rolinger, Analytical and Bioanalytical Chemistry 412 (9) 2123–2136 (2020).
19. S. Großhans, et al., Journal of Chromatography A 1547, 37–44 (2018).
20. A. Sanden, et al., Journal of Chromatography A 1608, 460410 (2019).
21. D.G. Sauer, et al., Biotechnology and Bioengineering 116 (8) 1999–2009 (2019).
22. C. Walther, Process Biochemistry 49 (7) 1113–1121 (2014).
23. N. Walch, et al., Biotechnology Journal 14, 1800521 (2019).
24. G. Thakur, V. Hebbi, and A. S. Rathore, Biotechnology and Bioengineering, 117 (3) 673-686 (2020).
25. V. Hebbi, G. Thakur, and A. S. Rathore, Journal of Biotechnology 325 (10) 303–311 (2020).
26. G. Thakur, S. Thori, and A. S. Rathore, Journal of Membrane Science, 613 (1) 118492 (2020).
27. F. Feidl, et al., Biotechnol. Prog. 35 (5) e2847 (2019).
28. S. Goldrick, et al., Processes 8 (9) 1179 (2020).
29. D. Yilmaz, et al., Biotechnol Progress 36 (3) e2947 (2020).
30. S.M. Mercier, et al., Eng. Life Sci. 16 (1) 25–35 (2015).
31. N.R. Abu-Absi, et al., Pharmaceutical Bioprocessing 2 (3) 267–284 (2014).
32. J. Claßen, et al., Anal. Bioanal. Chem. 409 (3) 651–666 (2017).
33. L. Chemmalil, et al., Biotechnology and Bioengineering 17 (12) 3757–3765 (2020).
34. F. Li, A. Shen, and A. Amanullah, Pharmaceutical Sciences Encyclopedia: Drug Discovery, Development, and Manufacturing 2, 1–38 (2010).
35. S. Hober, K. Nord, M. Linhult, Journal of Chromatography B 848 (1) 40–47 (2007).
36. A. Rathore, et al., “Re-use of Protein A Resin: Fouling and Economics,” BioPharm International 28 (3) 2015.
37. D. Pfister and M. Morbidelli, Journal of Controlled Release 180, 134–149 (2014).
38. S. Jevsevar, M. Kunstelj, and V.G. Porekar, Biotechnol. J. 5 (1) 113–128 (2010).
39. R. Shekhawat, et al., PLoS ONE 14 (3) e0212622 (2019).
40. J. Behi, et al., International Journal of Biological Macromolecules 109, 888–895 (2018).
41. D.H. Na, Y.S. Youn, and K.C. Lee, Rapid Commun. Mass Spectrom. 17 (19) 2241–2244 (2003).
42. H. Lutz, Ultrafiltration for Bioprocessing, (Woodhead Publishing, Cambridge, UK, 2015).
43. L.J. Zeman, and A.L. Zydney, Microfiltration and Ultrafiltration: Principles and Applications (CRC Press, Boca Raton, FL, 2017).
44. E. Thomas, et al., “Application of Single Pass TFF to Enable Intensified and Continuous Biological Manufacturing,” in Integrated Continuous Biomanufacturing III (ECI Symposium Series, 2017) pp. 1.
45. C. Casey, et al., Journal of Membrane Science 384, 82–88 (2011).
46. A. Arunkumar, et al., Journal of Membrane Science 524, 20–32 (2017).

About the authors

Garima Thakur is a graduate student in the Department of Chemical Engineering at the Indian Institute of Technology New Delhi, and Anurag S. Rathore*, asrathore@biotechcmz.com, is a professor in the Department of Chemical Engineering at the Indian Institute of Technology New Delhi and a member of BioPharm International’s Editorial Advisory Board.

*To whom all correspondence should be addressed.

Article Details

Pharmaceutical Technology
Vol. 45, No. 3
March 2021
Pages: 32–40

Citation

When referring to this article, please cite it as G. Thakur and A.S. Rathore, “Near Infrared Spectroscopy as a Versatile PAT Tool for Continuous Downstream Bioprocessing,” Pharmaceutical Technology 45 (3) 2021.