High-Throughput Monoclonal Antibody Peptide Mapping Using 15-s HPLC Gradients Coupled with Cyclic Ion Mobility-Mass Spectrometry

化学 色谱法 质谱法 单克隆抗体 离子迁移光谱法 吞吐量 高效液相色谱法 串联质谱法 环肽 分析化学(期刊) 抗体 生物化学 电信 计算机科学 免疫学 无线 生物
作者
Devin M. Makey,Brandon T. Ruotolo,Robert T. Kennedy
出处
期刊:Analytical Chemistry [American Chemical Society]
标识
DOI:10.1021/acs.analchem.5c00741
摘要

Identification and quantification of critical quality attributes (CQAs) such as sequence variants and post-translational modifications (PTMs) at the residue level are essential for ensuring the safety and efficacy of monoclonal antibody (mAb) therapeutics. Peptide mapping using liquid chromatography coupled to mass spectrometry (LC-MS) allows for the simultaneous monitoring of multiple CQAs, but conventional methods often suffer from low throughput, limiting their utility in applications requiring fast analysis. Here, we present a multidimensional high-throughput peptide mapping workflow that combines fast LC with cyclic ion mobility-mass spectrometry (LC-cIM-MS) for the high-resolution analysis of mAb tryptic digests. A 15-s LC gradient coupled to single-pass cIM-MS achieved a peak capacity of 490 and allowed for a 96-well plate to be analyzed in 37 min, including time for column re-equilibration and autosampler needle washing between each injection. The method yielded a 97% sequence coverage. Repeatability assessments demonstrated robust retention time, arrival time, and peak intensity reproducibility, and a linear dynamic range was observed across nearly 2 orders of magnitude. Mobility-aligned collision-induced dissociation was used to unambiguously localize PTM sites. The fast LC-cIM-MS platform provided site-specific tracking of the oxidation, deamidation, and isomerization kinetics during forced degradation studies. The method offers an approach to assessing CQAs in high-throughput analysis applications such as stability studies, formulation screening, and process monitoring and has the potential to accelerate mAb development and manufacturing. These results also demonstrate the potential of fast multidimensional separations for complex sample analysis.
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