An Fc engineering approach that modulates antibody-dependent cytokine release without altering cell-killing functions
Bibliographic record
Abstract
Cytotoxic therapeutic monoclonal antibodies (mAbs) often mediate target cell-killing by eliciting immune effector functions via Fc region interactions with cellular and humoral components of the immune system. Key functions include antibody-dependent cell-mediated cytotoxicity (ADCC), antibody-dependent cellular phagocytosis (ADCP), and complement-dependent cytotoxicity (CDC). However, there has been increased appreciation that along with cell-killing functions, the induction of antibody-dependent cytokine release (ADCR) can also influence disease microenvironments and therapeutic outcomes. Historically, most Fc engineering approaches have been aimed toward modulating ADCC, ADCP, or CDC. In the present study, we describe an Fc engineering approach that, while not resulting in impaired ADCC or ADCP, profoundly affects ADCR. As such, when peripheral blood mononuclear cells are used as effector cells against mAb-opsonized tumor cells, the described mAb variants elicit a similar profile and quantity of cytokines as IgG1. In contrast, although the variants elicit similar levels of tumor cell-killing as IgG1 with macrophage effector cells, the variants do not elicit macrophage-mediated ADCR against mAb-opsonized tumor cells. This study demonstrates that Fc engineering approaches can be employed to uncouple macrophage-mediated phagocytic and subsequent cell-killing functions from cytokine release.
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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) | 0.000 | 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) | 0.001 | 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".