Robust Translation of γ-Secretase Modulator Pharmacology across Preclinical Species and Human Subjects
Bibliographic record
Abstract
The amyloid-β peptide (Aβ)-in particular, the 42-amino acid form, Aβ1-42-is thought to play a key role in the pathogenesis of Alzheimer's disease (AD). Thus, several therapeutic modalities aiming to inhibit Aβ synthesis or increase the clearance of Aβ have entered clinical trials, including γ-secretase inhibitors, anti-Aβ antibodies, and amyloid-β precursor protein cleaving enzyme inhibitors. A unique class of small molecules, γ-secretase modulators (GSMs), selectively reduce Aβ1-42 production, and may also decrease Aβ1-40 while simultaneously increasing one or more shorter Aβ peptides, such as Aβ1-38 and Aβ1-37. GSMs are particularly attractive because they do not alter the total amount of Aβ peptides produced by γ-secretase activity; they spare the processing of other γ-secretase substrates, such as Notch; and they do not cause accumulation of the potentially toxic processing intermediate, β-C-terminal fragment. This report describes the translation of pharmacological activity across species for two novel GSMs, (S)-7-(4-fluorophenyl)-N2-(3-methoxy-4-(3-methyl-1H-1,2,4-triazol-1-yl)phenyl)-N4-methyl-6,7-dihydro-5H-cyclopenta[d]pyrimidine-2,4-diamine (BMS-932481) and (S,Z)-17-(4-chloro-2-fluorophenyl)-34-(3-methyl-1H-1,2,4-triazol-1-yl)-16,17-dihydro-15H-4-oxa-2,9-diaza-1(2,4)-cyclopenta[d]pyrimidina-3(1,3)-benzenacyclononaphan-6-ene (BMS-986133). These GSMs are highly potent in vitro, exhibit dose- and time-dependent activity in vivo, and have consistent levels of pharmacological effect across rats, dogs, monkeys, and human subjects. In rats, the two GSMs exhibit similar pharmacokinetics/pharmacodynamics between the brain and cerebrospinal fluid. In all species, GSM treatment decreased Aβ1-42 and Aβ1-40 levels while increasing Aβ1-38 and Aβ1-37 by a corresponding amount. Thus, the GSM mechanism and central activity translate across preclinical species and humans, thereby validating this therapeutic modality for potential utility in AD.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".