Selenium‐Based Molecules to Treat Alzheimer’s Disease
Bibliographic record
Abstract
BACKGROUND: The neurodegenerative disorder Alzheimer's disease (AD), is the major cause of dementia. As per the World Health Organization, >55 million people are affected with dementia worldwide. The health and economic burden of AD will only increase in the coming years due to the lack of effective and curative therapies. In this regard, the recent market launch of monoclonal antibodies lecanemab and donanemab to treat AD is an exciting development for patients and caregivers. This has renewed the interest in targeting the amyloid-beta (Aβ) as a viable strategy to treat AD. In this regard, designing small molecule therapies to prevent the aggregation and neurotoxicity of Aβ has the potential to discover novel anti-AD therapies. METHOD: The selenium-based N-benzylphenoselenazines were designed by investigating their interactions with Aβ40 assemblies and to incorporate suitable substituents. The N-benzylphenoselenazines library synthesis was carried out by optimizing the synthetic chemistry protocols. The synthesized compounds were characterized by analytical methods. The in vitro anti-Aβ40 activity was evaluated using thioflavin-T based fluorescence aggregation kinetics and electron microscopy studies. The antioxidant activity was also evaluated in vitro. The cytotoxicity was evaluated in mouse hippocampal HT22 neuronal cells. The blood-brain barrier permeability of novel N-benzylphenoselenazine derivatives was also evaluated. RESULT: C NMR, LCMS and HRMS confirmed their chemical structures and sample purity. In vitro studies demonstrated their Aβ40 aggregation inhibition activity ranging from 26-85% which was further confirmed by electron microscopy studies. Compounds from this series also demonstrated antioxidant activity ranging from 23-80.5%. Furthermore, these compounds were not toxic to hippocampal HT22 neuronal cells. CONCLUSION: This study led to the identification of novel selenium-based small molecules (N-benzylphenoselenazines) that are able to reduce Aβ40 aggregation and demonstrate antioxidant activity suggesting their potential application as disease-modifying agents to treat 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 | 0.001 |
| 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".