Automatic 1D 1H NMR Metabolite Quantification for Bioreactor Monitoring

High-throughput metabolomics can be used to optimize cell growth for enhanced production or for monitoring cell health in bioreactors. It has applications in cell and gene therapies, vaccines, biologics, and bioprocessing. NMR metabolomics is a method that allows for fast and reliable experimentation, requires only minimal sample preparation, and can be set up to take online measurements of cell media for bioreactor monitoring. This type of application requires a fully automated metabolite quantification method that can be linked with high-throughput measurements. In this review, we discuss the quantifier requirements in this type of application, the existing methods for NMR metabolomics quantification, and the performance of three existing quantifiers in the context of NMR metabolomics for bioreactor monitoring.

High-Throughput Analysis of Fluorescently Labeled N-Glycans Derived from Biotherapeutics Using an Automated LC-MS-Based Solution

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.