Modeling cannabinoids from a large-scale sample of Cannabis sativa chemotypes

Abstract

The widespread legalization of Cannabis has opened the industry to using contemporary analytical techniques for chemotype analysis. Chemotypic data has been collected on a large variety of oil profiles inherent to the cultivars that are commercially available. The unknown gene regulation and pharmacokinetics of dozens of cannabinoids offer opportunities of high interest in pharmacology research. Retailers in many medical and recreational jurisdictions are typically required to report chemical concentrations of at least some cannabinoids. Commercial cannabis laboratories have collected large chemotype datasets of diverse Cannabis cultivars. In this work a data set of 17,600 cultivars tested by Steep Hill Inc., is examined using machine learning techniques to interpolate missing chemotype observations and cluster cultivars into groups based on chemotype similarity. The results indicate cultivars cluster based on their chemotypes, and that some imputation methods work better than others at grouping these cultivars based on chemotypic identity. Due to the missing data and to the low signal to noise ratio for some less common cannabinoids, their behavior could not be accurately predicted. These findings have implications for characterizing complex interactions in cannabinoid biosynthesis and improving phenotypical classification of Cannabis cultivars.

Genomic evidence that governmentally producedCannabis sativapoorly represents genetic variation available in state markets

“…However, oversight is needed to assure the quality and consistency of Cannabis testing across laboratories (Jikomes and Zoorob 2018). Regulation and supervision will allow for a deeper understanding about all of the compounds produced by the plant particularly minor cannabinoids which are not always measured (Vergara et al 2020) and…”

“…However, oversight is needed to assure the quality and consistency of Cannabis testing across laboratories (Jikomes and Zoorob 2018). Regulation and supervision will allow for a deeper understanding about all of the compounds produced by the plant particularly minor cannabinoids which are not always measured (Vergara et al 2020) and that have multiple genes related to their production with complex interactions (Vergara et al 2019). This is particularly important because medical Cannabis use has outpaced its research (Hutchison et al 2019).…”

The Phytochemical Diversity of Commercial Cannabis in the United States

“…A variety of studies have looked at the chemical composition of Cannabis samples limited to a single geographic location (Hazekamp and Fischedick 2012; Orser et al 2017; Henry et al 2018; Reimann-Philipp et al 2019), included measurements of a limited number of cannabinoids (Hillig and Mahlberg 2004; Elzinga et al 2015; Hazekamp et al 2016; Vergara et al 2017; Jikomes and Zoorob 2018; Vergara et al 2020), or included measurements of terpenes without cannabinoid content (Hillig 2004). Few studies have investigated the major and minor cannabinoids together with the terpenes (Mudge et al 2019) and none have performed a thorough chemotaxonomic analysis on a dataset with tens of thousands of samples across several legal cannabis markets in the United States.…”

“…The lack of a standardized, regulated naming system for commercial Cannabis varieties has been discussed previously (Sawler et al 2015; Vergara et al 2016; Vergara et al 2020). Various studies, each limited in different ways, have investigated whether these labels capture real chemical variation.…”

“…A variety of studies have looked at the chemical composition of Cannabis samples limited to a single geographic location (Hazekamp and Fischedick 2012;Orser et al 2017;Henry et al 2018;Reimann-Philipp et al 2019), included measurements of a limited number of cannabinoids (Hillig and Mahlberg 2004;Hazekamp et al 2016;Vergara et al 2017;Jikomes and Zoorob 2018;Vergara et al 2020), or included measurements of terpenes without cannabinoid content (Hillig 2004).…”