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Record W2055237164 · doi:10.1021/jm101092u

Non-Natural Macrocyclic Inhibitors of Histone Deacetylases: Design, Synthesis, and Activity

2010· article· en· W2055237164 on OpenAlexaff
Luciana Auzzas, Andreas Larsson, Riccardo Matera, Annamaria Baraldi, B. Deschenes-Simard, Giuseppe Giannini, Walter Cabri, Gianfranco Battistuzzi, Grazia Gallo, Andrea Ciacci, Loredana Vesci, Claudio Pisano, Stephen Hanessian

Bibliographic record

VenueJournal of Medicinal Chemistry · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHistone Deacetylase Inhibitors Research
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsChemistryPharmacophoreTetrapeptideVorinostatHistone deacetylaseStereochemistryHistoneDepsipeptideHDAC6IsozymeStructure–activity relationshipCombinatorial chemistryBiochemistryEnzymePeptideDNAIn vitro

Abstract

fetched live from OpenAlex

Nonpeptidic chiral macrocycles were designed on the basis of an analogue of suberoylanilide hydroxamic acid (2) (SAHA, vorinostat) and evaluated against 11 histone deacetylase (HDAC) isoforms. The identification of critical amino acid residues highly conserved in the cap region of HDACs guided the design of the suberoyl-based macrocycles, which were expected to bear a maximum common substructure required to target the whole HDAC panel. A nanomolar HDAC inhibitory profile was observed for several compounds, which was comparable, if not superior, to that of 2. A promising cytotoxic activity was found for selected macrocycles against lung and colon cancer cell lines. Further elaboration of selected candidates led to compounds with an improved selectivity against HDAC6 over the other isozymes. Pair-fitting analysis was used to compare one of the best candidates with the natural tetrapeptide apicidin, in an effort to define a general pharmacophore that might be useful in the design of surrogates of peptidic macrocycles as potent and isoform-selective inhibitors.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.714

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.301
Teacher spread0.293 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations63
Published2010
Admission routes1
Has abstractyes

Explore more

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