Phase IIa trial in Duchenne muscular dystrophy shows vamorolone is a first-in-class dissociative steroidal anti-inflammatory drug
Bibliographic record
Abstract
We report a first-in-patient study of vamorolone, a first-in-class dissociative steroidal anti-inflammatory drug, in Duchenne muscular dystrophy. This 2-week, open-label Phase IIa multiple ascending dose study (0.25, 0.75, 2.0, and 6.0 mg/kg/day) enrolled 48 boys with Duchenne muscular dystrophy (4 to <7 years), with outcomes including clinical safety, pharmacokinetics and pharmacodynamic biomarkers. The study design included pharmacodynamic biomarkers in three contexts of use: 1. Secondary outcomes for pharmacodynamic safety (insulin resistance, adrenal suppression, bone turnover); 2. Exploratory outcomes for drug mechanism of action; 3. Exploratory outcomes for expanded pharmacodynamic safety. Vamorolone was safe and well-tolerated through the highest dose tested (6.0 mg/kg/day) and pharmacokinetics of vamorolone were similar to prednisolone. Using pharmacodynamic biomarkers, the study demonstrated improved safety of vamorolone versus glucocorticoids as shown by reduction of insulin resistance, beneficial changes in bone turnover (loss of increased bone resorption and decreased bone formation only at the highest dose level), and a reduction in adrenal suppression. Exploratory biomarkers of pharmacodynamic efficacy showed an anti-inflammatory mechanism of action and a beneficial effect on plasma membrane stability, as demonstrated by a dose-responsive decrease in serum creatine kinase activity. With an array of pre-selected biomarkers in multiple contexts of use, we demonstrate the development of the first dissociative steroid that preserves anti-inflammatory efficacy and decreases steroid-associated safety concerns. Ongoing extension studies offer the potential to bridge exploratory efficacy biomarkers to clinical outcomes.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".