ArticlemAbs
In-line product quality monitoring during biopharmaceutical manufacturing using computational Raman spectroscopy.
Article in mAbs. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- Automated high-throughput Raman detection of fluidic samples: measurement setup and methods preventing air bubble interference.Analytical and bioanalytical chemistry · 2026Article
- Raman Imaging-based Analysis of Drug Powder Dissolution on Calu-3 Cell Monolayers as a Mechanistic Predictor of Nasal Absorption.Pharmaceutical research · 2026Article
- A Review on Quantitative Process Analytical Technology for Continuous Downstream Processing of Monoclonal Antibodies.Biotechnology and bioengineering · 2026Review
- A Setup for Automatic Raman Measurements in High-Throughput Experimentation.Biotechnology and bioengineering · 2025Article
- Advances in Modeling the Inner Blood-Retinal Barrier: From Static Tissue Cell Cultures to Microphysiological Systems.Pharmaceuticals (Basel, Switzerland) · 2025Review
- Current PAT Landscape in the Downstream Processing of Biopharmaceuticals.Analytical science advances · 2025Review
- Raman-based PAT for multi-attribute monitoring during VLP recovery by dual-stage CFF: attribute-specific spectral preprocessing for model transfer.Frontiers in bioengineering and biotechnology · 2025Article
- Raman-based PAT for VLP precipitation: systematic data diversification and preprocessing pipeline identification.Frontiers in bioengineering and biotechnology · 2024Article
- Generative data augmentation and automated optimization of convolutional neural networks for process monitoring.Frontiers in bioengineering and biotechnology · 2024Article
- Artificial intelligence and machine learning-assisted digital applications for biopharmaceutical manufacturing.Biotechnology progressReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.