P3‐045: Novel Tricyclic Indole‐Based Compounds in the Prevention of Alzheimer’s Disease
Bibliographic record
Abstract
Alzheimer’s disease (AD) is a subtype of neurodegenerative disorder characterized by reduced hippocampal and cortical levels of acetylcholine (ACh), accumulation of amyloid-beta (Aβ) plaques and neurofibrillary tangles (NFTs) which leads to the loss of memory, hindered body coordination and eventually death. Current treatment approved by FDA and Health Canada includes medication such as rivastigmine, galantamine and donepezil which are capable of maintaining the levels of ACh in the brain and provides symptomatic relief. There are no drugs available which can prevent the accumulation of neurotoxic Aβ plaques or NFTs in the brain. We are developing a library of compounds based on a novel tricyclic indole-based ring system which is expected to target more than one aspect of the pathological signs of AD, i.e. having “multi-functional” activity and thus provide disease-modifying properties. The target compounds were synthesized and characterized by analytical techniques including NMR, LCMS and HPLC. The in vitro cholinesterase inhibition studies were carried out using Ellman’s method using human acetyl and butyrylcholinesterase enzymes by UV spectroscopy. The anti-Aβ aggregation activity was measured using the thioflavin T (ThT) based fluorescence spectroscopy. Molecular docking experiments were conducted using Discovery Studio: Structure-Based-Design software. Initial compound screening studies have shown that the indole based tricyclics are able to inhibit human cholinesterase enzymes (acetyl and butyrylcholinesterases). Furthermore, molecular modeling studies have identified a 3,4-dimethoxyphenyl substituent as a potential pharmacophore capable of exhibiting dual cholinesterase and Aβ-aggregation inhibition. Our investigations have shown that indole based tricyclic systems can be designed to develop small molecules with ability to target multiple factors associated with AD. Further studies are in progress to identify lead candidates.
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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) | 0.003 | 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".