Session 2: Intro to AOPs

Principles, Practices, Role of Computational Approaches
Daniel L. Villeneuve, US EPA Mid-Continent Ecology Division

Daniel L. Villeneuve, Ph.D


US EPA Mid-Continent Ecology Division


Daniel L. Villeneuve



High throughput and in silico methods are providing the regulatory toxicology community with capacity to rapidly and cost effectively generate data concerning a chemical’s ability to initiate one or more biological perturbations that may culminate in an adverse ecological or human health outcome. Translation of those data into scientifically-defensible predictions of outcome that help support regulatory decision-making depends on the ability to efficiently access and assemble the wealth of accumulated toxicological evidence and biological understanding distributed throughout the scientific community. We propose that this challenge can be met through the assembly and description adverse outcome pathway (AOPs) in a common knowledgebase. Adverse outcome pathways are frameworks for organizing knowledge in a manner that supports the extrapolation of mechanistic data, often measured at low levels of biological organization, into regulatory outcomes of concern, typically observed at higher levels of biological organization. A set of key principles and conventions for AOP development have been defined. Computational approaches can be leveraged to support the process of AOP discovery, quantitative prediction of dose-response time course behaviors and transitions between key events, and derivation and analysis of complex networks of AOPs. This presentation will provide an introduction to the AOP framework, key principles of AOP development, and highlight the envisioned role of computational approaches in enabling AOP-based approaches to predictive risk assessment. The contents of this abstract neither constitute nor necessarily represent official US EPA views and policies.