Driving the new generation histone deacetylase inhibitors in cancer therapy; manipulation of the histone abbreviation at the epigenetic level: an in-silico approach
Bibliographic record
Abstract
Histone deacetylase (HDAC) is an enzyme that deacetylates the histone protein by removing the acetyl group from the lysine residues. The overexpression of the HDAC enzyme alters the gene expressions and causes cancer development in the human body. The inhibition of HDAC is an excellent therapeutic way in current cancer therapy. In this regard, various inhibitors were selected, and the inhibitory potential of these inhibitors was examined by molecular dynamics (MD) simulation followed by trajectory analysis and binding energy calculations. The selected clinical trial II and III phase inhibitors are TSA (trichostatin-A), TFMK (trifluoromethyl-ketone-9,9,9-trifluoro-8-oxo- N-phenylnonanamide), AKA (alpha-ketoamide- N-cyclohexyl- N-methyl-2-oxononanediamide), ITF2357 (givinostat), MS275 (entinostat), CI994 (tacedinaline), and SAHA (suberoylanilide hydroxamic acid). This computational study examines the atomic level description of the drug binding site on the HDLP enzyme and investigates the interaction of the HDAC inhibitors with the amino acid residues attached to the active site of the histone deacetylase-like protein (HDLP). Root-mean-square deviation, radius of gyration, hydrogen bond analysis, MM-PBSA, linear interaction energy (LIE), and semi-LIE calculations have revealed that the HDLP enzyme is more stabilized when bound to TSA, ITF2357, and reference inhibitor SAHA. It was observed that the hydroxamic acid family inhibitors have more potent in inhibiting the HDLP enzyme than the benzamide and ketone families. The inhibitory efficacy of TSA and ITF2357 is much similar to that of SAHA. Therefore, these HDAC inhibitors have the potential to be used in future clinical practices for cancer-related treatments. The knowledge gathered from this study could also lead to discovering new HDAC inhibitors for clinical research.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".