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Evaluation and calibration of high-throughput predictions of chemical distribution to tissues

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Abstract

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). For thousands of chemicals that are present in our environment, in vivo TK data are lacking. The publicly available R package “httk” (version 1.8, 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 553 compounds. The PBTK model parameters include tissue:plasma partition coefficients (Kp) which the httk software predicts using the model of Schmitt (Toxicol In Vitro 22 (2):457–467, 2008). In this paper we evaluated and modified httk predictions, and quantified confidence using in vivo literature data. We used 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 possible deficiencies 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 10,000 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 adjustments, regressions were performed to calibrate and evaluate the predictions for 12 tissues. 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 (Vss) for 498 compounds reviewed by Obach et al. (Drug Metab Dispos 36(7):1385–1405, 2008). We found that calibration of the model improved performance: a regression of the measured values as a function of the predictions has a slope of 1.03, intercept of − 0.04, and R2 of 0.43. Through careful evaluation of predictive methods for chemical partitioning into tissues, we have improved and calibrated these methods and quantified confidence for TK predictions in humans and rats.

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Acknowledgements

This project was supported by an appointment to the Internship/Research Participation Program at the Office of Research and Development, U.S. Environmental Protection Agency, administered by the Oak Ridge Institute for Science and Education through an interagency agreement between the U.S. Department of Energy and EPA. The authors thank Drs. Hugh Barton and Rogelio Tornero-Velez for their helpful reviews of the manuscript. We also appreciate conversations with Drs. Weihsueh Chiu and Barbara Wetmore on protein binding.

Disclaimer

The United States Environmental Protection Agency, through its Office of Research and Development, funded and managed the research described here. However, it may not necessarily reflect official Agency policy, and reference to commercial products or services does not constitute endorsement.

Funding

Dr. Davis received no funding from Syngenta for this project, and Syngenta was not involved in the development or approval of this research or this report.

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  1. Jimena L. Davis

    Present address: Syngenta, Research Triangle Park, NC, 27709, USA

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  1. National Center for Computational Toxicology, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, 109 T.W. Alexander Dr, Durham, NC, 27711, USA

    Robert G. Pearce, R. Woodrow Setzer, Jimena L. Davis & John F. Wambaugh

  2. Oak Ridge Institute for Science and Education, Oak Ridge, TN, 37831, USA

    Robert G. Pearce

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Pearce, R.G., Setzer, R.W., Davis, J.L. et al. Evaluation and calibration of high-throughput predictions of chemical distribution to tissues. J Pharmacokinet Pharmacodyn 44, 549–565 (2017). https://doi.org/10.1007/s10928-017-9548-7

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