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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), 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.

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

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