Targeting the Endocannabinoid System in the Treatment of Posttraumatic Stress Disorder: A Promising Case of Preclinical-Clinical Translation?
Bibliographic record
Abstract
The endocannabinoid (eCB) system is one the most ubiquitous signaling systems of the brain and offers a rich pharmacology including multiple druggable targets. Preclinical research shows that eCB activity influences functional connectivity between the prefrontal cortex and amygdala and thereby influences an organism's ability to cope with threats and stressful experiences. Animal studies show that CB 1 receptor activation within the amygdala is essential for extinction of fear memories. Failure to extinguish traumatic memories is a core symptom of posttraumatic stress disorder, suggesting that potentiating eCB signaling may have a therapeutic potential in this condition. However, it has been unknown whether animal findings in this domain translate to humans. Data to inform this critical question are now emerging and are the focus of this review. We first briefly summarize the biology of the eCB system and the animal studies that support its role in fear extinction and stress responding. We then discuss the pharmacological eCB-targeting strategies that may be exploited for therapeutic purposes: direct CB 1 receptor activation, using D 9 -tetrahydrocannabinol or its synthetic analogs; or indirect potentiation, through inhibition of eCB-degrading enzymes, the anandamide-degrading enzyme fatty acid amide hydrolase; or the 2-AG (2-arachidonoyl glycerol)-degrading enzyme monoacylglycerol lipase. We then review recent human data on direct CB 1 receptor activation via D 9 -tetrahydrocannabinol and anandamide potentiation through fatty acid amide hydrolase blockade. The available human data consistently support a translation of animal findings on fear memories and stress reactivity and suggest a potential therapeutic utility in humans.
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".