Session 5: Scientific confidence framework

A scientific confidence framework for AOPs
Richard A Becker, American Chemistry Council

Richard A Becker


American Chemistry Council


Richard A Becker


Institute of Medicine (IOM). 2010. Evaluation of biomarkers and surrogate endpoints in chronic disease. ISBN: 978-0-309-15129-0.
National Research Council (NRC)., 2007. Toxicity Testing in the 21st Century: A Vision and a Strategy Washington, DC: National Academies Press.
OECD. 2004. INV/JM/MONO/(2004)24



Since the publication of the NAS report, there has been a shift in thinking of how toxicity testing should be undertaken in the future to address hazard and risk assessment purposes. Rather than toxicity testing based on phenotypic responses in animals, a mechanistically based approach taking advantage of in vitro tests including high throughput and high content screening methods is envisaged coupled with the application of a range of computational methods for data analysis and predictive modelling.

A key, overarching component will be a biological construct for appropriate interpretation of these data so that prediction models can guide regulatory uses and decision making. The adverse outcome pathway (AOP) framework could serve as such a construct. An adverse outcome pathway (AOP) describes the causal linkage between initial molecular events and an adverse outcome at the individual or population levels. Whilst there has been considerable momentum in AOP development, far less attention has been paid to how AOPs might be practically applied for different regulatory purposes.

Using the OECD QSAR validation principles (OECD, 2004) and the Institute of Medicine (IOM) biomarkers guidance (IOM, 2010), a framework was derived to assist in evaluating in vitro assays and their associated prediction models. The so-named scientific confidence framework is composed of three inter-related core elements, 1) analytical validation, 2) qualification and 3) utilization.

Here we demonstrate how this framework can be extended to assist in evaluating and applying a given AOP for different regulatory purposes ranging from prioritizing chemicals for further evaluation, to actual hazard prediction, and ultimately, risk assessment. We illustrate the applicability of this framework using several different AOPs for typical regulatory applications including prioritization of chemicals, grouping chemicals for subsequent read-across to testing strategies. The examples confirm how critical the richness of an AOP is for driving its practical application.