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High-Throughput Analysis of Ovarian Cycle Disruption by Mixtures of Aromatase Inhibitors
Frederic Y Bois1, Nazanin Golbamaki-Bakhtyari1, Simona Kovarich2
1Models for Ecotoxicology and Toxicology Unit (DRC/VIVA/METO), Institut National de l'Environnement Industriel et des Risques (INERIS) , Verneuil en Halatte, France.
Background:
Combining computational toxicology with ExpoCast exposure estimates and ToxCast™ 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™ 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, ∼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. https://doi.org/10.1289/EHP742.
Insights
Computational toxicology predicts that chemical mixtures can disrupt women's menstrual cycles. Simulations suggest up to 10% of exposures may impact ovulation, potentially increasing infertility risks.
Area of Science:
- Environmental Health Sciences
- Toxicology
- Endocrinology
Background:
- Computational toxicology integrates exposure data and assay results to predict chemical mixture risks.
- ToxCast™ assay data and ExpoCast exposure estimates aid in assessing human health impacts.
Purpose of the Study:
- To model the effects of random chemical mixtures on women's menstrual cycles.
- To quantify potential impacts of aromatase inhibitor mixtures on ovulation dynamics.
Main Methods:
- Simulated millions of potential mixtures involving 86 aromatase inhibitors.
- Utilized pharmacokinetic modeling for internal chemical concentrations and ToxCast™ assays for concentration-inhibition relationships.
- Employed a mathematical model of the hypothalamus-pituitary-ovarian axis to predict ovulation effects.
Main Results:
- Over 10% inhibition of aromatase activity predicted noticeable effects on ovulation.
- Individual chemical exposures did not cause significant effects.
- Estimated that approximately 10% of simulated exposures had mild to severe impacts on ovulation, with a lower bound of 0.3%.
Conclusions:
- Predictive toxicology can assess mixture effects of endocrine disruptors for high-throughput risk assessment.
- Predicted effects align with increased infertility risk from environmental chemical exposures.
- Highlights the potential for widespread reproductive health impacts from chemical mixtures.
