Protein glycosylation can impact the efficacy and safety of biotherapeutics and therefore needs to be well characterized and monitored throughout the drug product life cycle. Glycosylation is commonly assessed by fluorescent labeling of released glycans, which provides comprehensive information of the glycoprofile but can be resource-intensive regarding sample preparation, data acquisition, and data analysis. In this work, we evaluate a comprehensive solution from sample preparation to data reporting using a liquid chromatography–mass spectrometry (LC-MS)-based analytical platform for increased productivity in released glycan analysis. To minimize user intervention and improve assay robustness, a robotic liquid handling platform was used to automate the release and labeling of N-glycans within 2 h. To further increase the throughput, a 5 min method was developed on a liquid chromatography–fluorescence–mass spectrometry (LC-FLR-MS) system using an integrated glycan library based on retention time and accurate mass. The optimized method was then applied to 48 released glycan samples derived from six batches of infliximab to mimic comparability testing encountered in the development of biopharmaceuticals. Consistent relative abundance of critical species such as high mannose and sialylated glycans was obtained for samples within the same batch (mean percent relative standard deviation [RSD] = 5.3%) with data being acquired, processed, and reported in an automated manner. The data acquisition and analysis of the 48 samples were completed within 6 h, which represents a 90% improvement in throughput compared with conventional LC-FLR-based methods. Together, this workflow facilitates the rapid screening of glycans, which can be deployed at various stages of drug development such as process optimization, bioreactor monitoring, and clone selections, where high-throughput and improved productivity are particularly desired.

蛋白质糖基化会影响生物治疗药物的功效和安全性，因此需要在整个药品生命周期中进行充分表征和监测。糖基化通常通过对释放的聚糖进行荧光标记来评估，它提供了糖谱的全面信息，但在样品制备、数据采集和数据分析方面可能会占用大量资源。在这项工作中，我们使用基于液相色谱-质谱 (LC-MS) 的分析平台评估了从样品制备到数据报告的综合解决方案，以提高释放聚糖分析的效率。为了最大限度地减少用户干预并提高测定的稳健性，使用机器人液体处理平台在 2 小时内自动释放和标记 N-聚糖。为了进一步提高吞吐量，在液相色谱-荧光-质谱 (LC-FLR-MS) 系统上开发了一种 5 分钟的方法，该系统使用基于保留时间和精确质量的集成聚糖库。然后将优化的方法应用于来自六批英夫利昔单抗的 48 个释放聚糖样品，以模拟生物制药开发中遇到的可比性测试。以自动化方式采集、处理和报告数据，获得了同一批次内样品的关键物种（例如高甘露糖和唾液酸化聚糖）的一致相对丰度（平均百分比相对标准偏差 [RSD] = 5.3%）。48 个样品的数据采集和分析在 6 小时内完成，与基于 LC-FLR 的传统方法相比，吞吐量提高了 90%。一起，