No abstract

This paper presents a novel and comprehensive method to identify substances on the basis of electrical activity and is a substantial improvement for drug screening. The spontaneous activity of primary neuronal networks is influenced by neurotransmitters, ligands, and other substances in a similar fashion as known from in vivo pharmacology. However, quantitative methods for the identification of substances through their characteristic effects on network activity states have not yet been reported. We approached this problem by creating a database including native activity and five drug-induced oscillatory activity states from extracellular multisite recordings from microelectrode arrays. The response profiles consisted of 30 activity features derived from the temporal distribution of action potentials, integrated burst properties, calculated coefficients of variation, and features of Gabor fits to autocorrelograms. The different oscillatory states were induced by blocking neurotransmitter receptors for: (i) GABA(A); (ii) glycine; (iii) GABA(A) and glycine; (iv) all major synaptic types except AMPA, and (v) all major synapses except NMDA. To test the identification capability of the six substance-specific response profiles, five blind experiments were performed. The response features from the unknown substances were compared to the database using proximity measures using the normalized Euclidian distance to each activity state. This process created six identification coefficients where the smallest correctly identified the unknown substances. Such activity profiles are expected to become substance-specific 'finger prints' that classify unique responses to known and unknown substances. It is anticipated that this kind of approach will help to quantify pharmacological responses of networks used as biosensors.

Neuronal assemblies within the nervous system produce electrical activity that can be recorded in terms of action potential patterns. Such patterns provide a sensitive endpoint to detect effects of a variety of chemical and physical perturbations. They are a function of synaptic changes and do not necessarily involve structural alterations. In vitro neuronal networks (NNs) grown on micro-electrode arrays (MEAs) respond to neuroactive substances as well as the in vivo brain. As such, they constitute a valuable tool for investigating changes in the electrophysiological activity of the neurons in response to chemical exposures. However, the reproducibility of NN responses to chemical exposure has not been systematically documented. To this purpose six independent laboratories (in Europe and in USA) evaluated the response to the same pharmacological compounds (Fluoxetine, Muscimol, and Verapamil) in primary neuronal cultures. Common standardization principles and acceptance criteria for the quality of the cultures have been established to compare the obtained results. These studies involved more than 100 experiments before the final conclusions have been drawn that MEA technology has a potential for standard in vitro neurotoxicity/neuropharmacology evaluation. The obtained results show good intra- and inter-laboratory reproducibility of the responses. The consistent inhibitory effects of the compounds were observed in all the laboratories with the 50% Inhibiting Concentrations (IC50s) ranging from: (mean ± SEM, in μM) 1.53 ± 0.17 to 5.4 ± 0.7 (n = 35) for Fluoxetine, 0.16 ± 0.03 to 0.38 ± 0.16 μM (n = 35) for Muscimol, and 2.68 ± 0.32 to 5.23 ± 1.7 (n = 32) for Verapamil. The outcome of this study indicates that the MEA approach is a robust tool leading to reproducible results. The future direction will be to extend the set of testing compounds and to propose the MEA approach as a standard screen for identification and prioritization of chemicals with neurotoxicity potential.