A Rat α-Fetoprotein Binding Activity Prediction Model to Facilitate Assessment of the Endocrine Disruption Potential of Environmental Chemicals

Hong, Huixiao; Shen, Jie; Ng, Hui Wen; Sakkiah, Sugunadevi; Ye, Hao; Ge, Weigong; Gong, Ping; Xiao, Wenming; Tong, Weida

doi:10.3390/ijerph13040372

Title: A Rat α-Fetoprotein Binding Activity Prediction Model to Facilitate Assessment of the Endocrine Disruption Potential of Environmental Chemicals

Journal Article · Fri Mar 25 00:00:00 UTC 2016 · International Journal of Environmental Research and Public Health

DOI: https://doi.org/10.3390/ijerph13040372 · OSTI ID:1628335

Hong, Huixiao ^[1]; Shen, Jie ^[2]; Ng, Hui Wen ^[2];

^[2]; Ye, Hao ^[2]; Ge, Weigong ^[2]; Gong, Ping ^[3]; Xiao, Wenming ^[2]; Tong, Weida ^[2]

U.S. Food and Drug Administration (FDA), Jefferson, AR (United States). National Center for Toxicological Research. Division of Bioinformatics and Biostatistics; DOE/OSTI
U.S. Food and Drug Administration (FDA), Jefferson, AR (United States). National Center for Toxicological Research. Division of Bioinformatics and Biostatistics
US Army Engineer Research and Development Center, Vicksburg, MS (United States). Environmental Lab.

Endocrine disruptors such as polychlorinated biphenyls (PCBs), diethylstilbestrol (DES) and dichlorodiphenyltrichloroethane (DDT) are agents that interfere with the endocrine system and cause adverse health effects. Huge public health concern about endocrine disruptors has arisen. One of the mechanisms of endocrine disruption is through binding of endocrine disruptors with the hormone receptors in the target cells. Entrance of endocrine disruptors into target cells is the precondition of endocrine disruption. The binding capability of a chemical with proteins in the blood affects its entrance into the target cells and, thus, is very informative for the assessment of potential endocrine disruption of chemicals. α-fetoprotein is one of the major serum proteins that binds to a variety of chemicals such as estrogens. To better facilitate assessment of endocrine disruption of environmental chemicals, we developed a model for α-fetoprotein binding activity prediction using the novel pattern recognition method (Decision Forest) and the molecular descriptors calculated from two-dimensional structures by Mold2 software. The predictive capability of the model has been evaluated through internal validation using 125 training chemicals (average balanced accuracy of 69%) and external validations using 22 chemicals (balanced accuracy of 71%). Prediction confidence analysis revealed the model performed much better at high prediction confidence. Our results indicate that the model is useful (when predictions are in high confidence) in endocrine disruption risk assessment of environmental chemicals though improvement by increasing number of training chemicals is needed.

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Research Organization:: Oak Ridge Institute for Science and Education (ORISE), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER). Biological Systems Science Division

Grant/Contract Number:: SC0014664

OSTI ID:: 1628335

Journal Information:: International Journal of Environmental Research and Public Health, Journal Name: International Journal of Environmental Research and Public Health Journal Issue: 4 Vol. 13; ISSN IJERGQ; ISSN 1660-4601

Publisher:: MDPICopyright Statement

Country of Publication:: United States

Language:: English

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Mold ² , Molecular Descriptors from 2D Structures for Chemoinformatics and Toxicoinformatics Hong, Huixiao; Xie, Qian; Ge, Weigong Journal of Chemical Information and Modeling, Vol. 48, Issue 7 https://doi.org/10.1021/ci800038f	journal	June 2008
11.beta.-Methoxy-, 11.beta.-ethyl, and 17.alpha.-ethynyl-substituted 16.alpha.-fluoroestradiols: receptor-based imaging agents with enhanced uptake efficiency and selectivity Pomper, Martin G.; VanBrocklin, Henry; Thieme, Andrea M. Journal of Medicinal Chemistry, Vol. 33, Issue 12 https://doi.org/10.1021/jm00174a009	journal	December 1990
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Salicylhydrazine-Containing Inhibitors of HIV-1 Integrase: Implication for a Selective Chelation in the Integrase Active Site Neamati, Nouri; Hong, Huixiao; Owen, Joshua M. Journal of Medicinal Chemistry, Vol. 41, Issue 17 https://doi.org/10.1021/jm9801760	journal	August 1998
Rat α-Fetoprotein Binding Affinities of a Large Set of Structurally Diverse Chemicals Elucidated the Relationships between Structures and Binding Affinities Hong, Huixiao; Branham, William S.; Dial, Stacey L. Chemical Research in Toxicology, Vol. 25, Issue 11 https://doi.org/10.1021/tx3003406	journal	August 2012
Predicting Hepatotoxicity Using ToxCast in Vitro Bioactivity and Chemical Structure Liu, Jie; Mansouri, Kamel; Judson, Richard S. Chemical Research in Toxicology, Vol. 28, Issue 4 https://doi.org/10.1021/tx500501h	journal	March 2015
OEstrogenic Activity of Certain Synthetic Compounds Dodds, E. C.; Goldberg, L.; Lawson, W. Nature, Vol. 141, Issue 3562 https://doi.org/10.1038/141247b0	journal	February 1938
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Alpha-fetoprotein: the major high-affinity estrogen binder in rat uterine cytosols Uriel, J.; Bouillon, D.; Aussel, C. Proceedings of the National Academy of Sciences, Vol. 73, Issue 5 https://doi.org/10.1073/pnas.73.5.1452	journal	May 1976
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An integrated "4-phase" approach for setting endocrine disruption screening priorities--phase I and II predictions of estrogen receptor binding affinity Shi‡, L.; Tong, W.; Fang, H. SAR and QSAR in Environmental Research, Vol. 13, Issue 1 https://doi.org/10.1080/10629360290002235	journal	January 2002
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Effects-based chemical category approach for prioritization of low affinity estrogenic chemicals Hornung, M. W.; Tapper, M. A.; Denny, J. S. SAR and QSAR in Environmental Research, Vol. 25, Issue 4 https://doi.org/10.1080/1062936X.2014.898692	journal	April 2014
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