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Record W2332682928 · doi:10.1021/jm300997w

Synthesis and Biological Investigations of Nitric Oxide Releasing Nateglinide and Meglitinide Type II Antidiabetic Prodrugs: In-Vivo Antihyperglycemic Activities and Blood Pressure Lowering Studies

2012· article· en· W2332682928 on OpenAlexafffund
Jatinder Kaur, Atul Bhardwaj, Zhangjian Huang, Deepak Narang, Ting-Yueh Chen, Frances Plane, Edward E. Knaus

Bibliographic record

VenueJournal of Medicinal Chemistry · 2012
Typearticle
Languageen
FieldComputer Science
TopicComputational Drug Discovery Methods
Canadian institutionsUniversity of Alberta
FundersCanadian Institutes of Health ResearchAnimal Welfare Institute
KeywordsChemistryNateglinideProdrugIn vivoNitric oxidePharmacologyBlood pressureBiological activityIn vitroBiochemistryType 2 diabetesDiabetes mellitusOrganic chemistryEndocrinology

Abstract

fetched live from OpenAlex

A new group of hybrid nitric oxide-releasing type II antidiabetic drugs possessing a 1-(pyrrolidin-1-yl)diazen-1-ium-1,2-diolate (13 and 18), 1-(N,N-diethylamino)diazen-1-ium-1,2-diolate (14 and 19), or nitrooxyethyl (15 and 20) moiety attached to the carboxylic acid group of the type II antidiabetic drugs nateglinide and meglitinide were synthesized. These prodrugs, based on the beneficial properties of nitric oxide (NO), were designed to reduce the risk of adverse cardiovascular events in diabetic patients. Ester prodrugs (13-15 and 18-20) exhibited appreciable oral antihyperglycemic activity comparable to the parent drugs in nonfasted diabetic rats. Systolic and diastolic blood pressure profiles validated the beneficial hypotensive properties of these prodrugs. These prodrugs released NO (1.3-72.2% range) upon incubation with either phosphate buffer solution at pH 7.4 or in the presence of serum. This new type of hybrid NO donor prodrug represents an attractive approach for the rational design of type II antidiabetic drugs with a reduced risk of contraindicated cardiovascular events.

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)0.003
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 score0.055
Threshold uncertainty score0.435

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
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 head0.036
GPT teacher head0.302
Teacher spread0.266 · 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

Citations38
Published2012
Admission routes2
Has abstractyes

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