I014 Development of an immunological antibody approach for the treatment of Huntington’s disease
Bibliographic record
Abstract
The mutant huntingtin (mtHTT) protein is the principal cause of pathological changes observed in Huntington’s Disease (HD) patients. mtHTT is ubiquitously expressed and there is growing evidence that HD is a systemic disorder with functional interplay between the brain and the periphery. We have developed a murine monoclonal antibody (mAB), C6-17, targeting an exposed region of HTT near the aa586 Caspase 6 cleavage site. mAB C6-17 can block cell-to-cell propagation of mtHTT in vitro (Bartl et al. 2020). In a series of in vivo proof of concept experiments, administration of mAB C6-17 revealed an antibody distribution in peripheral and CNS tissues. Three months long treated YAC128 mice showed improved body weight, delayed progression in the motor deficits, reduced mtHTT in peripheral and CNS tissues and reduced striatal EM48 immunoreactivity compared to untreated or control AB treated YAC128 mice (Bartl et al. 2024). Based on these promising preclinical results, the development of an antibody treatment modality could be a potential new HD treatment strategy. The aim of HD Immune is to identify and isolate, via multiple approaches, human ABs which are binding to the same epitope of mAB C6-17. We are developing new clones featuring an improved target binding affinity. The novel clones are based on a humanized version of mAB C6-17 and a human AB targeting the same region. Here, we present the methods and initial results of an affinity maturation selection process. The isolated new human and humanized antibody candidates will be further developed as potential clinical candidates.
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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) | 0.002 | 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".