Abstract

This study aimed to explore the suitability of flow injection spectrophotometry (FIS) to analyze three degraded therapeutic monoclonal antibodies (bevacizumab, nivolumab, and rituximab). For this purpose, aggregates were generated with stirring, freeze-thaw, and heat stresses. The intact and stressed mab samples were filtered with 0.22 µm hydrophilic filters and analyzed by size exclusion chromatography (SEC), cation-exchange chromatography (CEX), and FIS. In terms of quantitative and qualitative analysis, protein loss and structural changes were assessed. Various aggregates profiles were obtained according to the mabs and the stresses. FIS allowed performing very satisfactory quantifications for each mab with intermediate precision RSD < 3.0 % and recovery between 97.9 and 102.0 %. From the protein loss measurements, it appears that SEC underestimates the mab aggregate proportions up to two times less as compared with FIS since the latter avoids any non-specific interactions (electrostatic or hydrophobic interactions). Using second derivative spectroscopy and multivariate data analysis, we noticed apparent structural differences, located in the regions 245–265 nm for rituximab and nivolumab and 280–300 nm for bevacizumab, depending on the stress. The FIS complementarity with the other techniques used in this study allowed us to demonstrate that the three mabs behave differently for a given stress condition. While extreme mechanical stress formed large aggregates irrespective of the mabs, rituximab showed to be less stable and more sensitive than the two other mabs under freeze-thaw and heat stresses, generating large aggregates (>200 nm) and partial unfolding. Nivolumab tends to form small aggregates less than 50 nm when heated and freeze-thawed. Moreover, freeze-thaw seems to generate native IgG-1 aggregates with rituximab. Similarly, bevacizumab showed to form these IgG-1 aggregates and was resistant to freeze-thaw, likely thanks to trehalose cryoprotectant from its formulation. Finally, FIS associated with multivariate analysis can provide rich information in one single run and appears to be a fast, simple, and reliable method to set complementary and orthogonal approaches for protein aggregates monitoring.

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