Inhibition of Tau Protein Aggregation Using Small Molecule Inhibitors and Immunotherapies
Bibliographic record
Abstract
Abstract Background Tau protein aggregation is one of the biomarkers of neurodegenerative diseases and is a viable drug target. Small molecules and immunotherapies have been tested targeting tauopathies. Understanding of the inhibitor mode‐of‐action is needed in order to develop effective drugs against neurodegeneration. Method The full length tau protein (2N4R, 441) was used in vitro with aggregation inducers: heparin and arachidonic acid. We have used small molecule inhibitors based of dopamine receptor agonist as inhibitors of tau aggregation in vitro. The inhibition of tau aggregation was also conducted in vitro using anti‐tau antibodies. The biophysical methods including electron microscopy and fluorescence spectroscopy were used to measure inhibitor efficiency. Result The anti‐tau antibodies which target epitope in R1‐R4 repeat domain were most effective in inhibition aggregation. The small molecules containing catechol groups were effective aggregation inhibitors and were in micromolar range, which was similar to the efficacy of methylene blue. Conclusion The tau aggregation was inhibited in vitro using dual approach, with small and large molecules, indicating that both strategies are viable against tauopathies. Future work will require design of multifunctional inhibitors to improve selectivity and potency.
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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) | 0.001 | 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".