Interlaboratory Evaluation of a User-Friendly Benchtop Mass Spectrometer for Multiple-Attribute Monitoring Studies of a Monoclonal Antibody

In the quest to market increasingly safer and more potent biotherapeutic proteins, the concept of the multi-attribute method (MAM) has emerged from biopharmaceutical companies to boost the quality-by-design process development. MAM strategies rely on state-of-the-art analytical workflows based on liquid chromatography coupled to mass spectrometry (LC-MS) to identify and quantify a selected series of critical quality attributes (CQA) in a single assay. Here, we aimed at evaluating the repeatability and robustness of a benchtop LC-MS platform along with bioinformatics data treatment pipelines for peptide mapping-based MAM studies using standardized LC-MS methods, with the objective to benchmark MAM methods across laboratories, taking nivolumab as a case study. Our results evidence strong interlaboratory consistency across LC-MS platforms for all CQAs (i.e., deamidation, oxidation, lysine clipping and glycosylation). In addition, our work uniquely highlights the crucial role of bioinformatics postprocessing in MAM studies, especially for low-abundant species quantification. Altogether, we believe that MAM has fostered the development of routine, robust, easy-to-use LC-MS platforms for high-throughput determination of major CQAs in a regulated environment.

Automated summary

The concept of multi-attribute method (MAM) has been introduced to enhance the quality-by-design process development in biopharmaceutical companies. MAM strategies rely on LC-MS technology to identify and quantify critical quality attributes (CQA) in a single assay. This study evaluated the repeatability and robustness of a benchtop LC-MS platform and bioinformatics data treatment pipelines for peptide mapping-based MAM studies, benchmarking MAM methods across laboratories using nivolumab as a case study. The results demonstrated strong interlaboratory consistency in all CQAs and emphasized the importance of bioinformatics postprocessing in MAM studies.