Emerging Drugs for Generalized Myasthenia Gravis
Bibliographic record
Abstract
Horizon Scan reports provide brief summaries of information regarding new and emerging health technologies. These technologies are identified through the CADTH Horizon Scanning Service as topics of potential interest to health care decision-makers in Canada. This Horizon Scan summarizes the available information regarding emerging targeted therapies for the treatment of generalized myasthenia gravis (MG). MG is a rare and chronic autoimmune disease in which autoantibodies attack specific proteins in the neuromuscular junction, resulting in muscle weakness. Many patients develop generalized MG resulting in severe fatigable muscle weakness with difficulties in facial expression, speech, swallowing, and mobility. Current treatments for MG include anticholinesterase inhibitors, systemic corticosteroids, and nonsteroidal immunosuppressive drugs. IV immunoglobulins or therapeutic plasma exchange are the current treatment options for patients with severe or acutely worsening generalized MG. Thymectomy is also considered a treatment option for patients with generalized MG who fail to respond to immunotherapy or have intolerable side effects. In this scan, we have reviewed 6 new treatment strategies that target specific areas of the immune system involved in the pathogenesis of MG: efgartigimod, rozanolixizumab, zilucoplan, ravulizumab, batoclimab, and nipocalimab. Phase II and III clinical trials have shown that these drugs may potentially benefit patients with generalized MG based on improvements in measures of disease severity and functional disability. The information presented is limited in that most of the available evidence comes from phase II trials that were designed to primarily investigate safety and tolerability based on small sample sizes, short trial duration, and narrow inclusion criteria. Therefore, findings do not reflect current standard of practice for maintenance therapy for generalized MG in the real-world setting. Considerations for future use include identifying the population that will most likely benefit from therapy and evaluating these drugs for rare and serious adverse events. Factors such as ease of administration, dosing schedule, and cost are all important factors that will help determine uptake and place in therapy for these emerging targeted therapies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".