Evidence map›Paper›PMID 38580890›Full record

ArticleAnalytical and bioanalytical chemistry2024

Assessment of monoclonal antibody glycosylation: a comparative study using HRMS, NMR, and HILIC-FLD.

Joshua Shipman, Michael Karfunkle, Hongbin Zhu, You Zhuo, Kang Chen, Milani Patabandige, Di Wu, Mercy Oyugi, Richard Kerr, Kui Yang and 1 more

Abstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Analytical and bioanalytical chemistry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
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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

11 authors.

Joshua ShipmanDivision of Complex Drug Analysis, Office of Testing and Research, Center for Drug Evaluation and Research, US Food and Drug Administration, St Louis, MO, 63110, USA.
Michael KarfunkleDivision of Pharmaceutical Analysis, Office of Testing and Research, Center for Drug Evaluation and Research, US Food and Drug Administration, St Louis, MO, 63110, USA.
Hongbin ZhuDivision of Complex Drug Analysis, Office of Testing and Research, Center for Drug Evaluation and Research, US Food and Drug Administration, St Louis, MO, 63110, USA.
You ZhuoDivision of Complex Drug Analysis, Office of Testing and Research, Center for Drug Evaluation and Research, US Food and Drug Administration, St Louis, MO, 63110, USA.
Kang ChenDivision of Complex Drug Analysis, Office of Testing and Research, Center for Drug Evaluation and Research, US Food and Drug Administration, St Louis, MO, 63110, USA.
Milani PatabandigeDivision of Complex Drug Analysis, Office of Testing and Research, Center for Drug Evaluation and Research, US Food and Drug Administration, St Louis, MO, 63110, USA.
Di WuImmediate Office, Office of Testing and Research, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, 20993, USA.
Mercy OyugiImmediate Office, Office of Testing and Research, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, 20993, USA.
Richard KerrDivision of Pharmaceutical Analysis, Office of Testing and Research, Center for Drug Evaluation and Research, US Food and Drug Administration, St Louis, MO, 63110, USA.
Kui YangDivision of Complex Drug Analysis, Office of Testing and Research, Center for Drug Evaluation and Research, US Food and Drug Administration, St Louis, MO, 63110, USA.
Sarah RogstadImmediate Office, Office of Testing and Research, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, 20993, USA. Sarah.Rogstad@fda.hhs.gov.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Monoclonal antibodies (mAbs) represent the largest class of therapeutic protein drug products. mAb glycosylation produces a heterogeneous, analytically challenging distribution of glycoforms that typically should be adequately characterized because glycosylation-based product quality attributes (PQAs) can impact product quality, immunogenicity, and efficacy. In this study, two products were compared using a panel of analytical methods. Two high-resolution mass spectrometry (HRMS) workflows were used to analyze N-glycans, while nuclear magnetic resonance (NMR) was used to generate monosaccharide fingerprints. These state-of-the-art techniques were compared to conventional analysis using hydrophilic interaction chromatography (HILIC) coupled with fluorescence detection (FLD). The advantages and disadvantages of each method are discussed along with a comparison of the identified glycan distributions. The results demonstrated agreement across all methods for major glycoforms, demonstrating how confidence in glycan characterization is increased by combining orthogonal analytical methodologies. The full panel of methods used represents a diverse toolbox that can be selected from based on the needs for a specific product or analysis.

Indexed as

Antibodies, MonoclonalHydrophobic and Hydrophilic InteractionsMagnetic Resonance SpectroscopyMass SpectrometryPolysaccharidesChromatography, LiquidGlycosylationAntibodies, MonoclonalPolysaccharidesMass spectrometryMonoclonal antibodyMulti-attribute methodN-Glycan analysisNuclear magnetic resonance

Identifiers

PMID38580890
PMCPMC11541336

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Registered trials

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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.