P2‐462: SEN12333 (WAY‐317538), a novel alpha7 nicotinic receptor agonist with neuroprotective efficacy: Implication for neurodegenerative diseases therapy
Bibliographic record
Abstract
There is a large body of literature demonstrating nicotine-mediated neuroprotection in both in vitro and in vivo models. More recently a role for nicotinic acetylcholine receptors of the alpha7 type have been implicated in these neuroprotective effects although the underlying mechanisms remains to be elucidated. Using a functional FLIPR-based calcium assay employing the rat alpha7 nAChR stably expressed in a GH4C1 cell line, we have recently identified SEN12333 (WAY-317538) as a full agonist with an EC50 of 1.2 microM and selective over related receptors. In previous experiments, this new alpha7 nAChR agonists has demonstrated pro-cognitive properties in both Alzheimer disease as well as schizophrenia oriented behavioural tests in rodents. Here we describe experiments aimed to evaluate the neuroprotective property of this compound in in vivo models of neurodegeneration in comparison with nicotine and to investigate the mechanisms of neuroprotection. Five microL of 0.5 M quisqualic acid solution injected into the nucleus basalis magnocellularis (NBM) of rats resulted in a highly significant decrease in the number of ChAT-positive neurons, massive reactive gliosis, involving both astrocytes and microglia, and a significant increase in p38-MAPK expression. A sub-chronic treatment with 3 mg/kg SEN12333 or 0.3 mg/kg nicotine for 7 days significantly attenuated the decrease in the number of ChAT-positive neurons in this brain region demonstrating neuroprotective effects. These effects may be relevant for treating neurodegenerative diseases such as Alzheimer's disease. The ability of nicotinic agonist-treatments to protect brain tissues from gliosis and inflammation will be 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".