Search

Thyroid uptake of iodide via the sodium-iodide symporter (NIS) is the first step in the biosynthesis of thyroid hormones that are critical for health and development in humans and wildlife. Despite having long been a known target of endocrine disrupting chemicals such as perchlorate, information regarding NIS inhibition activity is still unavailable for the vast majority of environmental chemicals. This study applied a previously validated high-throughput approach to screen for NIS inhibitors in the ToxCast phase I library, representing 293 important environmental chemicals. Here 310 blinded samples were screened in a tiered-approach using an initial single-concentration (100 μM) radioactive-iodide uptake (RAIU) assay, followed by 169 samples further evaluated in multi-concentration (0.001 μM-100 μM) testing in parallel RAIU and cell viability assays. A novel chemical ranking system that incorporates multi-concentration RAIU and cytotoxicity responses was also developed as a standardized method for chemical prioritization in current and future screenings. Representative chemical responses and thyroid effects of high-ranking chemicals are further discussed. This study significantly expands current knowledge of NIS inhibition potential in environmental chemicals and provides critical support to U.S. EPA's Endocrine Disruptor Screening Program (EDSP) initiative to expand coverage of thyroid molecular targets, as well as the development of thyroid adverse outcome pathways (AOPs).

There is a paucity of relevant experimental information available for the evaluation of the potential health and environmental effects of many man made chemicals. Knowledge of the potential pathways for activity provides a rational basis for the extrapolations inherent in the preliminary evaluation of risk and the establishment of priorities for obtaining missing data for environmental chemicals. The differential step in many mechanisms of toxicity may be generalized as the interaction between a small molecule (a potential toxicant) and one or more macromolecular targets. An approach based on computation of the interaction between a potential molecular toxicant and a library of macromolecular targets of toxicity has been proposed for preliminary chemical screening. In the current study, the interaction between a series of environmentally relevant chemicals and models of the rat estrogen receptors (ER) was computed and the results compared to an experimental data set of their relative binding affinities. The experimental data set consists of 281 chemicals, selected from the U.S. EPA's Toxic Substances Control Act (TSCA) inventory, that were initially screened using the rat uterine cytosolic ER-competitive binding assay. Secondary analysis, using Lineweaver-Burk plots and slope replots, was applied to confirm that only 15 of these test chemicals were true competitive inhibitors of ER binding with experimental inhibition constants (K(i)) less than 100 microM. Two different rapid computational docking methods have been applied. Each provides a score that is a surrogate for the strength of the interaction between each ligand-receptor pair. Using the score that indicates the strongest interaction for each pair, without consideration of the geometry of binding between the toxicant and the target, all of the active molecules were discovered in the first 16% of the chemicals. When a filter is applied on the basis of the geometry of a simplified pharmacophore for binding to the ER, the results are improved, and all of the active molecules were discovered in the first 8% of the chemicals. In order to obtain no false negatives in the model that includes the pharmacophore filter, only 8 molecules are false positives. These results indicate that molecular docking algorithms that were designed to find the chemicals that act most strongly at a receptor (and therefore are potential pharmaceuticals) can efficiently separate weakly active chemicals from a library of primarily inactive chemicals. The advantage of using a pharmacophore filter suggests that the development of filters of this type for other receptors will prove valuable.

Comparative molecular field analysis (CoMFA), a three-dimensional quantitative structure-activity relationship (3D-QSAR) paradigm, was used to examine the estrogen receptor (ER) binding affinities of a series of structurally diverse natural, synthetic, and environmental chemicals of interest. The CoMFA/3D-QSAR model is statistically robust and internally consistent, and successfully illustrates that the overall steric and electrostatic properties of structurally diverse ligands for the estrogen receptor are both necessary and sufficient to describe the binding affinity. The ability of the model to accurately predict the ER binding affinity of an external test set of molecules suggests that structure-based 3D-QSAR models may be used to supplement the process of endocrine disruptor identification through prioritization of novel compounds for bioassay. The general application of this 3D-QSAR model within a toxicological framework is, at present, limited only by the quantity and quality of biological data for relevant biomarkers of toxicity and hormonal responsiveness.