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Consensus Modeling for Prediction of Estrogenic Activity of Ingredients Commonly Used in Sunscreen Products

Author

Listed:
  • Huixiao Hong

    (Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA)

  • Diego Rua

    (Division of Nonprescription Drug Products, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, MD 20993, USA)

  • Sugunadevi Sakkiah

    (Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA)

  • Chandrabose Selvaraj

    (Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA)

  • Weigong Ge

    (Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA)

  • Weida Tong

    (Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA)

Abstract

Sunscreen products are predominantly regulated as over-the-counter (OTC) drugs by the US FDA. The “active” ingredients function as ultraviolet filters. Once a sunscreen product is generally recognized as safe and effective (GRASE) via an OTC drug review process, new formulations using these ingredients do not require FDA review and approval, however, the majority of ingredients have never been tested to uncover any potential endocrine activity and their ability to interact with the estrogen receptor (ER) is unknown, despite the fact that this is a very extensively studied target related to endocrine activity. Consequently, we have developed an in silico model to prioritize single ingredient estrogen receptor activity for use when actual animal data are inadequate, equivocal, or absent. It relies on consensus modeling to qualitatively and quantitatively predict ER binding activity. As proof of concept, the model was applied to ingredients commonly used in sunscreen products worldwide and a few reference chemicals. Of the 32 chemicals with unknown ER binding activity that were evaluated, seven were predicted to be active estrogenic compounds. Five of the seven were confirmed by the published data. Further experimental data is needed to confirm the other two predictions.

Suggested Citation

  • Huixiao Hong & Diego Rua & Sugunadevi Sakkiah & Chandrabose Selvaraj & Weigong Ge & Weida Tong, 2016. "Consensus Modeling for Prediction of Estrogenic Activity of Ingredients Commonly Used in Sunscreen Products," IJERPH, MDPI, vol. 13(10), pages 1-17, September.
    • Handle: RePEc:gam:jijerp:v:13:y:2016:i:10:p:958-:d:79511
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    References listed on IDEAS

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    1. Hui Wen Ng & Roger Perkins & Weida Tong & Huixiao Hong, 2014. "Versatility or Promiscuity: The Estrogen Receptors, Control of Ligand Selectivity and an Update on Subtype Selective Ligands," IJERPH, MDPI, vol. 11(9), pages 1-34, August.
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    Cited by:

    1. Sugunadevi Sakkiah & Tony Wang & Wen Zou & Yuping Wang & Bohu Pan & Weida Tong & Huixiao Hong, 2017. "Endocrine Disrupting Chemicals Mediated through Binding Androgen Receptor Are Associated with Diabetes Mellitus," IJERPH, MDPI, vol. 15(1), pages 1-17, December.

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