A framework for application of quantitative property-property relationships (QPPRs) in physiologically based pharmacokinetic (PBPK) models for high-throughput prediction of internal dose of inhaled organic chemicals.

New generation of toxicological tests and assessment strategies require validated toxicokinetic data or models that are lacking for most chemicals. This study aimed at developing a quantitative property-property relationship (QPPR)-based human physiologically based pharmacokinetic (PBPK) modeling framework for high-throughput predictions of inhalation toxicokinetics of organic chemicals. A PBPK model was parameterized with QPPR-derived values for hepatic clearance (CL) and partition coefficients (P) [blood:air (P) and tissue:air (P) and tissue:blood (P)]. The model was initially applied to an evaluation dataset of 40 organic chemicals in the applicability domain, and then to an expanded dataset of 249 organic chemicals from diverse chemical classes. 'Batch' analyses were performed for rapid assessments of hundreds of chemicals. The simulations of inhalation toxicokinetics following an 8-h exposure to 1 ppm of each chemical were successful. The mean ratios of their predicted-to-experimental values were within a factor of 1.36-2.36 for P and 1.18 for CL, for 80% of the chemicals in the evaluation dataset. The predicted 24-h area under the venous blood concentration-time curve (AUC) values were within the predicted envelopes obtained while using experimental values of P and considering either no or maximal hepatic extraction. The reliability analysis (based on combined sensitivity and uncertainty analyses) indicated that AUC predictions for 55% of the expanded dataset were moderately to highly reliable, with 46% exhibiting highly reliable values. Overall, the modeling framework suggests that molecular structure and chemical properties can together be effectively used to obtain first-cut estimates of the toxicokinetics of data-poor organic chemicals for screening and prioritization purposes.