RESUMEN
Cannabis sativa, a globally commercialized plant used for medicinal, food, fiber production, and recreation, necessitates effective identification to distinguish legal and illegal varieties in forensic contexts. This research utilizes multivariate statistical models and Machine Learning approaches to establish correlations between specific genotypes and tetrahydrocannabinol (Δ9-THC) content (%) in C. sativa samples. 132 cannabis leaves samples were obtained from legal growers in Piedmont, Italy, and illegal drug seizures in Turin. Samples were genetically profiled using a 13-loci STR multiplex and their Δ9-THC content was detected through quantitative GC-MS analysis. This study aims to assess the use of supervised classification modelling on genetic data to distinguish cannabis samples into legal and illegal categories, revealing distinct clusters characterized by unique allele profiles and THC content. t-distributed Stochastic Neighbor Embedding (t-SNE), Random Forest (RF) and Partial Least Squares Regression (PLS-R) were executed for the machine learning modelling. All the tested models resulted effective discriminating between legal samples and illegal. Although further validation is necessary, this study presents a novel forensic investigative approach, potentially aiding law enforcement in significant marijuana seizures or tracking illicit drug trafficking routes.
RESUMEN
Forensic laboratories are constantly required to identify new drugs and their metabolites. N-ethylhexedrone (NEH, HEXEN), N-Ethylpentedrone (NEP), and 4-Chloromethcathinone (4-CMC, clephedrone) are synthetic substances structurally related to natural cathinone, alkaloid present in the leaves of the Catha edulis (Khat) plant. These synthetic cathinones (SC) are members of the heterogenous family of new psychoactive substances (NPS) that raised major concerns in scientific and forensic communities over the past years due to their widespread consumption. In this context, we investigated their metabolic profile using of UHPLC-QTOF-HRMS to elucidate the distribution of the parent drug and its metabolites in urine samples over time. Initially, both male and female volunteers were divided into three groups and eight subjects of each group were administered intranasally or orally with one SC (20-40 mg of NEH or NEP intranasal, 100-150 mg of 4-CMC oral). Urine samples were collected at 0-2 and 2-4 or 2-5 h. Urine (50 µL) was diluted 1:2 with acetonitrile/methanol (95:5) and injected into the UHPLC-QTOF-HRMS. Phase-I and phase-II metabolites were identified on the basis of fragmentation patterns and exact masses. Several phase-I and glucuronide-phase-II metabolites were identified in urine samples. Keto group reduction, hydroxylation and dealkylation were the common metabolic pathways identified for all cathinones and the presence of NEH-glucuronide, NEP-glucuronide and 4-CMC-glucuronide was also relevant. Significant is the slower metabolite formation for 4-CMC, which was detected at high concentrations in its original form even 5 h after administration, due to its long half-life and low intrinsic clearance compared to the other SCs. UHPLC-QTOF-HRMS demonstrated a considerable capability to semi-quantify the three synthetic cathinones and identify the target metabolites with high reliability. The introduction of new target compounds improves the efficiency of toxicological screening analysis on real samples and extends the window of detection of the SCs in biological matrices.