ArticleBioengineered2020
Establishment of a highly precise multi-attribute method for the characterization and quality control of therapeutic monoclonal antibodies.
Article in Bioengineered, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Development and Integrated Application of the Multi-Attribute Method (MAM) in Quality Control of Biotechnological Drugs.Biotechnology and bioengineering · 2026Review
- Article
- Leveraging single B cell antibody platforms to develop countermeasures against animal viral diseases: recent advances and future perspectives.Frontiers in veterinary science · 2026Review
- Designing strong inducible synthetic promoters in yeasts.Nature communications · 2024Article
- Article
- Expanding the Analytical Toolbox: Developing New Lys-C Peptide Mapping Methods with Minimized Assay-Induced Artifacts to Fully Characterize Antibodies.Pharmaceuticals (Basel, Switzerland) · 2023Article
- Comprehensive multi-attribute method workflow for biotherapeutic characterization and current good manufacturing practices testing.Nature protocols · 2023Review
- Automated multi-attribute method sample preparation using high-throughput buffer exchange tips.Rapid communications in mass spectrometry : RCM · 2022Article
- Multi attribute method implementation using a High Resolution Mass Spectrometry platform: From sample preparation to batch analysis.PloS one · 2022Article
- Glycan engineering of the SARS-CoV-2 receptor-binding domain elicits cross-neutralizing antibodies for SARS-related viruses.The Journal of experimental medicine · 2021Article
- Enhancing the multi-attribute method through an automated and high-throughput sample preparation.mAbsArticle
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3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The multi-attribute method (MAM) has garnered attention as a new quality control method of therapeutic monoclonal antibodies (mAbs). MAM analysis allows multiple relative quantifications of several structural attributes of therapeutic mAbs; however, some issues remain to be addressed in its procedures especially for sample preparation. The goal of this study was to optimize the sample preparation method for MAM analysis of mAbs. Using a model mAb, we compared five sample preparation methods based on sequence coverage, peptide redundancy, missed cleavage and chemical deamidation. It was found that low pH buffer and short digestion time reduced artificial deamidation. The desalting process after carboxymethylation was essential to obtaining high sequence coverage by a short digestion time. The generation of missed cleavage peptides was also improved by using a trypsin/lysyl endopeptidase (Lys-C) mixture. Next, we evaluated the usefulness of our method as a part of MAM analysis. Finally, 17 glycopeptides, 2 deamidated peptides and N- and C-terminal peptides of the heavy chain were successfully monitored with acceptable mass accuracy and coefficient of variation (CV, %) of the relative peak area. On the other hand, 4 oxidated peptides indicated the unavoidable slightly higher inter-assay CV (%) of the peak area ratio due to the instability in the MS sample solution. Collectively, we demonstrated that our method was applicable as an easy and reliable sample preparation method for MAM analysis, and the variation in the relative peak area could be influenced by the modification type rather than by the amount of each peptide.
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