Statistical characterization of therapeutic protein modifications

• Published in: Scientific reports 2017-08, Vol.7 (1), p.7896-13, Article 7896
• Main Authors: Tsai, Tsung-Heng, Hao, Zhiqi, Hong, Qiuting, Moore, Benjamin, Stella, Cinzia, Zhang, Jeffrey H., Chen, Yan, Kim, Michael, Koulis, Theo, Ryslik, Gregory A., Verschueren, Erik, Jacobson, Fred, Haskins, William E., Vitek, Olga
• Format: Article
• Language: English
• Subjects: 631/114/2415; 631/154/51; 631/1647/296; 82/58; Biological Products - chemistry; Biological Products - pharmacology; Biostatistics; Chromatography, High Pressure Liquid; Computer simulation; Data processing; Humanities and Social Sciences; Liquid chromatography; Mass spectra; Mass spectrometry; Mass spectroscopy; Mathematical models; Monoclonal antibodies; multidisciplinary; Oxidation; Peptide Mapping; Post-translation; Protein Processing, Post-Translational; Proteins; Proteins - chemistry; Proteins - pharmacology; Science; Science (multidisciplinary); Statistical analysis; Statistical models; Tandem Mass Spectrometry; Technology, Pharmaceutical
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-017-08333-y

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.