Recent advancements in Peptide MAM: Improvement on chromatographic performance for CQA peptides and New Peak Detection

Description:
Multi-Attribute method (MAM) is an LC-MS assay introduced in recent years for biotherapeutic critical quality attribute (CQA) analysis. Despite the efforts to develop sensitive and reproducible MAM assays, the LC-MS based assay continues to present a challenge as system performance can impact data analysis. Specifically, the chelating interaction between “acidic” peptides and metal surfaces inside chromatographic hardware can impact the overall MAM assay quality by causing peak tailing, low recovery, or irreproducible chromatography of metal sensitive peptides. Often this is observed for peptides carrying acidic residues or modifications such as carboxyl groups, amines, and phosphates. In this study, we optimize an MAM analytical workflow for acidic CQA peptides known to have low chromatographic recovery, poor peak shapes and irreproducible retention times using an LC fluidic pathway engineered to be inert towards metal-sensitive peptides. Data collected from multiple UPLC-TOF MS systems were compared to show the reproducibility and robustness of the optimized method.
In addition to the improvements to the LC-MS instrument performance, this study also addresses improvements to the informatics handling of CQA peptide quantitation and the New Peak Detection (NPD). Novel peak picking algorithm with co-detection peak alignment and peak aggregation, and isotope modeling-based scoring used in NPD will be discussed to illustrate the improvements on relative quantitation of CQA peptides and NPD.

Speaker: Ying Qing Yu - Waters Corporation