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

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 (K) 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 measured by in vivo experiments for 143 compounds. Initially, predicted K were significantly larger than measured K for many lipophilic compounds (log octanol:water partition coefficient > 3). Hence the approach for predicting K 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 across 23 compounds with only 3 K worsening by more than a factor of 10. The vast majority (92%) of K were predicted within a factor of 10 of the measured value (overall root mean squared error of 0.59 on log-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) 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 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.

Development and evaluation of a high throughput inhalation model for organic chemicals

“…The PBTK model included within httk simulates perfusion- (flow) limited distribution of chemicals to tissues, whose capacity for chemical uptake (i.e., tissue:plasma partition coefficients) are predicted from physico-chemical and tissue properties. ( 14 ) Metabolism and excretion are characterized using in vitro chemical testing to estimate rate of hepatic metabolism and fraction unbound in plasma. ( 15 ) Despite the relative simplicity of these models, they provide significant utility.…”

A Computational Workflow for Probabilistic Quantitative in Vitro to in Vivo Extrapolation

“…The simulations in our own work (Figure 1B ) demonstrate that for a given PBPK model structure a range of model parameters might provide a similar quality of fit to data. Even for animal models, where the variability in physiology of the animal is relatively small, the uncertainty in chemical specific parameters predicted using computational tools/models is more substantial (Pearce et al, 2017 ). Therefore, even for animal models, parameter value uncertainty should also be considered when developing a QIVIVE approach.…”

Using the concordance of in vitro and in vivo data to evaluate extrapolation assumptions

“…We restricted our evaluation to chemicals for which effects were observed for both in vitro bioactivity and in vivo toxicity data. For each combination of in vitro bioactivity and in vivo endpoint a regression analysis [24, 33, 34] was used to evaluate the performance of the PBTK model relative to the untransformed values and randomized PBTK results. Results were summarized based on the count of the number of times the PBTK model performed better than both a randomized result (y-randomized TK parameters) and comparison of the untransformed values ( in vitro AC 50 vs. in vivo dose).…”

“…New measured values for f up for 67 chemicals are included in the httk R package version 1.9 and are available in S1 Table. For use in the models, a correction for non-specific binding in the RED assay was applied following previous methods [24].…”

“…The fraction absorbed was assumed to be 100%. We accounted for organ and tissue partitioning in the PBTK model following the methods described by Pearce et al [24]. When experimental values for log P (EPI Suite, [48]) were unavailable, predicted values from the OPERA model [49] were used.…”