Oral administration of a potent and selective non‐peptidic BACE‐1 inhibitor decreases β‐cleavage of amyloid precursor protein and amyloid‐β production <i>in vivo</i>
Bibliographic record
Abstract
Generation and deposition of the amyloid beta (Abeta) peptide following proteolytic processing of the amyloid precursor protein (APP) by BACE-1 and gamma-secretase is central to the aetiology of Alzheimer's disease. Consequently, inhibition of BACE-1, a rate-limiting enzyme in the production of Abeta, is an attractive therapeutic approach for the treatment of Alzheimer's disease. We have designed a selective non-peptidic BACE-1 inhibitor, GSK188909, that potently inhibits beta-cleavage of APP and reduces levels of secreted and intracellular Abeta in SHSY5Y cells expressing APP. In addition, we demonstrate that this compound can effectively lower brain Abeta in vivo. In APP transgenic mice, acute oral administration of GSK188909 in the presence of a p-glycoprotein inhibitor to markedly enhance the exposure of GSK188909 in the brain decreases beta-cleavage of APP and results in a significant reduction in the level of Abeta40 and Abeta42 in the brain. Encouragingly, subchronic dosing of GSK188909 in the absence of a p-glycoprotein inhibitor also lowers brain Abeta. This pivotal first report of central Abeta lowering, following oral administration of a BACE-1 inhibitor, supports the development of BACE-1 inhibitors for the treatment of Alzheimer's disease.
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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 | 0.000 | 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) | 0.002 | 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".