Is Duchenne gene therapy a suitable treatment despite its immunogenic class effect?
Bibliographic record
Abstract
INTRODUCTION: Duchenne muscular dystrophy (DMD) is a severe X-linked disorder characterized by progressive muscle weakness and eventual death due to cardiomyopathy or respiratory complications. Currently, there is no cure for DMD, with standard treatments primarily focusing on symptom management. Using immunosuppressive measures and optimized vector designs allows for gene therapies to better address the genetic cause of the disease. AREAS COVERED: This review evaluates the efficacy and safety of emerging DMD gene therapies as of 2024. It also discusses the potential of utrophin upregulation, gene editing, and truncated dystrophin as therapeutic strategies. It highlights safety concerns associated with these therapies, including adverse events and patient deaths. A comprehensive overview of developments covers topics such as CRISPR-Cas9 therapies, micro-dystrophin, and the potential delivery of full-length dystrophin. EXPERT OPINION: The FDA's recent approval of delandistrogene moxeparvovec (Elevidys) underscores the promise of gene replacement therapies for DMD patients. Understanding the mechanisms behind the adverse effects and excluding patients with specific pathogenic variants may enhance the safety profiles of these therapies. CRISPR/Cas9 therapies, while promising, face significant regulatory and safety challenges that hinder their clinical application. Optimal DMD therapies should target both skeletal and cardiac muscles to be effective.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".