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Record W2081802972 · doi:10.3390/ph3051530

Nonsteroidal Anti-Inflammatory Drugs (NSAIDs): Progress in Small Molecule Drug Development

2010· review· en· W2081802972 on OpenAlexaff
Praveen P. N. Rao, Saad N Kabir, Tarek Mohamed

Bibliographic record

VenuePharmaceuticals · 2010
Typereview
Languageen
FieldMedicine
TopicInflammatory mediators and NSAID effects
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsAspirinMedicineCyclooxygenasePharmacologyAnti-inflammatoryNonsteroidalSmall moleculeDrugDrug developmentDrug discoveryAntiinflammatory drugArachidonate 5-lipoxygenaseBioinformaticsInternal medicineChemistryEnzyme

Abstract

fetched live from OpenAlex

Ever since the discovery of aspirin, small molecule therapeutics have been widely prescribed to treat inflammation and pain. Aspirin and several small molecule NSAIDs are known to inhibit the enzymes cyclooxygenase-1 (COX-1) and -2 (COX-2). Despite the success of NSAIDs to treat inflammatory disorders, the development of a clinically useful small molecule NSAIDs with decreased side effect profiles is an ongoing effort. The recent discovery and development of selective COX-2 inhibitors was a step toward this direction. Emerging trends are represented by the progress in the development of hybrid agents such as nitric oxide donor-NSAIDs (NO-NSAIDs) and dual COX/lipoxygenase (LOX) inhibitors. This review focuses on the recent advances in the rational design of small molecule NSAIDs in therapy.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.980
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0000.001

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 head0.045
GPT teacher head0.375
Teacher spread0.330 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations103
Published2010
Admission routes1
Has abstractyes

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