Gentamicin‐induced readthrough of stop codons in duchenne muscular dystrophy
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to establish the feasibility of long-term gentamicin dosing to achieve stop codon readthrough and produce full-length dystrophin. Mutation suppression of stop codons, successfully achieved in the mdx mouse using gentamicin, represents an important evolving treatment strategy in Duchenne muscular dystrophy (DMD). METHODS: Two DMD cohorts received 14-day gentamicin (7.5mg/kg/day): Cohort 1 (n = 10) stop codon patients and Cohort 2 (n = 8) frameshift controls. Two additional stop codon DMD cohorts were gentamicin treated (7.5mg/kg) for 6 months: Cohort 3 (n = 12) dosed weekly and Cohort 4 (n = 4) dosed twice weekly. Pre- and post-treatment biopsies were assessed for dystrophin levels, as were clinical outcomes. RESULTS: In the 14-day study, serum creatine kinase (CK) dropped by 50%, which was not seen in frameshift DMD controls. After 6 months of gentamicin, dystrophin levels significantly increased (p = 0.027); the highest levels reached 13 to 15% of normal (1 in Cohort 3, and 2 in Cohort 4), accompanied by reduced serum CK favoring drug-induced readthrough of stop codons. This was supported by stabilization of strength and a slight increase in forced vital capacity. Pretreatment stable transcripts predicted an increase of dystrophin after gentamicin. Readthrough efficiency was not affected by the stop codon or its surrounding fourth nucleotide. In 1 subject, antigen-specific interferon-gamma enzyme-linked immunospot assay detected an immunogenic dystrophin epitope. INTERPRETATION: The results support efforts to achieve drug-induced mutation suppression of stop codons. The immunogenic epitope resulting from readthrough emphasizes the importance of monitoring T-cell immunity during clinical studies that suppress stop codons. Similar principles apply to other molecular strategies, including exon skipping and gene therapy.
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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) | 0.000 | 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 | 0.000 |
| 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".