PAK1 inhibitor NVS‐PAK1‐1 preserves dendritic spines in amyloid/tau exposed neurons and 5xFAD mice
Bibliographic record
Abstract
INTRODUCTION: Synaptic spine loss in Alzheimer's disease (AD) contributes to cognitive decline. p21-activated kinase 1 (PAK1), a regulator of spine integrity, is aberrantly activated in AD. We investigated whether PAK1 inhibition might preserve dendritic spines in vitro and in vivo. METHODS: Oligomeric amyloid beta (oAβ) or tau (oTau) were applied to hippocampal neurons ± NVS-PAK1-1, a selective PAK1 inhibitor. NVS-PAK1-1 was orally administered to 5xFAD mice. The effects of NVS-PAK1-1 treatment on PAK1 activity, spine density, and the proteome were assessed using phospho-PAK1 (pPAK1) western blotting, Golgi staining, and mass spectrometry for proteomic analyses. RESULTS: NVS-PAK1-1 prevented oAβ and oTau-induced spine loss in vitro. In 5xFAD mice, NVS-PAK1-1 demonstrated brain exposure after oral administration and reduced PAK1 activation, prevented spine loss, and partially normalized synaptic proteomic signatures in females in absence of alterations in brain or plasma Aβ. DISCUSSION: PAK1 inhibition enhances spine resilience in AD models, supporting its therapeutic potential. HIGHLIGHTS: = 2 nM). Oral administration of NVS-PAK1-1 achieves brain penetration and bioavailability in normal CD-1 mice, and target engagement in 5xFAD mice. Chronic NVS-PAK1-1 treatment mitigates spine loss in the somatosensory cortex of 6-month-old 5xFAD female mice. Chronic treatment with NVS-PAK1-1 restores proteomic abundance of actin cytoskeleton and dendritic spine-associated proteins, including cofilin 2 and pyruvate dehydrogenase kinases, downstream of PAK1 in young 5xFAD female mice showing spine resilience. Clinical oncology trials with other PAK1 inhibitors support potential repurposing or novel compound development for Alzheimer's disease trials.
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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 | 0.001 | 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".