Evidence map›Paper›PMID 42374623›Full record

ArticleBiotechnology progress

Advanced glucose control strategies leveraging Raman spectroscopy for optimized mammalian cell culture manufacturing.

Matthew Banner, Daniel Ray, Irfan Ahanger, Fabio Zurlo, Noah Hitchcock, Rajesh Mistry, Kasia Kozakowska-McDonnell, Matthew Cheeks, Richard Turner, Jonathan Welsh and 3 more

Abstract read
In one paragraph

Article in Biotechnology progress. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Matthew BannerThe Advanced Centre of Biochemical Engineering, Department of Biochemical Engineering, University College London, London, UK.ORCID https://orcid.org/0009-0008-9134-8366
Daniel RayAnalytical Sciences, Biopharmaceutical Development, R&D, AstraZeneca, Cambridge, UK.
Irfan AhangerCell Culture and Fermentation Sciences, Biopharmaceutical Development, R&D, AstraZeneca, Cambridge, UK.
Fabio ZurloCell Culture and Fermentation Sciences, Biopharmaceutical Development, R&D, AstraZeneca, Cambridge, UK.
Noah HitchcockCell Culture and Fermentation Sciences, Biopharmaceutical Development, R&D, AstraZeneca, Cambridge, UK.
Rajesh MistryCell Culture and Fermentation Sciences, Biopharmaceutical Development, R&D, AstraZeneca, Cambridge, UK.
Kasia Kozakowska-McDonnellAnalytical Sciences, Biopharmaceutical Development, R&D, AstraZeneca, Cambridge, UK.
Matthew CheeksCell Culture and Fermentation Sciences, Biopharmaceutical Development, R&D, AstraZeneca, Cambridge, UK.
Richard TurnerCell Culture and Fermentation Sciences, Biopharmaceutical Development, R&D, AstraZeneca, Cambridge, UK.
Jonathan WelshCell Culture and Fermentation Sciences, Biopharmaceutical Development, R&D, AstraZeneca, Cambridge, UK.
Suzanne S FaridThe Advanced Centre of Biochemical Engineering, Department of Biochemical Engineering, University College London, London, UK.ORCID https://orcid.org/0000-0001-8155-0538
Michael ThomasThe Advanced Centre of Biochemical Engineering, Department of Biochemical Engineering, University College London, London, UK.
Stephen GoldrickThe Advanced Centre of Biochemical Engineering, Department of Biochemical Engineering, University College London, London, UK.ORCID https://orcid.org/0000-0002-8319-1679

Funding

Biotechnology and Biological Sciences Research Council BB/V509607/1
6 · The paper itself

Abstract

Maintaining consistent quality in the manufacturing of biotherapeutic proteins in mammalian cell culture is challenging, with unplanned deviations causing inconsistencies and potential batch failure. Current methods for monitoring and controlling critical process parameters (CPPs) rely on slow, labor-intensive offline analyses. This is particularly problematic in upstream manufacturing, where infrequent measurements hinder real-time CPP monitoring and result in suboptimal control. This study developed and implemented a robust, standardized framework that integrates Raman spectroscopy with real-time machine learning models and bioreactor control via OPC-UA, enabling seamless communication and effective control of mammalian cell culture CPPs. High-accuracy ML models (R

Indexed as

Cell Culture TechniquesGlucoseSpectrum Analysis, RamanAnimalsBioreactorsCHO CellsCricetulusMachine LearningGlucosebiotherapeuticsmachine learningmammalian cell cultureprocess analytical technologyprocess controlRaman spectroscopy

Identifiers

PMID42374623
PMCPMC13636617

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.