Recent Progress of Antisense Oligonucleotide Therapy for Superoxide-Dismutase-1-Mutated Amyotrophic Lateral Sclerosis: Focus on Tofersen
Bibliographic record
Abstract
Amyotrophic lateral sclerosis (ALS) is a refractory neurodegenerative disease characterized by the degeneration and loss of motor neurons, typically resulting in death within five years of onset. There have been few effective treatments, making the development of robust therapies an urgent challenge. Genetic mutations have been identified as contributors to ALS, with mutations in superoxide dismutase 1 (SOD1), which neutralizes the harmful reactive oxygen species superoxide, accounting for approximately 2% of all ALS cases. To counteract the toxic gain of function caused by SOD1 mutations, therapeutic strategies aimed at suppressing SOD1 gene expression have shown promise. Antisense oligonucleotide (ASO) is an artificially synthesized, short, single-stranded DNA/RNA molecule that binds to target RNA to alter gene expression, representing a next-generation therapeutic approach. In 2023, tofersen became the first ASO drug approved by the FDA for ALS. Administered intrathecally, tofersen specifically binds to SOD1 mRNA, inhibiting the production of toxic SOD1 protein, thereby improving biomarkers of ALS. The long-term efficacy and safety of tofersen require further validation, and the development of more optimized treatment protocols is essential. A series of studies and therapeutic developments related to SOD1 mutations have advanced the understanding of ALS pathophysiology and significantly contributed to treatment strategies for central nervous system disorders. This review focuses on an overview of SOD1 mutations and the development process of tofersen, aiming to deepen the understanding of advancements in ALS research and discuss future challenges and directions for ASO therapy.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".