ArticleScientific reports2017
Statistical characterization of therapeutic protein modifications.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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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.
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Who cites it
2 citing papers in PubMed, 4 citations in OpenAlex.
- New Quality-Range-Setting Method Based on Between- and Within-Batch Variability for Biosimilarity Assessment.Pharmaceuticals (Basel, Switzerland) · 2021Article
- Systematic LC/MS/MS Investigations for the IND-Enabling Extended Characterization of Antibody-Drug Conjugate Modifications.Antibodies (Basel, Switzerland) · 2018Article
Corrections and comments
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Authors and funding
14 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Peptide mapping with liquid chromatography-tandem mass spectrometry (LC-MS/MS) is an important analytical method for characterization of post-translational and chemical modifications in therapeutic proteins. Despite its importance, there is currently no consensus on the statistical analysis of the resulting data. In this manuscript, we distinguish three statistical goals for therapeutic protein characterization: (1) estimation of site occupancy of modifications in one condition, (2) detection of differential site occupancy between conditions, and (3) estimation of combined site occupancy across multiple modification sites. We propose an approach, which addresses these goals in terms of summarizing the quantitative information from the mass spectra, statistical modeling, and model-based analysis of LC-MS/MS data. We illustrate the approach using an LC-MS/MS experiment from an antibody-drug conjugate and its monoclonal antibody intermediate. The performance was compared to a 'naïve' data analysis approach, by using computer simulation, evaluation of differential site occupancy in positive and negative controls, and comparisons of estimated site occupancy with orthogonal experimental measurements of N-linked glycoforms and total oxidation. The results demonstrated the importance of replicated studies of protein characterization, and of appropriate statistical modeling, for reproducible, accurate and efficient site occupancy estimation and differential analysis.
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