Chemical Hazard Prediction and Hypothesis Testing Using Quantitative Adverse Outcome Pathways

Current efforts in chemical safety are focused on utilizing human in vitro or alternatives to animal data in a bio­logical pathway context. However, it remains unclear how biological pathways, and toxicology data developed in that context, can be used to quantitatively facilitate decision-making. The objective of this work is to determine if hypothesis testing using adverse outcome pathways (AOPs) can provide quantitative chemical hazard predictions. Current methods for predicting hazards of chemicals in a biological pathway context were extensively reviewed, spe­cific case studies examined, and computational modeling used to demonstrate quantitative hazard prediction based on an AOP. Since AOPs are chemically agnostic, we propose that AOPs function as hypotheses for how specific chemicals may cause adverse effects via specific pathways. Three broad approaches were identified for testing the hypothesis with AOPs, semi-quantitative weight of evidence, probabilistic, and mechanistic modeling. We then demonstrate how these approaches could be used to test hypotheses using high throughput in vitro data and data from alternatives to animal testing. Finally, we discuss standards in development and documentation that would facilitate use in a regu­latory context. We conclude that quantitative AOPs provide a flexible hypothesis framework for predicting chemical hazards, which accommodates a wide range of approaches that are useful at many stages and build upon one another to become increasingly quantitative