Supporting Bioprocess Development and Method Transfer using Multi Attribute Monitoring Methods

Significance of the Topic

In modern biopharmaceutical development, monitoring multiple protein quality attributes in a single assay enhances efficiency and insight. Multi-Attribute Monitoring (MAM) by LC-MS reduces assay redundancy and delivers specific, sensitive data critical for process understanding and product quality.

Objectives and Study Overview

This study demonstrates a compliance-ready workflow for simultaneous tracking of multiple critical quality attributes (pCQAs) during process development, stability analysis, and QC release, using a single CDS platform with integrated MS control.

Methodology and Instrumentation

• Chromatographic separation on an ACQUITY UPLC system with a 50-minute gradient to resolve key peptide variants
• Mass detection using an ACQUITY QDa detector programmed for Selected Ion Recording (SIR) to target native and modified charge states of each pCQA
• Initial HRMS data to identify pCQAs followed by targeted SIR acquisition in Empower CDS for increased sensitivity
• Data management in Empower with system suitability summary plots and %CV metrics to detect random and systematic assay deviations across large sample sets

Results and Discussion

The workflow enabled concurrent monitoring of glycosylation variants, N-terminal pyroglutamate, C-terminal clipping, oxidation, and deamidation. Staggered SIR scheduling maximized dwell times, achieving baseline separation of oxidized and deamidated peptide pairs within a 50-minute run. Empower’s %CV analysis facilitated rapid identification of process or instrument drifts, supporting robust decision-making.

Benefits and Practical Applications

• Multiplexed detection reduces assay workload and streamlines data collection
• Mass-based specificity enhances confidence in attribute assignment
• Accelerated process development decisions and QC release testing
• Seamless method transfer across regulated and non-regulated labs
• Global deployment with minimal training and maintenance overhead

Future Trends and Potential Applications

Further improvements in detector sensitivity and data analytics will drive broader MAM adoption in both R&D and manufacturing. Integration with real-time monitoring, automation, and machine learning will enable continuous process verification, adaptive control, and deeper quality insights throughout the product lifecycle.

Conclusion

A scalable, compliance-ready LC-MS MAM workflow using ACQUITY QDa and Empower CDS delivers significant gains in productivity, data management, and product understanding, supporting efficient bioprocess development and QC operations.