Multivariate quantitative analysis of glycan impact on IgG1 effector functions

Development of novel therapeutic proteins and biosimilars requires a thorough understanding of the relationship between their structure and function. Particularly, how IgG glycosylation affects its effector functions is a point increasingly underscored in guidelines by the World Health Organization and regulatory agencies. Our results show that just a 1% decrease in Fc fucosylation can lead to a more than 25% increase in antibody-dependent cell-mediated cytotoxicity. The intercorrelated nature of glycan patterns, combined with the low variability and lack of well-defined glycan patterns in process development and manufacture samples, makes studying the effects of individual glycan structures challenging. The conventional approach to structure-function studies often relies on a suboptimal set of tools, such as the one-factor-at-a-time method for experimental planning and univariate data analysis. Here, we introduce a systematic approach to understanding and prediction of the impact of Fc glycans on effector functions, using a combination of the design of experiment, multivariate data analysis, and in-vitro glycoengineering. This approach adheres to quality-by-design principles and aligns with regulatory agency guidelines. A variety of analytical assays, including binding and cell-based assays, were applied to investigate the effect of individual glycans of the IgG1 molecule. The regression models developed here provide a quantitative explanation and prediction of the impact of individual glycan features on the binding to FcγRs and bioactivity of the therapeutic protein. To the best of our knowledge, this is the first report of a systematic approach to quantitatively understand the multivariate impact of glycosylation on the effector functionality of therapeutic monoclonal antibodies, providing valuable tools for advancing therapeutic protein development.