The sodium-iodide symporter (NIS, SLC5A5) plays a crucial role in thyroid hormone synthesis. Especially during brain development, correct thyroid signaling is of critical importance. Hence, inhibition of this transporter can lead to neurodevelopmental disorders, such as lowered IQ or autism. In order to uncover environmental chemicals with the potential of causing developmental neurotoxicity (DNT), NIS was selected for modeling. To support next-generation risk assessment, in silico-based methods were utilized. Docking-based virtual screening workflows of a library of compounds with experimentally determined inhibitory activity on NIS were applied. In addition, machine learning (ML) models based on random forest (RF), extreme gradient boosting (XGB), and support vector machines (SVM) were trained using extended-connectivity fingerprints 4 (ECFP4) and continuous and data-driven descriptors (CDDDs) with 9-fold cross validation to discriminate between NIS inhibiting and noninhibiting compounds. Ultimately, combining ML and docking predictions improved discrimination, achieving an area under the receiver operating characteristic curve (ROC AUC) of 0.77. Thresholds for optimal discrimination between actives and inactives were determined using kernel density estimate plots, at which a Matthews correlation coefficient (MCC) of 0.32, and a balanced accuracy (BA) of 0.78 were achieved on the internal test set. By combining ML predictions with docking scores and training on a larger, more diverse data set of 1412 compounds, this study provides a novel and robust framework for NIS inhibition prediction, which constitutes a new approach method in toxicological risk assessment.
Kandler et al. (Fri,) studied this question.