Developing, Validating, and Applying Pathway-Based Models for Endocrine Disruption

Screening for endocrine disrupting chemicals (EDCs) is a priority for U.S. and international regulatory authorities. People are exposed to tens of thousands of chemicals throughout their lives, and some of these are known to interact with the endocrine system, in particular the estrogen, androgen and thyroid pathways. Given the complexity of the in vivo animal tests currently used to identify and fully characterize EDCs, only a small fraction of the chemical exposure universe has been studied in any depth. This has led to the development of in vitro and in silico approaches, including the federal Tox21 and ToxCast high throughput screening (HTS) programs, allowing rapid testing of thousands of chemicals and identification of candidate EDCs. Multiple HTS assays that map to various key events along endocrine pathways have been integrated using mathematical models to provide predictions for chemical activity against estrogen receptor (ER) and androgen receptor (AR) pathways. The acceptance and use of these novel computational toxicology methods necessitates a similar shift in validation principles. The concept of validation must evolve to allow evaluation of testing strategies that integrate various in silico and in vitro data streams for a specific purpose: to screen or prioritize chemicals for their predicted activity against a biological pathway. Our group is applying this fit-for-purpose validation strategy to the computational toxicology models for ER and AR pathway activity, and comparing the results to current regulatory guideline tests from the U.S. Environmental Protection Agency (EPA). These novel approaches have been recently accepted by the EPA for ER bioactivity screening as an alternative to existing animal tests, and are under consideration for AR. Additional key aspects toward facilitating hazard characterization with computational methods include applying physiologically based models for in vitro to in vivo extrapolation (IVIVE), to translate HTS activity concentrations into relevant doses and exposure levels. We have created automated IVIVE workflows to predict equivalent administered in vivo doses from in vitro concentrations, and will demonstrate their application to endocrine pathways.