In Vivo Evaluation of Candidate Allele-specific Mutant Huntingtin Gene Silencing Antisense Oligonucleotides
Bibliographic record
Abstract
Huntington disease (HD) is a dominant, genetic neurodegenerative disease characterized by progressive loss of voluntary motor control, psychiatric disturbance, and cognitive decline, for which there is currently no disease-modifying therapy. HD is caused by the expansion of a CAG tract in the huntingtin (HTT) gene. The mutant HTT protein (muHTT) acquires toxic functions, and there is significant evidence that muHTT lowering would be therapeutically efficacious. However, the wild-type HTT protein (wtHTT) serves vital functions, making allele-specific muHTT lowering strategies potentially safer than nonselective strategies. CAG tract expansion is associated with single nucleotide polymorphisms (SNPs) that can be targeted by gene silencing reagents such as antisense oligonucleotides (ASOs) to accomplish allele-specific muHTT lowering. Here we evaluate ASOs targeted to HD-associated SNPs in acute in vivo studies including screening, distribution, duration of action and dosing, using a humanized mouse model of HD, Hu97/18, that is heterozygous for the targeted SNPs. We have identified four well-tolerated lead ASOs that potently and selectively silence muHTT at a broad range of doses throughout the central nervous system for 16 weeks or more after a single intracerebroventricular (ICV) injection. With further validation, these ASOs could provide a therapeutic option for individuals afflicted with HD.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".