The road toward AAV-mediated gene therapy of Duchenne muscular dystrophy
Bibliographic record
Abstract
Forty years after the dystrophin gene was cloned, significant progress has been made in developing gene therapy approaches for Duchenne muscular dystrophy (DMD). The disorder has presented numerous challenges, including the enormous size of the gene (2.2 MB), the need to target muscles body wide, and immunogenic issues against both vectors and dystrophin. Among human genetic disorders, DMD is relatively common, and the genetics are complicated since one-third of all cases arise from a spontaneous new mutation, resulting in thousands of independent lesions throughout the locus. Many approaches have been pursued in the goal of finding an effective therapy, including exon skipping, nonsense codon suppression, upregulation of surrogate genes, gene replacement, and gene editing. Here, we focus specifically on methods using AAV vectors, as these approaches have been tested in numerous clinical trials and are able to target muscles systemically. We discuss early advances to understand the structure of dystrophin, which are crucial for the design of effective DMD gene therapies. Included is a summary of efforts to deliver micro-, mini-, and full-length dystrophins to muscles. Finally, we review current approaches to adapt gene editing to the enormous DMD gene with prospects for improved therapies using all these methods.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".