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

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.