Evaluation of a cIEF Fractionation Workflow for Offline MS Analysis of Charge Variants of the Monoclonal Antibody Matuzumab

Abstract

Biological drugs like monoclonal antibodies require careful analysis and characterization to ensure product quality, safety, and efficacy. Charge variants of the molecule are of key interest and are analyzed using imaged capillary isoelectric focusing (icIEF). However, deeper characterization of these variants poses challenges. Two workflows for their characterization exist: an ion-exchange chromatography method for variant collection before mass spectrometry (MS) analysis, which is labor-intensive, and direct coupling of CE to MS, which allows detailed structural characterization but has limitations, for example, due to incompatibilities with ES ionization using high BGE concentrations. This study evaluates a platform that fractionates charge variants for offline MS analysis. The suitability of a procedure in which analytical icIEF methods are converted into preparative cIEF fractionation methods by increasing the sample concentration and adding 20 mM arginine as a cathodic spacer was tested. After chemical mobilization and fraction collection, the identity of the fractions was determined by fluorescence measurement and reinjection of the protein-containing fractions, using the previously developed analytical icIEF method. MS was subsequently performed. The general suitability of the workflow was demonstrated using Matuzumab. Transferring the analytical method from a concentration of 0.2 to 1.2 mg/mL in fractionation yielded an identical number of peaks and visually comparable peak profiles. The preparative separation took 50 min, with an additional 25 min for mobilization and 45 s per fraction collection, totaling approximately 2.5 h. Verification of charge variant isolation was straightforward via analytical icIEF. Following fractionation, MS allowed for the identification of the main peaks. Preliminary results with other antibodies indicated that the concentration range for MS experiments needs further investigation. Future work will aim to optimize sensitivity, selectivity, analysis time, and reproducibility.