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QSAR modeling for in vitro human NIS inhibition with blinded external validation and screening of 80,086 REACH substances

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  • Overview
Inhibition of the sodium/iodide symporter (NIS) can lead to learning and memory impairment in humans and rats (AOP 54). The aim of this study was to develop global binary QSAR models that can be applied for screening purposes and single-compound identification of possible NIS antagonists. For this purpose, we processed the HTS assays results from U.S EPA’s ToxCast Program phases I and II for NIS inhibition to develop the first QSAR model for this endpoint adopting a new curation procedure including tautomer treatment and accounting for volatility and lipophilicity, resulting in a training set of 579 substances (64 actives and 515 inactives). Two models were developed and robustly cross-validated, one with high sensitivity and another with high overall accuracy. The models were subsequently subjected to external validation with ToxCast NIS inhibition results blinded to the QSAR developers for 740 E1K substances. The external validation set underwent the same processing as the training set. Next, the training set was expanded with the E1K dataset and two final models were developed and cross-validated, applying the same methods as for the first versions. The final models were used to screen 54,971 U.S. EPA substances and 80,086 REACH substances for NIS inhibition including the 11,092 REACH registered. These QSAR predictions will be published in the free online Danish (Q)SAR Database (https://qsar.food.dtu.dk). Furthermore, the models will be published in the free online Danish (Q)SAR Models, accessible from the Danish (Q)SAR Database, for real-time prediction of user-submitted structures and download of detailed results in the QSAR Prediction Reporting Format. (This abstract does not necessarily reflect EPA policy).

Impact/Purpose

Validation of a predictive QSAR model for NIS inhibition, which is a targeted thyroid screening assay used to screen ToxCast chemicals.  An external validation set was used to test two models and a final model was used to test predictivity for larger chemical sets, such as the REACH substances.  This was included in CSS 1.1.7 HTT for StRAP 3.  

Citation

Martins, A., T. Stoker, J. Wang, N. Nikolov, AND E. Wedebye. QSAR modeling for in vitro human NIS inhibition with blinded external validation and screening of 80,086 REACH substances. SOT, San Diego, CA, March 26 - 31, 2022.
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Last updated on August 20, 2025
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