Evidence map›Paper›PMID 42571449›Full record

ArticleMetabolic engineering communications2026

Experimental and computational analysis of glycan processing at increased monoclonal antibody productivities.

Ranya Pranomphon, Sofia Gialamoidou, Montarop Yamabhai, Susan T Sharfstein, Ioscani Jiménez Del Val

Abstract read
In one paragraph

Article in Metabolic engineering communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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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

5 authors.

Ranya PranomphonCollege of Nanotechnology, Science and Engineering and The RNA Institute, University at Albany, State University of New York, Albany, NY, 12203, USA.
Sofia GialamoidouSchool of Chemical & Bioprocess Engineering, University College Dublin, Dublin, D04 V1W8, Ireland.
Montarop YamabhaiSchool of Biotechnology, Institute of Agricultural Technology, Suranaree University of Technology, Nakhon Ratchasima, Thailand.
Susan T SharfsteinCollege of Nanotechnology, Science and Engineering and The RNA Institute, University at Albany, State University of New York, Albany, NY, 12203, USA.
Ioscani Jiménez Del ValSchool of Chemical & Bioprocess Engineering, University College Dublin, Dublin, D04 V1W8, Ireland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Optimization of cell culture conditions is critically important for successful commercial production of therapeutic antibodies; however, improvements in productivity may be offset by changes in critical quality attributes. We assessed the impact of two different process conditions (high and low yield) on growth, productivity, and glycan distribution for two CHO-S clones producing adalimumab, clone 1 (DHFR clone) and clone 2 (GS clone) in fed-batch shake flask cultures. High-yield cultures showed approximately 1.6-fold and 7.4-fold increases in integral of viable cells (IVC) for clones 1 and 2, respectively, compared to the low-yield cultures. Although specific productivity was lower in the high-yield cultures, this decrease was offset by the increased IVC, yielding higher titers in both cell lines. High-yield cultures showed a marked decrease in galactosylation, marked by a decrease in G1F glycans compared with the low-yield cultures, across both clones. Man5 glycans remained unchanged in clone 1 cultures, while a slight increase was observed in the high-yield clone 2 cultures. Metabolic analysis showed significant changes in glucose utilization in high-yield cultures with substantial decreases in specific glucose uptake and lactate yield, particularly for clone 2. Calculation of nucleotide sugar fluxes revealed that the increased utilization of UDP-N-acetylglucosamine and UDP-galactose for cellular glycosylation limited their availability for monoclonal antibody (mAb) glycosylation in high IVC, high-yield cultures, consistent with the observed changes in mAb glycans.

Indexed as

AdalimumabBioprocess conditionsCHO cellsGlycosylationMonoclonal antibody

Identifiers

PMID42571449
PMCPMC13452297

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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.