Physico-chemical analysis, systematic benchmarking, and toxicological aspects of the JWH aminoalkylindole class-derived synthetic JWH cannabinoids

Abstract

We are presenting the results obtained by computing different toxicity indices for some of the newest JWH synthetic cannabinoids, by using (Q)SAR models, ADME(T) predictions, simulations of NMR spectral techniques and other different computational dedicated software packages and forensic analytical tools. We have examined the main physical and chemical properties and evaluated the behavioral neurotoxicity and pharmacokinetic profile of 16 aminoalkylindole class-derived synthetic cannabinoids JWH as compared to the Delta-9-tetrahydrocannabinol (Δ9-THC), which was chosen as a standard compound. For this purpose, the geometries of the molecules have been optimized by using the AM1 semi-empirical quantum method. The conclusions of a comparative analysis of the toxicities of synthetic and natural cannabinoids are presented.

Cannabinoids: an exploratory review

“…This property may be evaluated by assessing their hydrophilicity with the logP parameter, which was determined for the targeted cannabinoids with the ACD/ChemSketch software. The logP parameter is inversely proportional to permeability or absorption, meaning that high values of logP correspond to low absorption [3]. The Moriguchi octanol-water partition coefficient (MlogP) for the same compounds were also determined with the alvaMolecule software, while the values of LogP were computed by using the PROTOX software.…”

“…Table 1. Groups of synthetic cannabinoids [3] The molecular structure of synthetic cannabinoids is different from that of the natural ones. They are insoluble in water, highly lipophilic and highly volatile.…”

Sensitivity Analysis of Artificial Neural Networks Identifying JWH Synthetic Cannabinoids Built with Alternative Training Strategies and Methods

“…These designer drugs were divided into three classes referred to as "Class 1-JWH", "Class 2-non-JWH Cannabinoids" and "Class 3-Others". The group of positives contained 50 JWH synthetic cannabinoids, while the group of negatives included 100 compounds, i.e., 50 non-JWH cannabinoids and 50 other substances of forensic interest [5].…”

Computerized Detection of JWH Synthetic Cannabinoids Class Membership Based on Machine Learning Algorithms and Molecular Descriptors

“…Taking into account its variation, we have selected as final input the first 150 most relevant descriptors. Their list was reported in a previous paper [3]. The detailed definitions, mathematical formulas and chemical significance of these topological and 3D-MoRSE descriptors are presented in detail in Todeschini et al [4].…”