4.5 Article

Predicting binding between 55 cannabinoids and 4,799 biological targets by in silico methods

Journal

JOURNAL OF APPLIED TOXICOLOGY
Volume -, Issue -, Pages -

Publisher

WILEY
DOI: 10.1002/jat.4478

Keywords

cannabinoids; cannabis; computational toxicology; in silico; QSAR; receptors; safety pharmacology; secondary pharmacology; structure-activity relationship; target screening

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There has been a rise in cannabis-derived products being sold as food and dietary supplements. A study used an in silico tool to predict the binding between 55 cannabinoids and 4,799 biological targets. The predictions were validated with in vitro binding data, and clinical adverse effects associated with the predicted targets were identified.
Recently, 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 for in vitro testing, an in silico tool (Chemotargets Clarity software) was used to predict binding between 55 cannabinoids and 4,799 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 were available, and they agreed well with in silico binding data (median fourfold 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 be used to prioritize subsequent in vitro and in vivo testing.

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