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自動かつスケーラブルな質量分析データ解析による新たなバイオ医薬品の開発可能性向上

October 14, 2025

The biopharmaceutical landscape is rapidly evolving with increasingly complex and novel biotherapeutics that require rapid characterization tools to ensure developability. Early evaluation of critical attributes such as potency, stability, and safety is essential for mitigating risks and preventing costly late-stage failures.1 

Mass spectrometry (MS) technologies continue to offer deep insights into developability studies for in-depth characterization of drug candidates.2 However, the multidimensional MS datasets generated from diverse biotherapeutic constructs can create significant workflow bottlenecks for analytical teams.

To overcome these challenges, reproducible and standardized workflows rooted in Quality by Design principles are essential.3 Innovative analytical approaches, such as peptide mapping, intact mass, and released glycan analysis provide large volumes of data from multiple sources. Consolidating and comparing these datasets across laboratories is key to enabling confident, accelerated development decisions. When done successfully, harmonized MS strategies not only streamline data analysis and interpretation, but they also foster innovation and efficiency throughout the drug development pipeline.3

Analytical Challenges in Characterizing Complex Biotherapeutic Molecules

Conventional manual workflows and disparate software platforms present significant challenges in biotherapeutic characterization. These processes are often resource-intensive, requiring expert evaluation and multiple orthogonal assays to ensure accuracy.3 When analyses are performed across different instruments, software types, and laboratory sites, fragmentation introduces variability that undermines data consistency and increases the risk of bias.2,4 Additionally, siloed data systems further complicate data harmonization with the loss of valuable metadata, hampering knowledge and scalability amid growing biotherapeutic complexity.4,5

Successful biotherapeutic development relies on early-stage transparency in data quality, combined with thorough, in-depth characterization.5 This approach enables detailed insight into chemical stability, including molecular susceptibilities to modifications such as deamidation, oxidation, and aggregation.1,5 Advanced analytical techniques, supported by robust and harmonized methodologies, helps ensure molecular integrity throughout the development lifecycle. This approach guides lead candidate selection and enables proactive mitigation strategies to prevent the risk of late-stage attrition.1,5

Processing of Complex and Multidimensional MS Data Sets Across Multiple Instruments and Labs

The complexity of novel biotherapeutics produces extensive, multidimensional MS datasets. High-resolution MS technologies provide detailed insights into structural features such as glycosylation patterns, degradation patterns, and aggregation states.2 However, extracting actionable molecular information demands sophisticated platforms paired with expert data analysis.3,6 

Biopharmaceutical R&D frequently involves multiple instruments and distributed laboratories making it difficult to consolidate and compare basic signal characteristics such as intensity, retention time shifts, and peak shape. Fragmented software ecosystems and inconsistent workflows increase the risk of biased interpretations and hinder data comparability.

To address these challenges, harmonized and reproducible computational workflows are essential. They enable the translation of raw MS data into critical developability metrics, ensure consistency across instruments and locations, and foster confidence in decision-making. Ultimately, this standardization supports efficient technology transfer and accelerates development timelines. 2,4

Managing Increasing Sample Throughput with Limited Resources

As biotherapeutic pipelines expand and stability testing  expands, laboratory throughput is increasingly strained.1,5 Manual characterization workflows often become critical bottlenecks, impeding efficient drug discovery and development.3 

The adoption of automated, high-throughput analytical workflows streamlines candidate screening, accelerates data processing, and empowers agile, data-driven decision-making.1,3-5 Automation not only preserves resources but also shortens timelines and reduces costs associated with bringing innovative biologics to market.5

The Need for a Flexible, Scalable, and Automated MS Data Analysis Platform

The growing complexity of biotherapeutics and the expanding diversity of MS techniques place considerable demands on traditional manual data analysis approaches.1,2,4,5 Flexible, configurable, vendor-neutral platforms capable of processing large, multidimensional datasets tailored to specific analytical goals are urgently needed.4,5

End-to-end automated workflow solutions that harmonize data processing and elevate reproducibility across instruments and sites are critical for modern biotherapeutic characterization.2,4,6 These platforms provide transparent, high-confidence results with direct site-specific insights into critical quality attributes, enabling better decision-making and reducing risk throughout drug development.1,2,4,5

Integrating sample preparation, data acquisition, and analysis workflows, these platforms minimize manual intervention and reduce turnaround times. By leveraging advanced algorithms, they enhance data quality by automating noise reduction, peak detection, and anomaly identification, empowering scientists to focus on interpretation and decision-making instead of redundant tasks. 

Cloud-based deployment further enables seamless collaboration and real-time data sharing across global teams. This ensures harmonized results, accelerated development cycles, and strengthens regulatory compliance. By embracing such flexible and comprehensive platforms, biopharma organizations can significantly improve throughput, data quality, and scalability in their MS workflows.

Advancing Biotherapeutic Characterization with Automated Mass Spectrometry Workflows

To meet the challenges of increasingly complex biotherapeutics and vast multidimensional MS data, Sanofi has deployed the Genedata Biopharma Platform as a solution for the data workflows. The platform effectively addresses complexities related to extracting detailed molecular information and managing diverse biotherapeutic constructs, including multispecifics and fusion proteins.3,6

The core strengths of the platform lies in consolidating and standardizing diverse data from multiple MS instruments and analytical methods — such as peptide mapping, intact mass, and released glycan analyses — within a single, vendor-agnostic software ecosystem.3-5 Additionally, information from ion maps, chromatogram traces, and MS/MS fragment spectra peaks can be unified for confident identification and quantification. This integrated approach delivers transparent visualization and comprehensive quality control, enabling unbiased analysis and confident decision-making.

Sanofi benefits from intuitive data visualization, robust noise filtering, defined adduct-species search, and customizable reporting. These features reduce false positives, accelerate insights, and support deep trend analysis while reducing iterative tasks. Ultimately, Genedata supports Sanofi’s developability assessment process by driving the design of better biotherapeutic candidates. This enables faster delivery of best-in-class biotherapeutics while minimizing the risk of costly late-stage failures.5

Uncover Sanofi’s full story, following the adoption of Genedata Expressionist, in the complete case study.

 

References

  1. MMieczkowski, C., Zhang, X., Lee, D., Nguyen, K., Lv, W., Wang, Y., Zhang, Y., Way, J., Gries, J.-M. Blueprint for Antibody Biologics Developability. mAbs 2023, *15* (1), 2185924. https://doi.org/10.1080/19420862.2023.2185924.
  2. Beck, A., Wagner-Rousset, E., Ayoub, D., Van Dorsselaer, A., Sanglier-Cianférani, S. Characterization[SJ2]  of Therapeutic Antibodies and Related Products. Analytical Chemistry 2013, *85* (2), 715–736. https://doi.org/10.1021/ac3032355.
  3. Song, Y. E., Dubois, H., Hoffmann, M., D́eri, S., Fromentin, Y., Wiesner, J., Pfenninger, A., Clavier, S., Pieper, A., Duhau, L., Roth, U. Automated Mass Spectrometry Multi-Attribute Method Analyses for Process Development and Characterization of mAbs. Journal of Chromatography B 2021, *1166*, 122540. https://doi.org/10.1016/j.jchromb.2021.122540.
  4. Yang, F., Zhang, J., Buettner, A., Vosika, E., Sadek, M., Hao, Z., Reusch, D., Koenig, M., Chan, W., Bathke, A., Pallat, H., Lundin, V., Kepert, J. F., Bulau, P., Deperalta, G., Yu, C., Beardsley, R., Camilli, T., Harris, R., Stults, J. Mass Spectrometry-Based Multi-Attribute Method in Protein Therapeutics Product Quality Monitoring and QualityControl. mAbs 2023, *15* (1), 2197668. https://doi.org/10.1080/19420862.2023.2197668.
  5. Fernández-Quintero, M. L.. Ljungars, A., Waibl, F., Greiff, V., Andersen, J. T., Gjølberg, T. T., Jenkins, T. P., Voldborg, B. G., Grav, L. M., Kumar, S., Georges, G., Kettenberger, H., Liedl, K. R., Tessier, P. M., McCafferty, J., Laustsen, A. H. Assessing Developability Early in the Discovery Process for Novel Biologics. mAbs 2023, *15* (1), 2171248. https://doi.org/10.1080/19420862.2023.2171248.
  6. Hajnajafi, M. A., Iqbal, K. Proteome Science (2025) 23:5. Proteome Science 2025, *23*, 5. https://doi.org/10.1186/s12953-025-00241-8.