Evaluating the Impact of Uncertainties in Clearance and Exposure When Prioritizing Chemicals Screened in High-Throughput Assays

Abstract

The toxicity-testing paradigm has evolved to include high-throughput (HT) methods for addressing the increasing need to screen hundreds to thousands of chemicals rapidly. Approaches that involve in vitro screening assays, in silico predictions of exposure concentrations, and pharmacokinetic (PK) characteristics provide the foundation for HT risk prioritization. Underlying uncertainties in predicted exposure concentrations or PK behaviors can 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, PK properties, and in vitro dose-response data into a PK/pharmacodynamic (PD) model to allow for placement of chemicals into discrete priority bins. Literature-reported or predicted values for clearance rates and absorbed doses were used in the PK/PD model to evaluate the impact of their uncertainties on chemical prioritization. Scenarios using predicted absorbed doses resulted in a larger number of bin misassignments than those scenarios using predicted clearance rates, when comparing to bin placement using literature-reported values. Sensitivity of parameters on the model output of toxicological activity was examined across possible ranges for those parameters to provide insight into how uncertainty in their predicted values might impact uncertainty in activity.

Figure 1

Categories of acetylcholinesterase inhibiting chemicals used as a case study for prioritization, along with their in vitro potencies. Abbreviations are as follows: DDAC, didecyldimethylammonium chloride; 2,4-TDI, toluene 2,4-diisocyanate; DBSA, dodecylbenezene sulfonic acid.

Figure 2

Decision tree for selecting category of chemical of interest (A) and the appropriate pharmacokinetic (PK) and pharmacodynamic (PD) model inputs and parameters for that chemical category (B). The open circle in (A) represents a diverging step. Shaded boxes in (B) represent a need for the model input or parameter for that chemical category, whereas empty boxes represent exclusion of that parameter from the model. See text for how parameters were used in the PK and PD model.