title: Predicting the acute neurotoxicity of diverse organic solvents using probabilistic neural networks based QSTR modeling approaches.
abstract: Organic solvents are widely used chemicals and the neurotoxic properties of some are well established. In this study, we established nonlinear qualitative and quantitative structure-toxicity relationship (STR) models for predicting neurotoxic classes and neurotoxicity of structurally diverse solvents in rodent test species following OECD guideline principles for model development. Probabilistic neural network (PNN) based qualitative and generalized regression neural network (GRNN) based quantitative STR models were constructed using neurotoxicity data from rat and mouse studies. Further, interspecies correlation based quantitative activity-activity relationship (QAAR) and global QSTR models were also developed using the combined data set of both rodent species for predicting the neurotoxicity of solvents. The constructed models were validated through deriving several statistical coefficients for the test data and the prediction and generalization abilities of these models were evaluated. The qualitative STR models (rat and mouse) yielded classification accuracies of 92.86% in the test data sets, whereas, the quantitative STRs yielded correlation (R(2)) of >0.93 between the measured and model predicted toxicity values in both the test data (rat and mouse). The prediction accuracies of the QAAR (R(2) 0.859) and global STR (R(2) 0.945) models were comparable to those of the independent local STR models. The results suggest the ability of the developed QSTR models to reliably predict binary neurotoxicity classes and the endpoint neurotoxicities of the structurally diverse organic solvents.
authors.affiliation: ETRC, Gomtinagar, Lucknow 226010, India.
authors.forename: Nikita
authors.initials: N
authors.lastname: Basant
authors.affiliation: Environmental Chemistry Division, CSIR-Indian Institute of Toxicology Research, Post Box 80, Mahatma Gandhi Marg, Lucknow 226001, India.
authors.forename: Shikha
authors.initials: S
authors.lastname: Gupta
authors.affiliation: ETRC, Gomtinagar, Lucknow 226010, India. Electronic address: kpsingh_52@yahoo.com.
authors.forename: Kunwar P
authors.initials: KP
authors.lastname: Singh
mesh_terms: D000818:Animals; D066264:Datasets as Topic / Q000706:statistics & numerical data; D051379:Mice; D015233:Models, Statistical; D016571:Neural Networks, Computer; D020258:Neurotoxicity Syndromes / Q000175:diagnosis* / Q000209:etiology*; D017711:Nonlinear Dynamics; D009930:Organic Chemicals / Q000737:chemistry / Q000633:toxicity*; D011237:Predictive Value of Tests; D051381:Rats; D012044:Regression Analysis; D015203:Reproducibility of Results; D012997:Solvents / Q000737:chemistry / Q000633:toxicity*; D013329:Structure-Activity Relationship
chemical_list: D009930:Organic Chemicals; D012997:Solvents
keywords: Global structure–toxicity relationship; Interspecies correlations; Neurotoxicity; Nonlinear structure–toxicity relationships; Quantitative activity–activity relationships; Solvents