Citation:

Lefew, W., E. McConnell, J. Crooks, AND Tim Shafer. Evaluation of Microelectrode Array Data using Bayesian Modeling as an Approach to Screening and Prioritization for Neurotoxicity Testing*. NEUROTOXICOLOGY. Intox Press, Inc, Little Rock, AR, 36:34-41, (2013).

Impact/Purpose:

The National Academies report on Toxicity Testing in the 21 ‘ Century highlighted the need to characterize the toxicity of thousands of chemicals present ii: the environment (NAS 2007) to provide adequate protection of human health. As a result, there has been a substantial effort to develop rapid, cost-efficient methods to screen thousands of chemicals for their potential to cause toxicity. This effbrt includes new approaches to characterizing the potential for chemicals to disrupt lhnction of the nervous system. following both acute (Novellino ct al., 2010: Defranchi ct al., 2011: McConnell et al., 2012), and developmental exposure (Breier et a)., 2008, Radio Ct a)., 2008: Robinette et al., 2010; Hogbcrg et al., 2010).

Description:

The need to assess large numbers of chemicals for their potential toxicities has resulted in increased emphasis on medium- and high-throughput in vitro screening approaches. For such approaches to be useful, efficient and reliable data analysis and hit detection methods are also required. Assessment of chemical effects on neuronal network activity using rnicroelectrode arrays (MEAs) has been proposed as a screening tool for neurotoxicity. The current study examined a Bayesian data artalysis approach for assessing effects of a 30 chemical training set on activity of primary cortical neurons grown in multi-well MEA plates. Each well of the MEA plate contained 64 microelectrodes and the data set contains the number of electrical spikes registered by each electrode over the course of each experiment. A Bayesian data analysis approach was developed and then applied to several different parsings of the data set to produce probability determinations for hit selection and ranking. This methodology results in an approach that is approximately 74% sensitive in detecting chemicals in the training set known to alter neuronal function (23 expected positives) while being 100% specific in detecting chemicals expected to have no effect (7 expected negatives). Additionally, this manuscript demonstrates that the Bayesian approach may be combined with a previously published weighted mean firing rate approach in order to produce a more robust hit detection method. In particular, when combined with the weighted mean frring rate approach, the joint analysis produces a sensitivity of approximately 96% and a specificity of 100%. These results demonstrate the utility of a novel approach to analysis of MEA data and support the use of neuronal networks grown on MEAs as a for neurotoxicity screening approach.

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