Exon skipping for nonsense mutations in Duchenne muscular dystrophy: too many mutations, too few patients?
Bibliographic record
Abstract
INTRODUCTION: Duchenne muscular dystrophy (DMD), one of the most common and lethal genetic disorders, is caused by mutations of the dystrophin gene. Removal of an exon or of multiple exons using antisense molecules has been demonstrated to allow synthesis of truncated 'Becker muscular dystrophy-like' dystrophin. AREAS COVERED: Approximately 15% of DMD cases are caused by a nonsense mutation. Although patient databases have previously been surveyed for applicability to each deletion mutation pattern, this is not so for nonsense mutations. Here, we examine the world-wide database containing notations for more than 1200 patients with nonsense mutations. Approximately 47% of nonsense mutations can be potentially treated with single exon skipping, rising to 90% with double exon skipping, but to reach this proportion requires the development of exon skipping molecules targeting some 68 of dystrophin's 79 exons, with patient numbers spread thinly across those exons. In this review, we discuss progress and remaining hurdles in exon skipping and an alternative strategy, stop-codon readthrough. EXPERT OPINION: Antisense-mediated exon skipping therapy is targeted highly at the individual patient and is a clear example of personalized medicine. An efficient regulatory path for drug approval will be a key to success.
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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) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".