The endocannabinoid system as a putative target for the development of novel drugs for the treatment of psychiatric illnesses
Bibliographic record
Abstract
Cannabis is well established to impact affective states, emotion and perceptual processing, primarily through its interactions with the endocannabinoid system. While cannabis use is quite prevalent in many individuals afflicted with psychiatric illnesses, there is considerable controversy as to whether cannabis may worsen these conditions or provide some form of therapeutic benefit. The development of pharmacological agents which interact with components of the endocannabinoid system in more localized and discrete ways then via phytocannabinoids found in cannabis, has allowed the investigation if direct targeting of the endocannabinoid system itself may represent a novel approach to treat psychiatric illness without the potential untoward side effects associated with cannabis. Herein we review the current body of literature regarding the various pharmacological tools that have been developed to target the endocannabinoid system, their impact in preclinical models of psychiatric illness and the recent data emerging of their utilization in clinical trials for psychiatric illnesses, with a specific focus on substance use disorders, trauma-related disorders, and autism. We highlight several candidate drugs which target endocannabinoid function, particularly inhibitors of endocannabinoid metabolism or modulators of cannabinoid receptor signaling, which have emerged as potential candidates for the treatment of psychiatric conditions, particularly substance use disorder, anxiety and trauma-related disorders and autism spectrum disorders. Although there needs to be ongoing clinical work to establish the potential utility of endocannabinoid-based drugs for the treatment of psychiatric illnesses, the current data available is quite promising and shows indications of several potential candidate diseases which may benefit from this approach.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".