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Identification of Psychoactive Metabolites from Cannabis sativa, Its Smoke, and Other Phytocannabinoids Using Machine Learning and Multivariate Methods.

ABSTRACT: Cannabis sativa is a medicinal plant having a very complex matrix composed of mainly cannabinoids and terpenoids. The literature has numerous reports, which indicate that tetrahydrocannabinol (THC) is the only major psychoactive metabolite in C. sativa. It is important to explore other metabolites having the possibility of exhibiting the psychoactive character of various degrees and also to identify metabolites targeting other receptors such as opioid, ? amino butyric acid (GABA), glycine, serotonin, and nicotine present in C. sativa, the smoke of C. sativa, and other phytocannabinoid matrices. This article aims to achieve this goal by application of batteries of computational tools such as machine learning tools and multivariate methods on physiochemical and absorption, distribution, metabolism, excretion, and toxicity (ADMET) descriptors of 468 metabolites from C. sativa, its smoke and, other phytocannabinoids. The structure-activity relationship (SAR) showed that 54 metabolites from C. sativa have high scaffold homology with THC. Its implications on the route of administration and factors affecting the SAR are discussed. C. sativa smoke has metabolites that have possibility of interacting with GABA, and glycine receptors.

SUBMITTER: Jagannathan R

PROVIDER: S-EPMC6964292 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Identification of Psychoactive Metabolites from Cannabis sativa, Its Smoke, and Other Phytocannabinoids Using Machine Learning and Multivariate Methods.

Jagannathan Ramesh R

ACS omega 20200103 1

Cannabis sativa is a medicinal plant having a very complex matrix composed of mainly cannabinoids and terpenoids. The literature has numerous reports, which indicate that tetrahydrocannabinol (THC) is the only major psychoactive metabolite in C. sativa. It is important to explore other metabolites having the possibility of exhibiting the psychoactive character of various degrees and also to identify metabolites targeting other receptors such as opioid, γ amino butyric acid (GABA), ...[more]

PMID: 31956775

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Project description:Introduction: Cannabis sativa produces hundreds of bioactive compounds, including cannabinoids and terpenoids. It has been proposed that cannabinoids act in synergy with terpenoids to produce the entourage effect, a concept used to explain the therapeutic benefits of medicinal cannabis. One molecular explanation for the entourage effect is that the terpenoids augment the actions of cannabinoids at their molecular drug targets in cells. We recently reported that terpenoids commonly found in cannabis do not influence the functional effects of Δ9-tetrahydrocannabinol (Δ9-THC) on cannabinoid 1 and cannabinoid 2 receptors. The present study aimed to extend on this research by examining whether terpenoids influence the effects of phytocannabinoids and endocannabinoids on human transient receptor potential ankyrin 1 (hTRPA1) and human transient receptor potential vanilloid 1 (hTRPV1) channels heterologously expressed in mammalian cells. Materials and Methods: The activity of terpenoids, phytocannabinoids, and endocannabinoids was assessed in inducible HEK Flp-In T-Rex cells transfected with hTRPA1 and hTRPV1 channels, respectively. Real-time changes in intracellular calcium ([Ca]i) were measured using the Calcium 5 dye and a FlexStation 3 plate reader. Results: α-pinene, β-pinene, β-caryophyllene, linalool, limonene, β-myrcene or α-humulene did not affect [Ca]i in hTRPA1 and hTRPV1 overexpressing cells. Cinnamaldehyde (CA), Δ9-THC, and 2-arachidonoylglycerol (2-AG) activated TRPA1 receptors with high efficacy and similar potency (EC50s of ∼10 μM). Capsaicin and anandamide (AEA) activated TRPV1 receptors with an EC50 of 61 nM and 4.3 μM, respectively, but TRPV1 showed no response to Δ9-THC, cannabidiol, and other minor cannabinoids. Terpenoids did not significantly affect the responses of TRPA1 and TRPV1 receptors to submaximal and maximal concentrations of CA and Δ9-THC or the endocannabinoids AEA and 2-AG. Discussion: We could not find any evidence that the terpenoids tested here activate TRPA1 and TRPV1 channels or modulate their activation by Δ9-THC and other agonists, including endocannabinoids.

Dataset Information

Identification of Psychoactive Metabolites from Cannabis sativa, Its Smoke, and Other Phytocannabinoids Using Machine Learning and Multivariate Methods.

Publications

Identification of Psychoactive Metabolites from <i>Cannabis sativa</i>, Its Smoke, and Other Phytocannabinoids Using Machine Learning and Multivariate Methods.

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