Goods and Bads of the Endocannabinoid System as a Therapeutic Target: Lessons Learned after 30 Years
Bibliographic record
Abstract
The cannabis derivative marijuana is the most widely used recreational drug in the Western world, that is consumed by an estimated 83 million individuals (~3% of the world population). In recent years, there has been a marked transformation in society regarding the risk perception of cannabis, driven by its legalization and medical use in many states in the USA and worldwide. Compelling research evidence and the FDA cannabis-derived cannabidiol approval for severe childhood epilepsy have confirmed the large therapeutic potential of cannabidiol itself, Δ9-tetrahydrocannabinol (THC) and other plant-derived cannabinoids (phytocannabinoids). Of note, our body has a complex endocannabinoid system (ECS) - made of receptors, metabolic enzymes and transporters - that is also regulated by phytocannabinoids. The first endocannabinoid to be discovered 30 years ago was anandamide (N-arachidonoyl-ethanolamine); since then, distinct elements of ECS have been the target of drug design programs aimed at curing (or at least slowing down) a number of human diseases, both in the central nervous system and at the periphery. Here, a critical review of our knowledge of the goods and bads of ECS as a therapeutic target are presented, in order to define the benefits of ECS-active phytocannabinoids and ECS-oriented synthetic drugs for human health. Significance Statement The endocannabinoid system plays important roles everywhere in our body and is either involved in mediating key processes of central and peripheral diseases or represents a therapeutic target for treatment. Understanding structure, function, and pharmacology of the components of this complex system, and in particular of key receptors (like CB1R and CB2R) and metabolic enzymes (like FAAH and MAGL), will advance our understanding of endocannabinoid signaling and activity at molecular, cellular, and system levels providing new opportunities to treat patients.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".