Innovative analytical tools for the detection of new psychoactive substances by high-resolution mass spectrometry

The purpose of this work was to create a UHPLC-HRMS analytical platform for target and untarget determination of new and classic substances of abuse in seizures. The main difficulty in recognizing New Psychoactive Substances (NPS) lies in their dynamic nature, in the continuous synthesis and introduction on the market of new drugs, with similar but not identical structures to existing ones. These new drugs are not identified by classical data analysis software because they are not yet in the databases, but they may be potentially identifiable with predictive models, taking into account the similar fragmentation patterns. In this study, the analytical determination was conducted by ultrahigh-performance liquid chromatography coupled to high-resolution mass spectrometry by exploiting a spectrometer Synapt G2-Si HDMS (Waters, Milford, MA, USA). Initially exploiting 57 analytical standards belonging to different classes of chemicals, chromatographic and mass spectrometry acquisition parameters were defined. The first analyses were carried out in full scan mode, to obtain retention times for each analyte, and later in MS/MS mode, with reasoned collision energy values, chosen in relation to the chemical characteristics of the precursor ion molecule, in order to obtain fragmentation spectra. Subsequently, to allow untargeted analysis, a chemometric model that would highlight the similarities between the chemical characteristic of each analyte was done. Specifically, a data matrix was created containing all values of m/z related to the fragments with their respective intensities, normalized to the intensity of the ion precursor. A principal component analysis (PCA) was then performed to obtain a chemometric model that would highlight common fragments within the various classes; also the PCA lays the foundation for creating a model that can predict the class of an unknown substance based on its fragmentation pattern. PCA was performed using MatLab, in total 19,614 accurate masses rounded to the third decimal place. With further analysis, the starting data can be expanded, and a model can be obtained that can characterize more substances and highlight the fragments they have in common. It will be possible, in this way, to take important steps forward in the analysis and recognition of New Psychoactive Substances.