Amyloid-β1-15/16 as a Marker for γ-Secretase Inhibition in Alzheimer's Disease
Bibliographic record
Abstract
Amyloid-β (Aβ) producing enzymes are key targets for disease-modifying Alzheimer's disease (AD) therapies since Aβ trafficking is at the core of AD pathogenesis. Development of such drugs might benefit from the identification of markers indicating in vivo drug effects in the central nervous system. We have previously shown that Aβ(1-15) is produced by concerted β-and α-secretase cleavage of amyloid-β protein precursor (AβPP). Here, we test the hypothesis that this pathway is more engaged upon γ-secretase inhibition in humans, and cerebrospinal fluid (CSF) levels of Aβ(1-15/16) represent a biomarker for this effect. Twenty healthy men were treated with placebo (n = 5) or the γ-secretase inhibitor semagacestat (100 mg [n = 5], 140 mg [n = 5], or 280 mg [n = 5]). CSF samples were collected hourly over 36 hours and 10 time points were analyzed by immunoassay for Aβ(1-15/16), Aβ(x-38), Aβ(x-40), Aβ(x-42), sAβPPα, and sAβPPβ. The CSF concentration of Aβ(1-15/16) showed a dose-dependent response over 36 hours. In the 280 mg treatment group, a transient increase was seen with a maximum of 180% relative to baseline at 9 hours post administration of semagacestat. The concentrations of Aβ(x-38), Aβ(x-40), and Aβ(x-42) decreased the first 9 hours followed by increased concentrations after 36 hours relative to baseline. No significant changes were detected for CSF sAβPPα and sAβPPβ. Our data shows that CSF levels of Aβ(1-15/16) increase during treatment with semagacestat supporting its feasibility as a pharmacodynamic biomarker for drug candidates aimed at inhibiting γ-secretase-mediated AβPP-processing.
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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) | 0.000 | 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 | 0.000 |
| 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".