Chemistry, Crystal Structure, and <i>In Vitro</i> Receptor Binding of Δ10-THC Isomers
Bibliographic record
Abstract
Introduction:The psychoactive properties of Δ10-THC isomers (trans- and cis-Δ10-THC) are poorly understood. To shed more light on the biological effects of these compounds, we studied in vitro receptor binding of Δ10-THC isomers at cannabinoid CB1 and CB2 receptors. Materials and Methods:We first optimized and simplified catalytic synthesis of trans- and cis-Δ10-THC to allow their safe and cheap large-scale synthesis. In our synthesis, BuLi was replaced with KOtBu, and DMSO/anisole or NEt3/heptane solvent systems were used instead of HMPA/toluene. Single crystal X-ray analysis confirmed the structure of both isomers and the configuration of their chiral centers. Results:In the radioligand replacement assay, both isomers showed strong affinity toward the CB1 receptor, with IC50=29.1 nM for the trans isomer and IC50=294.2 nM for the cis counterpart. However, the IC50 values were significantly higher than that of Δ9-THC (2.1 nM), a naturally occurring psychoactive component of cannabis sativa, suggesting a lower affinity of Δ10-THCs toward this receptor. In function assays, in contrast to Δ9-THC, both isomers failed to show any agonist properties at concentrations up to 10 μM suggesting a lack of THC-like psychoactivity for trans- and cis-Δ10-THC. Conclusions:Our results established Δ10-THC isomers among antagonists of the CB1 receptor as both cis and trans isomers antagonized CP55,490 with IC50=460 nM for trans and IC50=1040 nM for cis. This functional property has not been previously observed for any other THC isomers.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".