Click Chemistry‐Aided Synthesis of Triazole‐Tethered Benzothiazoles as Novel Multifunctional Agents Against Alzheimer's Disease
Bibliographic record
Abstract
ABSTRACT In the current medical era, Alzheimer's disease (AD) stands as a challenging multifaceted neurodegenerative disorder characterized by diverse pathological features that necessitate the development of multi‐target directed ligands (MTDLs) as a promising therapeutic approach. This study reports the design and synthesis of triazole‐tethered benzothiazole derivatives ( 10a–r and 11a–e ) as MTDLs for AD. These molecules have been evaluated for their potential to inhibit cholinesterases, amyloid‐β (Aβ) aggregation, and their reactive oxygen species (ROS) scavenging ability. Benzothiazoles 10f , 10l , and 11c exhibited good inhibition of human acetylcholinesterase ( h AChE) with IC 50 values of 100, 110, and 140 nM, respectively, and were better than tacrine (IC 50 = 160 nM). Furthermore, they inhibited Aβ42 aggregation with percent inhibition of 49.4%, 45.1%, and 39.3%, respectively. It should be emphasized that the most potent h AChE and Aβ42 dual inhibitors, 10f and 10l , displayed efficient antioxidant activities (57.2% and 47.5%, respectively) and were better than resveratrol (40.8%). Noteworthy, the developed molecules were not cytotoxic to mouse hippocampal neuronal cells (HT22) at 25 µM, with cell viability ranging from 79.2% to 113.3%, highlighting their potential to be considered as novel scaffolds for CNS drug development. A molecular docking study proposed that 10f and 10l interacted with both the catalytic and peripheral active sites of h AChE similar to donepezil and displayed favorable binding. Also, the docking study suggested the binding mode of 10f to Aβ40 and Aβ42. These results show that benzothiazoles 10f and 10l are promising candidates for the development of novel MTDLs for the effective management of 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 | 0.000 | 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) | 0.002 | 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".