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Automated multi-attribute method sample preparation using high-throughput buffer exchange tips.

Yuko Ogata1, Pamela M Quizon2, Nancy S Nightlinger1

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Summary

An automated multi-attribute method (MAM) sample preparation protocol significantly enhances throughput for biotherapeutic analysis. This method achieves rapid desalting and maintains product attribute integrity, supporting process development and quality control.

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Area of Science:

  • Biopharmaceutical analysis
  • Mass spectrometry
  • Analytical chemistry

Background:

  • The multi-attribute method (MAM) is a key mass spectrometry (MS)-based technique for characterizing biotherapeutics like monoclonal antibodies (mAbs).
  • Enhancing sample preparation throughput is crucial for expanding MAM applications in drug development without compromising data quality.

Purpose of the Study:

  • To develop and validate a fully automated sample preparation protocol for MAM analysis.
  • To improve the speed and efficiency of sample preparation for biotherapeutic characterization.

Main Methods:

  • A fully automated protocol utilizing miniaturized size-exclusion chromatography columns in pipette tips for rapid desalting (<15 minutes).
  • Sample analysis via electrospray ionization (ESI) orbitrap mass spectrometry coupled with ultra-high-performance liquid chromatography (UHPLC) and dual column switching.

Main Results:

  • Automated sample preparation showed no significant changes in product attributes or quantities compared to manual methods.
  • Enhanced sample recovery and excellent reproducibility were achieved across various sample concentrations.
  • Individual columns offered flexibility in sample batch size and plate location, unlike plate-based systems.

Conclusions:

  • The automated MAM protocol significantly reduces "at-bench" time for preparing up to 96 samples.
  • This automation facilitates process development and routine quality control applications of MAM.
  • The method provides a robust and efficient solution for high-throughput biotherapeutic characterization.