Neuroprotective strategies for NMDAR-mediated excitotoxicity in Huntington’s Disease
Bibliographic record
Abstract
Abstract BACKGROUND Huntington’s Disease (HD) is an autosomal dominant neurodegenerative disease causing severe neurodegeneration of the striatum as well as marked cognitive and motor disabilities. Excitotoxicity, caused by overstimulation of NMDA receptors (NMDARs) has been shown to have a key role in the neuropathogenesis of HD, suggesting that targeting NMDAR-dependent signaling may be an effective clinical approach for HD. However, broad NMDAR antagonists are generally poor therapeutics in clinical practice. It has been suggested that GluN2A-containing, synaptically located NMDARs activate cell survival signaling pathways, while GluN2B-containing, primarily extrasynaptic NMDARs trigger cell death signaling. A better approach to development of effective therapeutics for HD may be to target, specifically, the cell-death specific pathways associated with extrasynaptic GluN2B NMDAR activation, while maintaining or potentiating the cell-survival activity of GluN2A-NMDARs. OBJECTIVE This review outlines the role of NMDAR-mediated excitotoxicity in HD and overviews current efforts to develop better therapeutics for HD where NMDAR excitotoxicity is the target. METHODS A systematic review process was conducted using the PubMed search engine focusing on research conducted in the past 5-10 years. 250 articles were consulted for the review, with key search terms including “Huntington’s Disease”, “excitotoxicity”, “NMDAR” and “therapeutics”. RESULTS A wide range of NMDAR excitotoxicity-based targets for HD were identified and reviewed, including targeting NMDARs directly by blocking GluN2B, extrasynaptic NMDARs and/or potentiating GluN2A, synaptic NMDARs, targeting glutamate release or uptake, or targeting specific downstream cell-death signaling of NMDARs. CONCLUSION The current review identifies NMDAR-mediated excitotoxicity as a key player in HD pathogenesis and points to various excitotoxicity-focused targets as potential future preventative therapeutics for 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".