Profiling of counterfeit medicines by vibrational spectroscopy

Counterfeit pharmaceutical products have become a widespread problem in the last decade. Various analytical techniques have been applied to discriminate between genuine and counterfeit products. Among these, Near-infrared (NIR) and Raman spectroscopy provided promising results.The present study offers a methodology allowing to provide more valuable information fororganisations engaged in the fight against counterfeiting of medicines.A database was established by analyzing counterfeits of a particular pharmaceutical product using Near-infrared (NIR) and Raman spectroscopy. Unsupervised chemometric techniques (i.e. principal component analysis - PCA and hierarchical cluster analysis - HCA) were implemented to identify the classes within the datasets. Gas Chromatography coupled to Mass Spectrometry (GC-MS) and Fourier Transform Infrared Spectroscopy (FT-IR) were used to determine the number of different chemical profiles within the counterfeits. A comparison with the classes established by NIR and Raman spectroscopy allowed to evaluate the discriminating power provided by these techniques. Supervised classifiers (i.e. k-Nearest Neighbors, Partial Least Squares Discriminant Analysis, Probabilistic Neural Networks and Counterpropagation Artificial Neural Networks) were applied on the acquired NIR and Raman spectra and the results were compared to the ones provided by the unsupervised classifiers.The retained strategy for routine applications, founded on the classes identified by NIR and Raman spectroscopy, uses a classification algorithm based on distance measures and Receiver Operating Characteristics (ROC) curves. The model is able to compare the spectrum of a new counterfeit with that of previously analyzed products and to determine if a new specimen belongs to one of the existing classes, consequently allowing to establish a link with other counterfeits of the database.

L'apport d'une approche criminalistique pour lutter contre la contrefaçon de médicaments

Dans le domaine de l'analyse et la détection de produits pharmaceutiques contrefaits, différentes techniques analytiques ont été appliquées afin de discriminer les produits authentiques des contrefaçons. Parmi celles-ci, les spectroscopies proche infrarouge (NIR) et Raman ont fourni des résultats prometteurs. L'objectif de cette étude était de développer une méthodologie, basée sur l'établissement de liens chimiques entre les saisies de produits contrefaits, permettant de fournir des informations utiles pour les acteurs impliqués dans la lutte à la prolifération de ces produits. Une banque de données de spectres NIR et Raman a été créée et différents algorithmes de classification non-supervisée (i.e., analyse en composantes principales (ACP) et analyse factorielle discriminante (AFD) - elus ter onolysis) ont été utilisées afin d'identifier les différents groupes de produits présents. Ces classes ont été comparées aux profils chimiques mis en évidence par la spectroscopie infrarouge à transformée de Fourier (FT-IR) et par la chromatographie gazeuse couplée à la spectrométrie de masse (GC -MS). La stratégie de classification proposée, fondée sur les classes identifiées par spectroscopie NIR et Raman, utilise un algorithme de classification basé sur des mesures de distance et des courbes Receiver Operating Characteristics (ROC). Le modèle est capable de comparer le spectre d'une nouvelle contrefaçon à ceux des saisies précédemment analysées afin de déterminer si le nouveau spécimen appartient à l'une des classes existantes, permettant ainsi de le lier à d'autres saisies dans la base de données.

Diffusion-weighted spectroscopy: a novel approach to determine macromolecule resonances in short-echo time 1H-MRS.

Quantification of short-echo time proton magnetic resonance spectroscopy results in >18 metabolite concentrations (neurochemical profile). Their quantification accuracy depends on the assessment of the contribution of macromolecule (MM) resonances, previously experimentally achieved by exploiting the several fold difference in T(1). To minimize effects of heterogeneities in metabolites T(1), the aim of the study was to assess MM signal contributions by combining inversion recovery (IR) and diffusion-weighted proton spectroscopy at high-magnetic field (14.1 T) and short echo time (= 8 msec) in the rat brain. IR combined with diffusion weighting experiments (with δ/Δ = 1.5/200 msec and b-value = 11.8 msec/μm(2)) showed that the metabolite nulled spectrum (inversion time = 740 msec) was affected by residuals attributed to creatine, inositol, taurine, choline, N-acetylaspartate as well as glutamine and glutamate. While the metabolite residuals were significantly attenuated by 50%, the MM signals were almost not affected (< 8%). The combination of metabolite-nulled IR spectra with diffusion weighting allows a specific characterization of MM resonances with minimal metabolite signal contributions and is expected to lead to a more precise quantification of the neurochemical profile.