Development of small-molecule Tau-SH3 interaction inhibitors that prevent amyloid-β toxicity and network hyperexcitability
Bibliographic record
Abstract
Alzheimer's disease (AD) is the leading cause of dementia and lacks highly effective treatments. Tau-based therapies hold promise. Tau reduction prevents amyloid-β-induced dysfunction in preclinical models of AD and also prevents amyloid-β-independent dysfunction in diverse disease models, especially those with network hyperexcitability, suggesting that strategies exploiting the mechanisms underlying Tau reduction may extend beyond AD. Tau binds several SH3 domain-containing proteins implicated in AD via its central proline-rich domain. We previously used a peptide inhibitor to demonstrate that blocking Tau interactions with SH3 domain-containing proteins ameliorates amyloid-β-induced dysfunction. Here, we identify a top hit from high-throughput screening for small molecules that inhibit Tau-FynSH3 interactions and describe its optimization with medicinal chemistry. The resulting lead compound is a potent cell-permeable Tau-SH3 interaction inhibitor that binds Tau and prevents amyloid-β-induced dysfunction, including network hyperexcitability. These data support the potential of using small molecule Tau-SH3 interaction inhibitors as a novel therapeutic approach to 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) | 0.001 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".