CNS Amyloid- , Soluble APP- and - Kinetics during BACE Inhibition
Bibliographic record
Abstract
BACE, a β-secretase, is an attractive potential disease-modifying therapeutic strategy for Alzheimer's disease (AD) as it results directly in the decrease of amyloid precursor protein (APP) processing through the β-secretase pathway and a lowering of CNS amyloid-β (Aβ) levels. The interaction of the β-secretase and α-secretase pathway-mediated processing of APP in the rhesus monkey (nonhuman primate; NHP) CNS is not understood. We hypothesized that CNS inhibition of BACE would result in decreased newly generated Aβ and soluble APPβ (sAPPβ), with increased newly generated sAPPα. A stable isotope labeling kinetics experiment in NHPs was performed with a (13)C6-leucine infusion protocol to evaluate effects of BACE inhibition on CNS APP processing by measuring the kinetics of sAPPα, sAPPβ, and Aβ in CSF. Each NHP received a low, medium, or high dose of MBI-5 (BACE inhibitor) or vehicle in a four-way crossover design. CSF sAPPα, sAPPβ, and Aβ were measured by ELISA and newly incorporated label following immunoprecipitation and liquid chromatography-mass spectrometry. Concentrations, kinetics, and amount of newly generated APP fragments were calculated. sAPPβ and sAPPα kinetics were similar, but both significantly slower than Aβ. BACE inhibition resulted in decreased labeled sAPPβ and Aβ in CSF, without observable changes in labeled CSF sAPPα. ELISA concentrations of sAPPβ and Aβ both decreased and sAPPα increased. sAPPα increased by ELISA, with no difference by labeled sAPPα kinetics indicating increases in product may be due to APP shunting from the β-secretase to the α-secretase pathway. These results provide a quantitative understanding of pharmacodynamic effects of BACE inhibition on NHP CNS, which can inform about target development.
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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) | 0.001 | 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".