Phytocannabinomics: Untargeted metabolomics as a tool for cannabis chemovar differentiation

Cannabis sativa is traditionally classified according to five chemotypes based on the concentration of the main phytocannabinoids tetrahydrocannabinol (THC), cannabidiol (CBD), and cannabigerol (CBG). However, cannabis chemovars and varieties very often present similar concentrations of such phytocannabinoids but different chemical profiles, which is unavoidably translated into different pharmacological effects when used for therapeutic purposes. For this reason, a more refined approach is needed for chemovar distinction, which is described in this study and named phytocannabinomics. The classification was achieved by a comprehensive characterization of the phytocannabinoid composition, by liquid chromatography coupled to high-resolution mass spectrometry untargeted metabolomics for the detection of over a hundred phytocannabinoids, and data analysis by chemometrics for chemovars differentiation. The method was developed on fifty cannabis varieties, grown under the same conditions, and was validated to discriminate between the standard chemotypes by partial least squares discriminant analysis. Then, the method was extended to consider the entire chemical variety of the cannabis accessions, by an unsupervised approach based on the principal component analysis. The latter approach clearly indicated several new subgroups within the traditional classifications, which arise from a unique composition of the minor phytocannabinoids. The existence of these subgroups, which were never described before, is of critical importance for evaluating the pharmacological effects of cannabis chemovars.