In-line product quality monitoring during biopharmaceutical manufacturing using computational Raman spectroscopy

ABSTRACT

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.

KEYWORDS:

• automation
• clinical manufacturing
• high throughput process development
• liquid handling robotics
• machine learning
• process analytical technology
• Raman spectroscopy

Abbreviations

CHO	=	Chinese hamster ovary
CNN	=	Convolution neural network
CV	=	Column volumes
DLS	=	Dynamic light scattering
GMP	=	Good manufacturing practices
HCCF	=	Harvested cell culture fluid
HCP	=	Host cell protein
HIC	=	Hydrophobic interaction chromatography
HMW	=	High molecular weight
IEX	=	Ion-exchange chromatography
KNN	=	k-Nearest Neighbor
LMW	=	Low molecular weight
mAbs	=	Monoclonal antibodies
MAE	=	Mean absolute error
NMR	=	Nuclear magnetic resonance
PAT	=	Process analytical technologies
PCR	=	Principal component regressor
PLS	=	Partial least squares
SVR	=	Support vector regressor
UFDF	=	Ultrafiltration/diafiltration
UPSEC	=	Ultra-performance liquid size exclusion chromatography

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request and require legal agreements prior to sharing.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/19420862.2023.2220149

Additional information

Funding

This work was funded by Boehringer Ingelheim Pharma GmbH & Co. KG.