An Updated Analysis of Exon-Skipping Applicability for Duchenne Muscular Dystrophy Using the UMD-DMD Database
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Antisense oligonucleotide (ASO)-mediated exon-skipping is an effective approach to restore the disrupted reading frame of the dystrophin gene for the treatment of Duchenne muscular dystrophy (DMD). Currently, four FDA-approved ASOs can target three different exons, but these therapies are mutation-specific and only benefit a subset of patients. Understanding the broad applicability of exon-skipping approaches is essential for prioritizing the development of additional therapies with the greatest potential impact on the DMD population. This review offers an updated analysis of all theoretical exon-skipping strategies and their applicability across the patient population, with a specific focus on DMD-associated mutations documented in the UMD-DMD database. Unlike previous studies, this approach leverages the inclusion of phenotypic data for each mutation, providing a more comprehensive and clinically relevant perspective. METHODS: The theoretical applicability of all single and double exon-skipping strategies, along with multi exon-skipping strategies targeting exons 3-9 and 45-55, was evaluated for all DMD mutations reported in the UMD-DMD database. RESULTS: Single and double exon-skipping approaches were applicable for 92.8% of large deletions, 93.7% of small lesions, 72.4% of duplications, and 90.3% of all mutations analyzed. Exon 51 was the most relevant target and was applicable for 10.6% of all mutations and 17.2% of large deletions. Additionally, two multi-exon-skipping approaches, targeting exons 45-55 and 3-9, were relevant for 70.6% of large deletions and 19.2% of small lesions. CONCLUSIONS: Current FDA-approved ASOs were applicable to 27% of the UMD-DMD population analyzed, leaving a significant portion of patients without access to exon-skipping therapies. The clinical translation of alternative approaches is critical to expanding the accessibility of these therapies for the DMD population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".