Characterization of Therapeutic Antibody Charge Heterogeneity Under Stress Conditions by Microfluidic Capillary Electrophoresis Coupled with Mass Spectrometry

Therapeutic antibodies are a major class of biopharmaceutics that are applied in disease treatment because of their many advantages, including high specificity and high affinity to molecular targets. Between their production and administration, therapeutic antibodies are exposed to multiple stress conditions. Forced degradation and stress stability studies are conducted to simulate the risk of degradation and the effects of these stresses, thereby enhancing understanding of the drug product to support strategies to mitigate the impact from stressed conditions. These types of studies are also routinely conducted to evaluate product comparability when major process changes are implemented during the production. Charge variant analysis helps understand the changes in the electrostatic environment of biotherapeutics and can uncover underlying molecular level alterations associated with charge variants. Herein, we used ZipChip native capillary electrophoresis-mass spectrometry (nCE-MS) to elucidate the changes in charge variant profiles at the molecular level. In two case studies under thermal stress conditions, we observed that charge variants arose from both post-translational modifications (including deamidation, oxidation, and pyroglutamate formation) and sequence truncations at the hinge regions. Under oxidative stress conditions, oxidation was found to be the major contributor to the changes in the charge variant profiles. Under pH stress conditions, the changes in the charge variant profile were due to increased levels of deamidation, oxidation, and pyroglutamate formation. ZipChip nCE-MS analysis enables identification of charge variant species under various stress conditions, thus supporting process and formulation development of biotherapeutics. (c) 2024 American Pharmacists Association. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.