α-Pyrrolidinovalerophenone (α-PVP) is a synthetic cathinone belonging to the group of \"second generation\" pyrrolidinophenones that becomes more and more popular as a designer psychostimulant. Here we provide toxicological analytical support for a severe poisoning with α-PVP. Serum and urine samples that were sent to our laboratory were subjected to a general unknown screening procedure. The procedure includes immunoassay-based screening of drugs of abuse in serum and systematic toxicological analysis of urine and serum after neutral and basic liquid-liquid extraction followed by gas chromatography-mass spectrometry (GC-MS). Whereas the immunoassay delivered negative results, analyzing the urine sample by GC-MS in full scan mode disclosed the presence of α-PVP and its metabolites α-(2″-oxo-pyrrolidino)valerophenone (2″-oxo-α-PVP) and 1-phenyl-2-(pyrrolidin-1-yl)pentan-1-ol (OH-α-PVP). In the acetylated urine sample we found additionally N,N-bis-dealkyl-PVP. In serum, α-PVP could be detected after solid phase extraction and a concentration of 29ng/mL was determined. Other forensic relevant substances were not detected. The presented data can explain the psychotic symptoms and behavioural pattern of the subject after abuse of α-PVP, leading to a clinical condition similar to excited delirium syndrome.

Limited information is available regarding the methodology required to characterize hashish seizures for assessing the presence or the absence of a chemical link between two seizures. This casework report presents the methodology applied for assessing that two different police seizures were coming from the same block before this latter one was split. The chemical signature was extracted using GC-MS analysis and the implemented methodology consists in a study of intra- and inter-variability distributions based on the measurement of the chemical profiles similarity using a number of hashish seizures and the calculation of the Pearson correlation coefficient. Different statistical scenarios (i.e., a combination of data pretreatment techniques and selection of target compounds) were tested to find the most discriminating one. Seven compounds showing high discrimination capabilities were selected on which a specific statistical data pretreatment was applied. Based on the results, the statistical model built for comparing the hashish seizures leads to low error rates. Therefore, the implemented methodology is suitable for the chemical profiling of hashish seizures.