Working Paper Brief Report Version 1 This version is not peer-reviewed

Linking Cannabis spp. Metabolite Profiles to Effects and Classifications

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.

Subject Areas

Cannabis; Metabolite; Principal Component Analysis; Random Forest

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