Future treatments for myelin oligodendrocyte glycoprotein antibody-associated disease: the clinical trial landscape
Bibliographic record
Abstract
INTRODUCTION: Myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) is an emerging autoimmune demyelinating disorder distinct from multiple sclerosis and AQP4-IgG-positive neuromyelitis optica. Despite increasing recognition, no therapies are currently approved for MOGAD, and treatment remains empirical, with significant variability in clinical response and access to care. AREAS COVERED: This review explores the evolving treatment landscape of adult MOGAD, with a focus on immunotherapies under active clinical investigation: azathioprine, tocilizumab, satralizumab, and rozanolixizumab. For each agent, we discuss mechanisms of action, pharmacokinetics, dosing, safety, and efficacy based on clinical trials and observational data. Literature was identified through PubMed and ClinicalTrials.gov, including ongoing phase 2/3 studies (MOGwAI, TOMATO, METEOROID, and cosMOG). EXPERT OPINION: Targeted immunotherapies have the potential to transform MOGAD management. In the next five years, one or more of these agents may achieve regulatory approval, particularly if biomarker-driven strategies and trial designs are refined. Addressing unmet needs in pediatric populations and low-resource settings will be essential to ensure equitable, personalized treatment.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 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 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".