In-line product quality monitoring during biopharmaceutical manufacturing using computational Raman spectroscopy
The implementation of process analytical technologies is positioned to play a critical role in advancing biopharmaceutical manufacturing by simultaneously resolving clinical, regulatory, and cost challenges. Raman spectroscopy is emerging as a key technology enabling in-line product quality monitoring, but laborious calibration and computational modeling efforts limit the widespread application of this promising technology. In this study, we demonstrate new capabilities for measuring product aggregation and fragmentation in real-time during a bioprocess intended for clinical manufacturing by applying hardware automation and machine learning data analysis methods. We reduced the effort needed to calibrate and validate multiple critical quality attribute models by integrating existing workflows into one robotic system. The increased data throughput resulting from this system allowed us to train calibration models that demonstrate accurate product quality measurements every 38 s. In-process analytics enable advanced process understanding in the short-term and will lead ultimately to controlled bioprocesses that can both safeguard and take necessary actions that guarantee consistent product quality.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:15 |
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| Enthalten in: |
mAbs - 15(2023), 1 vom: 23. Jan., Seite 2220149 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Wang, Jiarui [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Completed 09.06.2023 Date Revised 12.06.2023 published: Print Citation Status MEDLINE |
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| doi: |
10.1080/19420862.2023.2220149 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM357906853 |
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| 520 | |a The implementation of process analytical technologies is positioned to play a critical role in advancing biopharmaceutical manufacturing by simultaneously resolving clinical, regulatory, and cost challenges. Raman spectroscopy is emerging as a key technology enabling in-line product quality monitoring, but laborious calibration and computational modeling efforts limit the widespread application of this promising technology. In this study, we demonstrate new capabilities for measuring product aggregation and fragmentation in real-time during a bioprocess intended for clinical manufacturing by applying hardware automation and machine learning data analysis methods. We reduced the effort needed to calibrate and validate multiple critical quality attribute models by integrating existing workflows into one robotic system. The increased data throughput resulting from this system allowed us to train calibration models that demonstrate accurate product quality measurements every 38 s. In-process analytics enable advanced process understanding in the short-term and will lead ultimately to controlled bioprocesses that can both safeguard and take necessary actions that guarantee consistent product quality | ||
| 650 | 4 | |a Journal Article | |
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| 700 | 1 | |a Wang, Gang |e verfasserin |4 aut | |
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