Anandamide transport inhibition by <scp>ARN</scp>272 attenuates nausea‐induced behaviour in rats, and vomiting in shrews (<i><scp>S</scp>uncus murinus</i>)
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: To understand how anandamide transport inhibition impacts the regulation of nausea and vomiting and the receptor level mechanism of action involved. In light of recent characterization of an anandamide transporter, fatty acid amide hydrolase-1-like anandamide transporter, to provide behavioural support for anandamide cellular reuptake as a facilitated transport process. EXPERIMENTAL APPROACH: The systemic administration of the anandamide transport inhibitor ARN272 ([(4-(5-(4-hydroxy-phenyl)-3,4-diaza-bicyclo[4.4.0]deca-1(6),2,4,7,9-pentaen-2-ylamino)-phenyl)-phenylamino-methanone]) was used to evaluate the prevention of LiCl-induced nausea-induced behaviour (conditioned gaping) in rats, and LiCl-induced emesis in shrews (Suncus murinus). The mechanism of how prolonging anandamide availability acts to regulate nausea in rats was explored by the antagonism of cannabinoid 1 (CB1) receptors with the systemic co-administration of SR141716. KEY RESULTS: The systemic administration of ARN272 produced a dose-dependent suppression of nausea-induced conditioned gaping in rats, and produced a dose-dependent reduction of vomiting in shrews. The systemic co-administration of SR141716 with ARN272 (at 3.0 mg·kg(-1)) in rats produced a complete reversal of ARN272-suppressed gaping at 1.0 mg·kg(-1). SR141716 alone did not differ from the vehicle solution. CONCLUSIONS AND IMPLICATIONS: These results suggest that anandamide transport inhibition by the compound ARN272 tonically activates CB1 receptors and as such produces a type of indirect agonism to regulate toxin-induced nausea and vomiting. The results also provide behavioural evidence in support of a facilitated transport mechanism used in the cellular reuptake of anandamide.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".