Antibody-based therapies for Huntington’s disease: current status and future directions
Bibliographic record
Abstract
The types of treatments and interventions being developed for chronic neurodegenerative disorders have expanded considerably in recent years. In addition to the variety of targets being pursued, strategies have moved from symptom management to more directed disease-modifying approaches. Among them are antibody-based therapies, which are not only being evaluated for a range of tauopathies and synucleinopathies, but are also emerging as a potential application for monogenic disorders of the central nervous system (CNS), including Huntington's disease (HD). Despite the excitement around the early trial data of anti-sense oligonucleotides (ASO) treatment for such disorders, antibody therapies may hold the key to tackling another aspect of the disease that could be critical to its pathogenesis. While gene-based methodologies are designed to lower, predominantly within cellular elements, mutant huntingtin protein (mHtt) - the genetic product of HD - the pathological protein is abundant in free forms and in several compartments including the cerebrospinal fluid, the plasma and the extracellular matrix. With accumulating evidence for the spreading and seeding capacities of mHtt, it may indeed be essential to target the protein both intracellularly and extracellularly. Therefore, free forms of mHtt not only represents an ideal target for antibodies, but one that needs to be addressed if meaningful and maximal clinical benefits are to be expected. This review explores the potential use of antibody-based therapies to treat HD, including the rationale for this approach as well as the pre-clinical data supporting it. The potential challenges that will need to be considered if such route is to be pursued clinically are also discussed.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".