Stereoisomers of cannabidiols and their pharmacological activities – A potentially novel direction for cannabinoids
Bibliographic record
Abstract
Cannabidiol (CBD), a bicyclic non-psychoactive cannabinoid biosynthesized by Cannabis spp. of plants, has attracted significant interest in the past decade due to its therapeutic properties. In 2018, the US FDA approved Epidiolex®, a CBD-based drug for the treatment of two rare epileptic seizure disorders. CBD possesses two chiral centers at C3 and C4 on its terpenoid moiety and exhibits cis-trans stereoisomerism along the C3-C4 bond axis. (-)-trans-(3R,4R)-CBD, the natural CBD, is biosynthesized by the cannabis plant, while the unnatural (+)-trans-(3S,4S)-CBD is obtained via chemical synthesis. Both trans isomers exhibit broad in vitro and in vivo biological activities; typically, the unnatural stereoisomer (+)-trans-CBD and its derivatives exhibited more potent activities in comparison to the corresponding (-)-trans isomers. On the other hand, cis-CBD isomers have only been reported recently and can undergo epimerization into trans isomers. There is a significant opportunity to explore unique synthetic methods and biological activities of stereoisomers of CBD that may pave the path for the development of novel therapeutics. Herein, as a novel direction in cannabinoids, we review the chemistry of CBD stereoisomers, their structure-activity relationships, target selectivity and efficacy in animal models.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".