Unveiling Piperazine-Quinoline Hybrids as Potential Multi-Target Directed Anti- Alzheimer’s Agents: Design, Synthesis and Biological Evaluation
Bibliographic record
Abstract
Abstract Multi-target directed ligands (MTDLs) have recently been popularized due to their outstanding efficacy in combating the complicated features of Alzheimer's disease. This study details the synthesis of piperazine-quinoline-based MTDLs through a multicomponent Petasis reaction, targeting multiple factors such as AChE, BuChE, metal chelation to restore metal dyshomeostasis, and antioxidant activity. Some of the synthesized compounds exhibited notable inhibitory activity against AChE and BuChE enzymes at specific concentrations. Among the synthesized compounds compound (95) containing a 4-chloroaniline moiety and a 4-methoxybenzyl group displayed the most promising inhibitory activities against AChE (IC50 3.013 µM) and BuChE (IC50 = 3.144 µM). Compound (83) featuring 2-methoxyaniline and 4-fluorobenzyl substituents, exhibited the highest BuChE inhibition (IC50 1.888 µM). Notably, compound (79) demonstrated 93-times higher selectivity for BuChE over AChE. Out of these compounds nine compounds were assessed for antioxidant activity, displaying significant potential at a concentration of 100 μM. Moreover, all the compounds demonstrated metal chelating activity with Cu+2, Zn+2, Fe+2, Fe+3 and Al+3. This study provides insights into the design of novel MTDLs, highlighting compound (95) as a potential candidate for Inhibiting Alzheimer's disease and emphasizing its role in the development of anti-AD medication.
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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 | 0.000 |
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".