Skipping Multiple Exons to Treat DMD—Promises and Challenges
Bibliographic record
Abstract
gene. Antisense-mediated exon-skipping is a promising therapeutic strategy that makes use of synthetic nucleic acids to skip frame-disrupting exon(s) and allows for short but functional protein expression by restoring the reading frame. In 2016, the U.S. Food and Drug Administration (FDA) approved eteplirsen, which skips DMD exon 51 and is applicable to approximately 13% of DMD patients. Multiple exon skipping, which is theoretically applicable to 80-90% of DMD patients in total, have been demonstrated in animal models, including dystrophic mice and dogs, using cocktail antisense oligonucleotides (AOs). Although promising, current drug approval systems pose challenges for the use of a cocktail AO. For example, both exons 6 and 8 need to be skipped to restore the reading frame in dystrophic dogs. Therefore, the cocktail of AOs targeting these exons has a combined therapeutic effect and each AO does not have a therapeutic effect by itself. The current drug approval system is not designed to evaluate such circumstances, which are completely different from cocktail drug approaches in other fields. Significant changes are needed in the drug approval process to promote the cocktail AO approach.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".