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Studying Biology to Understand Risk: Dosimetry Models and Quantitative Adverse Outcome Pathways
Citation:
Conolly, R. Studying Biology to Understand Risk: Dosimetry Models and Quantitative Adverse Outcome Pathways. SOT RASS webinar, NA, April 13, 2016.
Impact/Purpose:
This is an abstract of a presentation that will be given as a webinar for the Society of Toxicology Risk Assessment Specialty Section. The presentation will provide an historical overview of biologically-motivated computational models of toxicant pharmacokinetics and of adverse outcome pathways. A quantitative AOP being developed in CSS 17.01 will be described. The potential for these kinds of models to support regulatory decision-making will be considered.
Description:
Confidence in the quantitative prediction of risk is increased when the prediction is based to as great an extent as possible on the relevant biological factors that constitute the pathway from exposure to adverse outcome. With the first examples now over 40 years old, physiologically based pharmacokinetic (PBPK) models and inhalation dosimetry models using realistic descriptions of the respiratory tract have been developed. These models excel at identifying how nonlinearities associated with transport and metabolism affect the internal dose and thereby influence the shapes of dose-response curves. In the case of respiratory tract dosimetry, these models have aided in the characterization for some chemicals of large gradients of dose within the respiratory tract, emphasizing the need for appreciation of spatial factors in the characterization of inhalation risk. The key feature of these models is quantitative description of the biology that is relevant to and determines the dosimetry. More limited, though gradually increasing efforts have been made to computationally model the biology that links internal measures of dose with adverse outcomes of regulatory interest. What we called biologically based dose response (BBDR) models 20 years ago have evolved into quantitative adverse outcome pathways today (qAOP). While the relevant biology hasn’t changed (of course!), the language we are using has evolved to a considerable degree. The new terminology around AOPs – molecular initiating events (MIE), key events (KE), key event relationships (KER), and adverse outcomes (AO) provides a structured, formal language for thinking about and describing the biology. In this presentation I will briefly review biologically motivated dosimetry modeling and then describe in some detail a qAOP linking the molecular-level effect of aromatase inhibition in fathead minnows with effects on fecundity and population trajectory. The kinds of experimental designs that facilitate qAOP development, the question of confidence in model predictions and of how confidence can be increased through cycles of new data acquisition and model refinement, the data-intensive nature of qAOP development and the associated costs will all be discussed. This abstract reflects the views of the author and does not necessarily reflect EPA policy.