Targeting FcRn for immunomodulation: Benefits, risks, and practical considerations
Bibliographic record
Abstract
The neonatal fragment crystallizable (Fc) receptor (FcRn) functions as a recycling mechanism to prevent degradation and extend the half-life of IgG and albumin in the circulation. Several FcRn inhibitors selectively targeting IgG recycling are now moving rapidly toward clinical practice in neurology and hematology. These molecules accelerate the destruction of IgG, reducing pathogenic IgG and IgG immune complexes, with no anticipated effects on IgA, IgM, IgE, complement, plasma cells, B cells, or other cells of the innate or adaptive immune systems. FcRn inhibitors have potential for future use in a much wider variety of antibody-mediated autoimmune diseases. Given the imminent clinical use, potential for broader utility, and novel mechanism of action of FcRn inhibitors, here we review data from 4 main sources: (a) currently available activity, safety, and mechanism-of-action data from clinical trials of FcRn inhibitors; (b) other procedures and treatments that also remove IgG (plasma donation, plasma exchange, immunoadsorption); (c) diseases resulting in loss of IgG; and (d) primary immunodeficiencies with potential mechanistic similarities to those induced by FcRn inhibitors. These data have been evaluated to provide practical considerations for the assessment, monitoring, and reduction of any potential infection risk associated with FcRn inhibition, in addition to highlighting areas for future research.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| 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".