MAM Data Processing Automation with Genedata Expressionist

Alexander Julian Veach1, Aude Tartiere2, Maurizio Bronzetti2, Da Ren1

1Amgen Inc., Thousand Oaks, CA, United States, 2Genedata, Inc., San Francisco, California, United States

I will be presenting work at upcoming industry symposia. Please find the abstract below.

Physiochemical properties of therapeutic proteins are crucial for product quality control (QC) in the biopharmaceutical industry. Robust and compliant software is needed to rapidly identify and quantify multi-attribute method (MAM) LC/MS-based spectra. For GMP application we assessed ThermoFisher Chromeleon and Genedata Expressionist, which are both 21 CFR Part 11 compliant. Our comparison of these MS data processing software with Amgen’s internal software MassAnalyzer establishes that results from Expressionist are mostly aligned with conventional approaches without significant differences. Due to its readiness to support QC method validation, Expressionist may be a viable fit for end-to-end MAM lifecycle management. The workflows and plugins we create reduce effort during method transfer from early stage molecule assessment to GMP environments. And, we find that Expressionist reduces analysis time for large data sets and provides an accurate orthogonal validation of results. Using a dedicated server, we use a retention time alignment algorithm and merge MS/MS spectra across samples to improve data quality and reduce false positives. Our proposed methods for calculating relative abundance of PTMs such as oxidation, deamidation, glycosylation, and clips increase data accuracy by including more peaks, charge states, adduct ions, and peptides with non-specific cleavage. In Expressionist, manual integration is not necessary as attribute identification and quantification processes are automated. With Expressionist’s flexible system we optimize novel processes to test system suitability, detect new and missing peaks, and generate control charts. Expressionist shows performance benefits in alleviating limitations in the number of attributes monitored and reducing minimum training requirements for a QC analyst. With advanced statistical tools, we present solutions to monitor trends and investigate unexpected results in development and GMP environments employing MAM.