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Evaluating the Value of Augmenting In Vitro Hazard Assessment with Exposure and Pharmacokinetics Considerations for Chemical Prioritization
Citation:
Tan, C., J. Leonard, S. Scholle, M. Winter, P. Key, J. Lu, AND D. Chang. Evaluating the Value of Augmenting In Vitro Hazard Assessment with Exposure and Pharmacokinetics Considerations for Chemical Prioritization. Society of Toxicology Conf, New Orleans, LA, March 13 - 17, 2016.
Impact/Purpose:
The National Exposure Research Laboratory (NERL) Computational Exposure Division (CED) develops and evaluates data, decision-support tools, and models to be applied to media-specific or receptor-specific problem areas. CED uses modeling-based approaches to characterize exposures, evaluate fate and transport, and support environmental diagnostics/forensics with input from multiple data sources. It also develops media- and receptor-specific models, process models, and decision support tools for use both within and outside of EPA.
Description:
Over time, toxicity-testing paradigms have progressed from low-throughput in vivo animal studies for limited numbers of chemicals to high-throughput (HT) in vitro screening assays for thousands of chemicals. Such HT in vitro methods, along with HT in silico predictions of population exposure levels and pharmacokinetic (PK) properties, act as the foundation for HT risk assessment. The impact of uncertainties inherent in predicted exposure levels and PK behaviors has not been evaluated in overall risk estimates, however. In the current study, this impact was investigated using a medium-throughput approach that integrates exposure, PK, and hazard data using a one compartment PK/pharmacodynamic (PD) model to prioritize 25 acetylcholinesterase (AChE) inhibiting chemicals. The PK model was used to generate steady state plasma concentrations (Css), which were then incorporated with in vitro potency data in the PD model to predict inhibition activities, allowing these chemicals to be placed into four priority classes. Compared to a reference scenario in which all model parameters were based on measured values, the scenario in which clearance values were predicted using in silico models resulted in 4% misclassification. In contrast, a scenario in which measured clearance values were used in conjunction with absorbed doses predicted using a HT exposure model resulted in 65% misclassification, and a scenario using predicted values for both clearance and exposure resulted in 72% misclassification. Our findings suggest that proper estimates of exposure levels are needed in order to increase confidence in screening level risk assessment.
