A Mechanistic Framework for Integrating Chemical Structure and High-Throughput Screening Results to Improve Toxicity Predictions

Abstract

Adverse Outcome Pathways (AOPs) establish a connection between a molecular initiating event (MIE) and an adverse outcome. As the point within the AOP where chemicals directly interact with the biology, a detailed understanding of the MIE itself, and the proximal events that occur following this perturbation, provide the ideal data for determining chemical properties required to elicit the MIE. This study utilized high-throughput screening data from the ToxCast program coupled with chemical structural information to generate chemical clusters using three similarity methods pertaining to the MIEs within an AOP network for hepatic steatosis (fatty liver disease). Three case studies demonstrate the utility of the mechanistic information held by the MIE for integrating both biological and chemical data. Evaluation of the chemical clusters that activate the glucocorticoid receptor (GR) identified activity differences in chemicals within a chemical cluster. Comparison of the estrogen receptor (ER) results with previous work showed that bioactivity data and structural alerts can be combined to improve predictions in a customizable way where bioactivity data are limited. The aryl hydrocarbon receptor (AHR) highlighted that while structural data can be used to offset limited data for new screening efforts, not all ToxCast targets have sufficient data to define robust chemical clusters. In this context, an alternative to additional receptor assays is proposed where assays for proximal key events downstream of AHR activation could be used to enhance confidence in active calls. These case studies highlight the value provided by AOP-informed chemical clusters when attempting to determine the activity of chemicals for which limited toxicity data exist.