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High-throughput analysis of ovarian cycle disruption by mixtures of aromatase inhibitors

Abstract : BACKGROUND: Combining computational toxicology with ExpoCast exposure estimates and ToxCast (TM) assay data gives us access to predictions of human health risks stemming from exposures to chemical mixtures. OBJECTIVES: We explored, through mathematical modeling and simulations, the size of potential effects of random mixtures of aromatase inhibitors on the dynamics of women's menstrual cycles. METHODS: We simulated random exposures to millions of potential mixtures of 86 aromatase inhibitors. A pharmacokinetic model of intake and disposition of the chemicals predicted their internal concentration as a function of time (up to 2 y). A ToxCast (TM) aromatase assay provided concentration inhibition relationships for each chemical. The resulting total aromatase inhibition was input to a mathematical model of the hormonal hypothalamus pituitary-ovarian control of ovulation in women. RESULTS: Above 10% inhibition of estradiol synthesis by aromatase inhibitors, noticeable (eventually reversible) effects on ovulation were predicted. Exposures to individual chemicals never led to such effects. In our best estimate, similar to 10% of the combined exposures simulated had mild to catastrophic impacts on ovulation. A lower bound on that figure, obtained using an optimistic exposure scenario, was 0.3%. CONCLUSIONS: These results demonstrate the possibility to predict large-scale mixture effects for endocrine disrupters with a predictive toxicology approach that is suitable for high-throughput ranking and risk assessment. The size of the effects predicted is consistent with an increased risk of infertility in women from everyday exposures to our chemical environment.
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Submitted on : Tuesday, August 28, 2018 - 11:31:06 AM
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Frédéric Y. Bois, Nazanin Golbamaki, Simona Kovarich, Cléo Tebby, Henry A. Gabb, et al.. High-throughput analysis of ovarian cycle disruption by mixtures of aromatase inhibitors. Environmental Health Perspectives, National Institute of Environmental Health Sciences, 2017, 125 (7), 077012. ⟨10.1289/EHP742⟩. ⟨ineris-01863248⟩



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