Simultaneous analysis of amphetamine-type stimulants in plasma by solid-phase microextraction and gas chromatography-mass spectrometry

Brazil is considered one of the countries with the highest number of amphetamine-type stimulant (ATS) users worldwide, mainly diethylpropion (DIE) and fenproporex (FEN). The use of ATS is mostly linked to diverted prescription stimulants and this misuse is widely associated with (ab)use by drivers. A validated method was developed for the simultaneous analysis of amphetamine (AMP), DIE and FEN in plasma samples employing direct immersion-solid-phase microextraction, and gas chromatographic/mass spectrometric analysis. Trichloroacetic acid 10% was used for plasma deproteinization. In situ derivatization with propylchloroformate was employed. The linear range of the method covered from 5.0 to 100 ng/mL. The detection limits were 1.0 (AMP), 1.5 (DIE) and 2.0 ng/mL (FEN). The accuracy assessment of the control samples was within 85.58-108.33% of the target plasma concentrations. Recoveries ranged from 46.35 to 84.46% and precision was <15% of the value of relative standard deviation. This method is appropriate for screening and confirmation in plasma forensic toxicology analyses of these basic drugs.

Exploring the chemical profile of designer drugs by ESI(+) and PSI(+) mass spectrometry-An approach on the fragmentation mechanisms and chemometric analysis

The consumption of design drugs, frequently known as new psychoactive substances (NPS), has increased considerably worldwide, becoming a severe issue for the responsible governmental agencies. These illicit substances can be defined as synthetic compounds produced in clandestine laboratories in order to act as analogs of schedule drugs mimetizing its chemical structure and improving its pharmacological effects while hampering the control and making regulation more complicated. In this way, the development of new methodologies for chemical analysis of NPS drugs is indispensable to determine a novel class of drugs arising from the underground market. Therefore, this work shows the use of high-resolution mass spectrometry Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) applying different ionization sources such as paper spray ionization (PSI) and electrospray ionization (ESI) in the evaluation of miscellaneous of seized drugs samples as blotter paper (n = 79) and tablet (n = 100). Also, an elucidative analysis was performed by ESI(+)MS/MS experiments, and fragmentation mechanisms were proposed to confirm the chemical structure of compounds identified. Besides, the results of ESI(+) and PSI(+)-FT-ICR MS were compared with those of GC-MS, revealing that ESI(+)MS showed greater detection efficiency among the methodologies employed in this study. Moreover, this study stands out as a guide for the chemical analysis of NPS drugs, highlighting the differences between the techniques of ESI(+)-FT-ICR MS, PSI(+)-FT-ICR MS, and GC-MS.

Wavenumber selection based on Singular Value Decomposition for sample classification

The dissemination of falsified medicines is a public health risk. Techniques such as attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy are commonly adopted for fraudulent drug detection. However, the spectrum generated by the ATR-FTIR typically results in hundreds of wavenumbers, reducing the performance of classification methods aimed at discriminating between authentic and falsified medicines. This article proposes a novel method for selecting a reduced size subset of wavenumbers that improves the classifier performance. The singular value decomposition SVD is used to generate a wavenumber importance index. An iterative process creates k-nearest neighbor (KNN) models by adding the wavenumbers in a decreasing order according to the importance index. Wavenumbers that increase classification accuracy are selected. When applied to Cialis® ATR-FTIR data, the proposed approach retained average 0.51% of the original wavenumbers with 100% accurate classifications; as for the Viagra® data set, the method yielded perfect classifications retaining average 0.17% of the original wavenumbers.

Enhancing counterfeit and illicit medicines grouping via feature selection and X-ray fluorescence spectrometry

In this paper, we propose a novel framework to select the most relevant X-Ray Fluorescence (XRF) energy values (i.e., features) to enhance the clustering (grouping) of counterfeit and illicit medical tablets. The framework is based on the integration of multidimensional scaling (MDS) and Procrustes analysis (PA) multivariate techniques. MDS provides a projection of the original data into a lower dimension, while PA finds a projection matrix from the original data. Such outputs give rise to a feature importance index that guides an iterative feature selection process; after each feature is inserted in the subset, an optimization procedure based on a greedy search method is carried out to maximize the clustering quality assessed through the Silhouette Index (SI). The inorganic chemical fingerprinting of 41 commercial samples (Viagra®, Cialis®, Lazar®, Libiden®, Maxfil®, Plenovit®, Potent 75®, Rigix®, V-50®, Vimax® and Pramil®) and 56 seized counterfeit samples (Viagra and Cialis) was used to validate the proposed framework. From the original 2048 data points in the full spectra, we identified a subset comprised of 41 energy values that substantially improved clustering quality; the obtained groups were assessed by visual inspection of the PCA plots.

Ortiz R. Counterfeit medicines: a pilot study for chemical profiling employing a different proposal of an usual technique

Gas chromatography (GC) is a gold standard technique used in forensic laboratories, including for the characterization of counterfeit medicines. When coupled simultaneously to flame ionization (FID) and mass detector (MS) allow the identification and quantification of medicines and drugs employing a single method, besides permitting the application of chemometric tools for forensic intelligence purposes. Here is presented a pilot project that developed and applied a qualitative method for the analysis of counterfeit medicines comprised by amphetamine-type stimulants and antidepressants, through a simple extraction procedure followed by GC-FID/MS analysis, with application of exploratory tools by Hierarchical Cluster Analysis (HCA) and Principal Component Analysis (PCA). The main purpose was to identify similarities between the all compounds detected in the irregular medicines allowing the traceability of illicit producers with the creation of a common data base. Through the analyses it was verified that different producers of counterfeit medicines labeled as Sibutramine, added a mixture of Caffeine and Benzocaine in their formulation, respecting the same ratio of 2.2:1. HCA was able to confirm these results, showing the presence of both falsifications in the same cluster, representing the best tool to identify similar characteristics among the samples – when compared to PCA. Other interesting finding was the use of Fluoxetine as a falsification of counterfeit medicines labeled as Sibutramine and Diethylpropion. Another seized sample labeled as “Nobesio Forte”, marketed as a mix of stimulants, showed only Caffeine and Lidocaine in its formulation. The pilot project applied primarily to 45 samples of counterfeit medicines containing amphetamine-type stimulants and antidepressants, showed the capability of perform the chemical profiling of counterfeit medicines in the solid form - powder, capsules and tablets. Further analysis can be performed for different types of medicines in solid form using the developed method, allowing the construction of a single database to perform the chemical profiling of counterfeit medicines, enabling the traceability of illicit producers.

Gaussian process regression coupled with mRMR to predict adulterant concentration in cocaine

Street cocaine is often mixed with various substances that intensify its harmful effects. This paper proposes a framework to identify attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR) intervals that best predict the concentration of adulterants in cocaine samples. Wavelengths are ranked according to their relevance through ReliefF and mRMR feature selection approaches, and an iterative process removes less relevant wavelengths based on the ranking suggested by each approach. Gaussian Process (GP) regression models are constructed after each wavelength removal and the prediction performance is evaluated using RMSE. The subset balancing a low RMSE value and a small percentage of retained wavelengths is chosen. The proposed framework was validated using a dataset consisting of 345 samples of cocaine with different amounts of levamisole, caffeine, phenacetin, and lidocaine. Averaged over the four adulterants, the GP regression coupled with the mRMR retained 1.07 % of the 662 original wavelengths, outperforming PLS and SVR regarding prediction performance.