4.8 Article

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

期刊

ENVIRONMENTAL SCIENCE & TECHNOLOGY
卷 56, 期 12, 页码 8363-8372

出版社

AMER CHEMICAL SOC
DOI: 10.1021/acs.est.1c07762

关键词

conformal prediction; environmental health; endocrine disruption; QSAR

资金

  1. Swedish Research Council FORMAS [2018-02264]
  2. Swedish Environmental Protection Agency's Health-Related Environmental Monitoring (HAMI) [215-20-010]
  3. Swedish Foundation for Strategic Environmental Research, MISTRA [DIA 2018/11]
  4. Safe and Efficient Chemistry by Design (SafeChem)
  5. RiskMix project
  6. Formas [2018-02264] Funding Source: Formas

向作者/读者索取更多资源

Data on toxic effects of industrial chemicals are lacking in the current understanding. This study developed in silico models using high-throughput screening data to identify potential thyroid hormone system-disrupting chemicals. The models were applied to two different databases, identifying chemicals of concern for thyroid hormone disruption.
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.

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