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Integrating Data Gap Filling Techniques for Predicting Neurotoxicity TEQs to Facilitate the Hazard Assessment of Polychlorinated Biphenyls
Citation:
Pradeep, P., L. Carlson, R. Judson, G. Lehmann, AND G. Patlewicz. Integrating Data Gap Filling Techniques for Predicting Neurotoxicity TEQs to Facilitate the Hazard Assessment of Polychlorinated Biphenyls. Presented at Symposium on Halogenated Persistent Organic Pollutants :DIOXIN 2018, Krakow, N/A, POLAND, August 26 - 31, 2018.
Impact/Purpose:
Abstract for presentation at 8th International Symposium on Halogenated Persistent Organic Pollutants (POPs) & 10th International PCB Workshop : DIOXIN August 2018
Description:
The application of toxic equivalency factors (TEFs) to estimate toxic potencies for mixtures of chemicals which contribute to a biological effect through a common mechanism is one approach for filling data gaps. TEFs have been used to express the toxicity of dioxin-like compounds (i.e., dioxins, furans, and dioxin-like polychlorinated biphenyls (PCBs)) relative to the most toxic form of dioxin: 2,3,7,8-tetrachlorodibenzo-p-dioxin (2,3,7,8-TCDD). This study sought to integrate two data gap filling techniques, quantitative structure–activity relationships (QSARs) and TEFs, to predict neurotoxicity for PCBs. Simon et al. (2007) previously derived neurotoxic equivalent (NEQ) values (scale 0-1) for a dataset of 87 PCB congeners, of which 83 congeners had experimental data. These data were taken from a set of four different studies measuring different effects related to neurotoxicity, each of which tested overlapping subsets of the 83 PCB congeners. The goals of the current study were to: (1) derive and evaluate alternative NEQ values from the experimental data, relative to those derived by Simon et al., and (2) develop QSAR models to provide estimate NEQ values for the large number of untested PCB congeners. The QSAR models used multiple linear regression, support vector regression, k-nearest neighbor and random forest algorithms within a 5-fold cross validation scheme and position-specific chlorine substitution patterns on the biphenyl scaffold as descriptors. Alternative NEQ values were derived but the resulting QSAR models had relatively low predictivity (RMSE ~0.25), largely due to uncertainties in the underlying experimental data and NEQ values. Using derived NEQs or QSAR predicted NEQs to fill data gaps should be applied with caution. The views expressed in this abstract are those of the authors and do not necessarily reflect the views or policies of the U.S. EPA.