First and Second Generation γ-Secretase Modulators (GSMs) Modulate Amyloid-β (Aβ) Peptide Production through Different Mechanisms
Bibliographic record
Abstract
γ-Secretase-mediated cleavage of amyloid precursor protein (APP) results in the production of Alzheimer disease-related amyloid-β (Aβ) peptides. The Aβ42 peptide in particular plays a pivotal role in Alzheimer disease pathogenesis and represents a major drug target. Several γ-secretase modulators (GSMs), such as the nonsteroidal anti-inflammatory drugs (R)-flurbiprofen and sulindac sulfide, have been suggested to modulate the Alzheimer-related Aβ production by targeting the APP. Here, we describe novel GSMs that are selective for Aβ modulation and do not impair processing of Notch, EphB2, or EphA4. The GSMs modulate Aβ both in cell and cell-free systems as well as lower amyloidogenic Aβ42 levels in the mouse brain. Both radioligand binding and cellular cross-competition experiments reveal a competitive relationship between the AstraZeneca (AZ) GSMs and the established second generation GSM, E2012, but a noncompetitive interaction between AZ GSMs and the first generation GSMs (R)-flurbiprofen and sulindac sulfide. The binding of a (3)H-labeled AZ GSM analog does not co-localize with APP but overlaps anatomically with a γ-secretase targeting inhibitor in rodent brains. Combined, these data provide compelling evidence of a growing class of in vivo active GSMs, which are selective for Aβ modulation and have a different mechanism of action compared with the original class of GSMs described.
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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) | 0.000 | 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 | 0.000 |
| 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".