Comprehensive review of adverse reactions and toxicology in ASO-based therapies for Duchenne Muscular Dystrophy: From FDA-approved drugs to peptide-conjugated ASO
Bibliographic record
Abstract
Duchenne Muscular Dystrophy (DMD) is a devastating X-linked genetic disorder characterized by progressive muscle degeneration due to mutations in the dystrophin gene. This results in the absence or dysfunction of the dystrophin protein, leading to muscle weakness, loss of ambulation, respiratory issues, and cardiac complications, often leading to premature death. Recently, antisense oligonucleotide (ASO)-mediated exon skipping has emerged as a promising therapeutic strategy for DMD. Notably, the FDA has conditionally approved four ASO therapies for DMD, with numerous others in various stages of clinical development, indicating the growing interest and potential in this field. To enhance ASO-based therapies, researchers have explored the novel concept of conjugating peptides to the phosphorodiamidate morpholino backbone (PMO) of ASOs, leading to the development of peptide-conjugated PMOs (PPMOs). These PPMOs have demonstrated significantly improved pharmacokinetic profiles, potentially augmenting their therapeutic effectiveness. Despite the optimism surrounding ASOs and PPMOs, concerns persist regarding their efficacy and safety. To comprehensively evaluate these therapies, it is imperative to expand patient populations in clinical trials and conduct thorough investigations into the associated risks. This article provides a comprehensive review and discussion of the available data pertaining to adverse reactions and toxicology associated with FDA-approved ASO drugs for DMD. Furthermore, it offers insights into the emerging category of peptide-conjugated ASO drugs those are clinical and preclinical trials, shedding light on their potential benefits and challenges.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".