FcRN receptor antagonists in the management of myasthenia gravis
Bibliographic record
Abstract
Myasthenia gravis (MG) is an autoimmune disorder characterized by autoantibodies specifically directed against proteins located within the postsynaptic membrane of the neuromuscular junction. These pathogenic autoantibodies can be reduced by therapies such as plasma exchange, IVIG infusions and other immunosuppressive agents. However, there are significant side effects associated with most of these therapies. Since there is a better understanding of the molecular structure and the biological properties of the neonatal Fc receptors (FcRn), it possesses an attractive profile in treating myasthenia gravis. FcRn receptors prevent the catabolism of IgG by impeding their lysosomal degradation and facilitating their extracellular release at physiological pH, consequently extending the IgG half-life. Thus, the catabolism of IgG can be enhanced by blocking the FcRn, leading to outcomes similar to those achieved through plasma exchange with no significant safety concerns. The available studies suggest that FcRn holds promise as a versatile therapeutic intervention, capable of delivering beneficial outcomes in patients with distinct characteristics and varying degrees of MG severity. Efgartigimod is already approved for the treatment of generalized MG, rozanolixizumab is under review by health authorities, and phase 3 trials of nipocalimab and batoclimab are underway. Here, we will review the available data on FcRn therapeutic agents in the management of MG.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".