Challenges and future perspective of antisense therapy for spinal muscular atrophy: A review
Bibliographic record
Abstract
Spinal muscular atrophy (SMA), the most common genetic cause of infantile death, is caused by a mutation in the survival of motor neuron 1 gene (SMN1), leading to the death of motor neurons and progressive muscle weakness. SMN1 normally produces an essential protein called SMN. Although humans possess a paralogous gene called SMN2, ∼90% of the SMN it produces is non-functional. This is due to a mutation in SMN2 that causes the skipping of a required exon during splicing of the pre-mRNA. The first treatment for SMA, nusinersen (brand name Spinraza), was approved by the FDA in 2016 and by the EMU in 2017. Nusinersen is an antisense oligonucleotide-based therapy that alters the splicing of SMN2 to make functional full-length SMN protein. Despite the recent advancements in antisense oligonucleotide therapy and SMA treatment development, nusinersen is faced with a multitude of challenges, such as intracellular and systemic delivery. In recent years, the use of peptide-conjugated phosphorodiamidate morpholino oligomers (PPMOs) in antisense therapy has gained interest. These are antisense oligonucleotides conjugated to cell-penetrating peptides such as Pips and DG9, and they have the potential to address the challenges associated with delivery. This review focuses on the historic milestones, development, current challenges, and future perspectives of antisense therapy for SMA.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".