MétaCan
Menu
Back to cohort

Identifying Simultaneous Matrix Metalloproteinases/Soluble Epoxide Hydrolase Inhibitors

2015· article· en· W2953437064 on OpenAlexafffund
Ahmed A. El-Sherbeni, Rabia Bhatti, Ayman O.S. El‐Kadi

Bibliographic record

VenueThe FASEB Journal · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEicosanoids and Hypertension Pharmacology
Canadian institutionsUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsChemistryEpoxide hydrolase 2Matrix metalloproteinaseZymographyRecombinant DNABiochemistryEnzymeChromatographyPharmacology

Abstract

fetched live from OpenAlex

Matrix metalloproteinase (MMP) and soluble epoxide hydrolase (sEH) have completely unrelated biological functions, however, their dysregulation produce similar effects on biological systems. Based on the similarity in the reported structural requirements for their inhibition, the current study aimed to identify a simultaneous inhibitor for MMP and sEH. Six compounds were identified as potential simultaneous MMP/sEH inhibitors and tested for their capacity to inhibit MMP and sEH. Inhibition of MMP and sEH activity using their endogenous and exogenous substrates was measured by liquid chromatography/mass spectrometry, spectrophotometry and zymography. Two compounds, CTK8G1143 and ONO-4817, were identified to inhibit both MMP and sEH activity. CTK8G1143 and ONO-4817 inhibited the recombinant human sEH activity by an average of 67.4% and 55.2%, respectively. The IC50 for CTK8G1143 and ONO-4817 to inhibit recombinant human sEH were 5.2 and 3.5 µM, respectively, whereas, their maximal inhibition values were 71.4% and 42.8%, respectively. Also, MMP and sEH activity of human cardiomyocytes were simultaneously inhibited by CTK8G1143 and ONO-4817. Regarding other compounds, they showed either MMP or sEH inhibitory activity but not both. In conclusion, these two simultaneous inhibitors of MMP and sEH could provide a promising intervention for the prevention and control of several diseases, especially cardiovascular diseases. This work was supported by a grant from the CIHR to AOSE.

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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.027
GPT teacher head0.295
Teacher spread0.268 · 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

Citations0
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueThe FASEB JournalSame topicEicosanoids and Hypertension PharmacologyFrench-language works237,207