- 第3日 6月24日（金） P会場（501，502，503）
Automated Data Analysis Workflow Enabling Unbiased New Peak Detection for Implementations of the Multi-Attribute Method (MAM)
The multi-attribute method (MAM) leverages mass spectrometry (MS) for the simultaneous identification, quantitation, and monitoring of product quality attributes (PQAs) at the molecular level. By replacing multiple conventional analytical approaches, MAM provides significant operational cost savings while enabling a true Quality-by-Design (QbD) approach for the development and production of novel biotherapeutics. The implementation of MAM in Quality Control requires a new peak detection (NPD) data analysis procedure that provides identification of potential process- and/or product-related impurities. We present an innovative automated data analysis workflow that delivers sensitive and truly unbiased NPD.
A dedicated data workflow was designed to automatically process LC-MS/MS peptide mapping data from predigested samples of the NIST mAb. NPD was performed by comparing an untreated reference sample to samples spiked with a set of MS calibration peptides. MS data from the reference sample was loaded, subjected to RT alignment and noise reduction, MS peaks were detected, and peptide signals were annotated based on matching mass and MS/MS fragment spectra. All data processing was performed using the Genedata Expressionist® software platform (Genedata AG, Basel, Switzerland). This approach provided highly sensitive NPD, while minimizing false positive identifications.