Target and suspect screening of psychoactive substances in seizures and oral fluid exploiting retention time prediction and LC-MS/MS analysis.

BACKGROUND: Novel psychoactive substances (NPS) are a group of substances, mainly of synthetic origin, characterized by toxicological properties extremely dangerous. The main difficulty in recognizing NPS in seizures and biological samples lies in their dynamic nature, related to the continuous synthesis and introduction on the market of new drugs, often with very similar structures to existing ones. The aim of this study was the creation of a robust and versatile method for the analysis of traditional drugs and NPS in different matrices. RESULTS: Both target analysis and suspect screening methodologies were developed. The strategy used for suspect screening allowed to collect data through a scheduled multi reaction monitoring (sMRM) survey which triggered the collection of enhanced product ion (EPI) spectra when a compound met information dependent acquisition (IDA) criteria. The retention time of the different drugs, which was crucial to define the sMRM survey scan parameters, was predicted with a Quantitative Structure Retention (Chromatographic) Relationship (QSRR) model by Multiple Linear Regression. The model was validated through the evaluation of training set predictions, an external validation set and a leave-one out strategy; the results showed that the method fit for its purpose. The target method was validated in oral fluid as a testing matrix, with excellent results in term of recovery, accuracy, precision and matrix effect. Finally, the performances of the suspect method were evaluated by analysing a mixture containing 8 reference standards not included in the initial dataset, as well as seizures and real oral fluid samples. Four NPS were putatively identified in the analysed samples. SIGNIFICANCE: The advantage of the proposed approach is the possibility of quantifying 65 classical drugs of abuse and NPS and, at the same time, detect and putatively identify 146 additional drugs in one single LC-MS/MS run. This is an innovative strategy for multi analyte detection and enables detection of low concentrations of drugs in complex biological matrices such as oral fluid. Considering the highly dynamic drug market, a strength of this strategy is that the analytical method can be kept up to date through the addition of new compounds based on the last drug monitoring bodies alerts without the need of authentic standards.

Tackling new psychoactive substances through metabolomics: UHPLC-HRMS study on natural and synthetic opioids in male and female murine models.

Novel psychoactive substances (NPS) represent a broad class of drugs new to the illicit market that often allow passing drug-screening tests. They are characterized by a variety of structures, rapid transience on the drug scene and mostly unknown metabolic profiles, thus creating an ever-changing scenario with evolving analytical targets. The present study aims at developing an indirect screening strategy for NPS monitoring, and specifically for new synthetic opioids (NSOs), based on assessing changes in endogenous urinary metabolite levels as a consequence of the systemic response following their intake. The experimental design involved in-vivo mice models: 16 animals of both sex received a single administration of morphine or fentanyl. Urine was collected before and after administration at different time points; the samples were then analysed with an untargeted metabolomics LC-HRMS workflow. According to our results, the intake of opioids resulted in an elevated energy demand, that was more pronounced on male animals, as evidenced by the increase in medium and long chain acylcarnitines levels. It was also shown that opioid administration disrupted the pathways related to catecholamines biosynthesis. The observed alterations were common to both morphine and fentanyl: this evidence indicate that they are not related to the chemical structure of the drug, but rather on the drug class. The proposed strategy may reinforce existing NPS screening approaches, by identifying indirect markers of drug assumption.