MétaCan
Menu
Back to cohort
Record W2323252287 · doi:10.1021/ml500161v

Two Potent OXE-R Antagonists: Assignment of Stereochemistry

2014· article· en· W2323252287 on OpenAlexafffund
Pranav Patel, Chintam Nagendra Reddy, Vivek Gore, Shishir Chourey, Qiuji Ye, Yannick P. Ouedraogo, Sylvie Gravel, William S. Powell, Joshua Rokach

Bibliographic record

VenueACS Medicinal Chemistry Letters · 2014
Typearticle
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsMcGill University
FundersCanadian Institutes of Health ResearchAmerican Asthma Foundation
KeywordsEnantiomerChemistryMetaboliteArachidonic acidStereochemistryHigh-performance liquid chromatographyEnzymeStereoisomerismBiochemistryCombinatorial chemistryChromatographyCatalysis

Abstract

fetched live from OpenAlex

5-Oxo-6,8,11,14-eicosatetraenoic acid (5-oxo-ETE) is formed by the oxidation of 5-hydroxy-6E,8Z,11Z,14Z-eicosatetraenoic acid (5-HETE), which is a major metabolite of enzymatic oxidation of arachidonic acid (AA). 5-Oxo-ETE is the most potent lipid chemoattractant for human eosinophils. Its actions are mediated by the selective OXE receptor, which is therefore an attractive target in eosinophilic diseases such as allergic rhinitis and asthma. Recently, we have reported two excellent OXE receptor antagonists that have IC50 values at low nanomolar concentrations. Each of these antagonists has a chiral center, and the isolation of the individual enantiomers by chiral high-performance liquid chromatography (HPLC) revealed that in each case one enantiomer is over 300 times more potent than the other. To unambiguously assign the stereochemistry of these enantiomers and to provide access to larger amounts of the active compounds for biological testing, we report here their total synthesis.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.007
GPT teacher head0.250
Teacher spread0.243 · 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 source (direct Gemma or distilled Codex), 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

Citations15
Published2014
Admission routes2
Has abstractyes

Explore more

Same venueACS Medicinal Chemistry LettersSame topicAsthma and respiratory diseasesFrench-language works237,207