<i>sym</i>-Triazines for Directed Multitarget Modulation of Cholinesterases and Amyloid-β in Alzheimer’s Disease
Bibliographic record
Abstract
Alzheimer's disease (AD) is a complex neurodegenerative disorder marked by numerous causative factors of disease progression, termed pathologies. We report here the synthesis of a small library of novel sym-triazine compounds designed for targeted modulation of multiple pathologies related to AD, specifically human acetylcholinesterase (AChE), butyrylcholinesterase (BuChE), and Aβ aggregation. Rational targeting of AChE was achieved by the incorporation of acetylcholine substrate analogues into a sym-triazine core in either a mono-, di-, or trisubstituted regime. A subset of these derivatives demonstrated improved activity compared to several commercially available cholinesterase inhibitors. High AChE/BuChE selectivity was characteristic of all derivatives, and AChE steady-state kinetics indicated a mixed-type inhibition mechanism. Further integration of multiple hydrophobic phenyl units allowed for improved β-sheet intercalation into amyloid aggregates. Several highly effective structures exhibited fibril inhibition greater than the previously reported β-sheet-disrupting penta-peptide, iAβ5p, evaluated by thioflavin T fluorescence spectroscopy and transmission electron microscopy. Highly effective sym-triazines were shown to be well tolerated by differentiated human neuronal cells, as demonstrated by the absence of adverse effects on cellular viability at a wide range of concentrations. Parallel targeting of multiple pathologies using sym-triazines is presented here as an effective strategy to address the complex, multifactorial nature of AD progression.
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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 | 0.001 |
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".