Towards MAM in QC: Accelerating Adoption with Streamlined Data Analysis Strategies

The multi-attribute method (MAM) offers significant potential to replace certain conventional release and stability assays, yet widespread adoption has been slowed by challenges such as reliable new peak detection (NPD) while minimizing false positives. These hurdles, combined with the need to monitor critical quality attributes (CQAs) and support tradition in-depth characterization activities, often force analysts to rely on multiple software solutions to meet their goals.

In this presentation, Michelle English, Ph.D., introduces a comprehensive, GxP-ready MAM workflow designed for both CQA monitoring and robust NPD. The workflow integrates advanced features to reduce false positives, including statistical scoring, artifact filtering, and curated known-peak libraries. Users have successfully validated this approach in accordance with ICH Q2 guidelines and applied it across development and MAM applications, achieving sensitive and accurate identification of new peaks with zero false positives. This streamlined solution enables efficient implementation of MAM while maintaining compliance and analytical rigor.

This presentation is part of a full webinar. Learn more about the full event and watch all four speakers here.