Small molecule<i>β</i>‐amyloid inhibitors that stabilize protofibrillar structures<i>in vitro</i>improve cognition and pathology in a mouse model of Alzheimer’s disease
Bibliographic record
Abstract
Beta-amyloid (Abeta) peptides are thought to play a major role in the pathogenesis of Alzheimer's disease. Compounds that disrupt the kinetic pathways of Abeta aggregation may be useful in elucidating the role of oligomeric, protofibrillar and fibrillar Abeta in the etiology of the disease. We have previously reported that scyllo-inositol inhibits Abeta(42) fibril formation but the mechanism(s) by which this occurs has not been investigated in detail. Using a series of scyllo-inositol derivatives in which one or two hydroxyl groups were replaced with hydrogen, chlorine or methoxy substituents, we examined the role of hydrogen bonding and hydrophobicity in the structure-function relationship of scyllo-inositol-Abeta binding. We report here that all scyllo-inositol derivatives demonstrated reduced effectiveness in preventing Abeta(42) fibrillization compared with scyllo-inositol, suggesting that scyllo-inositol interacts with Abeta(42) via key hydrogen bonds that are formed by all hydroxyl groups. Increasing the hydrophobicity of scyllo-inositol by the addition of two methoxy groups (1,4-di-O-methyl-scyllo-inositol) produced a derivative that stabilized Abeta(42) protofibrils in vitro. Prophylactic administration of 1,4-di-O-methyl-scyllo-inositol to TgCRND8 mice attenuated spatial memory impairments and significantly decreased cerebral amyloid pathology. These results suggest that Abeta aggregation can be targeted at multiple points along the kinetic pathway for the improvement of Alzheimer's disease-like pathology.
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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) | 0.001 | 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".