Positive allosteric modulation of M1 mAChRs with VU0486846 reverses cognitive deficits in male APPswe/PSEN1ΔE9 alzheimer's mice
Bibliographic record
Abstract
Alzheimer's disease (AD) is an age-associated neurodegenerative disease marked by progressive cognitive deterioration and beta-amyloid (Aβ) protein buildup, which currently lacks therapeutic interventions to decelerate its pathogenesis. The M1 muscarinic acetylcholine receptor (mAChR) is integral to synaptic plasticity and memory processes and has emerged as a critical target for ameliorating AD-associated cognitive deficits. Although M1 mAChR agonists have pro-cognitive potential, their clinical application is limited by significant cholinergic side effects. Our recent findings demonstrate that VU0486846, an M1 mAChR positive allosteric modulator (PAM) devoid of cholinergic toxicity, exhibits therapeutic benefits in a female APPswe/PSEN1ΔE9 (APP/PS1) Alzheimer's disease mouse model. This compound reversed memory deficits, alleviated anxiety-like behaviours, reduced Aβ pathology, and attenuated neuroinflammation in female mice. However, its therapeutic potential in male AD models remains to be fully characterized. In this study, we find that VU0486846 treatment restored cognitive function in male APP/PS1 mice, as evidenced by improved performance in the novel object recognition and Morris water maze tasks, and reduced anxiety-like behaviours in the open field test. VU0486846 ameliorates impaired autophagy signaling in the hippocampus, however, it does not alter hippocampal Aβ oligomer or plaque burden, despite decreasing BACE1 expression. These findings suggest that VU0486846 exerts behavioural and cognitive benefits via Aβ-independent mechanism(s). Collectively, this study highlights the therapeutic potential of VU0486846 in modulating AD pathophysiology, albeit via sex-specific signaling pathways.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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".