Computation-assisted discovery of bioactive minor cannabinoids from hemp

Oregon State University, Corvallis OR

Investigators

Abstract

This project aims to conduct mechanistic research of potential therapeutic benefits of minor cannabinoids in hemp. We propose to integrate Artificial Intelligence in our workflow to accelerate the discovery of bioactive minor cannabinoids. We focus on chronic pain as our therapeutic benefit of non-psychoactive cannabinoids. A large body of literature suggests that terpenophenolics from hemp may have potential in the treatment of pain and other conditions that are driven by low-grade chronic inflammation. The conventional, classic approach to identify potential therapeutics derived from natural products (NPs) is laborious and time-consuming bioassay-guided fractionation. Our AI-assisted approach for the discovery of bioactive NPs accelerates the discovery pipeline, as our feasibility studies show. We expect that we will succeed in identifying bioactive cannabinoids that have previously not been studied in clinically relevant bioassays. Our Specific Aims are: Specific Aim 1: To predict and validate the effects of individual cannabinoids in CBD-depleted hemp extracts in cell culture models relevant to nociception. Columbia Basin Bioscience operates an industrial scale CBD manufacturing facility which makes use of recrystallization of CBD from cannabinoid-enriched hemp extracts. We will use the supernatants of various recrystallization stages (“mother liquors”) as our source of minor cannabinoids in our computation-assisted discovery of bioactives. We will perform calcium flux assays using HEK-293 cells stably over-expressing recombinant human TRPV1 as a cell culture model relevant to nociception. We will also test for FAAH/COX-2 inhibitory activity as dual acting compounds targeting the endocannabinoid and endovanilloid systems are emerging as a novel treatment option for chronic pain management. We will validate the most potent predictors of activity by testing individual, pure cannabinoids in the same assays. We will also determine the bioactivity of 17 minor cannabinoids from our repository of cannabinoid standards, which may or may not include predictors of bioactivity from our discovery pipeline. Specific Aim 2: To predict and validate synergistic/interactive effects between bioactive cannabinoids. Our AI-discovery pipeline has the ability to detect synergistic, additive, or antagonistic effects based on measurements of absolute concentrations of NPs in crude fractions containing bioactive cannabinoids for which we will establish concentration-effect curves. We will quantify the pharmacological interactions among a subset of authentic cannabinoids including 17 minor cannabinoids from our repository of standards which show bioactivity in our assays or which have been reported to attenuate TRPV1-related pain signaling. We expect that the proposed AI-assisted discovery approach will generate cannabinoid candidates that attenuate nociceptive TRPV1-FAAH/COX-1 signaling pathways for further mechanistic testing in preclinical models of pain sensation. The development of minor cannabinoids as pain mitigators from waste products of the CBD-industry will benefit not only chronic pain patients but also a sustainable agro-industry.