30 Years After Wingspread: Development of Quantitative Adverse Outcome Pathways for Endocrine Disrupting Chemicals (EDCs) that Alter Reproductive Development in Utero
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Since the origin of the term “Endocrine Disruptors” at the 1991 Wingspread Meeting, led by Dr Theo Colborn, the field has evolved from description of the in vitro and in vivo effects EDCs to the utilization of mechanisms of toxicity and key events in Adverse Outcome Pathways (AOP) to predict the latent, life-long adverse effects of in utero exposure to EDCs on reproductive development. Our research team has developed an AOP Network (N) of the androgen signaling pathway that includes multiple AOPs with diverse mechanisms of toxicity. These AOPs converge on common key events in the AOP_N resulting in the development of common adverse outcomes in male offspring.
The remainder of the presentation will present two examples of how this AOP_N can be utilized to predict the effects of individual chemicals and mixtures of chemicals that disrupt androgen signaling in utero.
The first example demonstrates how the effects of mixtures of chemicals with diverse mechanisms of toxicity can be predicted using dose addition models based upon the individual dose response data of each of the components. The mixtures studies include binary mixtures to those including 15 to 18 chemicals. These chemicals include those that act as androgen receptor (AR) antagonists, AR degraders and chemicals that inhibit fetal hormone function. There are several key observations from these studies. For example, that dose addition should be the default model to predict mixtures because they consistently provide more accurate quantitative predictions of the adverse effects on androgen-dependent tissues than do response (RA) or integrated addition (IA) models. RA and IA mixture models may grossly underpredict the toxicity of the mixture. Secondly, mixtures of chemicals can produce adverse reproductive effects well below the individual reproductive NOAELs and LOAELs of each chemical.
Using the data from a nine phthalate mixture study, the second example in this presentation will describe several different approaches to predict the effects of phthalates or mixtures of phthalates. For example, accurate predictions of the effects of this mixture on male rat offspring can be predicted using postnatal dose response data on each chemical. However, this approach requires the availability of dose response data from a one generation study of each phthalate. Other approaches that provide accurate predictions include using statistical models of the effects of the individual phthalates or the mixture of phthalates on key events in the AOP to predict the adverse effects of the nine chemical mixture. Using alterations of key events in the phthalate AOP to predict the adverse effects of the nine phthalate mixture requires fewer resources and uses fewer animals than the approach using one-generation dose response data on each of the nine phthalates.
This is an abstract of a proposed presentation and does not necessarily reflect Agency opinion.