Brief Report
Version 1
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Linking Cannabis spp. Metabolite Profiles to Effects and Classifications
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and
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Eric Lane
Version 1
: Received: 4 September 2020 / Approved: 5 September 2020 / Online: 5 September 2020 (07:51:50 CEST)
How to cite: Monk, A.; Lane, E. Linking Cannabis spp. Metabolite Profiles to Effects and Classifications. Preprints 2020, 2020090127 Monk, A.; Lane, E. Linking Cannabis spp. Metabolite Profiles to Effects and Classifications. Preprints 2020, 2020090127
Abstract
The many strains of Cannabis spp. are associated with many effects on users and contain many different potentially psychoactive metabolites, but the links between metabolite profiles and user effects are unclear. Here we take a statistical approach to linking cause (i.e. metabolites) to effects in Cannabis spp. through the prism of strains, using quantitative data for metabolite composition and user effects. We find that species (indica vs. sativa) explains <2% of the variability in metabolite profiles, while strain explains 1/3 of variability, indicating species is nonindicative of metabolite composition, while strain is approximately indicative. Using random forests we generate a table of potential metabolite-effect links. We also find that effect-weighted metabolite composition can effectively be described in terms of four values representing the concentrations of pairs or triplets of particular compounds.
Keywords
Cannabis; Metabolite; Principal Component Analysis; Random Forest
Subject
Medicine and Pharmacology, Pharmacology and Toxicology
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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