An Fc engineering approach that modulates antibody-dependent cytokine release without altering cell-killing functions
Why this work is in the frame
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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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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 it