Back

Activation of cannabinoid CB2 receptors by (-)-cannabichromene but not (+)-cannabichromene

Udoh, M.; Santiago, M.; Haneef, S.; Rodger, A.; Marlowe, C. K.; Barr, P. J.; Connor, M.

2023-08-02 pharmacology and toxicology
10.1101/2023.08.01.551199 bioRxiv
Show abstract

IntroductionCannabichromene (CBC) is a minor constituent of Cannabis that is a selective cannabinoid CB2 receptor agonist and activator of TRPA1. To date, it has not been shown whether (-)-CBC, (+)-CBC (or both) can mediate these effects. In this study we investigate the activity of the CBC enantiomers at CB1, CB2 and TRPA1 in vitro. Materials and MethodsCBC enantiomers were purified from synthetic CBC by chiral chromatography, and their optical activity was confirmed by spectroscopy. Human CB1 and CB2 receptor activity was measured using a fluorescent assay of membrane potential in stably transfected AtT20 cells. TRPA1 activation was measured using a fluorescent assay of intracellular calcium in stably transfected HEK293 cells. Results(-)-CBC activated CB2 with an EC50 of 1.5 {micro}M, to a maximum of 60 % of CP55940. (+)-CBC did not activate CB2 at concentrations up to 30 {micro}M. Only 30 {micro}M (-)- CBC produced detectable activation of CB1, (+)-CBC was inactive. Both (-)-CBC and (+)- CBC activated TRPA1; at 30 {micro}M (-)-CBC produced an activation 50% of that of the reference agonist cinnamaldehyde (300 {micro}M), 30 {micro}M (+)-CBC activated TRPA1 to 38% of the cinnamaldehyde maximum. DiscussionIt is unclear whether (-)-CBC is the sole or even the predominant enantiomer of CBC enzymatically synthesized in Cannabis. This study shows that (-)-CBC is the active isomer at CB2 receptors, while both isomers activate TRPA1. The results suggest that medicinal preparations of CBC that target cannabinoid receptors would be most effective when (-)-CBC is the dominant isomer.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.