Real‐Time Monitoring of Quality Attributes by In‐Line Fourier Transform Infrared Spectroscopic Sensors at Ultrafiltration and Diafiltration of Bioprocess
Abstract
Technologies capable of monitoring product quality attributes and process parameters in real time are becoming popular due to the endorsement of regulatory agencies and also to support the agile development of biotherapeutic pipelines. The utility of vibrational spectroscopic techniques such as Fourier transform mid‐infrared (Mid‐IR) and multivariate data analysis (MVDA) models allows the prediction of multiple critical attributes simultaneously in real time. This study reports the use of Mid‐IR and MVDA model sensors for monitoring of multiple attributes (excipients and protein concentrations) in real time (measurement frequency of every 40 s) at ultrafiltration and diafiltration (UF/DF) unit operation of biologics manufacturing. The platform features integration of fiber optic Mid‐IR probe sensors to UF/DF set up at the bulk solution and through a flow cell at the retentate line followed by automated Mid‐IR data piping into a process monitoring software platform with pre‐loaded partial least square regression (PLS) chemometric models. Data visualization infrastructure is also built‐in to the platform so that upon automated PLS prediction of excipients and protein concentrations, the results were projected in a graphical or numerical format in real time. The Mid‐IR predicted concentrations of excipients and protein show excellent correlation with the offline measurements by traditional analytical methods. Absolute percent difference values between Mid‐IR predicted results and offline reference assay results were ≤5% across all the excipients and the protein of interest; which shows a great promise as a reliable process analytical technology tool.
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ATR-FTIR has been implemented in-line during the UFDF step to monitor protein concentration with a simple one-point calibration algorithm or both protein and various excipient concentrations with a multivariate data analysis model.
“Recently, ATR‐FTIR has also been implemented in‐line during the UFDF step, to monitor the protein concentration with a simple one‐point calibration algorithm [55] or both protein and various excipient concentrations, with a multi‐variate data analysis (MVDA) model [56].”
From Current PAT Landscape in the Downstream Processing of Biopharmaceuticals (2025) · cites this paper as [56]
In ATR-FTIR monitoring during UFDF, rapid measurements in seconds and accurate concentration predictions were achieved.
“In both cases, rapid measurements in seconds and accurate concentration predictions were achieved.”
From Current PAT Landscape in the Downstream Processing of Biopharmaceuticals (2025) · cites this paper as [56]
Cites
Biopharmaceutical benchmarks 2018
Walsh, G. (2018) · Nature Biotechnology · doi:10.1038/nbt.4305 · Cited by 978 · see citing works
Wasalathanthri, D. P., Rehmann, M. S., Song, Y., Gu, Y., Mi, L., Shao, C., Chemmalil, L., Lee, J., Ghose, S., Borys, M. C., et al. (2020) · Biotechnology and Bioengineering · doi:10.1002/bit.27461 · Cited by 177 · see citing works
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Challenges in the Development of Soft Sensors for Bioprocesses: A Critical Review
Brunner, V., Siegl, M., Geier, D., & Becker, T. (2021) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2021.722202 · Cited by 100 · see citing works
Current PAT Landscape in the Downstream Processing of Biopharmaceuticals
Sathiyapriyan, P., Mukherjee, S., Vogel, T., Essen, L., Boerema, D., Vey, M., & Kalina, U. (2025) · Analytical Science Advances · doi:10.1002/ansa.70013 · Cited by 45 · see citing works
- Authors
- Wasalathanthri, D. P., Feroz, H., Puri, N., Hung, J., Lane, G., Holstein, M., Chemmalil, L., Both, D., Ghose, S., Ding, J., & Li, Z. J.
- Affiliations
- Biologics Process Development Bristol Myers Squibb Company Devens Massachusetts
- Engineering Technologies Bristol Myers Squibb Company New Brunswick New Jersey
- Venue
- Biotechnology and Bioengineering
- Publisher
- Wiley
- Published
- 2020-08-18
- Volume
- 117
- Issue
- 12
- Pages
- 3766-3774
- ISSN
- 0006-3592
- 1097-0290
- Cited by
- 35
- References
- 2
- Language
- en
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