A system suitability testing platform for untargeted, high-resolution mass spectrometry

Modular, Scalable, and Customizable LC-HRMS for Exposomics

In this chapter, we describe a multi-purpose, reversed-phase liquid chromatography-high-resolution mass spectrometry (LC-HRMS) workflow for acquiring high-quality, non-targeted exposomics data utilizing data-dependent acquisition (DDA) combined with the use of toxicant inclusion lists for semi-targeted analysis. In addition, we describe expected retention times for >160 highly diverse xenobiotics in human plasma and serum samples. The method described is intended to serve as a generic LC-HRMS exposomics workflow for research and educational purposes. Moreover, it may be employed as a primer, allowing for further adaptations according to specialized research needs, e.g., by including reference and/or internal standards, by expanding to data-independent acquisition (DIA), or by modifying the list of compounds prioritized in fragmentation experiments (MS2).

Qualification of a LC-HRMS platform method for biosimilar development using NISTmab as a model

Biosimilars are a cost-effective alternative to biopharmaceuticals, necessitating rigorous analytical methods for consistency and compliance. Liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS) is a versatile tool for assessing key attributes, encompassing molecular mass, primary structure, and post-translational modifications (PTMs). Adhering to ICH Q2R1, we validated an LC-HRMS based peptide mapping method using NISTmab as a reference. The method validation parameters, covering system suitability, specificity, accuracy, precision, robustness, and carryover, were comprehensively assessed. The method effectively differentiated the NISTmab from similar counterparts as well as from artificially introduced spiked conditions. Notably, the accuracy of mass error for NISTmab specific complementarity determining region peptides was within a maximum of 2.42 parts per million (ppm) from theoretical and the highest percent relative standard deviation (%RSD) observed for precision was 0.000219 %. It demonstrates precision in sequence coverage and PTM detection, with a visual inspection of total ion chromatogram approach for variability assessment. The method maintains robustness when subjected to diverse storage conditions, encompassing variations in column temperature and mobile phase composition. Negligible carryover was noted during the carryover analysis. In summary, this method serves as a versatile platform for multiple biosimilar development by effectively characterizing and identifying monoclonal antibodies, ultimately ensuring product quality.

Current Practices in LC-MS Untargeted Metabolomics: A Scoping Review on the Use of Pooled Quality Control Samples

Untargeted metabolomics is an analytical approach with numerous applications serving as an effective metabolic phenotyping platform to characterize small molecules within a biological system. Data quality can be challenging to evaluate and demonstrate in metabolomics experiments. This has driven the use of pooled quality control (QC) samples for monitoring and, if necessary, correcting for analytical variance introduced during sample preparation and data acquisition stages. Described herein is a scoping literature review detailing the use of pooled QC samples in published untargeted liquid chromatography-mass spectrometry (LC-MS) based metabolomics studies. A literature query was performed, the list of papers was filtered, and suitable articles were randomly sampled. In total, 109 papers were each reviewed by at least five reviewers, answering predefined questions surrounding the use of pooled quality control samples. The results of the review indicate that use of pooled QC samples has been relatively widely adopted by the metabolomics community and that it is used at a similar frequency across biological taxa and sample types in both small- and large-scale studies. However, while many studies generated and analyzed pooled QC samples, relatively few reported the use of pooled QC samples to improve data quality. This demonstrates a clear opportunity for the field to more frequently utilize pooled QC samples for quality reporting, feature filtering, analytical drift correction, and metabolite annotation. Additionally, our survey approach enabled us to assess the ambiguity in the reporting of the methods used to describe the generation and use of pooled QC samples. This analysis indicates that many details of the QC framework are missing or unclear, limiting the reader's ability to determine which QC steps have been taken. Collectively, these results capture the current state of pooled QC sample usage and highlight existing strengths and deficiencies as they are applied in untargeted LC-MS metabolomics.