P2‐398: Targeting alzheimeric pathologies using small‐molecule therapies: A multi‐targeted approach
Bibliographic record
Abstract
The complex pathophysiology of Alzheimer's disease (AD) mandates the phase-out of the ‘one drug, one target’ method in favor of a multi-targeted approach for the development of novel AD pharmacotherapies. Multifunctional treatments that target various Alzheimeric pathologies are likely to inflict a disease-modifying effect (DME) that can halt and/or reverse disease progression. Our research encompasses the design, development and biological evaluation of novel heterocyclic compounds with an Alzheimeric multifunctional profile. Emphasis is on targeting cholinergic dysfunction (via cholinesterase inhibition), amyloid toxicities (via Aβ-aggregation inhibition, radical scavenging) and tau pathology (via T-aggregation inhibition). Methodology utilizes advanced computational, chemical and biological tools to generate a biological profile for compounds within a chemical library. In vitro, in situ and in vivo assessments are conducted and combined with molecular docking studies to acquire structure-activity relationship data (SAR) that can identify novel pharmacophores for a multi-targeted therapeutic strategy. Our recent efforts with developing a 2,4-disubstituted pyrimidine ring (DPR) template yielded promising results in achieving multifunctional activity against the cholinesterases, Aβ aggregation and β-secretase. In our quest to improve cholinesterase potency and add to the biological profile, we are investigating the potential of heteroatom-based, bicyclic rings as a substitute for the 2,4 DPR template. These novel bicyclic-based compounds will incorporate features necessary to attain dual cholinesterase inhibition, multi-mode Aβ aggregation inhibition and antioxidant capabilities. The design also focuses on establishing a good pharmacological profile with favorable blood-brain-barrier passage. Preliminary docking studies demonstrated the ability of these bicyclic compounds to closely interact with the catalytic site of both cholinesterases as well as extend toward the peripheral site of AChE. In addition, the heterocyclic nature of the template ring allows it to interact with various key aggregation residues of the Aβ and tau peptides. The size and chemical nature of the substituents attached to the bicyclic template govern the compounds biological capabilities.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".