A monoclonal antibody with anti‐D–like activity in murine immune thrombocytopenia requires Fc domain function for immune thrombocytopenia ameliorative effects
Bibliographic record
Abstract
BACKGROUND: The mechanism of action of anti-D in ameliorating immune thrombocytopenia (ITP) remains unclear. The monoclonal antibody (MoAb) Ter119, which targets murine red blood cells (RBCs), has been shown to mimic the effect of anti-D in improving antibody-mediated murine ITP. The mechanism of Ter119-mediated ITP amelioration, especially the role of the antigen-binding and Fc domains, remains untested. A functional Fc domain is crucial for many therapeutic MoAb activity; therefore, the requirement of Ter119 Fc domain in ITP amelioration is investigated using outbred CD-1 mice. STUDY DESIGN AND METHODS: Ter119 variants, including Ter119 F(ab')2 fragments, deglycosylated Ter119, and afucosylated Ter119, were generated to test their effect in ameliorating antibody-induced murine ITP. In vivo inhibition of FcγRIII and FcγRIIB was achieved using the Fab fragment of the FcγRIII/FcγRIIB-specific MoAb 2.4G2. RESULTS: Ter119 F(ab')2 fragments and deglycosylated Ter119 were unable to ameliorate murine ITP or mediate phagocytosis of RBCs by RAW264.7 macrophages in vitro. Inhibition of FcγRIII and FcγRIIB, as well as Ter119 defucosylation, do not affect Ter119-mediated ITP amelioration. CONCLUSION: The Fc domain of Ter119, as well as its Fc glycosylation, is required for Ter119-mediated ITP amelioration. Moreover, both Fc and Fc glycosylation are required for Ter119-mediated phagocytosis in vitro. These findings demonstrate the importance of the Fc domain in a therapeutic MoAb with anti-D-like activity.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".