Evaluation of N- and O-Linked Indole Triazines for a Dual Effect on α-Synuclein and Tau Aggregation
Bibliographic record
Abstract
Alzheimer’s disease (AD) is the most prevalent neurodegenerative disorder underlying dementia in the geriatric population. AD manifests by two pathological hallmarks: extracellular amyloid-β (Aβ) peptide-containing senile plaques and intraneuronal neurofibrillary tangles comprised of aggregated hyperphosphorylated tau protein (p-tau). However, more than half of AD cases also display the presence of aggregated α-synuclein (α-syn)-containing Lewy bodies. Conversely, Lewy bodies disorders have been reported to have concomitant Aβ plaques and neurofibrillary tangles. Our drug discovery program focuses on the synthesis of multitarget-directed ligands to abrogate aberrant α-syn, tau (2N4R), and p-tau (1N4R) aggregation and to slow the progression of AD and related dementias. To this end, we synthesized 11 compounds with a triazine-linker and evaluated their effectiveness in reducing α-syn, tau isoform 2N4R, and p-tau isoform 1N4R aggregation. We utilized biophysical methods such as thioflavin T (ThT) fluorescence assays, transmission electron microscopy (TEM), photoinduced cross-linking of unmodified proteins (PICUP), and M17D intracellular inclusion cell-based assays to evaluate the antiaggregation properties and cellular protection of our best compounds. We also performed disaggregation assays with isolated Aβ-plaques from human AD brains. Our results demonstrated that compound 10 was effective in reducing both oligomerization and fibril formation of α-syn and tau isoform 2N4R in a dose-dependent manner via ThT and PICUP assays. Compound 10 was also effective at reducing the formation of recombinant α-syn, tau 2N4R, and p-tau 1N4R fibrils by TEM. Compound 10 reduced the development of α-syn inclusions in M17D neuroblastoma cells and stopped the seeding of tau P301S using biosensor cells. Disaggregation experiments showed smaller Aβ-plaques and less paired helical filaments with compound 10 . Compound 10 may provide molecular scaffolds for further optimization and preclinical studies for neurodegenerative proteinopathies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".