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Record W4409149541 · doi:10.1016/j.isci.2025.112336

Development of a succinyl CoA:3-ketoacid CoA transferase inhibitor selective for peripheral tissues that improves glycemia in obesity

2025· article· en· W4409149541 on OpenAlexafffund
Seyed Amirhossein Tabatabaei Dakhili, Kunyan Yang, Hamdah Al Nebaihi, Amanda A. Greenwell, Melinda Wuest, Jenilee Woodfield, Rabih Abou Farraj, Christina T. Saed, Jordan S. F. Chan, Rakesh Bhat, Indiresh A Mangra-Bala, Tanin Shafaati, Keshav Gopal, Farah Eaton, Sally R. Ferrari, Cory S. Wagg, Megan E. Capozzi, Jonathan E. Campbell, Michael Overduin, Carlos A. Velázquez‐Martínez, J. N. Mark Glover, Frank Wuest, Dion R. Brocks, John R. Ussher

Bibliographic record

VenueiScience · 2025
Typearticle
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsUniversity of Alberta
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchCanada Foundation for Innovation
KeywordsPeripheralPreferenceTransferaseChemistryBiochemistryNeuroscienceInternal medicineEndocrinologyEnzymePsychologyBiologyMedicineEconomics

Abstract

fetched live from OpenAlex

studies identified that the antipsychotic diphenylbutylpiperidines can inhibit SCOT and alleviate obesity-related hyperglycemia. Because ketones are a major brain fuel, whereas the diphenylbutylpiperidines have central nervous system-related adverse effects, we aimed to develop a peripheral selective SCOT inhibitor (PSSI). Using a pharmacophore derived from the diphenylbutylpiperidine-SCOT interaction, we synthesized PSSI-51, which inhibited SCOT activity in peripheral but not brain tissue, while decreasing myocardial ketone oxidation. Importantly, PSSI-51 treatment improved glycemia in obese mice and demonstrated reduced brain accumulation compared to the diphenylbutylpiperidine pimozide. We propose that PSSI-51 can lay the foundation for optimizing a new class of brain-impermeable SCOT inhibitors for treating T2D.

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 score0.291
Threshold uncertainty score0.433

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 head0.019
GPT teacher head0.297
Teacher spread0.279 · 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

Citations2
Published2025
Admission routes2
Has abstractyes

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