Skipping Multiple Exons of Dystrophin Transcripts Using Cocktail Antisense Oligonucleotides
Bibliographic record
Abstract
Duchenne muscular dystrophy (DMD) is one of the most common and lethal genetic disorders, with 20,000 children per year born with DMD globally. DMD is caused by mutations in the dystrophin (DMD) gene. Antisense-mediated exon skipping therapy is a promising therapeutic approach that uses short DNA-like molecules called antisense oligonucleotides (AOs) to skip over/splice out the mutated part of the gene to produce a shortened but functional dystrophin protein. One major challenge has been its limited applicability. Multiple exon skipping has recently emerged as a potential solution. Indeed, many DMD patients need exon skipping of multiple exons in order to restore the reading frame, depending on how many base pairs the mutated exon(s) and adjacent exons have. Theoretically, multiple exon skipping could be used to treat approximately 90%, 80%, and 98% of DMD patients with deletion, duplication, and nonsense mutations, respectively. In addition, multiple exon skipping could be used to select deletions that optimize the functionality of the truncated dystrophin protein. The proof of concept of systemic multiple exon skipping using a cocktail of AOs has been demonstrated in dystrophic dog and mouse models. Remaining challenges include the insufficient efficacy of systemic treatment, especially for therapies that target the heart, and limited long-term safety data. Here we review recent preclinical developments in AO-mediated multiple exon skipping and discuss the remaining challenges.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".