Bentazepam

In this study, we present an approach to neuropharmacological research by integrating few-shot meta-learning algorithms with brain activity mapping (BAMing) to enhance the discovery of central nervous system (CNS) therapeutics. By utilizing patterns from previously validated CNS drugs, our approach facilitates the rapid identification and prediction of potential drug candidates from limited datasets, thereby accelerating the drug discovery process. The application of few-shot meta-learning algorithms allows us to adeptly navigate the challenges of limited sample sizes prevalent in neuropharmacology. The study reveals that our meta-learning-based convolutional neural network (Meta-CNN) models demonstrate enhanced stability and improved prediction accuracy over traditional machine-learning methods. Moreover, our BAM library proves instrumental in classifying CNS drugs and aiding in pharmaceutical repurposing and repositioning. Overall, this research not only demonstrates the effectiveness in overcoming data limitations but also highlights the significant potential of combining BAM with advanced meta-learning techniques in CNS drug discovery.

In the dynamic universe of new psychoactive substances (NPS), the identification of multiple and chemically diverse compounds remains a challenge for forensic laboratories. Since hair analysis represents a gold-standard to assess the prevalence of NPS, which are commonly detected together with classical drugs of abuse (DoA), our study aimed at developing a wide-screen method to detect and quantify 127 NPS and 15 DoA on hair.

A multi-analyte ultra-high performance liquid chromatography mass spectrometry method for the identification and quantification of 127 NPS (phenethylamines, arylcyclohexylamines, synthetic opioids, tryptamines, synthetic cannabinoids, synthetic cathinones, designer benzodiazepines) and 15 DoA in hair samples was developed. A full validation was performed according to the European medicines Agency (EMA) guidelines, by assessing selectivity, linearity, accuracy, precision, limit of quantification (LOQ), limit of detection (LOD), matrix effect and recovery. As a proof of the applicability, the method was applied to 22 authentic hair samples collected for forensic purposes.

Successful validation was achieved, by meeting the required technical parameters, for 137 compounds (122 NPS and 15 DoA), with LOQ set at 4 pg/mg for 129 compounds, at 10 pg/mg for 6 and at 40 pg/mg for 2. The method was not considered validated for 5 NPS, as LLOQ resulted too high for a forensic analysis (80 pg/mg). Among authentic forensic samples, 17 tested positive for DoA, and 10 to NPS, most samples showing positivity for both. Detected NPS were ketamine and norketamine, 5-MMPA, ritalinic acid, methoxyacetyl fentanyl, methylone and RCS-4.

The present methodology represents an easy, low cost, wide-panel method for the quantification of 122 NPS and 15 DoA, for a total of 137 analytes, in hair samples. The method can be profitably applied by forensic laboratories. Similar multi-analyte methods on the hair matrix might be useful in the future to study the prevalence of NPS and the co-occurrence of NPS-DoA abuse.