A low‐temperature SPR‐based assay for monoclonal antibody galactosylation and fucosylation assessment using FcγRIIA/B
Bibliographic record
Abstract
Monoclonal antibodies (MAbs) are powerful therapeutic tools in modern medicine and represent a rapidly expanding multibillion USD market. While bioprocesses are generally well understood and optimized for MAbs, online quality control remains challenging. Notably, N-glycosylation is a critical quality attribute of MAbs as it affects binding to Fcγ receptors (FcγRs), impacting the efficacy and safety of MAbs. Traditional N-glycosylation characterization methods are ill-suited for online monitoring of a bioreactor; in contrast, surface plasmon resonance (SPR) represents a promising avenue, as SPR biosensors can record MAb-FcγR interactions in real-time and without labeling. In this study, we produced five lots of differentially glycosylated Trastuzumab (TZM) and finely characterized their glycosylation profile by HILIC-UPLC chromatography. We then compared the interaction kinetics of these MAb lots with four FcγRs including FcγRIIA and FcγRIIB at 5°C and 25°C. When interacting with FcγRIIA/B at low temperature, the differentially glycosylated MAb lots exhibited distinct kinetic behaviors, contrary to room-temperature experiments. Galactosylated TZM (1) and core fucosylated TZM (2) could be discriminated and even quantified using an analytical technique based on the area under the curve of the signal recorded during the dissociation phase of a SPR sensorgram describing the interaction with FcγRIIA (1) or FcγRII2B (2). Because of the rapidity of the proposed method (<5 min per measurement) and the small sample concentration it requires (as low as 30 nM, exact concentration not required), it could be a valuable process analytical technology for MAb glycosylation monitoring.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".