Brogidirsen and Exon 44 Skipping for Duchenne Muscular Dystrophy: Advances and Challenges in RNA-Based Therapy
Bibliographic record
Abstract
Duchenne muscular dystrophy (DMD) is a severe inherited muscle-wasting disorder that is associated with severe morbidity and mortality globally. Current treatment options have improved the quality of life of patients, but these treatments are only palliative. There is a need for more DMD treatment options. Antisense oligonucleotide (ASO) therapies have emerged as a promising personalized treatment option for patient groups that possess specific mutations. A subset of these therapies can skip over frame-disrupting exons in the DMD gene and can partially restore dystrophin production for individuals with DMD. One novel exon skipping therapy currently being investigated is brogidirsen, an exon 44 that targets ASO using a novel dual-targeting approach. This article will provide an overview of brogidirsen’s history and current clinical trial developments. It will summarize how this investigational therapy compares with other pre-clinical and clinical trial-stage ASO therapies targeting exon 44. Current advances and challenges faced by RNA-based therapies will also be discussed. Overall, brogidirsen is a promising potential addition to existing DMD treatment options, with its clinical trial results showing expression levels above that of the maximum amount of dystrophin expression achieved by current FDA- and EMA-approved exon-skipping DMD therapies. Further research will be needed to determine its overall efficacy and ability to overcome the known limitations faced by other existing ASO therapies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".