Glycosylated tetrahydrosalens as multifunctional molecules for Alzheimer's therapy
Bibliographic record
Abstract
The tetrahydrosalens N,N'-bis(2-hydroxybenzyl)-ethane-1,2-diamine ((2)(1)), N,N'-bis(2-hydroxybenzyl)-(-)-1,2-cyclohexane-(1R,2R)-diamine ((2)(2)), N,N'-bis(2-hydroxybenzyl)-N,N'-dimethyl-ethane-1,2-diamine ((2)(3)), N,N'-bis(2-hydroxybenzyl)-N,N'-dibenzyl-ethane-1,2-diamine ((2)(4)), and N,N'-bis(2-(4-tert-butyl)hydroxybenzyl)-ethane-1,2-diamine ((2)(5)), as well as their prodrug glycosylated forms, (1-5), have been prepared and evaluated in vitro for their potential use as Alzheimer's disease (AD) therapeutics. Dysfunctional interactions of metal ions, especially those of Cu, Zn, and Fe, with the amyloid-beta (Abeta) peptide are hypothesised to play an important role in the aetiology of AD, and disruption of these aberrant metal-peptide interactions via chelation therapy holds considerable promise as a therapeutic strategy. Tetrahydrosalens such as (2)(1-5) have a significant affinity for metal ions, and thus should be able to compete with the Abeta peptide for Cu, Zn, and Fe in the brain. This activity was assayed in vitrovia a turbidity assay; (2)(1) and (2)(3) were found to attenuate Abeta(1-40) aggregation after exposure to Cu(2+) and Zn(2+). In addition, (2)(1-5) were determined to be potent antioxidants on the basis of an in vitro antioxidant assay. (1-5) were prepared as metal binding prodrugs; glycosylation is intended to prevent systemic metal binding, improve solubility, and enhance brain uptake. Enzymatic (beta-glucosidase) deprotection of the carbohydrate moieties was facile, with the exception of (4), demonstrating the general feasibility of this prodrug approach. Finally, a representative prodrug, (3), was determined to be non-toxic over a large concentration range in a cell viability assay.
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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 | 0.001 |
| 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".