| Autor: |
Tsukidate T; Analytical Research & Development Mass Spectrometry, Merck & Co., Inc., 126 East Lincoln Avenue, Rahway, New Jersey 07065, United States., Stiving AQ; Analytical Research & Development Mass Spectrometry, Merck & Co., Inc., 126 East Lincoln Avenue, Rahway, New Jersey 07065, United States., Rivera S; Analytical Research & Development Mass Spectrometry, Merck & Co., Inc., 126 East Lincoln Avenue, Rahway, New Jersey 07065, United States., Sahoo A; Biologics Process Research & Development, Merck & Co., Inc., 126 East Lincoln Avenue, Rahway, New Jersey 07065, United States., Madabhushi S; Biologics Process Research & Development, Merck & Co., Inc., 126 East Lincoln Avenue, Rahway, New Jersey 07065, United States., Li X; Analytical Research & Development Mass Spectrometry, Merck & Co., Inc., 126 East Lincoln Avenue, Rahway, New Jersey 07065, United States. |
| Abstrakt: |
Monitoring and quantifying host cell proteins (HCPs) in biotherapeutic production processes is crucial to ensure product quality, stability, and safety. Liquid chromatography-mass spectrometry (LC-MS) analysis has emerged as an important tool for identifying and quantifying individual HCPs. However, LC-MS-based approaches face challenges due to the wide dynamic range between HCPs and the therapeutic protein as well as laborious sample preparation and long instrument time. To address these limitations, we evaluated the application of parallel accumulation-serial fragmentation combined with data-independent acquisition (diaPASEF) to HCP analysis for biopharmaceutical process development applications. We evaluated different library generation strategies and LC methods, demonstrating the suitability of these workflows for various HCP analysis needs, such as in-depth characterization and high-throughput analysis of process intermediates. Remarkably, the diaPASEF approach enabled the quantification of hundreds of HCPs that were undetectable by a standard data-dependent acquisition mode while considerably improving sample requirement, throughput, coverage, quantitative precision, and data completeness. |
