A Surface Plasmon Resonance‐Based Integrated Assay for Quantification and Glycosylation Characterization of Monoclonal Antibodies in Crude Heterogeneous Samples
Bibliographic record
Abstract
The rise in cancer, autoimmune, inflammatory, and infectious diseases in recent decades has led to a surge in the development of monoclonal antibodies (mAbs) therapies, now the most widely used family of biologics. To meet the growing global demand, biopharmaceutical industries are intensifying their production processes. One approach to achieve more efficient production of effective mAbs is to develop tools for real-time quality monitoring. Specifically, the glycosylation profile of mAbs must be closely monitored, since it greatly impacts their therapeutic efficacy and innocuity, making it a critical quality attribute. In this study, we developed a surface plasmon resonance-based integrated assay allowing for the simultaneous quantification and glycosylation characterization of mAbs in crude samples, hence permitting the at-line analysis of bioreactor cell cultures. Thanks to the high specificity of the interaction between biosensor surface-bound protein A and the Fc region of mAbs, we quantified crude IgG samples under mass transport limitations. Next, by flowing running buffer on the surface, impurities contained in the mAbs samples were washed away from the biosensor surface, allowing subsequent recording of the kinetics between the captured mAbs and injected FcγRII receptors. Of interest, with this strategy, we were able to quantify terminal galactosylation and core fucosylation of IgG lots, two important glycan modifications for mAb efficacy.
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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".