Bivalent non-human gal-α1-3-gal glycan epitopes in the Fc region of a monoclonal antibody model can be recognized by anti-Gal-α1-3-Gal IgE antibodies
Why this work is in the frame
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Bibliographic record
Abstract
Monoclonal antibody (mAb) production using non-human cells can introduce non-human glycan epitopes including terminal galactosyl-α1-3-galactose (α1-3-Gal) moieties. Cetuximab is a commercial mAb associated with causing anaphylaxis in some patients due to the binding of endogenous anti-α1-3-Gal IgE to the Fab (containing bi-α1-3-galactosylated glycans) but not to the Fc region (containing mono-α1-3-galactosylated glycans). Despite being low in abundance in typical commercial mAbs, the inherent sensitivity of cell culture conditions on glycosylation profiles, and the development of novel glycoengineering strategies, novel antibody-based modalities, and biosimilars by various manufacturers with varying procedures, necessitates a better understanding of the structural requirements for anti-α1-3-Gal IgE binding to the Fc region. Herein, we synthesized mAb glycoforms with varying degrees and regioisomers of α1-3-galactosylation and tested their binding to two commercial anti-α1-3-Gal human IgE antibodies derived from a human patient with allergies to red meat (comprising α1-3-Gal epitopes), as well as to the FcγRIIIA receptor. Our results demonstrate that unexpectedly, anti-α1-3-Gal human IgE antibodies can bind to Fc glycans, with bi-α1-3-galactosylation being the most important factor, highlighting that their presence in the Fc region may be considered as a potential critical quality attribute, particularly when using novel platforms in mAb-based biotherapeutics.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it