The importance of synthetic pharmacotherapy for recessive cerebellar ataxias
Bibliographic record
Abstract
INTRODUCTION: The last decade has witnessed major breakthroughs in identifying novel genetic causes of hereditary ataxias, deepening our understanding of disease mechanisms, and developing therapies for these debilitating disorders. AREAS COVERED: This article reviews the currently approved and most promising candidate pharmacotherapies in relation to the known disease mechanisms of the most prevalent autosomal recessive ataxias. Omaveloxolone is an Nrf2 activator that increases antioxidant defense and was recently approved for treatment of Friedreich ataxia. Its therapeutic effect is modest, and further research is needed to find synergistic treatments that would halt or reverse disease progression. Promising approaches include upregulation of frataxin expression by epigenetic mechanisms, direct protein replacement, and gene replacement therapy. For ataxia-telangiectasia, promising approaches include splice-switching antisense oligonucleotides and small molecules targeting oxidative stress, inflammation, and mitochondrial function. Rare recessive ataxias for which disease-modifying therapies exist are also reviewed, emphasizing recently approved therapies. Evidence supporting the use of riluzole and acetyl-leucine in recessive ataxias is discussed. EXPERT OPINION: Advances in genetic therapies for other neurogenetic conditions have paved the way to implement feasible approaches with potential dramatic benefits. Particularly, as we develop effective treatments for these conditions, we may need to combine therapies, consider newborn testing for pre-symptomatic treatment, and optimize non-pharmacological approaches.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.004 | 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".