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(searched for: doi:10.1016/j.scitotenv.2017.09.033)
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, John F. Wambaugh, Amanda Bernstein, Mark Sfeir,
Journal of Exposure Science & Environmental Epidemiology, Volume 32, pp 855-863; https://doi.org/10.1038/s41370-022-00491-0

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Dustin F. Kapraun, Mark Sfeir, Robert Pearce, Sarah Davidson, Annie Lumen, André Dallmann, Richard Judson, John F. Wambaugh
Published: 1 September 2022
, , Robert Pearce, , Barbara Wetmore,
Published: 26 September 2020
Computational Toxicology, Volume 16; https://doi.org/10.1016/j.comtox.2020.100136

Abstract:
The toxicokinetic (TK) parameters fraction of the chemical unbound to plasma proteins and metabolic clearance are critical for relating exposure and internal dose when building in vitro-based risk assessment models. However, experimental toxicokinetic studies have only been carried out on limited chemicals of environmental interest (~1000 chemicals with TK data relative to tens of thousands of chemicals of interest). This work evaluated the utility of chemical structure information to predict TK parameters in silico; development of cluster-based read-across and quantitative structure–activity relationship models of fraction unbound or fub (regression) and intrinsic clearance or Clint (classification and regression) using a dataset of 1487 chemicals; utilization of predicted TK parameters to estimate uncertainty in steady-state plasma concentration (Css); and subsequent in vitroin vivo extrapolation analyses to derive bioactivity-exposure ratio (BER) plot to compare human oral equivalent doses and exposure predictions using androgen and estrogen receptor activity data for 233 chemicals as an example dataset. The results demonstrate that fub is structurally more predictable than Clint. The model with the highest observed performance for fub had an external test set RMSE/σ = 0.61 and R2 = 0.57, for Clint classification had an external test set accuracy = 73.2%, and for intrinsic clearance regression had an external test set RMSE/σ = 0.92 and R2 = 0.16. This relatively low performance is in part due to the large uncertainty in the underlying Clint data. We show that Css is relatively insensitive to uncertainty in Clint. The models were benchmarked against the ADMET Predictor software. Finally, the BER analysis allowed identification of 14 out of 136 chemicals for further risk assessment demonstrating the utility of these models in aiding risk-based chemical prioritization.
, Jean-Daniel Berset, Billy W. Day
Chemical Research in Toxicology, Volume 33, pp 2010-2021; https://doi.org/10.1021/acs.chemrestox.0c00092

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Journal of Chemical Information and Modeling, Volume 60, pp 3792-3803; https://doi.org/10.1021/acs.jcim.0c00574

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, Barbara A Wetmore, , Chantel I Nicolas, Robert G Pearce, Gregory S Honda, Roger Dinallo, Derek Angus, Jon Gilbert, Teresa Sierra, et al.
Published: 18 September 2019
Toxicological Sciences, Volume 172, pp 235-251; https://doi.org/10.1093/toxsci/kfz205

Abstract:
High(er) throughput toxicokinetics (HTTK) encompasses in vitro measures of key determinants of chemical toxicokinetics and reverse dosimetry approaches for in vitro-in vivo extrapolation (IVIVE). With HTTK, the bioactivity identified by any in vitro assay can be converted to human equivalent doses and compared with chemical intake estimates. Biological variability in HTTK has been previously considered, but the relative impact of measurement uncertainty has not. Bayesian methods were developed to provide chemical-specific uncertainty estimates for 2 in vitro toxicokinetic parameters: unbound fraction in plasma (fup) and intrinsic hepatic clearance (Clint). New experimental measurements of fup and Clint are reported for 418 and 467 chemicals, respectively. These data raise the HTTK chemical coverage of the ToxCast Phase I and II libraries to 57%. Although the standard protocol for Clint was followed, a revised protocol for fup measured unbound chemical at 10%, 30%, and 100% of physiologic plasma protein concentrations, allowing estimation of protein binding affinity. This protocol reduced the occurrence of chemicals with fup too low to measure from 44% to 9.1%. Uncertainty in fup was also reduced, with the median coefficient of variation dropping from 0.4 to 0.1. Monte Carlo simulation was used to propagate both measurement uncertainty and biological variability into IVIVE. The uncertainty propagation techniques used here also allow incorporation of other sources of uncertainty such as in silico predictors of HTTK parameters. These methods have the potential to inform risk-based prioritization based on the relationship between in vitro bioactivities and exposures.
Published: 18 September 2019
Journal of Cheminformatics, Volume 11, pp 1-20; https://doi.org/10.1186/s13321-019-0384-1

Abstract:
Background: The logarithmic acid dissociation constant pKa reflects the ionization of a chemical, which affects lipophilicity, solubility, protein binding, and ability to pass through the plasma membrane. Thus, pKa affects chemical absorption, distribution, metabolism, excretion, and toxicity properties. Multiple proprietary software packages exist for the prediction of pKa, but to the best of our knowledge no free and open-source programs exist for this purpose. Using a freely available data set and three machine learning approaches, we developed open-source models for pKa prediction. Methods: The experimental strongest acidic and strongest basic pKa values in water for 7912 chemicals were obtained from DataWarrior, a freely available software package. Chemical structures were curated and standardized for quantitative structure–activity relationship (QSAR) modeling using KNIME, and a subset comprising 79% of the initial set was used for modeling. To evaluate different approaches to modeling, several datasets were constructed based on different processing of chemical structures with acidic and/or basic pKas. Continuous molecular descriptors, binary fingerprints, and fragment counts were generated using PaDEL, and pKa prediction models were created using three machine learning methods, (1) support vector machines (SVM) combined with k-nearest neighbors (kNN), (2) extreme gradient boosting (XGB) and (3) deep neural networks (DNN). Results: The three methods delivered comparable performances on the training and test sets with a root-mean-squared error (RMSE) around 1.5 and a coefficient of determination (R2) around 0.80. Two commercial pKa predictors from ACD/Labs and ChemAxon were used to benchmark the three best models developed in this work, and performance of our models compared favorably to the commercial products. Conclusions: This work provides multiple QSAR models to predict the strongest acidic and strongest basic pKas of chemicals, built using publicly available data, and provided as free and open-source software on GitHub.
, Jane C. Bare, Courtney C. Carignan, Kathie L. Dionisio, Robin E. Dodson, Olivier Jolliet, Xiaoyu Liu, David E. Meyer, Seth R. Newton, , et al.
Published: 1 June 2019
Current Opinion in Toxicology, Volume 15, pp 76-92; https://doi.org/10.1016/j.cotox.2019.07.001

Gregory S. Honda, Robert G. Pearce, Ly L. Pham, , Barbara A. Wetmore, , Jon Gilbert, Briana Franz, Russell S. Thomas,
Published: 28 May 2019
Journal: PLOS ONE
Abstract:
Linking in vitro bioactivity and in vivo toxicity on a dose basis enables the use of high-throughput in vitro assays as an alternative to traditional animal studies. In this study, we evaluated assumptions in the use of a high-throughput, physiologically based toxicokinetic (PBTK) model to relate in vitro bioactivity and rat in vivo toxicity data. The fraction unbound in plasma (fup) and intrinsic hepatic clearance (Clint) were measured for rats (for 67 and 77 chemicals, respectively), combined with fup and Clint literature data for 97 chemicals, and incorporated in the PBTK model. Of these chemicals, 84 had corresponding in vitro ToxCast bioactivity data and in vivo toxicity data. For each possible comparison of in vitro and in vivo endpoint, the concordance between the in vivo and in vitro data was evaluated by a regression analysis. For a base set of assumptions, the PBTK results were more frequently better associated than either the results from a “random” model parameterization or direct comparison of the “untransformed” values of AC50 and dose (performed best in 51%, 28%, and 21% of cases, respectively). We also investigated several assumptions in the application of PBTK for IVIVE, including clearance and internal dose selection. One of the better assumptions sets–restrictive clearance and comparing free in vivo venous plasma concentration with free in vitro concentration–outperformed the random and untransformed results in 71% of the in vitro-in vivo endpoint comparisons. These results demonstrate that applying PBTK improves our ability to observe the association between in vitro bioactivity and in vivo toxicity data in general. This suggests that potency values from in vitro screening should be transformed using in vitro-in vivo extrapolation (IVIVE) to build potentially better machine learning and other statistical models for predicting in vivo toxicity in humans.
Heather L. Ciallella,
Chemical Research in Toxicology, Volume 32, pp 536-547; https://doi.org/10.1021/acs.chemrestox.8b00393

Abstract:
In 2016, the Frank R. Lautenberg Chemical Safety for the 21st Century Act became the first US legislation to advance chemical safety evaluations by utilizing novel testing approaches that reduce the testing of vertebrate animals. Central to this mission is the advancement of computational toxicology and artificial intelligence approaches to implementing innovative testing methods. In the current big data era, the terms volume (amount of data), velocity (growth of data), and variety (the diversity of sources) have been used to characterize the currently available chemical, in vitro, and in vivo data for toxicity modeling purposes. Furthermore, as suggested by various scientists, the variability (internal consistency or lack thereof) of publicly available data pools, such as PubChem, also presents significant computational challenges. The development of novel artificial intelligence approaches based on public massive toxicity data is urgently needed to generate new predictive models for chemical toxicity evaluations and make the developed models applicable as alternatives for evaluating untested compounds. In this procedure, traditional approaches (e.g., QSAR) purely based on chemical structures have been replaced by newly designed data-driven and mechanism-driven modeling. The resulting models realize the concept of adverse outcome pathway (AOP), which can not only directly evaluate toxicity potentials of new compounds, but also illustrate relevant toxicity mechanisms. The recent advancement of computational toxicology in the big data era has paved the road to future toxicity testing, which will significantly impact on the public health.
Lisa Bittner, , Isabel Keddi, , Nils Klüver
Published: 19 February 2019
Environmental Toxicology and Chemistry, Volume 38, pp 1012-1022; https://doi.org/10.1002/etc.4395

Abstract:
Reported off‐target effects of antihistamines in humans draw interest in ecotoxicity testing of first‐ and second‐generation antihistamines, the latter of which have fewer reported side effects in humans. As antihistamines are ionizable compounds, the pH influences uptake and toxicity and thus is highly relevant when conducting toxicity experiments. Zebrafish embryo toxicity tests were performed with the 3 first‐generation antihistamines ketotifen, doxylamine and dimethindene and the 2 second‐generation antihistamines cetirizine and levocabastine at pH 5.5, 7.0 and 8.0. We detected effects on survival, phenotype, swimming activity and heart rate for 4 antihistamines with exception of levocabastine, which did not show any lethal or sublethal effects. When compared to lethal concentrations, effect concentrations neither of phenotype malformation, nor of swimming activity or heart rate, deviated by more than a factor of 10 from lethal concentrations, indicating that all sublethal effects were fairly non‐specific. First‐generation antihistamines are weak bases and showed decreasing external effect concentrations with increasing neutral fraction, accompanied by increased uptake in the fish embryo. As a result, internal effect concentrations were independent from external pH. The pH‐dependent toxicity originates from speciation‐dependent uptake, with neutral species taken up in higher amounts than the corresponding ionic species. Cetirizine, which shifts from zwitterionic to anionic state in the measured pH‐range, did not show any pH‐dependent uptake or toxicity. This article is protected by copyright. All rights reserved
Published: 8 October 2018
Journal: Chemosphere
Chemosphere, Volume 215, pp 634-646; https://doi.org/10.1016/j.chemosphere.2018.10.041

Abstract:
New generation of toxicological tests and assessment strategies require validated toxicokinetic data or models that lack 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 (CLh) and partition coefficients (P) [blood:air (Pba) and tissue:air (Pta) and tissue:blood (Ptb)]. The model was initially applied to an evaluation dataset of 40 organic chemicals in the applicability domain, then to an expanded dataset of 249 organic chemicals from diverse chemical classes. ‘Batch’ analyses were performed for fast assessments of hundreds of chemicals. The simulated toxicokinetics following an 8-h exposure to 1 ppm of each chemical were successful. For the chemicals in the evaluation dataset, while the mean ratios of their predicted-to-experimental values were within a factor of 1.36–2.36 for Ptb and 1.18 for CLh, for 80% of those chemicals, the predicted 24-h area under the venous blood concentration-time curves (AUC24) values were within the predicted envelopes obtained while using experimental values of Pba and considering either no or maximal hepatic extraction. The reliability analysis (based on crossed sensitivity and uncertainty analyses) indicated that AUC24 predictions for 55% of the expanded dataset were moderately to highly reliable, with 46% exhibiting highly reliable values. Overall, the modeling framework suggests that chemical properties alone can enable predictions of first-cut estimates of the toxicokinetics of data-poor organic chemicals for screening and prioritization purposes.
, , , Denise K Macmillan, Jermaine Ford, , Sherry R Black, Rodney W Snyder, Nisha S Sipes, Barbara A Wetmore, et al.
Published: 27 January 2018
Toxicological Sciences, Volume 163, pp 152-169; https://doi.org/10.1093/toxsci/kfy020

Abstract:
Prioritizing the risk posed by thousands of chemicals potentially present in the environment requires exposure, toxicity, and toxicokinetic (TK) data, which are often unavailable. Relatively high throughput, in vitro TK (HTTK) assays and in vitro-to-in vivo extrapolation (IVIVE) methods have been developed to predict TK, but most of the in vivo TK data available to benchmark these methods are from pharmaceuticals. Here we report on new, in vivo rat TK experiments for 26 non-pharmaceutical chemicals with environmental relevance. Both intravenous and oral dosing were used to calculate bioavailability. These chemicals, and an additional 19 chemicals (including some pharmaceuticals) from previously published in vivo rat studies, were systematically analyzed to estimate in vivo TK parameters (e.g., volume of distribution [Vd], elimination rate). For each of the chemicals, rat-specific HTTK data were available and key TK predictions were examined: oral bioavailability, clearance, Vd, and uncertainty. For the non-pharmaceutical chemicals, predictions for bioavailability were not effective. While no pharmaceutical was absorbed at less than 10%, the fraction bioavailable for non-pharmaceutical chemicals was as low as 0.3%. Total clearance was generally more under-estimated for nonpharmaceuticals and Vd methods calibrated to pharmaceuticals may not be appropriate for other chemicals. However, the steady-state, peak, and time-integrated plasma concentrations of nonpharmaceuticals were predicted with reasonable accuracy. The plasma concentration predictions improved when experimental measurements of bioavailability were incorporated. In summary, HTTK and IVIVE methods are adequately robust to be applied to high throughput in vitro toxicity screening data of environmentally relevant chemicals for prioritizing based on human health risks.
Robert G. Pearce, R. Woodrow Setzer, Jimena L. Davis,
Journal of Pharmacokinetics and Pharmacodynamics, Volume 44, pp 549-565; https://doi.org/10.1007/s10928-017-9548-7

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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