Citation:

Perkins, E., R. Ashauer, L. Burgoon, R. Conolly, B. Landesmann, C. Mackay, C. Murphy, N. Pollesch, J. Wheeler, A. Zupanic, AND S. Scholz. Building and applying quantitative adverse outcome pathway models for chemical hazard and risk assessment. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY. Society of Environmental Toxicology and Chemistry, Pensacola, FL, 38(9):1850-1865, (2019). https://doi.org/10.1002/etc.4505

Impact/Purpose:

Adverse outcome pathways (AOP) organize toxicological knowledge in a uniform framework. Starting at the molecular initiating event and progressing to adverse outcomes at higher levels of biological organization, AOPs encode causal dependencies. Quantitative adverse outcome pathways (qAOP) use the AOP knowledge base to build models that can be utilized for a variety of applications, such as weight of evidence descriptions, probabilistic activation of key events, and other predictive models. This research gives an overview of an emerging set of techniques suited for development of qAOP models.

Description:

An important goal in toxicology is the development of new ways to increase the speed, accuracy and applicability of chemical hazard and risk assessment approaches by incorporating in vitro assays and biological pathway information. Here we examine how the Adverse Outcome Pathway (AOP) framework can be used to develop pathway based quantitative models useful for regulatory chemical safety assessment. By using AOPs as initial conceptual models and the AOP knowledge base as a source of data on key event relationships, different methods can be applied to develop computational quantitative AOP models (qAOPs) relevant for decision making. A qAOP model may not necessarily have the same structure as the AOP it is based on. Useful AOP modeling methods range from statistical, Bayesian networks, regression, and ordinary differential equations to individual-based and population models and should be chosen according to the problem being addressed and the data available. An example of using qAOPs for hazard assessment is presented where a Bayesian network model for a liver steatosis AOP network is used to examine interactions between perfluorooctanoic acid and rosiglitizone, an anti diabetic. We discuss the need for toxicokinetic models to provide linkages between exposure and qAOPs, to extrapolate from in vitro to in vivo, and to extrapolate across species. Finally, we identified best practices for modeling, model building and the necessity for transparent and comprehensive documentation to gain confidence in the use of a quantitative AOP models and ultimately their use in regulatory applications.

URLs/Downloads:

Record Details: