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A method for ranking compounds based on their relative toxicity using neural networking, C. elegans, axenic liquid culture, and the COPAS parameters TOF and EXT

DOI: http://dx.doi.org/10.2147/OAB.S13466

Keywords: neural network, TOF, EXT, COPAS, C. elegans, rat oral LD-50

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Abstract:

method for ranking compounds based on their relative toxicity using neural networking, C. elegans, axenic liquid culture, and the COPAS parameters TOF and EXT Original Research (2870) Total Article Views Authors: Martine Ferguson, Marc Boyer, Robert Sprando Published Date October 2010 Volume 2010:2 Pages 139 - 144 DOI: http://dx.doi.org/10.2147/OAB.S13466 Martine Ferguson1, Marc Boyer1, Robert Sprando2 1United States Food and Drug Administration, Center for Food Safety and Applied Nutrition, Office of Food Defense Communication and Emergency Response, Division of Public Health and Biostatistics, College Park, MD, USA; 2United States Food and Drug Administration, Center for Food Safety and Applied Nutrition, Office of Applied Research and Safety Assessment, Division of Toxicology, Laurel, MD, USA Abstract: Caenorhabditis elegans (L1s) were exposed to (in order of decreasing toxicity) sodium arsenite, sodium fluoride, caffeine, valproic acid, sodium borate, or dimethyl sulfoxide in C. elegans habitation medium (CeHM) for 72 consecutive hours. At this time point nematode growth and development were assessed using a Complex Object Parametric Analyzer and Sorter (COPAS ). The COPAS generated biomarkers of growth (time of flight [TOF] – a measure of axial length) and development (extinction [EXT] – a measure of optical density) were subsequently utilized to rank compounds according to their relative toxicity, as measured by the rat oral LD-50, using artificial neural network methods. Neural network methods were utilized to analyze this data because of their ability to model nonlinear endpoints and a multilayer perceptron neural network method was used because of its capability to function well in the presence of collinearity. Using a neural network approach we found that the LD-50 was correctly predicted 96% of the time. The present study demonstrates that neural network methods can be utilized to rank compounds according to their relative toxicity using COPAS-generated data (TOF and EXT) obtained from exposing a large number of nematodes to water-soluble compounds in axenic liquid culture.

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