Single and Synergistic Effects of Cannabidiol and Δ-9-Tetrahydrocannabinol on Zebrafish Models of Neuro-Hyperactivity
Bibliographic record
Abstract
In this study, we aimed to investigate the effect of the two main active cannabinoids extracted from cannabis: Δ-9-tetrahydrocannabinol (THC) and cannabidiol (CBD) on two distinct behavioural models of induced neuro-hyperactivity. We have taken advantage of two previously developed zebrafish models of neuro-hyperactivity: a chemically induced pentylenetetrazole model and a genetic model caused by loss-of-function mutations in the GABA receptor subunit alpha 1 (GABRA1-/-). Both CBD and THC have a significant effect on the behavioural changes induced by both models. Importantly, we have also shown that when applied together at different ratios of THC to CBD (1:1, 1:5 and 1:10) there was a synergistic effect at a ratio of 1:1. This was particularly important for the genetically-induced neuro-hyperactivity as it brought the concentrations of THC and CBD required to oppose the induced behavioural changes to levels that had much less of an effect on baseline larval behavior. The results of this study help to validate the ability of THC and CBD to oppose neuro-hyperactivity linked to seizure modalities. Additionally, it appears that individually, each cannabinoid may be more effective against the chemically induced model than against the GABRA1-/- transgenic model. However, when applied together, the concentration of each compound required to oppose the GABRA1-/- light induced activity was lowered. This is of particular interest since the use of cannabinoids as therapeutics can be dampened by their side effect profile. Reducing the level of each cannabinoid required may help to prevent off target effects that lead to side effects. Additionally, this study provides a validation of the complimentary nature of the two zebrafish models and sets a platform for future work with cannabinoids, in particular in the context of neuro-hyperactivity disorders such as epilepsy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".