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Record W4413755118 · doi:10.1021/acs.jmedchem.5c00479

Improved Pharmacokinetic Profiles of HDAC6 Inhibitors via Cap Group Modifications

2025· article· en· W4413755118 on OpenAlexafffund
Olasunkanmi O. Olaoye, Fettah Erdogan, Maria Gracia-Hernandez, Harsimran Kaur Garcha, Abootaleb Sedighi, Qirat F. Ashraf, Nabanita Nawar, Mulu Geletu, Hyuk‐Soo Seo, Diaaeldin I. Abdallah, Ayah Abdeldayem, Muhammad Murtaza Hassan, Sirano Dhe‐Paganon, Elvin D. de Araujo, Alejandro Villagra, Patrick T. Gunning

Bibliographic record

VenueJournal of Medicinal Chemistry · 2025
Typearticle
Languageen
FieldMedicine
TopicPeptidase Inhibition and Analysis
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health ResearchCanada Research ChairsCanada Foundation for InnovationOntario Research Foundation
KeywordsChemistryPharmacokineticsGroup (periodic table)PharmacologyStereochemistryCombinatorial chemistryBiochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Hydroxamic acid (HA)-based HDAC inhibitors often suffer from poor pharmacokinetic (PK) profiles, limiting their in vivo applications. Cap group modification offers a promising strategy to address these challenges. Here, we optimized the cap group of TO-317, a selective HDAC6 inhibitor with a bisected cap structure, generating 26 analogs with comparable or improved HDAC6 binding affinity and selectivity. Replacing the redundant tetrafluorobenzene sulfonamide cap while retaining the essential picolyl cap group preserved the critical H614 hydrogen bond, as confirmed by X-ray crystallography (1.24–1.27 Å resolution) of five analogs. Analog 14, featuring a 2-chlorobenzene sulfonamide cap, demonstrated a 120-fold enhancement in plasma concentration in mice compared to that of TO-317. Preclinical studies showed that analog 14 achieved 56% tumor growth inhibition in an SM1 melanoma murine model without observed toxicity. These findings highlight cap group optimization as a powerful approach to enhance HA-based HDAC inhibitors for advanced preclinical and clinical development.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.015
Threshold uncertainty score0.480

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.009
GPT teacher head0.289
Teacher spread0.280 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

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