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Background <p>Controlled disulfide bond reduction is critical in antibody–drug conjugate (ADC) manufacturing, enabling site-specific conjugation and desired drug-to-antibody ratios. Current characterization relies on time-consuming offline capillary electrophoresis, creating bottlenecks in process development where multiple conditions must be rapidly screened. This study evaluates Raman spectroscopy as a process analytical technology (PAT) to elucidate reduction kinetics and accelerate optimization for ADC development.</p> Methods <p>A Design of Experiments approach investigated TCEP-mediated reduction of an IgG1 antibody under varying TCEP/mAb ratios (5–15) and pH conditions (5.5–7.5). Raman spectra were collected throughout reduction reactions. Principal component analysis (PCA) characterized reduction kinetics, while partial least squares (PLS) regression quantified fragment formation against non-reduced capillary electrophoresis sodium dodecyl sulfate (nrCE-SDS) measurements.</p> Results <p>PCA effectively captured reduction kinetics, with PC1 trajectories correlating strongly with heavy chain (HC) formation measured by nrCE-SDS. The analysis revealed pH-dependent TCEP saturation effects: higher pH showed convergent kinetics at elevated ratios while lower pH maintained ratio-dependent rates. PLS models successfully predicted HC formation, demonstrating potential for endpoint detection despite the interchain disulfide bonds representing only ~ 0.2% of total protein mass.</p> Conclusions <p>Raman spectroscopy with chemometric analysis provides valuable insights for ADC process development. PCA enables rapid screening of reduction conditions without nrCE-SDS confirmation, reducing analytical burden during early development. Unlike offline nrCE-SDS, Raman offers potential for real-time online PAT implementation. Future work focusing on narrower operating ranges could enhance model performance for manufacturing applications.</p>

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Raman Spectroscopy for Characterizing Monoclonal Antibody Reduction: A Process Analytical Technology Approach for Antibody–Drug Conjugation Process Development

  • 期刊论文

  • 发表日期：

    2026年7月14日

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  • David Pople,
  • Zhenshu Wang,
  • Anton Kozyryev,
  • Bhumit Patel,
  • Emmanuel Appiah-Amponsah,
  • Hanzhou Feng

摘要

Background

Controlled disulfide bond reduction is critical in antibody–drug conjugate (ADC) manufacturing, enabling site-specific conjugation and desired drug-to-antibody ratios. Current characterization relies on time-consuming offline capillary electrophoresis, creating bottlenecks in process development where multiple conditions must be rapidly screened. This study evaluates Raman spectroscopy as a process analytical technology (PAT) to elucidate reduction kinetics and accelerate optimization for ADC development.

Methods

A Design of Experiments approach investigated TCEP-mediated reduction of an IgG1 antibody under varying TCEP/mAb ratios (5–15) and pH conditions (5.5–7.5). Raman spectra were collected throughout reduction reactions. Principal component analysis (PCA) characterized reduction kinetics, while partial least squares (PLS) regression quantified fragment formation against non-reduced capillary electrophoresis sodium dodecyl sulfate (nrCE-SDS) measurements.

Results

PCA effectively captured reduction kinetics, with PC1 trajectories correlating strongly with heavy chain (HC) formation measured by nrCE-SDS. The analysis revealed pH-dependent TCEP saturation effects: higher pH showed convergent kinetics at elevated ratios while lower pH maintained ratio-dependent rates. PLS models successfully predicted HC formation, demonstrating potential for endpoint detection despite the interchain disulfide bonds representing only ~ 0.2% of total protein mass.

Conclusions

Raman spectroscopy with chemometric analysis provides valuable insights for ADC process development. PCA enables rapid screening of reduction conditions without nrCE-SDS confirmation, reducing analytical burden during early development. Unlike offline nrCE-SDS, Raman offers potential for real-time online PAT implementation. Future work focusing on narrower operating ranges could enhance model performance for manufacturing applications.