Exploring the multifunctionality of thioflavin- and deferiprone-based molecules as acetylcholinesterase inhibitors for potential application in Alzheimer's disease
Bibliographic record
Abstract
Alzheimer's disease (AD) is a devastating neurodegenerative disease that affects millions of people worldwide. With no prevention or cure available, this progressive disease has a significant impact on society - dementia patients and their caretakers, healthcare systems, and the economy. Previously, we have reported initial developments of multifunctional drug candidates for AD based on two scaffolds - thioflavin-T and deferiprone. Individually, these molecules have shown several favorable functionalities, including dissociation of toxic amyloid-β aggregates, antioxidant and/or metal chelating ability that can pacify reactive oxygen species, plaque targeting, and blood-brain barrier penetration. In this work, the two scaffolds are augmented with a new functionality - acetylcholinesterase inhibition. This functionality is incorporated by derivatization with a carbamate group, which is the active group in some AD drugs currently in the market. We present the rationale for designing three novel compounds, their synthesis and characterization, including X-ray crystallographic data, and encouraging results from in vitro and computational acetylcholinesterase inhibition studies. Also, we evaluate the compounds as potential drug candidates by Lipinski's rules and cytotoxicity studies in a neuronal cell line. Overall, we demonstrate the feasibility of improving on two well established scaffolds, as well as show in vitro efficacy plus initial mode of action and biological compatibility data.
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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) | 0.000 | 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".