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Tracking the Behavior of Monoclonal Antibody Product Quality Attributes Using a Multi-Attribute Method Workflow
Author(s) -
Craig Jakes,
Silvia MillánMartín,
Sara Carillo,
Kai Scheffler,
Izabela Zaborowska,
Jonathan Bones
Publication year - 2021
Publication title -
journal of the american society for mass spectrometry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.961
H-Index - 127
eISSN - 1879-1123
pISSN - 1044-0305
DOI - 10.1021/jasms.0c00432
Subject(s) - workflow , chemistry , robustness (evolution) , mass spectrometry , monoclonal antibody , chromatography , data mining , computer science , computational biology , database , antibody , biochemistry , biology , immunology , gene
The multi-attribute method (MAM) is a liquid chromatography-mass spectrometry based method that is used to directly characterize and monitor many product quality attributes and impurities on biotherapeutics, most commonly at the peptide level. It utilizes high-resolution accurate mass spectral data which are analyzed in an automated fashion. MAM is a promising approach that is intended to replace or supplement several conventional assays with a single LC-MS analysis and can be implemented in a Current Good Manufacturing Practice environment. MAM provides accurate site-specific quantitation information on targeted attributes and the nontargeted new peak detection function allows to detect new peaks as impurities, modifications, or sequence variants when comparing to a reference sample. The high resolution MAM workflow was applied here for three independent case studies. First, to monitor the behavior of monoclonal antibody product quality attributes over the course of a 12-day cell culture experiment providing an insight into the behavior and dynamics of product attributes throughout the process. Second, the workflow was applied to test the purity and identity of a product through analysis of samples spiked with host cell proteins. Third, through the comparison of a drug product and a biosimilar with known sequence variants. The three case studies presented here, clearly demonstrate the robustness and accuracy of the MAM workflow that implies suitability for deployment in the regulated environment.

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