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Record W2980559264 · doi:10.3389/fnmol.2019.00257

Indomethacin Enhances Type 1 Cannabinoid Receptor Signaling

2019· article· en· W2980559264 on OpenAlexaff
Robert B. Laprairie, Kawthar A. Mohamed, Ayat Zagzoog, Lesley Stevenson, Roger G. Pertwee, Eileen M. Denovan‐Wright, Ganesh A. Thakur

Bibliographic record

VenueFrontiers in Molecular Neuroscience · 2019
Typearticle
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsUniversity of SaskatchewanDalhousie University
FundersNational Eye InstituteNational Institute on Drug AbuseNational Institutes of Health
KeywordsCannabinoidNeuroscienceCannabinoid receptorEndocannabinoid systemReceptorCannabinoid receptor type 2PharmacologyMedicineBiologyInternal medicineAntagonist

Abstract

fetched live from OpenAlex

In addition to its known actions as a non-selective cyclooxygenase (COX) 1 and 2 inhibitor, we hypothesized that indomethacin can act as an allosteric modulator of the type 1 cannabinoid receptor (CB1R) because of its shared structural features with the known allosteric modulators of CB1R GAT211. Indomethacin enhanced the binding of [3H]CP55940 to hCB1R and enhanced AEA-dependent [35S]GTPγS binding to hCB1R in CHO cell membranes. Indomethacin (1 µM) also enhanced CP55940-dependent arrestin1 recruitment, cAMP inhibition, ERK1/2 and PLCβ3 phosphorylation in HEK293A cells expressing hCB1R, but not in cells expressing hCB2R. Finally, indomethacin enhanced the magnitude and duration of CP55940-induced hypolocomotion, immobility, hypothermia, and anti-nociception in C57BL/6J mice. Together, these data support the hypothesis that indomethacin acted as a positive allosteric modulator of hCB1R. The identification of structural and functional features shared amongst allosteric modulators of CB1R may lead to the development of novel compounds designed for greater CB1R or COX selectivity and compounds designed to modulate both the prostaglandin and endocannabinoid systems.

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 score0.084
Threshold uncertainty score0.639

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.010
GPT teacher head0.274
Teacher spread0.264 · 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

Citations13
Published2019
Admission routes1
Has abstractyes

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