Monovalent Fc receptor blockade by an anti–Fcγ receptor/albumin fusion protein ameliorates murine ITP with abrogated toxicity
Bibliographic record
Abstract
Patients with immune thrombocytopenia (ITP) commonly have antiplatelet antibodies that cause thrombocytopenia through Fcγ receptors (FcγRs). Antibodies specific for FcγRs, designed to inhibit antibody-FcγR interaction, had been shown to improve ITP in refractory human patients. However, the development of such FcγR-specific antibodies has stalled because of adverse events, a phenomenon recapitulated in mouse models. One hypothesis behind these adverse events involved the function of the Fc region of the antibody, which engages FcγRs, leading to inflammatory responses. Unfortunately, inhibition of Fc function by deglycosylation failed to prevent this inflammatory response. In this work, we hypothesize that the bivalent antigen-binding fragment regions of immunoglobulin G are sufficient to trigger adverse events and have reasoned that designing a monovalent targeting strategy could circumvent the inflammatory response. To this end, we generated a fusion protein comprising a monovalent human FcγRIIIA-specific antibody linked in tandem to human serum albumin, which retained FcγR-binding activity in vitro. To evaluate clinically relevant in vivo FcγR-blocking function and inflammatory effects, we generated a murine version targeting the murine FcγRIII linked to murine albumin in a passive murine ITP model. Monovalent blocking of FcγR function dramatically inhibited antibody-dependent murine ITP and successfully circumvented the inflammatory response as assessed by changes in body temperature, basophil activation, and basophil depletion. Consistent with our hypothesis, in vivo cross-linking of the fusion protein induced these inflammatory effects, recapitulating the adverse events of the parent antibody. Thus, monovalent blocking of FcγR function demonstrates a proof of concept to successfully treat FcγR-mediated autoimmune diseases.
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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) | 0.001 | 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) | 0.001 | 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".