Automated mass spectrometry multi-attribute method analyses for process development and characterization of mAbs.

Area of Science:

• Biopharmaceutical Development
• Analytical Chemistry
• Process Engineering

Background:

• The biopharmaceutical industry is expanding, necessitating efficient process development and optimization.
• Quality by Design (QbD) principles require robust analytical methods for product characterization.
• Existing analytical techniques face challenges with variability, hindering effective process development.

Purpose of the Study:

• To develop and validate a harmonized, automated analytical platform for multi-attribute methods (MAM).
• To demonstrate inter-site comparability of the automated MAM platform, including sample preparation and LC-MS.
• To showcase the applicability of the automated MAM platform in supporting various stages of biopharmaceutical process development.

Main Methods:

• Implementation of an automated analytical platform comprising automated sample preparation, LC-MS based MAM, and automated data treatment.
• Global harmonization of the automated MAM platform across three different sites.
• Validation of inter-site comparability for automated sample preparation and LC-MS instrumentation.

Main Results:

• Successful global harmonization of an automated MAM platform across three independent sites.
• Demonstrated inter-site comparability of automated sample preparation and LC-MS measurements.
• Validated the utility of the automated MAM platform for supporting cell line, cell culture, and downstream process development.

Conclusions:

• The developed automated MAM platform provides a harmonized and comparable analytical solution for biopharmaceutical process development.
• This platform addresses the challenge of instrument and method variability, enabling reliable data for process optimization.
• The automated MAM platform is expected to significantly guide and accelerate process development across multiple projects.