A Robust and Versatile New Peak Detection Workflow to Facilitate the Multi-Attribute Method (MAM) Implementation From Research to Commercialization

June 2, 2025

The MS-based Multi-Attribute Method (MAM) enables robust monitoring of Product Quality Attributes (PQAs) by leveraging deep product knowledge in GMP environments. A key component, New Peak Detection (NPD), is critical for identifying novel impurities or changes; however, its adoption has been limited by false positive detection. To address this challenge, we present a novel, sensitivity-optimized MAM NPD workflow implemented within Genedata Expressionist^®, featuring enhanced statistical scoring, smart library integration, and advanced artifact filtering. These innovations—such as detection of co-eluting adducts, polymer signatures, and chemical noise—streamline data review and improve the identification of true sample differences in complex LC-MS datasets.

By embedding this advanced NPD workflow into a user-friendly interface, the solution not only enhances data consistency and traceability but also enables non-expert users to execute complex analyses efficiently. This streamlined approach supports research, development, and QC environments, delivering significant time savings and facilitating broader adoption of NPD in regulated biopharmaceutical workflows.