In Silico Identification of Potential Thyroid Hormone System Disruptors among Chemicals in Human Serum and Chemicals with a High Exposure Index

Data on toxic effects
are at large missing the prevailing understanding
of the risks of industrial chemicals. Thyroid hormone (TH) system
disruption includes interferences of the life cycle of the thyroid
hormones and may occur in various organs. In the current study, high-throughput
screening data available for 14 putative molecular initiating events
of adverse outcome pathways, related to disruption of the TH system,
were used to develop 19 in silico models for identification of potential
thyroid hormone system-disrupting chemicals. The conformal prediction
framework with the underlying Random Forest was used as a wrapper
for the models allowing for setting the desired confidence level and
controlling the error rate of predictions. The trained models were
then applied to two different databases: (i) an in-house database
comprising xenobiotics identified in human blood and ii) currently
used chemicals registered in the Swedish Product Register, which have
been predicted to have a high exposure index to consumers. The application
of these models showed that among currently used chemicals, fewer
were overall predicted as active compared to chemicals identified
in human blood. Chemicals of specific concern for TH disruption were
identified from both databases based on their predicted activity.