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先進的な質量分析データ解析技術基盤によるバイオ医薬品開発の変革

October 14, 2025

Biologics such as monoclonal antibodies (mAbs), continue to grow robustly as a therapeutic class. Posttranslational modifications (PTMs) can strongly influence the therapeutic properties of biologic drugs. Glycosylation is the most common posttranslational modification, though others like carboxylation, hydroxylation, sulfation, and amidation also occur in certain products.1 Understanding how PTMs govern both biological function and clinical outcomes is central to advancing biopharmaceutical development. Moreover, PTMs are a critical quality attribute whose monitoring is required by regulatory bodies.2

Mass spectrometry (MS) is an increasingly important method in discovering and developing protein therapeutics in the biopharmaceutical industry. A study reported that mass spectrometry was found to be essential for characterizing biotherapeutics, with 79 out of 80 submitted biologics license applications (BLAs) using MS workflows for protein or impurity analysis.3 This includes using MS to detect and characterize PTMs.

In this blog, we discuss major challenges surrounding MS-based characterization of PTMs in biotherapeutics, especially surrounding data processing and analysis. We highlight how one major biopharmaceutical company, Merck, overcame these challenges using a single digital platform to overcome data silos and increase analytical sensitivity.

Key Challenges in MS-Based Characterization of Biotherapeutics

Post-translational modifications influence protein folding by affecting local secondary structure and guiding polypeptide chain folding. These modifications also also impact protein stability, affect solubility, and influence protein activity and trafficking. Additionally, certain posttranslational modification patterns, especially from plants and yeast, can trigger immunogenic responses in mammals.1

The biopharmaceutical industry needs faster, real-time protocols that allow intervention during production to optimize the product’s glycoform profile.1 MS approaches for characterizing PTMs range from intact mass analysis and top-down sequencing to released glycan analysis.4 Charge variant analysis is a specific approach in which cation exchange chromatography is combined with MS to provide a fingerprint of posttranslational modifications in monoclonal antibodies.5  Significant challenges encountered in with MS-based characterization approaches like charge variant analysis include:

Developing and Optimizing Novel MS-Based Analytical Methods

Optimization of sample preparation and separation is a key consideration prior to MS-based characterization of biotherapeutics. In the case of charge variant analysis with MS, conventional salt- or pH-based eluents used during cation exchange chromatography are incompatible with MS owing to their nonvolatility and high ionic strength. As a result, researchers have relied on labor-intensive offline fractionation before MS measurement, which is laborious, time-consuming, and biased towards major variants, often overlooking low-abundance modifications.2

Alternative strategies have been developed using volatile buffers have enabled direct coupling of cation exchange chromatography with high-resolution MS. Such strategies--involving adjusted gradient slopes for antibodies of different pI values, or combined salt and pH gradients for universal separation--have thus achieved efficient acquisition of high-quality spectra.5,6 

Ensuring High-Quality, Harmonized MS Data Analysis and Interpretation

MS produces high volume and complex data that require intensive computation as well as expert analysis and interpretation. In the case of charge variant analysis, information can be lost depending on the analysis approach. Deconvolution of averaged mass spectra can obscure mass shifts and mask variants under co-eluting peaks or noise. Overlaying chromatograms from different fractions creates similar issues with hidden peaks and noise. Finally, database search-based methods are biased towards search parameters.2

Moreover, different MS instruments from different vendors have unique data formats, complicating data integration and result comparison across the organization.6 Lack of harmonization creates a data readiness gap that hinders knowledge sharing and future data use.

How Genedata Overcomes Data Silos and Complexity in Molecular Characterization of Biopharmaceuticals

Companies like Merck and Novartis have tackled the challenges above, not only by optimizing sample preparation and separation conditions to achieve online MS analysis, but by optimizing analysis using the Genedata platform.2,6 The vendor-agnostic platform imports raw MS data and automates peak detection and analysis, to enable high-throughput screening. The software could handle gigabytes of data in parallel and rapidly produce results—within hours.2

At the same time, the Genedata platform has offered these companies the ability to adapt their analysis workflows and the flexibility to apply different deconvolution methods, including full-automated, semi-automated, spectral, and time-resolved deconvolution approaches.6 With this versatility, for instance, Novartis observed that with a time-resolved deconvolution algorithm they could  detect modifications present at <1% abundance.2 Similarly, Merck could obtain more in-depth and unbiased characterization using time-resolved, scan-by-scan deconvolution in the Genedata software.6 

Furthermore, 3D visualization maps (mass vs. time vs. intensity) offered by Genedata solution allow fast and intuitive result interpretation. This allowed rapid recognition of variants, as well as degradation products and artifacts.2 In one specific instance, for Merck, this capability revealed two coeluting low molecular weight impurities, allowing them to confirm superior separation with strong cation over size exclusion chromatography.6

The Merck structural characterization lab in Guidonia, Italy, applied these innovations to develop novel MS-based methods for analyzing charge variants, glycoforms, and released glycans in biopharmaceuticals. By optimizing sample preparation, separation procedures, and data workflows, they achieved more efficient and comprehensive characterization, further boosting the total number of detected glycoforms with a striking 2.5-fold increase with respect to the conventional sample preparation. Learn more about how Merck revolutionized its biopharmaceutical characterization with Genedata Expressionist.

 

References

  1. Walsh, G., Jefferis, R. Post-translational modifications in the context of therapeutic proteins. Nat Biotechnol.24, 1241–1252 (2006). https://www.nature.com/articles/nbt1252
  2. Griaud, F., Denefeld, B., Lang, M., Hensinger, H., Haberl, P., & Berg, M. Unbiased in-depth characterization of CEX fractions from a stressed monoclonal antibody by mass spectrometry. mAbs, 9(5), 820–830 (2017).
  3. Rogstad, S., Faustino, A., Ruth, A., Keire, D., Boyne, M., Park, J. A Retrospective Evaluation of the Use of Mass Spectrometry in FDA Biologics License Applications. J. Am. Soc. Mass Spectrom. 28 (5), 786–794 (2016). https://pubs.acs.org/doi/10.1007/s13361-016-1531-9
  4. Guodong, C., Bethanne M. W., Angela K. G., Hui W., David B. W-I., Adrienne A. T. Characterization of protein therapeutics by mass spectrometry: recent developments and future directions, Drug Discovery Today, 16(1–2): 58–64 (2011), (https://www.sciencedirect.com/science/article/pii/S1359644610008068)
  5. Füssl, F., Cook, K., Scheffler, K., Farrell, A., Mittermayr, S., Bones, J. Charge Variant Analysis of Monoclonal Antibodies Using Direct Coupled pH Gradient Cation Exchange Chromatography to High-Resolution Native Mass Spectrometry. Anal. Chem.90 (7), 4669–4676 (2018). https://pubs.acs.org/doi/10.1021/acs.analchem.7b05241
  6. Ma, F., Raoufi, F., Bailly, M. A., Fayadat-Dilman, L., Tomazela, D. Hyphenation of strong cation exchange chromatography to native mass spectrometry for high throughput online characterization of charge heterogeneity of therapeutic monoclonal antibodies. mAbs, 12(1) (2020). https://www.tandfonline.com/doi/full/10.1080/19420862.2020.1763762#abstract