Onasemnogene abeparvovec for the treatment of spinal muscular atrophy
Bibliographic record
Abstract
Introduction Gene therapy for spinal muscular atrophy (SMA) represents a significant milestone in the treatment of neurologic diseases. SMA is a neurodegenerative disease that results in motor neuron loss because of mutations of the survival motor neuron 1 gene, which directs survival motor neuron (SMN) protein production. Onasemnogene abeparvovec, a one-time gene replacement therapy, delivers a functional transgene to restore SMN protein expression. Onasemnogene abeparvovec has demonstrated improved survival and motor milestone achievements for presymptomatic infants and patients with SMA type 1.Areas covered This expert review describes the current state of gene therapy for SMA, reviews the mechanism of and clinical experience with onasemnogene abeparvovec, explains future efforts to expand applications of gene therapy for SMA, and provides context for developing gene therapy for other conditions.Expert opinion Onasemnogene abeparvovec has demonstrated efficacy in clinical trials and, because of this, is a valuable treatment option for patients with symptomatic infantile SMA and those identified by newborn screening. Gene therapy is still in its infancy, and challenges and uncertainties associated with transgene delivery must be addressed. With ongoing development of vector technology, more specific tissue tropism, reduced ‘off-target’ effects, and an enhanced safety profile will continue to evolve.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".