Long-Term Efficacy of Systemic Multiexon Skipping Targeting Dystrophin Exons 45–55 With a Cocktail of Vivo-Morpholinos in Mdx52 Mice
Bibliographic record
Abstract
Antisense-mediated exon skipping, which can restore the reading frame, is a most promising therapeutic approach for Duchenne muscular dystrophy. Remaining challenges include the limited applicability to patients and unclear function of truncated dystrophin proteins. Multiexon skipping targeting exons 45-55 at the mutation hotspot of the dystrophin gene could overcome both of these challenges. Previously, we described the feasibility of exons 45-55 skipping with a cocktail of Vivo-Morpholinos in vivo; however, the long-term efficacy and safety of Vivo-Morpholinos remains to be determined. In this study, we examined the efficacy and toxicity of exons 45-55 skipping by intravenous injections of 6 mg/kg 10-Vivo-Morpholino cocktail (0.6 mg/kg each vPMO) every 2 weeks for 18 weeks to dystrophic exon-52 knockout (mdx52) mice. Systemic skipping of the entire exons 45-55 region was induced, and the Western blot analysis exhibited the restoration of 5-27% of normal levels of dystrophin protein in skeletal muscles, accompanied by improvements in histopathology and muscle strength. No obvious immune response and renal and hepatic toxicity were detected at the end-point of the treatment. We demonstrate our new regimen with the 10-Vivo-Morpholino cocktail is effective and safe for long-term repeated systemic administration in the dystrophic mouse model.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".