Gene Editing for Duchenne Muscular Dystrophy: From Experimental Models to Emerging Therapies
Bibliographic record
Abstract
The CRISPR system has emerged as a ground-breaking gene-editing tool, offering promising therapeutic potential for Duchenne muscular dystrophy (DMD), a severe genetic disorder affecting approximately 1 in 5000 male births globally. DMD is caused by mutations in the dystrophin gene, which encodes a critical membrane-associated protein essential for maintaining muscle structure, function and repair. Patients with DMD experience progressive muscle degeneration, loss of ambulation, respiratory insufficiency, and cardiac failure, with most succumbing to the disease by their third decade of life. Despite the well-characterized genetic basis of DMD, curative treatments- such as exon skipping therapies, micro-dystrophin, and steroids- remain elusive. Recent preclinical studies have demonstrated the promise of CRISPR-based approaches in restoring dystrophin expression across various models, including human cells, murine systems, and large animal models. These advancements highlight the potential of gene editing to fundamentally alter the trajectory of the disease. However, significant challenges persist, including immunogenicity, off-target effects, and limited editing efficiency, which hinder clinical translation. This review provides a comprehensive analysis of the latest developments in CRISPR-based therapeutic strategies for DMD. It emphasizes the need for further innovation in gene-editing technologies, delivery systems, and rigorous safety evaluations to overcome current barriers and harness the full potential of CRISPR/Cas as a durable and effective treatment for DMD.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".