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Complementing in vitro hazard assessment with exposure and pharmacokinetics considerations for chemical prioritization
Citation:
Leonard, J. AND C. Tan. Complementing in vitro hazard assessment with exposure and pharmacokinetics considerations for chemical prioritization. 2017 ISES Annual Meeting, Durham, NC, October 15 - 19, 2017.
Impact/Purpose:
Traditional toxicity testing involves a large investment in resources, often using low-throughput in vivo animal studies for limited numbers of chemicals. An alternative strategy is the emergence of high-throughput (HT) in vitro assays as a rapid, cost-efficient means to screen thousands of chemicals across hundreds of pathway-based toxicity endpoints and to aid in chemical prioritization for more extensive testing. Such HT in vitro methods, along with integration of HT in silico predictions of population exposure levels and pharmacokinetic (PK) characteristics, act as the foundation for HT risk assessment.
Description:
Traditional toxicity testing involves a large investment in resources, often using low-throughput in vivo animal studies for limited numbers of chemicals. An alternative strategy is the emergence of high-throughput (HT) in vitro assays as a rapid, cost-efficient means to screen thousands of chemicals across hundreds of pathway-based toxicity endpoints and to aid in chemical prioritization for more extensive testing. Such HT in vitro methods, along with integration of HT in silico predictions of population exposure levels and pharmacokinetic (PK) characteristics, act as the foundation for HT risk assessment. Underlying uncertainties in predicted exposure concentrations or PK behaviors could significantly influence the prioritization of chemicals, though the impact of such influences is unclear. In the current study, a framework was developed to incorporate absorbed doses, clearance, and in vitro dose-response data into a PK/pharmacodynamic (PD) model to allow for placement of chemicals into discreet priority bins. In addition, both measured (from literature) and predicted values for absorbed doses or clearance were used in the PK/PD model to evaluate the impact of their uncertainties on the prioritization process. Scenarios using predicted absorbed doses resulted in a larger number of bin misassignments than scenarios using predicted clearance rates, when compared to placement of chemicals into bins using literature-reported values. Prioritization is more robust to uncertainties in clearance due to physiological constraints, whereas the large magnitude of differences between exposure predictions resulting from numerous possible exposure scenarios is the cause of increased errors in prioritization of chemicals into bins.
