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Evaluation and Calibration of High-Throughput Predictions of Chemical Distribution to Tissues
Citation:
Pearce, R., Woodrow Setzer, J. Davis, AND J. Wambaugh. Evaluation and Calibration of High-Throughput Predictions of Chemical Distribution to Tissues. JOURNAL OF PHARMACOKINETICS AND PHARMACODYNAMICS. Springer, New York, NY, 44(6):549-565, (2017). https://doi.org/10.1007/s10928-017-9548-7
Impact/Purpose:
We have used literature rat measurements to evaluate and improve the predictive ability of “httk” (an open-source EPA tool for physiologically-based pharmacokinetics (PBTK)). This product supports CSS RED project deliverable: “Development of pharmacokinetic models predicting internal doses for hundreds of chemicals and facilitating exposure based risk prioritization of chemicals.”
Description:
Toxicokinetics (TK) provides critical information for integrating chemical toxicity and exposure assessments in order to determine potential chemical risk (i.e., the margin between toxic doses and plausible exposures). The publicly available R package “httk” (named for “high throughput TK”) draws from a database of in vitro data and physico-chemical properties in order to run physiologically-based TK (PBTK) models for 543 compounds (version 1.4). The PBTK model parameters include tissue:plasma partition coefficients, Kp, which the httk software predicts using the model of Schmitt [1]. In this paper we evaluate, modify, and quantify confidence in these predictions using literature in vivo data. We evaluated and modified our initial model parameters based on 964 rat Kp measured by in vivo experiments for 143 compounds. Initially, predicted Kp were significantly larger than measured Kp for many lipophilic compounds (log10 octanol:water partition coefficient > 3). Hence the approach for predicting Kp was revised to account for the lack of lipid in the in vitro protein binding assay, and the method for predicting membrane affinity was revised. These changes yielded improvements ranging from a factor of 10 to nearly a factor of 10000 for 83 Kp across 23 compounds with only 3 Kp worsening by more than a factor of 10. The vast majority (92%) of Kp were predicted within a factor of 10 of the measured value (overall root mean squared error of 0.59 on log10-transformed scale). After applying the corrections, regressions were performed to calibrate and evaluate the predictions for each tissue. The evaluation of the predictions allowed quantification of the performance of in silico predictive tools for Kp. Predictions for some tissues (e.g., spleen, bone, gut, lung) were observed to be better than predictions for other tissues (e.g., skin, brain, fat), indicating that confidence in the application of in silico tools to predict chemical partitioning varies depending upon the tissues involved. Our calibrated model was then evaluated using a second data set of human in vivo measurements of volume of distribution (V¬d) for 498 compounds reviewed by Obach et al. 2008. We find that our calibrated model performs well: a regression of the measured values as a function of the predictions has a slope of 1.02, intercept of -0.05, and R2 of 0.42. Through careful evaluation of predictive methods for chemical partitioning into tissues, we have improved and calibrated these methods and quantified confidence for humans and rats.
URLs/Downloads:
DOI: Evaluation and Calibration of High-Throughput Predictions of Chemical Distribution to Tissues