Application of multielectrode array (MEA) chips for studying the neurotoxicity of mixtures

In vitro neuronal networks are a simplified and accessible model of the central nervous system. Moreover, they exhibit morphological and physiological properties and activity-dependent path-specific synaptic modification similar to the in vivo tissue. Cortical neurons grown on multi electrode array (MEA) chips have been shown to be a valuable tool to study fundamental properties of neuronal network activity, synaptic plasticity, learning in vitro, and functional pharmacological screening. The variation of spontaneous activity of in vitro neuronal networks coupled to MEAs has been studied using several binary mixtures (inhibitors with different mode of action: Verapamil and Muscimol, Fluoxetine and Muscimol; inhibitors with the same mode of action: Deltamethrin and Permethrin; and an excitatory and an inhibitory compound with different mode of action: Kainic acid and Muscimol) with the aim of characterize and assess their combined effects. Individual dose-response and binary mixtures curves have been generated. Concentration Addition (CA) and Independent Action (IA) frameworks have been used to compare calculated and experimental results. In addition, Nuclear Magnetic Resonance (NMR) spectroscopy has been employed to assess that no chemical reaction or complexation took place between mixtures components, as well as to monitor the presence of potential impurities and, in this case, to evaluate their relative amount in the tested samples. The results suggest that additivity: CA and IA are able to predict in most of the cases the total toxicity of the mixture. The variability of the results makes difficult to assess which of both approaches is the most accurate. The presence of both excitatory and inhibitory effects as in the case of Kainic acid may further complicate the analysis of the experimental datasets and biphasic concentration-dose response curves may be need to analyze the joint effect.JRC.I.6-Systems toxicolog

Metabolomics: moving towards personalized medicine

In many fields of medicine there is a growing interest in characterizing diseases at molecular level with a view to developing an individually tailored therapeutic approach. Metabolomics is a novel area that promises to contribute significantly to the characterization of various disease phenotypes and to the identification of personal metabolic features that can predict response to therapies. Based on analytical platforms such as mass spectrometry or NMR-based spectroscopy, the metabolomic approach enables a comprehensive overview of the metabolites, leading to the characterization of the metabolic fingerprint of a given sample. These metabolic fingerprints can then be used to distinguish between different disease phenotypes and to predict a drug's effectiveness and/or toxicity

Report on characterisation of New Psychoactive Substances (NPS)

The emergence of designer drugs as abused substances has seen a dramatic increase over the past few years. About 70 new psychoactive substances were discovered in 2012 and more than 80 in 2013. Customs and forensic laboratories are faced with a challenge in identifying the chemical structure of these new compounds. Their analytical controls based on infrared spectroscopy and gas chromatography-mass spectrometry allow the recognition of known substances already recorded in spectroscopic libraries. However the identification of new derivatives as well as new chemical structures requires highly sophisticated analytical techniques such as Nuclear Magnetic Resonance (NMR) and High Resolution Mass Spectrometry (HR-MS). The report introduces an analytical strategy allowing the characterisation of unknown compounds based on the experience of the JRC in the use of these techniques. These approaches have been tested in the laboratory of the Joint Research Centre (JRC) and the efficiency of the proposed approach has been successfully demonstrated on several study cases. The report gives an overview of the analytical strategies and modern laboratory techniques needed to perform a fast unambiguous identification and characterisation of unknown organic chemical substances such as New Psychoactive Substances (NPS).JRC.I.1-Chemical Assessment and Testin

Systematic analytical characterization of new psychoactive substances: A case study

AbstractNew psychoactive substances (NPS) are synthesized compounds that are not usually covered by European and/or international laws. With a slight alteration in the chemical structure of existing illegal substances registered in the European Union (EU), these NPS circumvent existing controls and are thus referred to as “legal highs”. They are becoming increasingly available and can easily be purchased through both the internet and other means (smart shops). Thus, it is essential that the identification of NPS keeps up with this rapidly evolving market.In this case study, the Belgian Customs authorities apprehended a parcel, originating from China, containing two samples, declared as being “white pigments”. For routine identification, the Belgian Customs Laboratory first analysed both samples by gas-chromatography mass-spectrometry and Fourier-Transform Infrared spectroscopy. The information obtained by these techniques is essential and can give an indication of the chemical structure of an unknown substance but not the complete identification of its structure. To bridge this gap, scientific and technical support is ensured by the Joint Research Centre (JRC) to the European Commission Directorate General for Taxation and Customs Unions (DG TAXUD) and the Customs Laboratory European Network (CLEN) through an Administrative Arrangement for fast recognition of NPS and identification of unknown chemicals. The samples were sent to the JRC for a complete characterization using advanced techniques and chemoinformatic tools.The aim of this study was also to encourage the development of a science-based policy driven approach on NPS.These samples were fully characterized and identified as 5F-AMB and PX-3 using 1H and 13C nuclear magnetic resonance (NMR), high-resolution tandem mass-spectrometry (HR-MS/MS) and Raman spectroscopy. A chemoinformatic platform was used to manage, unify analytical data from multiple techniques and instruments, and combine it with chemical and structural information

Application of Multivariate Analysis, Support Vector Machines and Artificial Neural Network to the Processing of Nuclear Magnetic Resonance data of olive oil and fish oil samples for classification of geographic origin and discrimination between wild and farm fish.

Motivations Traceability and control of origin of food products are very important for the Consumers and for the European enforcement laboratories. For instance, The high added value of olive oil makes its control an important goal for EU producers and consumers. There is thus a need in developing analytical methods to ensure compliance with labeling, i.e.the control of geographical origin giving also support to the denominated protected origin (DPO) policy, and the determination of the genuineness of the product by the detection of eventual adulterations. Futhermore , EU regulations requires that origin, wild or farmed as well as geographic origin, of fish sold on the retail market be available to the consumers. Modern analytical techniques such as Nuclear Magnetic Resonance (NMR) provide very informative data on the composition in fatty acids and in other constituents of vegetable oils and fish oils. The combination of 1H NMR fingerprinting with multivariate analysis provides an original approach to study the profile of these oils in relation with geographical origin of olive oil or for discrimination between wild or farm origin for fish like salmons. Methods Concerning the experiment on fish oil, we used Support vector machines (SVMs) as a novel learning machine in the authentication of the origin of salmon. SVMs have the advantage of relying on a well-developed theory and have already proved to be successful in a number of practical applications. The method requires a very simple sample preparation of the fish oils extracted from the white muscle of salmon samples. Multivariate (chemometric) techniques are able to filter out the most relevant information from a spectrum, e.g. for a classification. In the experiment on olive oil samples, the principal component analysis (PCA) was carried out on the ~12,000 variables (chemical shifts) and four data sets were defined prior to PCA. Linear discriminant analysis (LDA) of the first 50 PC\u2019s was applied for classification of olive oil samples according to the geographic origin and year of production. The data analysis has been carried out with and without outliers, as well. Variable selection for LDA was achieved using: (i) the best five variables and (ii) an interactive forward stepwise manner. Results The use of SVMs for the discrimination between wild and farm salmon provides a new and effective method that eliminates the possibility of fraud through misrepresentation of the country of origin of salmon. The SVM has been able to distinguish correctly between the wild and farmed salmon; however ca. 5% of the country of origins were misclassified. Using LDA on the external validation sets the correct classification of olive oil varied between 47 and 75% (random selection), and between 35 and 92% (Kennard\u2013Stone selection (KS)) depending on geographic origin (country) and production years. A similar success rate could be achieved using partial least squares discriminant analysis (PLS DA). The success rate can be considerably improved by using probabilistic neural networks (PNN). Correct classification by PNN varied between 58 and 100% on the external validation sets. Other chemometric techniques, such as multiple linear regression, or generalized pair-wise correlation, did not give better results. Acknowledgements The authors are grateful to the Europeanproject COFAWS (European Commission DG RTD FP5 project GRD2\u20132000\u201331813) and to all the collaborators from the partners of this project (Eurofins Scientific (Nantes- France), North Atlantic Fisheries College (Scalloway, Shetland Islands - United Kingdom), SINTEF Fisheries and Aquaculture (Trondheim-Norway), Joint Research Centre (Ispra-Italy)) who contributed to the collection and preparation of fish samples, and for the authorization to exploit their NMR data in this work

NMR And Isotopic Fingerprinting For Food Characterisation

Numerous analytical methods have been developed during the past decades and have proven to be extremely efficient, for instance, in the case of single, high purity compounds for the measurements of concentration and/or structure elucidation. However, real-world applications often require the characterization of complex mixtures containing tens to thousands of compounds, such as biofluids, food matrices, industrial products, etc. The complete characterisation of such mixtures would be tedious, not to say impossible in the case of mixtures containing hundreds of compounds, and certainly unfeasible for monitoring purposes. In fact, one can concentrate on one or a few molecules which entail the non-negligible issue of the choice of the molecules of interest, and therefore require an a priori knowledge. Nevertheless this approach usually requires molecular separation and purification, which is time, money and human resource consuming. In contrast the Nuclear Magnetic Resonance (NMR) fingerprinting aims at establishing a holistic approach: the mixture is submitted to the NMR experiment as a whole. A simple quantification of the major compounds, which are characterised by one or several signals in the NMR spectrum, can be performed. This type of analysis is particularly attractive for several reasons: it is non-destructive, non selective and cost effective; requires little or no sample pre-treatment; uses small amounts of organic solvents or reagents; and typically takes only a few minutes per sample. The spectra of complex mixtures show hundreds of signals, coming from numerous molecules. This and the overlap of signal make it difficult to extract information, either visually or by simple processing of the data. The most effective way to analyse these holistic profiles is by using chemometric tools which enable the visualisation of the data in a reduced dimension and the classification of the samples into established classes based on inherent patterns in a set of spectral measurements. Moreover, these techniques also allow to trace the NMR spectral variables responsible of this classification, and thus, identify molecular markers of interest. Isotopic measurements such as Isotopic Ratio Mass Spectroscopy (IRMS) or Site-specific Natural Isotopic Fractionation (SNIF-NMR) provide few variables, but these contain unique information on geographical origin and metabolic or production pathways. Thus, isotopic measurements provide complementary data to NMR fingerprinting.JRC.I.5-Physical and chemical exposure

Metodi Isotopici per il Controllo delle Bevande. Tecniche per la Caratterizzazione dell'Autenticità di un Prodotto e per la Rivelazione di Frodi.

Abstract not availableJRC.I-Institute for Health and Consumer Protection (Ispra

Proceedings of the 1st Workshop on Application of Isotopic Methods in Wine Control, 11-13 June 2003, Ispra (VA)

Abstract not availableJRC.I-Institute for Health and Consumer Protection (Ispra

An Oligostilbene from Vitis Roots

Abstract not availableJRC.(EI)-Environment Institut

Carbon Stable Isotops and Olive Oil Adulteration. Note II.

This research has evidenced that the delta13C values for aliphatic aloholic fraction of pomace olive oils isolated after UE method were significantly more negative than those of virgin and refined olive oils. These findings were explained by the presence in pomace olive oil and virgin or refined olive oils of very different quantities of isoprenoids and methylsterols.JRC.(EI)-Environment Institut