Isolation, Synthesis And Structure Determination Of Cannabidiol Derivatives And Their Cytotoxic Activities

Antibiofilm and Immune-Modulatory Activity of Cannabidiol and Cannabigerol in Oral Environments-In Vitro Study

OBJECTIVE

To evaluate the in vitro antimicrobial and antibiofilm properties and the immune modulatory activity of cannabidiol (CBD) and cannabigerol (CBG) on oral bacteria and periodontal ligament fibroblasts (PLF).

METHODS

Cytotoxicity was assessed by propidium iodide flow cytometry on fibroblasts derived from the periodontal ligament. The minimum inhibitory concentration (MIC) of CBD and CBG for S. mutans and C. albicans and the metabolic activity of a subgingival 33-species biofilm under CBD and CBG treatments were determined. The Quantification of cytokines was performed using the LEGENDplex kit (BioLegend, Ref 740930, San Diego, CA, USA).

RESULTS

CBD-treated cell viability was greater than 95%, and for CBG, it was higher than 88%. MIC for S. mutans with CBD was 20 µM, and 10 µM for CBG. For C. albicans, no inhibitory effect was observed. Multispecies biofilm metabolic activity was reduced by 50.38% with CBD at 125 µg/mL (p = 0.03) and 39.9% with CBG at 62 µg/mL (p = 0.023). CBD exposure at 500 µg/mL reduced the metabolic activity of the formed biofilm by 15.41%, but CBG did not have an effect. CBG at 10 µM caused considerable production of anti-inflammatory mediators such as TGF-β and IL-4 at 12 h. CBD at 10 µM to 20 µM produced the highest amount of IFN-γ.

CONCLUSION

Both CBG and CBD inhibit S. mutans; they also moderately lower the metabolic activity of multispecies biofilms that form; however, CBD had an effect on biofilms that had already developed. This, together with the production of anti-inflammatory mediators and the maintenance of the viability of mammalian cells from the oral cavity, make these substances promising for clinical use and should be taken into account for future studies.

Development and Validation of a GC-FID Method for the Quantitation of Δ 8-Tetrahydrocannabinol and Impurities Found in Synthetic Δ 8-Tetrahydrocannabinol and Vaping Products

Concerns about health hazards associated with the consumption of trans-delta-8-tetrahydrocannabinol products were highlighted in public health advisories from the U. S. Food and Drug Administration and U. S. Centers for Disease Control and Prevention. Simple and rapid quantitative methods to determine trans-delta-8-tetrahydrocannabinol impurities are vital to analyze such products. In this study, a gas chromatography-flame ionization detection method was developed and validated for the determination of delta-8-tetrahydrocannabinol and some of its impurities (recently published) found in synthesized trans-delta-8-tetrahydrocannabinol raw material and included olivetol, cannabicitran, Δ 8-cis-iso-tetrahydrocannabinol, Δ 4-iso-tetrahydrocannabinol, iso-tetrahydrocannabifuran, cannabidiol, Δ 4,8-iso-tetrahydrocannabinol, Δ 8-iso-tetrahydrocannabinol, 4,8-epoxy-iso-tetrahydrocannabinol, trans-Δ 9-tetrahydrocannabinol, 8-hydroxy-iso-THC, 9α-hydroxyhexahydrocannabinol, and 9β-hydroxyhexahydrocannabinol. Validation of the method was assessed according to the International Council for Harmonization guidelines and confirmed linearity with R2 ≥ 0.99 for all the target analytes. The limit of detection and limit of quantitation were 1.5 and 5 µg/mL, respectively, except for olivetol, which had a limit of detection of 3 µg/mL and a limit of quantitation of 10 µg/mL. Method precision was calculated as % relative standard deviation and the values were less than 8.4 and 9.9% for the intraday precision and inter-day precision, respectively. The accuracy ranged from 85 to 118%. The method was then applied to the analysis of 21 commercially marketed vaping products claiming to contain delta-8-tetrahydrocannabinol. The products analyzed by this method have various levels of these impurities, with all products far exceeding the 0.3% of trans-Δ 9-tetrahydrocannabinol limit for hemp under the Agriculture Improvement Act of 2018. The developed gas chromatography-flame ionization detection method can be an important tool for monitoring delta-8-tetrahydrocannabinol impurities in commercial products.

Neuroprotective Effects of Cannabispirenone A against NMDA-Induced Excitotoxicity in Differentiated N2a Cells

The endocannabinoid system is found throughout the central nervous system, and its cannabinoids receptor 1 is critical in preventing neurotoxicity caused by N-methyl-D-aspartate receptor activation (NMDARs). The activity of NMDARs places demands on endogenous cannabinoids to regulate their calcium currents. Endocannabinoids keep NMDAR activity within safe limits, protecting neural cells from excitotoxicity. Cannabinoids are remembered to deliver this outcome by repressing presynaptic glutamate discharge or obstructing postsynaptic NMDAR-managed flagging pathways. The endocannabinoid system must exert a negative influence proportional to the strength of NMDAR signaling for such control to be effective. The goal of this paper is to draw the attention towards the neuroprotective mechanism of constituents of Cannabis sativa against NMDA-induced excitotoxic result. Phytochemical investigation of the cannabis flowers led to the isolation of nine secondary metabolites. A spiro-compound, Cannabispirenone A, which on treatment of the cells prior to NMDA exposure significantly increases cell survival while decreasing ROS production, lipid peroxidation, and intracellular calcium. Our findings showed that this compound showed neuroprotection against NMDA-induced excitotoxic insult, has antioxidative properties, and increased cannabinoid receptor 1 expression, which may be involved in the signaling pathway for this neuroprotection.

Isolation and Characterization of Impurities in Commercially Marketed Δ8-THC Products

Qualitative analysis of several commercial products containing Δ⁸-tetrahydrocannabinol (Δ⁸-THC) as a major component using GC-MS resulted in the identification of several impurities along with Δ⁸-THC. In an attempt to isolate and identify these impurities, a commercial Δ⁸-THC distillate was selected for the isolation work. Eleven impurities were isolated using a variety of chromatographic techniques, and their chemical structures were determined. These include Δ^4,8-iso-tetrahydrocannabinol (1), Δ⁴-iso-tetrahydrocannabinol (2), Δ⁸-cis-iso-tetrahydrocannabinol (3), 4,8-epoxy-iso-tetrahydrocannabinol (4), 8-hydroxy-iso-tetrahydrocannabinol (5), 9β-hydroxyhexahydrocannabinol (6), 9α-hydroxyhexa-hydrocannabinol (7), iso-tetrahydrocannabifuran (8), cannabicitran (CBT, 9), olivetol (10), and Δ⁹-THC (11). The chemical structures of the purified compounds were determined using several spectroscopic methods, including 1D (¹H, ¹³C, and DEPT-135) and 2D (COSY, HMQC, HMBC, and NOESY) NMR, LC-MS, and GC-MS. Other naturally occurring cannabinoids and impurities were also identified in GC-MS chromatograms but were not isolated. These were cannabidiol (CBD, 12), cannabinol (CBN, 13), hexahydrocannabinol (HHC, 14), and Δ⁸-tetrahydrocannabivarin (Δ⁸-THCV, 15). The chemical structure of Δ⁸-THCV (15), for which a standard was not available, was confirmed by partial synthesis and NMR analysis. This is the first report for many of the above compounds as well as Δ⁸-THCV as impurities in Δ⁸-THC products.

The Dark Side of Cannabidiol: The Unanticipated Social and Clinical Implications of Synthetic Δ8-THC

Introduction: The explosive growth of the cannabis industry in the United States over the past decade has spurred a multitude of products derived from phytocannabinoids produced by Cannabis sativa L. Decades of cannabis prohibition coupled with the more recent 2018 Farm Bill have lead to several unanticipated consequences and the widespread availability of synthetic cannabinoids derived from hemp CBD, including Δ8-THC, Δ10-THC and HHC. Methods: Herein, we review the available literature of the complexity of the chemistry of its current manufacture, namely, the acid-catalyzed ring closure of cannabidiol (ACRCC), the myriad of issues involving the unsolved technical problems with quality control of ACRCC-Δ8-THC and the multitude of isomerized byproducts, and the lack of consistent regulation regarding consumer safety and labeling. Results: We provide what we believe is the first comprehensive listing of all the documented ACRCC-Δ8-THC byproducts. Perhaps, most importantly, we highlight the growing concern that, other than Δ8-THC itself, the compounds in ACRCC-Δ8-THC product mixtures have not been subjected to any human toxicological evaluation. This is especially troubling as ACRCC-Δ8-THC products relate to vaping, and their contribution to a growing and lethal epidemic of electronic cigarette, or vaping, product use-associated lung injury (EVALI). Conclusions: Quality control is totally inadequate in the newly emerging Δ8-THC industry. American consumers are ingesting products that are mislabeled with many compounds that have never received any toxicological testing. EVALI cases continue to be reported with a fatality rate approaching 2% (in California).

A critical review on the use of DP4+ in the structural elucidation of natural products: the good, the bad and the ugly. A practical guide

Covering: 2015 up to the end of 2020Even in the golden age of NMR, the number of natural products being incorrectly assigned is becoming larger every day. The use of quantum NMR calculations coupled with sophisticated data analysis provides ideal complementary tools to facilitate the elucidation process in challenging cases. Among the current computational methodologies to perform this task, the DP4+ probability is a popular and widely used method. This updated version of Goodman's DP4 synergistically combines NMR calculations at higher levels of theory with the Bayesian analysis of both scaled and unscaled data. Since its publication in late 2015, the use of DP4+ to solve controversial natural products has substantially grown, with several predictions being confirmed by total synthesis. To date, the structures of more than 200 natural products were determined with the aid of DP4+. However, all that glitters is not gold. Besides its intrinsic limitations, on many occasions it has been improperly used with potentially important consequences on the quality of the assignment. Herein we present a critical revision on how the scientific community has been using DP4+, exploring the strengths of the method and how to obtain optimal results from it. We also analyze the weaknesses of DP4+, and the paths to by-pass them to maximize the confidence in the structural elucidation.

In Silico Infrared Characterization of Synthetic Cannabinoids by Quantum Chemistry and Chemometrics

The concept of forensic sciences as mere trace analysis has been modified by the idea of forensic intelligence, which entails applying data to make decisions within the investigative process. Many countries are engaged in combating drug trafficking and drug use because they are related to public health and safety issues. Prohibiting the consumption of traditional drugs has led new psychoactive substances (NPSs) to emerge. NPSs consist of compounds that resemble the initially banned substance and which aim to mimic the traditional drug recreational effects while circumventing drug legislation. For example, synthetic cannabinoids are sprayed on herbal products to reproduce the cannabis recreational effects. According to the United Nations Office on Drugs and Crime (UNODC), the toxic effects of synthetic cannabis types are unknown, and harm and fatalities associated with the use of these drugs have been reported. Information on the characterization related to these species is lacking. The rate at which NPSs appear poses a significant challenge because employing conventional methods to understand the characteristics of these compounds may require time and be costly. This work uses in silico practices as an alternative to understand how NPSs related to cannabis behave. We apply quantum chemistry methods to evaluate several synthetic cannabinoids recognized in forensic samples. More specifically, we generate infrared spectra that can be employed as a benchmark for NPSs. We apply a multivariate classification to evaluate the results. We conclude that in silico methods are an alternative that provide information about the spectra of undetected substances. This information can help to identify new drugs, to increase knowledge about them, and to feed information procedures.