Endocannabinoids in the Treatment of Mood Disorders: Evidence from Animal Models
Bibliographic record
Abstract
Among all mental disorders, major depression has the highest rate of prevalence and incidence of morbidity. Currently available antidepressant therapies have limited efficacies; consequently, research on new drugs for the treatment of mood disorders has become increasingly critical. Recent preclinical evidences that cannabinoid agonists and endocannabinoid enhancers, such as the fatty acid amide hydrolase (FAAH) inhibitors, can impact mood regulation have opened a new line of research in antidepressant drug discovery. However, the neurobiological mechanisms linking the endocannabinoid system with the pathophysiology of mood disorders and antidepressant action remain unclarified. In this review, we have presented an update on preclinical data indicating the antidepressant potential of cannabinoid agonists and endocannabinoid enhancers in comparison to standard antidepressants. Data obtained from CB(1) knockout (CB(1)-/-) and FAAH knockout (FAAH-/-) mice have also been examined within this context. We have illustrated how the various classes of antidepressants exert their therapeutic action. In particular, all antidepressants increase the neurotransmission of serotonin after long-term treatment, enhance the tonic activity of hippocampal 5-HT(1A) receptors, promote neurogenesis, and modulate (decrease or increase) the firing activity of noradrenergic neurons. Interestingly, cannabinoid agonists and endocannabinoid enhancers increase serotonin and noradrenergic neuronal firing activity, increase serotonin release in the hippocampus, as well as promote neurogenesis. Since cannabinoid-derived drugs potentiate monoaminergic neurotransmission and hippocampal neurogenesis through distinct pathways compared to classical antidepressants, they may represent an alternative drug class in the pharmacotherapy of mood and other neuropsychiatric disorders.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".