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

Abstract

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

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