Predicting binding between 55 cannabinoids and 4799 biological targets by in silico methods

AbstractRecently, there has been an increase in cannabis derived products being marketed as foods, dietary supplements, and other consumer products. Cannabis contains over a hundred cannabinoids, many of which have unknown physiological effects. Since there are large numbers of cannabinoids, and many are not commercially available, anin silico tool (Chemotargets Clarity software) was used to predict binding between 55 cannabinoids and 4799 biological targets (enzymes, ion channels, receptors, and transporters). This tool relied on quantitative structure activity relationships (QSAR), structural similarity, and other approaches to predict binding. From this screening, 827 cannabinoid-target binding pairs were predicted, which included 143 unique targets. Many cannabinoids sharing core structures (cannabinoid “types”) had similar binding profiles, whereas most cannabinoids containing carboxylic acid groups were similar without regards to their core structure. For some of the binding predictions (43),in vitro binding data was available, and it agreed well within silico binding data (median 4-fold difference in binding concentrations). Finally, clinical adverse effects associated with 22 predicted targets were identified from an online database (Clarivate Off-X), providing important insights on potential human health hazards. Overall,in silico biological target predictions are a rapid means to identify potential hazards due to cannabinoid-target interactions, and the data can...
Source: Journal of Applied Toxicology - Category: Toxicology Authors: Tags: RESEARCH ARTICLE Source Type: research