A comprehensive analytical strategy based on characteristic fragments to detect synthetic cannabinoid analogs in seized products and hair samples

Nontarget screen and identify sulfate and sulfonate surfactants in personal care products using UHPLC-Q-Orbitrap-HRMS based on fragmentation characteristics and sulfur isotopologue pattern

Sulfate and sulfonate compounds are extensively used as anionic surfactants in personal care products (PCPs), which might pose adverse potential to human health. However, available research mostly identified certain subsets of sulfated and sulfonated surfactants based on target analysis. In this study, we developed a comprehensive nontarget strategy for identification of sulfated and sulfonated surfactants in PCPs using UHPLCHRMS supplemented by an in-lab R script based on characteristic fragment ions and sulfur isotope patterns. A total of 20 sulfate and 12 sulfonate surfactants of confidence level 3 and above were identified in the range of alkyl chain length from C12 to C26 with 0-7 ethoxy groups and molecular weights of 200-600 Da in the PCP samples. The sulfates included 4 alkyl sulfates and 16 alkyl ether sulfates. In addition to commonly reported 4 alkyl benzene sulfonates, this study identified eight sulfonate surfactants for the first time, which were 3 alkyl sulfonates, 3 methyl ammonium sulfonates, and 2 bis-sulfonate sulfonates in the PCPs. Interestingly, 22 sulfate and sulfonate compounds were identified in the negatively labeled PCP samples which were not supposed to contain sulfate and sulfonate surfactants. The results demonstrated robustness of the developed nontarget analyzing strategy in identifying and characterizing sulfate and sulfonate surfactants and consequently providing guidance for management and regulation of chemical addition in PCPs to ensure safe use.

Establishment of Broad-Specificity Monoclonal Antibody-Based Immunoassay for Rapid Detection of Indole-Type and Indazole-Type Synthetic Cannabinoids and Metabolites

Synthetic cannabinoids (SCs) have emerged as one of the most severely abused categories of new psychoactive substances (NPS), exacerbating the global drug problem and posing significant threats to public health. Presently, a class of new amide-type SCs featuring an indazole or indole core has been identified in numerous cases of illegal drug use, but there is still a lack of comprehensive analysis methods of SC detection. Herein, monoclonal antibodies (mAbs) 2E4 and AE6 targeting 36 indole-type and indazole-type SCs and their metabolites with IC50 ranging from 0.14 to 85.28 ng/mL were prepared and the molecular mechanism of antibody recognition was elaborated. We established the indirect competitive enzyme-linked immunosorbent assay (ic-ELISA) and gold immunochromatography assay (GICA) based on mAbs 2E4 and AE6 to detect indazole-type and indole-type SCs in urine and hair samples. Under optimal conditions, the proposed method detected ADB-BUTINACA (an indazole-type SC) with limits of detection (LODs) of 0.11 ng/mL for urine and 0.024 ng/mg for hair by ic-ELISA, and 1.02 ng/mL for urine and 0.046 ng/mg for hair by GICA; the LODs of 4F-MDMB-BUTICA (an indole-type SC) detection was 0.036 ng/mL for urine and 0.012 ng/mg for hair by ic-ELISA, and 0.54 ng/mL for urine and 0.03 ng/mg for hair by GICA. Collectively, our study provides a comprehensive foundation for the rapid screening and quantitation of SC derivatives in biological samples.

Analyte protectant approach to protect amide-based synthetic cannabinoids from degradation and esterification during GC-MS analysis

The forensic analysis of amide-based synthetic cannabinoids (SCs) in seized materials is routinely performed using gas chromatography-mass spectrometry (GC-MS); however, a major challenge associated with GC-MS is the thermolytic degradation of substances with sensitive functional groups. Herein, we report the comprehensive thermal degradation and ester transformation of amide-based SCs, such as AB-FUBINACA, AB-CHMINACA, and MAB-CHMINACA, during GC-MS analysis and their treatment with analyte protectants (APs). These SCs were found to undergo thermolytic degradation during GC-MS in the presence of non-alcohol solvents. Using methanol as an injection solvent resulted in the conversion of the amide group to an ester group, producing other SCs such as AMB-FUBINACA, MA-CHMINACA, and MDMB-CHMINACA. Degradant and ester product formation has been interpreted as the adsorption of target SCs on glass wool via hydrogen bonding interactions between the active silanol and amide groups of the SCs, followed by an addition and/or elimination process. The factors found to influence the thermal degradation and/or esterification of the amide functional group include residence time, activity of glass wool, and injection volume. This report presents the fragmentation patterns of all compounds that were produced by degradation and esterification. Using 0.5 % sorbitol (AP) in MeOH as an injection solvent resulted in complete protection and improvement of the chromatographic shape of the compounds. This method has been successfully confirmed in terms of sensitivity, linearity, accuracy, and precision for standard solutions and tablet extraction using 0.5 % sorbitol in MeOH. Using AP increased the sensitivity by ten times or more compared to the use of only MeOH. The limit of detection for all analytes was determined as 25 ng/mL, and the calibration curves were linear over the concentration range of 50-2000 ng/mL. The values of accuracy error were below 11 %, and precision was less than 13 %. The effects of phytochemicals of herbal products, tablet ingredients, and biological matrices on the degradation and/or esterification and APs performance have also been evaluated in this work.

Deep learning-assisted mass spectrometry imaging for preliminary screening and pre-classification of psychoactive substances

Currently, it is of great urgency to develop a rapid pre-classification and screening method for suspected drugs as the constantly springing up of new psychoactive substances. In most researches, psychoactive substances classification approaches depended on the similar chemical structures and pharmacological action with known drugs. Such approaches could not face the complicated circumstance of emerging new psychoactive substances. Herein, mass spectrometry imaging and convolutional neural networks (CNN) were used for preliminary screening and pre-classification of suspected psychoactive substances. Mass spectrometry imaging was performed simultaneously on two brain slices as one was from blank group and another one was from psychoactive substance-induced group. Then, fused neurotransmitter variation mass spectrometry images (Nv-MSIs) reflecting the difference of neurotransmitters between two slices were achieved through two homemade programs. A CNN model was developed to classify the Nv-MSIs. Compared with traditional classification methods, CNN achieved better estimation accuracy and required minimal data preprocessing. Also, the specific region on Nv-MSIs and weight of each neurotransmitter that affected the classification most could be unraveled by CNN. Finally, the method was successfully applied to assist the identification of a new psychoactive substance seized recently. This sample was identified as cannabinoids, which greatly promoted the screening process.