A Rational Approach for the Identification of Non-Hydroxamate HDAC6-Selective Inhibitors
Bibliographic record
Abstract
The human histone deacetylase isoform 6 (HDAC6) has been demonstrated to play a major role in cell motility and aggresome formation, being interesting for the treatment of multiple tumour types and neurodegenerative conditions. Currently, most HDAC inhibitors in preclinical or clinical evaluations are non-selective inhibitors, characterised by a hydroxamate zinc-binding group (ZBG) showing off-target effects and mutagenicity. The identification of selective HDAC6 inhibitors with novel chemical properties has not been successful yet, also because of the absence of crystallographic information that makes the rational design of HDAC6 selective inhibitors difficult. Using HDAC inhibitory data retrieved from the ChEMBL database and ligand-based computational strategies, we identified 8 original new non-hydroxamate HDAC6 inhibitors from the SPECS database, with activity in the low μM range. The most potent and selective compound, bearing a hydrazide ZBG, was shown to increase tubulin acetylation in human cells. No effects on histone H4 acetylation were observed. To the best of our knowledge, this is the first report of an HDAC6 selective inhibitor bearing a hydrazide ZBG. Its capability to passively cross the blood-brain barrier (BBB), as observed through PAMPA assays, and its low cytotoxicity in vitro, suggested its potential for drug 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".