Current state and potential applications of neonatal Fc receptor (FcRn) inhibitors in hematologic conditions
Bibliographic record
Abstract
The neonatal fragment crystallizable (Fc) receptor (FcRn) transports IgG across mucosal surfaces and the placenta and protects IgG from degradation. Numerous clinical trials are investigating therapeutic FcRn inhibition for various immune-mediated neuromuscular and rheumatologic conditions; however, FcRn inhibition also represents a potential therapy for IgG-mediated hematologic conditions (e.g., immune thrombocytopenia, autoimmune hemolytic anemia, immune thrombotic thrombocytopenic purpura, acquired hemophilia, red blood cell/platelet alloimmunization). Current evidence derived from both in vitro and in vivo studies suggests that FcRn inhibitors effectively reduce total IgG levels without impacting its production or altering the levels of other immunoglobulin isotypes. Moreover, the risk of serious adverse events, including serious infections, appears to be lower than that seen with other commonly used immunomodulatory/immunosuppressive therapies, albeit in the setting of limited clinical trial data. Ultimately, additional clinical trials that include varied patient populations are required prior to incorporating these agents into standard treatment algorithms for most hematologic conditions. However, based on the pathophysiology of IgG-mediated hematologic disorders and the mechanism of action of FcRn inhibitors, these agents may represent a future novel therapeutic strategy for patients with hematologic conditions caused by IgG antibodies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".