A high-throughput analysis of ovarian cycle disruption by mixtures of aromatase inhibitors

TL;DR

This study uses computational modeling to predict how combined exposure to aromatase inhibitors may disrupt women's menstrual cycles, highlighting potential risks of infertility from everyday chemical exposures.

Contribution

It introduces a high-throughput simulation framework combining toxicology data and exposure estimates to assess mixture effects on ovulation.

Findings

Approximately 10% of simulated mixtures caused ovulation disruption.

Individual chemicals did not cause effects, but mixtures did.

Predicted effects are consistent with increased infertility risk.

Abstract

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: To explore, 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 two years). 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, 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%. Conclusions: 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. The size of the effects predicted is consistent with an increased risk of infertility in women from everyday exposures to our chemical environment.