Thioflavin-based molecular probes for application in Alzheimer's disease: from in silico to in vitro models
Bibliographic record
Abstract
Alzheimer's disease (AD) is a neurological disease of confusing causation with no cure or prevention available. The definitive diagnosis is made postmortem, in part through the presence of amyloid-beta plaques in the brain tissue, which can be done with the small molecule thioflavin-T (ThT). Plaques are also found to contain elevated amounts of metal ions Cu(ii) and Zn(ii) that contribute to the neurotoxicity of amyloid-beta (Aβ). In this paper, we report in silico, in vitro, and ex vivo studies with ThT-derived metal binders 2-(2-hydroxyphenyl)benzoxazole (HBX), 2-(2-hydroxyphenyl)benzothiazole (HBT) and their respective iodinated counterparts, HBXI and HBTI. They exhibit low cytotoxicity in a neuronal cell line, potential blood-brain barrier penetration, and interaction with Aβ fibrils from senile plaques present in human and transgenic mice AD models. Molecular modelling studies have also been undertaken to understand the prospective ligand-Aβ complexes as well as to rationalize the experimental findings. Overall, our studies demonstrate that HBX, HBT, HBXI, and HBTI are excellent agents for future use in in vivo models of AD, as they show in vitro efficacy and biological compatibility. In addition to this, we present the glycosylated form of HBX (GBX), which has been prepared to take advantage of the benefits of the prodrug approach. Overall, the in vitro and ex vivo assays presented in this work validate the use of the proposed ThT-based drug candidate series as chemical tools for further in vivo development.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| 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".