Synthetic cannabinoids: In silico prediction of the cannabinoid receptor 1 affinity by a quantitative structure-activity relationship model

Application of ensemble learning for predicting GABAA receptor agonists

BACKGROUND

Over the past few decades, agonists binding to the benzodiazepine site of the GABAA receptor have been successfully developed as clinical drugs. Different modulators (agonist, antagonist, and reverse agonist) bound to benzodiazepine sites exhibit different or even opposite pharmacological effects, however, their structures are so similar that it is difficult to distinguish them based solely on molecular skeleton. This study aims to develop classification models for predicting the agonists.

METHODS

306 agonists or non-agonists were collected from literature. Six machine learning algorithms including RF, XGBoost, AdaBoost, GBoost, SVM, and ANN algorithms were employed for model development. Using six descriptors including 1D/2D Descriptors, ECFP4, 2D-Pharmacophore, MACCS, PubChem, and Estate fingerprint to characterize chemical structures. The model interpretability was explored by SHAP method.

RESULTS

The best model demonstrated an AUC value of 0.905 and an MCC value of 0.808 for the test set. The PubMac-based model (PubMac-GB) achieved best AUC values of 0.935 for test set. The SHAP analysis results emphasized that MaccsFP62, ECFP_624, ECFP_724, and PubchemFP213 were the crucial molecular features. Applicability domain analysis was also performed to determine reliable prediction boundaries for the model. The PubMac-GB model was applied to virtual screening for potential GABAA agonists and the top 100 compounds were given.

CONCLUSION

Overall, our ensemble learning-based model (PubMac-GB) achieved comparable performance and would be helpful in effectively identifying agonists of GABAA receptors.

Advances in urinary biomarker research of synthetic cannabinoids

New psychoactive substances (NPS) are chemical compounds designed to mimic the action of existing illicit recreational drugs. Synthetic cannabinoids (SCs) are a subclass of NPS which bind to the cannabinoid receptors, CB1 and CB2, and mimic the action of cannabis. SCs have dominated recent NPS seizure reports worldwide. While urine is the most common matrix for drug-of-abuse testing, SCs undergo extensive Phase I and Phase II metabolism, resulting in almost undetectable parent compounds in urine samples. Therefore, the major urinary metabolites of SCs are usually investigated as surrogate biomarkers to identify their consumption. Since seized urine samples after consuming novel SCs may be unavailable in a timely manner, human hepatocytes, human liver microsomes and human transporter overexpressed cell lines are physiologically-relevant in vitro systems for performing metabolite identification, metabolic stability, reaction phenotyping and transporter experiments to establish the disposition of SC and its metabolites. Coupling these in vitro experiments with in vivo verification using limited authentic urine samples, such a two-pronged approach has proven to be effective in establishing urinary metabolites as biomarkers for rapidly emerging SCs.

Au-doped MOFs catalyzed electrochemiluminescence platform coupled with target-induced self-enrichment for detection of synthetic cannabinoid RCS-4

A ternary composite material with Au, Co-based organic frameworks (ZIF-67) and perylene derivatives (PTCD-cys) has been synthesized for identification of synthetic cannabinoids. Through contact with Au-S, Au-ZIF-67 increased electrochemiluminescence (ECL) sensitivity and stability and efficiently catalyzed the ECL of PTCD-cys. Compared with the ECL response of PTCD-cys monomer, the ECL signal value of the composite material was significantly increased, and the onset potential of Au-ZIF-67/PTCD-cys favorably shifted more than that of PTCD-cys/GCE. When the target cannabinoid molecule RCS-4 appeared, Au-ZIF-67 captured and immobilized it on the sensor surface by adsorption to achieve target-induced self-enrichment of RCS-4. Under optimal conditions, the ECL sensor was found to be linearly related to the logarithm of the RCS-4 concentration ranging from 3.1 × 10^-15 to 3.1 × 10^-9 mol/L with a detection limit (LOD) of 6.0 × 10^-16 mol/L (S/N = 3). The approach had the advantages of being simple to use, having a high sensitivity, a wide detection range, and good stability, making it a novel platform for RSC-4 detection in public health safety monitoring.

QSAR Model for Predicting the Cannabinoid Receptor 1 Binding Affinity and Dependence Potential of Synthetic Cannabinoids

In recent years, there have been frequent reports on the adverse effects of synthetic cannabinoid (SC) abuse. SCs cause psychoactive effects, similar to those caused by marijuana, by binding and activating cannabinoid receptor 1 (CB1R) in the central nervous system. The aim of this study was to establish a reliable quantitative structure–activity relationship (QSAR) model to correlate the structures and physicochemical properties of various SCs with their CB1R-binding affinities. We prepared tetrahydrocannabinol (THC) and 14 SCs and their derivatives (naphthoylindoles, naphthoylnaphthalenes, benzoylindoles, and cyclohexylphenols) and determined their binding affinity to CB1R, which is known as a dependence-related target. We calculated the molecular descriptors for dataset compounds using an R/CDK (R package integrated with CDK, version 3.5.0) toolkit to build QSAR regression models. These models were established, and statistical evaluations were performed using the mlr and plsr packages in R software. The most reliable QSAR model was obtained from the partial least squares regression method via Y-randomization test and external validation. This model can be applied in vivo to predict the addictive properties of illicit new SCs. Using a limited number of dataset compounds and our own experimental activity data, we built a QSAR model for SCs with good predictability. This QSAR modeling approach provides a novel strategy for establishing an efficient tool to predict the abuse potential of various SCs and to control their illicit use.

Virtual Screening of C. Sativa Constituents for the Identification of Selective Ligands for Cannabinoid Receptor 2

The selective targeting of the cannabinoid receptor 2 (CB2) is crucial for the development of peripheral system-acting cannabinoid analgesics. This work aimed at computer-assisted identification of prospective CB2-selective compounds among the constituents of Cannabis Sativa. The molecular structures and corresponding binding affinities to CB1 and CB2 receptors were collected from ChEMBL. The molecular structures of Cannabis Sativa constituents were collected from a phytochemical database. The collected records were curated and applied for the development of quantitative structure-activity relationship (QSAR) models with a machine learning approach. The validated models predicted the affinities of Cannabis Sativa constituents. Four structures of CB2 were acquired from the Protein Data Bank (PDB) and the discriminatory ability of CB2-selective ligands and two sets of decoys were tested. We succeeded in developing the QSAR model by achieving Q² 5-CV > 0.62. The QSAR models helped to identify three prospective CB2-selective molecules that are dissimilar to already tested compounds. In a complementary structure-based virtual screening study that used available PDB structures of CB2, the agonist-bound, Cryogenic Electron Microscopy structure of CB2 showed the best statistical performance in discriminating between CB2-active and non-active ligands. The same structure also performed best in discriminating between CB2-selective ligands from non-selective ligands.

Study of the effectiveness of various cannabinoid receptor 1 (CB1) agonists using molecular docking and molecular dynamics modeling

The binding of a series of small organic molecules, acting as agonists of the cannabinoid receptor CB1, was investigated by means of three methods of computational chemistry. Binding modes were predicted by means of molecular docking, and binding free energy was estimated via docking, molecular-mechanics Poisson-Boltzmann surface area method, and multistate Bennett acceptance ratio. No evident correlation was observed for the molecules between the experimental characteristics of affinity and three computed binding free energy estimates. The reasons for the discrepancy were discussed.

Cannabinoid Interactions with Proteins: Insights from Structural Studies

Cannabinoids have been widely used for recreational and medicinal purposes. The increasing legalization of cannabinoid use and the growing success in Medicinal Chemistry of cannabinoids have fueled recent interest in cannabinoid-sensing sites in receptor proteins. Here, we review structural data from high-resolution cryo-EM and crystallography studies that depict phytocannabinoid, endocannabinoid, and synthetic cannabinoid molecules bound to various proteins. The latter include antigen-binding fragment (Fab), cellular retinol binding protein 2 (CRBP2), fatty acid-binding protein 5 (FABP5), peroxisome proliferator-activated receptor γ (PPAR γ), and cannabinoid receptor types 1 and 2 (CB1 and CB2). Cannabinoid-protein complexes reveal the complex design of cannabinoid binding sites that are usually presented by conventional ligand-binding pockets on respective proteins. However, subtle differences in cannabinoid interaction with amino acids within the binding pocket often result in diverse consequences for protein function. The rapid increase in available structural data on cannabinoid-protein interactions will ultimately direct drug design efforts toward rendering highly potent cannabinoid-related pharmacotherapies that are devoid of side effects.

Receptor Binding Affinities of Synthetic Cannabinoids Determined by Non-Isotopic Receptor Binding Assay

A major predictor of the efficacy of natural or synthetic cannabinoids is their binding affinity to the cannabinoid type I receptor (CB1) in the central nervous system, as the main psychological effects of cannabinoids are achieved via binding to this receptor. Conventionally, receptor binding assays have been performed using isotopes, which are inconvenient owing to the effects of radioactivity. In the present study, the binding affinities of five cannabinoids for purified CB1 were measured using a surface plasmon resonance (SPR) technique as a putative non-isotopic receptor binding assay. Results were compared with those of a radio-isotope-labeled receptor binding assay. The representative natural cannabinoid Δ9-tetrahydrocannabinol and four synthetic cannabinoids, JWH-015, JWH-210, RCS-4, and JWH-250, were assessed using both the SPR biosensor assay and the conventional isotopic receptor binding assay. The binding affinities of the test substances to CB1 were determined to be (from highest to lowest) 9.52 × 10-13 M (JWH-210), 6.54 × 10-12 M (JWH-250), 1.56 × 10-11 M (Δ9-tetrahydrocannabinol), 2.75 × 10-11 M (RCS-4), and 6.80 ×10-11 M (JWH-015) using the non-isotopic method. Using the conventional isotopic receptor binding assay, the same order of affinities was observed. In conclusion, our results support the use of kinetic analysis via SPR in place of the isotopic receptor binding assay. To replace the receptor binding affinity assay with SPR techniques in routine assays, further studies for method validation will be needed in the future.

Screening methods for rapid determination of new psychoactive substances (NPS) in conventional and non-conventional biological matrices

In the last years, a global awareness has arisen from the reported harmful effects and public health risks associated with the consumption of new psychoactive substances (NPSs). Improving efforts in the detection and identification of these substances have emerged as a global analytical challenge involving the large range of NPSs' chemical structures and the variety of conventional and non-conventional biological matrices. Indeed, detection capabilities and screening tools impact many fields and settings, including seized products analysis, workplace and roadside drug controls, emergency rooms, drug addiction treatment clinics, post-mortem and criminal caseworks, law enforcement and health interventions. Colorimetric, immunochemical and chromatographic-mass spectrometry techniques have been investigated and developed for the rapid identification of NPSs. Considering the continuous emergence of new substances, this review offers a panoramic view on the current status of analytical approaches for the rapid screening of NPSs, including, when available, data on conventional and non-conventional biological matrices. Although some of the presented methods are sound and promising, their applications are still limited, thus proving the importance of further investigations. New screening and sensitive targeted methods for NPS and their metabolites should be developed in different types of biological matrices, where concentration of substances and matrix effects can be significantly different.

The ongoing challenge of novel psychoactive drugs of abuse. Part I. Synthetic cannabinoids (IUPAC Technical Report)

In the past decade, the world has experienced a large increase in the number of novel compounds appearing on the illicit drug market for recreational purposes. Such substances are designed to circumvent governmental regulations; the illegal drug manufacturers take a known psychoactive compound reported in the scientific literature and slightly modify its chemical structure in order to produce analogues that will mimic the pharmacological activity of the original substance. Many of these novel substances are sold via the Internet. Among the various chemical classes, synthetic cannabinoid receptor modulators, commonly referred to as “synthetic cannabinoids” have been at the forefront, as demonstrated by the frequency of drug seizures, numerous severe toxic effects, and fatalities associated with some of these substances. This review presents the chemical structures of relevant synthetic cannabinoids and describes their mechanism of action, pharmacological features, metabolic pathways, and structure-activity relationships. It illustrates the approaches used in forensic testing, both for bulk analysis (drug seizures) and for analytical toxicology (biological matrices) and discusses aspects of regulation surrounding this drug class. This report is intended to provide pertinent information for the purposes of informing scientific, medical, social, and governmental bodies about this ever-evolving recreational drug class and the challenges it poses worldwide.

Focus on cannabinoids and synthetic cannabinoids

The recent emergence of a multitude of synthetic cannabinoids (SCs) has generated a wealth of new information, suggesting the usefulness of state-of-the-art on lato sensu cannabinoids. By modulating a plurality of neurotransmission pathways, the endocannabinoid system is involved in many physiological processes that are increasingly explored. SCs desired and adverse effects are considered to be more intense than those observed with cannabis smoking, which is partly explained by the full agonist activity and higher affinity for cannabinoid receptors. Neurological and cardiovascular side effects observed after cannabinoid poisoning generally respond to conventional supportive care, but severe outcomes may occur in a minority of cases, mainly observed with SCs. The likelihood of severe abuse and addiction produced by SCs are of concern for the scientific community also interested in the potential therapeutic value of cannabinoids.

New psychoactive substances: an overview on recent publications on their toxicodynamics and toxicokinetics

This review article covers English-written and PubMed-listed review articles and original studies published between January 2015 and April 2016 dealing with the toxicodynamics and toxicokinetics of new psychoactive substances. Compounds covered include stimulants and entactogens, synthetic cannabinoids, tryptamines, NBOMes, phencyclidine-like drugs, benzodiazepines, and opioids. First, an overview and discussion is provided on timely review articles followed by an overview and discussion on recent original studies. Both sections are then concluded by an opinion on these latest developments. This review shows that the NPS market is still highly dynamic and that the data published on their toxicodynamics and toxicokinetics can hardly keep pace with the appearance of new entities. However, data available are very helpful to understand and predict how NPS may behave in severe intoxication. The currently best-documented parameter is the in vitro metabolism of NPS, a prerequisite to allow detection of NPS in biological matrices in cases of acute intoxications or chronic consumption. However, additional data such as their chronic toxicity are still lacking.