PMID- 29032447 OWN - NLM STAT- MEDLINE DCOM- 20180702 LR - 20240402 IS - 1573-8744 (Electronic) IS - 1567-567X (Print) IS - 1567-567X (Linking) VI - 44 IP - 6 DP - 2017 Dec TI - Evaluation and calibration of high-throughput predictions of chemical distribution to tissues. PG - 549-565 LID - 10.1007/s10928-017-9548-7 [doi] AB - 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 (K(p)) 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 K(p) measured by in vivo experiments for 143 compounds. Initially, predicted K(p) were significantly larger than measured K(p) for many lipophilic compounds (log(10) octanol:water partition coefficient > 3). Hence the approach for predicting K(p) 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 K(p) across 23 compounds with only 3 K(p) worsening by more than a factor of 10. The vast majority (92%) of K(p) were predicted within a factor of 10 of the measured value (overall root mean squared error of 0.59 on log(10)-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 (V(ss)) 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 R(2) 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. FAU - Pearce, Robert G AU - Pearce RG AD - 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. AD - Oak Ridge Institute for Science and Education, Oak Ridge, TN, 37831, USA. FAU - Setzer, R Woodrow AU - Setzer RW AD - 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. FAU - Davis, Jimena L AU - Davis JL AD - 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. AD - Syngenta, Research Triangle Park, NC, 27709, USA. FAU - Wambaugh, John F AU - Wambaugh JF AUID- ORCID: 0000-0002-4024-534X AD - 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. Wambaugh.John@epa.gov. LA - eng GR - EPA999999/Intramural EPA/United States PT - Journal Article DEP - 20171014 PL - United States TA - J Pharmacokinet Pharmacodyn JT - Journal of pharmacokinetics and pharmacodynamics JID - 101096520 RN - 0 (Pharmaceutical Preparations) SB - IM MH - Animals MH - Calibration MH - Computer Simulation/statistics & numerical data MH - Drug Evaluation, Preclinical/methods MH - Drug-Related Side Effects and Adverse Reactions/*metabolism MH - High-Throughput Screening Assays/methods/*standards MH - Humans MH - *Models, Biological MH - Pharmaceutical Preparations/administration & dosage/*metabolism MH - Rats MH - Tissue Distribution/drug effects/physiology MH - Toxicity Tests/methods/standards PMC - PMC6186149 MID - NIHMS1505428 OTO - NOTNLM OT - Distribution OT - High throughput toxicokinetics OT - PBPK OT - PBTK OT - Partition coefficients OT - Physiologically based toxicokinetics OT - Statistical analysis OT - Volume of distribution OT - httk EDAT- 2017/10/17 06:00 MHDA- 2018/07/03 06:00 PMCR- 2018/12/01 CRDT- 2017/10/17 06:00 PHST- 2017/05/26 00:00 [received] PHST- 2017/09/30 00:00 [accepted] PHST- 2017/10/17 06:00 [pubmed] PHST- 2018/07/03 06:00 [medline] PHST- 2017/10/17 06:00 [entrez] PHST- 2018/12/01 00:00 [pmc-release] AID - 10.1007/s10928-017-9548-7 [pii] AID - 10.1007/s10928-017-9548-7 [doi] PST - ppublish SO - J Pharmacokinet Pharmacodyn. 2017 Dec;44(6):549-565. doi: 10.1007/s10928-017-9548-7. Epub 2017 Oct 14.