Enhancing activity of FcαRI-bispecific antibodies using glycoengineering
Bibliographic record
Abstract
Macrophages and natural killer (NK) cells can effectively kill tumor cells in the presence of anti-cancer IgG monoclonal antibodies (mAbs), but neutrophils are less effective. We previously showed that IgG1 bispecific antibodies (BsAb), which target the IgA Fc receptor (FcαRI, CD89) and a tumor associated antigen induce effective neutrophil recruitment and tumor cell killing in vivo. Here we investigated if the efficacy of an anti-EGFR (CetuximAb)/FcαRI-bispecific antibody could be further improved by implementing glycoengineering of the IgG-Fc, aimed at increasing FcγRIIIa/b binding and/or complement activity. Fc afucosylation was introduced to enhance antibody-dependent cellular cytotoxicity (ADCC) by FcγRIIIa on NK/macrophages, which can also reduce neutrophil-mediated ADCC through their GPI-linked FcγRIIIb. Fc galactylation was found to enhance antibody hexamerization and thereby complement dependent cytotoxicity (CDC). Low fucosylated BsAbs moderately increased NK cell-mediated tumor cell killing, but did not affect neutrophil-mediated tumor cell killing nor phagocytosis by macrophages. Glycoengineering of these EGFR-specific BsAb, which normally are devoid of CDC-activity, did not enable their complement activities. In conclusion, glycoengineered FcαRI BsAbs increased ADCC by NK cells but had little effect on neutrophil or macrophage mediated tumor killing.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".