Recent advancements in exon-skipping therapies using antisense oligonucleotides and genome editing for the treatment of various muscular dystrophies
Bibliographic record
Abstract
Muscular dystrophy is a group of genetic disorders characterised by degeneration of muscles. Different forms of muscular dystrophy can show varying phenotypes with a wide range of age, severity and location of muscle deterioration. Many palliative care options are available for muscular dystrophy patients, but no curative treatment is available. Exon-skipping therapy aims to induce skipping of exons with disease-causing mutations and/or nearby exons to restore the reading frame, which results in an internally truncated, partially functional protein. In antisense-mediated exon-skipping synthetic antisense oligonucleotide binds to pre-mRNA to induce exon skipping. Recent advances in exon skipping have yielded promising results; the US Food and Drug Administration (FDA) approved eteplirsen (Exondys51) as the first exon-skipping drug for the treatment of Duchenne muscular dystrophy, and in vivo exon skipping has been demonstrated in animal models of dysferlinopathy, limb-girdle muscular dystrophy type 2C and congenital muscular dystrophy type 1A. Novel methods that induce exon skipping utilizing Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) are also being developed where splice site mutations are created within the genome to induce exon skipping. Challenges remain as exon-skipping agents can have deleterious non-specific effects and different in-frame deletions show phenotypic variance. This article reviews the state of the art of exon skipping for treating muscular dystrophy and discusses challenges and future prospects.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.004 | 0.002 |
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".