Prédictions de l’effet de mélanges perturbateurs endocriniens sur le cycle ovarien chez la femme

Abstract : Combining modeling with databases of exposures and toxicity can predict human health risks from exposures to chemical mixtures. To assess the potential effects of random mixtures of aromatase inhibitors on the dynamics of women’s menstrual cycles, we simulated millions of exposures scenarios to mixtures of 86 aromatase inhibitors. A pharmacokinetic model was used to predict the internal concentration of each chemical for up to two years. A ToxCast aromatase assay provided concentrationinhibition 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. Above 10 % inhibition of estradiol synthesis, noticeable (eventually reversible) effects on ovulation were predicted. About 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 %. These results demonstrate the possibility to predict large-scale mixture effects for endocrine disrupters with a predictive toxicology approach, suitable for high throughput ranking and risk assessment
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Frédéric Bois. Prédictions de l’effet de mélanges perturbateurs endocriniens sur le cycle ovarien chez la femme. Rapport Scientifique INERIS, 2018, 2017-2018, pp.58-59. ⟨ineris-02044876⟩

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