Duchenne muscular dystrophy: promising early-stage clinical trials to watch
Bibliographic record
Abstract
INTRODUCTION: Current therapies are unable to cure Duchenne muscular dystrophy (DMD), a severe and common form of muscular dystrophy, and instead aim to delay disease progression. Several treatments currently in phase I trials could increase the number of therapeutic options available to patients. AREAS COVERED: This review aims to provide an overview of current treatments undergoing or having recently undergone early-stage trials. Several exon-skipping and gene therapy approaches are currently being investigated at the clinical stage to address an unmet need for DMD treatments. This article also covers Phase I trials from the last 5 years that involve inhibitors, small molecules, a purified synthetic flavanol, a cell-based therapy, and repurposed cardiac or tumor medications. EXPERT OPINION: With antisense oligonucleotide (AON) treatments making up the majority of conditionally approved DMD therapies, most of the clinical trials occurring within the last 5 years have also evaluated exon-skipping AONs. The approval of Elevidys, a micro-dystrophin therapy, is reflected in a recent trend toward gene transfer therapies in phase I DMD clinical trials, but their safety and efficacy are being established in this phase of development. Other Phase I clinical-stage approaches are diverse, but have a range in efficacy, safety, and endpoint measures.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".