Small-molecule targeting AMPA-mediated excitotoxicity has therapeutic effects in mouse models for multiple sclerosis
Bibliographic record
Abstract
While most research and treatments for multiple sclerosis (MS) focus on autoimmune reactions causing demyelination, it is possible that neurodegeneration precedes the autoimmune response. Hence, glutamate receptor antagonists preventing excitotoxicity showed promise in MS animal models, though blocking glutamate signaling prevents critical neuronal functions. This study reports the discovery of a small molecule that prevents AMPA-mediated excitotoxicity by targeting an allosteric binding site. A machine learning approach was used to screen for small molecules targeting the AMPA receptor GluA2 subunit. The lead candidate has potent effects in restoring neurological function and myelination while reducing the immune response in experimental autoimmune encephalitis and cuprizone MS mouse models without affecting basal neurotransmission or learning and memory. These findings facilitate development of a treatment for MS with a different mechanism of action than current immune modulatory drugs and avoids important off-target effects of glutamate receptor antagonists. This class of MS therapeutics could be useful as an alternative or complementary treatment to existing therapies.
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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 | 0.000 |
| 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 | 0.000 |
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".