Tetrahydrocannabivarin (THCV) Dose Dependently Blocks or Substitutes for Tetrahydrocannabinol (THC) in a Drug Discrimination Task in Rats
Bibliographic record
Abstract
Delta-9-Tetrahydrocannabivarin (THCV), a naturally occurring cannabinoid and structural analog of THC, exhibits a dual pharmacological profile as a CB1 receptor agonist/antagonist and a partial CB2 agonist. This study evaluated the effects of THCV in a THC discrimination model in rats. Male Sprague-Dawley rats (n = 16, 300–340 g, PND60) were trained under a fixed ratio 20 (FR20) schedule to discriminate THC (3 mg/kg) from vehicle. Substitution tests were conducted with THC (0.325–3 mg/kg), THCV (0.75–6 mg/kg), and THC-THCV combinations. THCV produced an inverted U-shaped substitution curve, significantly differing from vehicle (p = 0.008). At 3 mg/kg, THCV partially substituted for THC (54.6% ± 17.82, p = 0.003). Response rate significantly increased during the substitution test with 3 mg/kg of THCV (p = 0.042). THCV (6 mg/kg) reversed THC (0.75 mg/kg)-induced responding (p = 0.040), with no significant change in response rate (p = 0.247). However, THCV combined with THC (1.5 mg/kg) affected response rates (p = 0.012), with 6 mg/kg significantly reducing rates vs. 3 mg/kg (p = 0.013). Blood THC and 11-OH-THC levels remained unchanged when THC was combined with THCV. The findings suggest THCV can partially mimic or block THC’s discriminative effects in a dose-dependent manner, possibly acting as a partial CB1 agonist.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".