FcγRII Binding Prediction via Glycan Correlation
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Solution Overview
Problem
There is a need in the biopharmaceutical industry for simple and efficient methods to predict the level of effector function or binding to an FcγR based on the glycoform profile of an antibody composition, and to determine the levels of particular glycans that will achieve a desired level of effector function or FcγR binding.
Innovation Solution
The disclosure provides statistically significant associations between the FcγRII binding level of an antibody composition and the levels of β-galactosylated glycans and/or afucosylated glycans. Expressions and equations correlate FcγRII binding with the percentage content of these glycans, enabling methods to predict FcγRII binding levels and identify glycoprofiles for desired antibody compositions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If glycan profiling methods are used to predict FcγR binding, then prediction accuracy is improved, but measurement complexity increases
Solution Approach 1:
The patent segments the complex FcγR binding prediction into multiple measurable glycan components (core fucose, terminal galactose, high mannose, β-galactosylated glycans). Each glycan type is quantified separately using HILIC chromatography, and their individual contributions to binding are determined through statistical analysis, making the overall complex measurement manageable and accurate
Solution Approach 2:
The patent uses glycan profiles as intermediary markers to predict FcγR binding. Instead of directly measuring complex FcγR binding interactions, the method measures glycan structures (which are easier to quantify) and uses established statistical correlations to infer binding levels, simplifying the measurement process while maintaining prediction accuracy
2Measurement precision
If multiple glycan types are measured to improve binding prediction, then prediction accuracy is improved, but analysis time increases
Solution Approach 1:
The patent combines multiple glycan measurements into a single integrated HILIC chromatography analysis. Different glycan types (core fucose, terminal galactose, high mannose, β-galactosylated glycans) are simultaneously separated and quantified in one analytical run, rather than requiring separate assays for each glycan type, thus reducing total analysis time while maintaining comprehensive prediction accuracy
3Reliability
If glycan content is optimized for FcγRII binding, then binding level is improved, but effector function may be compromised
Solution Approach 1:
The patent uses parameter optimization through statistical modeling to determine the specific glycan content ranges that achieve desired FcγRII binding levels. By establishing quantitative relationships between glycan profiles and binding, the method identifies optimal parameter windows that satisfy binding requirements while maintaining compatibility with effector functions through multi-parameter optimization rather than single-parameter maximization
Data Source
AI summary
Provided herein are methods of determining product quality of an antibody composition, wherein the product quality is based on the Fcγ receptor II (FcγRI) binding level of the antibody composition. In exemplary embodiments, the method comprises (a) determining the afucosylated glycan content and/or β-galactosylated glycan content of a sample of the antibody composition; (b) optionally, calculating a predicted FcγRII binding level based on the afucosylated glycan content and/or β-galactosylated glycan content as determined in (a); and (c) determining the product quality of the antibody composition as acceptable when (i) the afucosylated glycan content and/or β-galactosylated glycan content is within a target range and/or (ii) the predicted FcγRII binding level is within a target range. Related methods of monitoring product quality and methods of producing an antibody composition are further provided herein.