Identification of a New Amphetamine Type Stimulant : 3,4-Methylenedioxy-N-(2-hydroxyethyl)amphetamine (MDHOET)

3,4-Methylenedioxy-N-(2-hydroxyethyl)-amphetamine (MDHOET), an MDA derivative, was identified in an illicit exhibit from France. This new amphetamine type stimulant (ATS) was analyzed within a round robin performed between seven laboratories. Outcome of this study was that six out of seven laboratories were able to identify MDHOET. Analytical data from gas chromatography, infrared spectroscopy, mass spectrometry and proton nuclear magnetic resonance spectroscopy are presented

Drug intelligence based on MDMA tablets data : (1) Organic impurities profiling.

One major objective of the European project "Collaborative Harmonisation of Methods for Profiling of Amphetamine Type Stimulants" (CHAMP) funded by the sixth framework programme of the European Commission consisted in the harmonisation of a Gas Chromatography / Mass Spectrometry (GC/MS) method for the analysis of organic impurities found in 3,4-MethyleneDioxyMethAmphetamine (MDMA) samples in a drug intelligence perspective. Statistical analysis provided a selection of pertinent variables among the 46 organic impurities identified in the chromatograms. Correlation coefficients were used to yield separation between populations of linked samples (from the same seizure) and unlinked samples (from different seizures). It was shown that correlation measurements based on Pearson and cosine functions applied to the data pre-treated by the square root provided an excellent discrimination between the two populations. The organic impurities profiling method was proved to be an excellent technique to differentiate samples from different seizures and to highlight operational links between samples

Drug intelligence based on MDMA tablets data : (2) Physical characteristics profiling

One of the tasks of the project entitled "Collaborative Harmonisation of Methods for Profiling of Amphetamine Type Stimulants" (CHAMP) was to develop a methodology to highlight links between MDMA samples coming from different countries and to detect traffic tendencies, based on physical characteristics of the MDMA tablets. Diameter, thickness, weight and score were demonstrated to be reliable and relevant features in this drug intelligence perspective. Distributions of linked samples (i.e. coming from the same tabletting batch) and unlinked samples (i.e. coming from different tabletting batches) were very well discriminated by using the squared Euclidean or the Manhattan distance on standardised data. Our findings confirmed the hypothesis of the possibility to discriminate between MDMA samples issued form different tabletting batches. Furthermore, as no pattern was found between countries, the hypothesis that most of the MDMA samples found on the international market come from the same countries is supported