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Computational Toxicology: Expanding Frontiers in Risk Assessment
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Computational Toxicology: Expanding Frontiers in Risk Assessment

A special issue of Toxics (ISSN 2305-6304). This special issue belongs to the section "Novel Methods in Toxicology Research".

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 46511

Special Issue Editors

Ms. Annie M. Jarabek

E-Mail Website
Guest Editor
Senior Science Advisor, Human and Environmental Effects Assessment Division (HEEAD), Center for Public Health and Environmental Assessment (CPHEA), Office of Research and Development (ORD), U.S. Environmental Protection Agency (US EPA), North Carolina 27711, USA
Interests: multi-scale dosimetry modeling; source-to-outcome modeling; risk assessment; inhalation toxicology; decision analysis
Dr. Alicia Paini

E-Mail Website
Guest Editor
Scientific Officer, European Commission - Directorate General Joint Research Centre, Health, Consumers and Reference Materials, Chemicals Safety and Alternative Methods hosting EURL ECVAM
Interests: biokinetics; in vitro fate modeling; PBK modeling; linking exposure to effect; chemical risk assessment
Dr. Peter P. Egeghy

E-Mail Website
Guest Editor
Chief of Computational Exposure and Toxicokinetics Branch, Center for Computational Toxicology and Exposure (CCTE), Office of Research and Development (ORD), U.S. Environmental Protection Agency (US EPA), North Carolina 27711, USA
Interests: exposure-based chemical screening and prioritization; accelerated exposure assessment; rapid risk assessment; household product chemical exposure; exposure database development; exposure variability; biological markers
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear colleagues,

Emerging technologies and advances in computational toxicology coupled with an improved understanding of mechanistic adverse outcome pathways (AOPs), in a new era of systems-focused thinking, are radically expanding the frontiers for risk assessment. The scientific community is capitalizing on these innovations to transform traditional approaches used for prioritization and standard setting regarding potential toxicity from exposures. Likewise, improved measurement capabilities and computational techniques are better characterizing emissions, transport, and transformation processes from source to media to target site exposures in various species as part of aggregate exposure pathways (AEPs) that can be integrated with AOP key events to refine dose–response analyses. New approach methodologies (NAMs) are increasingly providing biokinetic and mechanistic data from in vitro assays and in silico predictions that can aid evidence integration. A comprehensive characterization of the exposome promises to revolutionize how we approach protection of public health, including consideration of both human and ecological risks, with the help of artificial intelligence and machine learning. Similar computational approaches are used in the medical arena and can be mutually informative. In this Special Issue, we explore this new computational capacity across the source-to-outcome spectrum with examples of specific models and informatics approaches in both the environmental and medical arenas, including strategies for systematic review and harnessing big data. Conceptual constructs as well as quantitative examples are highlighted to illustrate impacts. Cross-cutting challenges such as how to foster the FAIR (findable, accessible, interoperable, and reproducible) principles and management of data repositories are also featured.

Ms. Annie M. Jarabek
Dr. Alicia Paini
Dr. Peter P. Egeghy
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Toxics is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • computational toxicology
  • dosimetry
  • PBPK modeling
  • risk assessment
  • exposure
  • adverse outcome pathway (AOP)
  • aggregate exposure pathway (AEP)
  • exposure
  • new approach methodologies (NAMs)
  • artificial intelligence

Published Papers (16 papers)

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Research

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11 pages, 1338 KiB  
Article
Utilizing a Population-Genetic Framework to Test for Gene-Environment Interactions between Zebrafish Behavior and Chemical Exposure
by Preethi Thunga, Lisa Truong, Yvonne Rericha, Jane La Du, Mackenzie Morshead, Robyn L. Tanguay and David M. Reif
Toxics 2022, 10(12), 769; https://doi.org/10.3390/toxics10120769 - 09 Dec 2022
Cited by 1 | Viewed by 1268
Abstract
Individuals within genetically diverse populations display broad susceptibility differences upon chemical exposures. Understanding the role of gene-environment interactions (GxE) in differential susceptibility to an expanding exposome is key to protecting public health. However, a chemical’s potential to elicit GxE is often not considered [...] Read more.
Individuals within genetically diverse populations display broad susceptibility differences upon chemical exposures. Understanding the role of gene-environment interactions (GxE) in differential susceptibility to an expanding exposome is key to protecting public health. However, a chemical’s potential to elicit GxE is often not considered during risk assessment. Previously, we’ve leveraged high-throughput zebrafish (Danio rerio) morphology screening data to reveal patterns of potential GxE effects. Here, using a population genetics framework, we apportioned variation in larval behavior and gene expression in three different PFHxA environments via mixed-effect modeling to assess significance of GxE term. We estimated the intraclass correlation (ICC) between full siblings from different families using one-way random-effects model. We found a significant GxE effect upon PFHxA exposure in larval behavior, and the ICC of behavioral responses in the PFHxA exposed population at the lower concentration was 43.7%, while that of the control population was 14.6%. Considering global gene expression data, a total of 3746 genes showed statistically significant GxE. By showing evidence that heritable genetics are directly affecting gene expression and behavioral susceptibility of individuals to PFHxA exposure, we demonstrate how standing genetic variation in a heterogeneous population such as ours can be leveraged to test for potential GxE. Full article
(This article belongs to the Special Issue Computational Toxicology: Expanding Frontiers in Risk Assessment)
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12 pages, 3167 KiB  
Article
Towards Whole Health Toxicology: In-Silico Prediction of Diseases Sensitive to Multi-Chemical Exposures
by Olatunbosun Arowolo, Victoria Salemme and Alexander Suvorov
Toxics 2022, 10(12), 764; https://doi.org/10.3390/toxics10120764 - 08 Dec 2022
Cited by 2 | Viewed by 1638
Abstract
Chemical exposures from diverse sources merge on a limited number of molecular pathways described as toxicity pathways. Changes in the same set of molecular pathways in different cell and tissue types may generate seemingly unrelated health conditions. Today, no approaches are available to [...] Read more.
Chemical exposures from diverse sources merge on a limited number of molecular pathways described as toxicity pathways. Changes in the same set of molecular pathways in different cell and tissue types may generate seemingly unrelated health conditions. Today, no approaches are available to predict in an unbiased way sensitivities of different disease states and their combinations to multi-chemical exposures across the exposome. We propose an inductive in-silico workflow where sensitivities of genes to chemical exposures are identified based on the overlap of existing genomic datasets, and data on sensitivities of individual genes is further used to sequentially derive predictions on sensitivities of molecular pathways, disease states, and groups of disease states (syndromes). Our analysis predicts that conditions representing the most significant public health problems are among the most sensitive to cumulative chemical exposures. These conditions include six leading types of cancer in the world (prostatic, breast, stomach, lung, colorectal neoplasms, and hepatocellular carcinoma), obesity, type 2 diabetes, non-alcoholic fatty liver disease, autistic disorder, Alzheimer’s disease, hypertension, heart failure, brain and myocardial ischemia, and myocardial infarction. Overall, our predictions suggest that environmental risk factors may be underestimated for the most significant public health problems. Full article
(This article belongs to the Special Issue Computational Toxicology: Expanding Frontiers in Risk Assessment)
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18 pages, 2629 KiB  
Article
A Physiologically Based Pharmacokinetic (PBPK) Modeling Framework for Mixtures of Dioxin-like Compounds
by Rongrui Liu, Tim R. Zacharewski, Rory B. Conolly and Qiang Zhang
Toxics 2022, 10(11), 700; https://doi.org/10.3390/toxics10110700 - 17 Nov 2022
Cited by 6 | Viewed by 1900
Abstract
Humans are exposed to persistent organic pollutants, such as dioxin-like compounds (DLCs), as mixtures. Understanding and predicting the toxicokinetics and thus internal burden of major constituents of a DLC mixture is important for assessing their contributions to health risks. PBPK models, including dioxin [...] Read more.
Humans are exposed to persistent organic pollutants, such as dioxin-like compounds (DLCs), as mixtures. Understanding and predicting the toxicokinetics and thus internal burden of major constituents of a DLC mixture is important for assessing their contributions to health risks. PBPK models, including dioxin models, traditionally focus on one or a small number of compounds; developing new or extending existing models for mixtures often requires tedious, error-prone coding work. This lack of efficiency to scale up for multi-compound exposures is a major technical barrier toward large-scale mixture PBPK simulations. Congeners in the DLC family, including 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), share similar albeit quantitatively different toxicokinetic and toxicodynamic properties. Taking advantage of these similarities, here we reported the development of a human PBPK modeling framework for DLC mixtures that can flexibly accommodate an arbitrary number of congeners. Adapted from existing TCDD models, our mixture model contains the blood and three diffusion-limited compartments—liver, fat, and rest of the body. Depending on the number of congeners in a mixture, varying-length vectors of ordinary differential equations (ODEs) are automatically generated to track the tissue concentrations of the congeners. Shared ODEs are used to account for common variables, including the aryl hydrocarbon receptor (AHR) and CYP1A2, to which the congeners compete for binding. Binary and multi-congener mixture simulations showed that the AHR-mediated cross-induction of CYP1A2 accelerates the sequestration and metabolism of DLC congeners, resulting in consistently lower tissue burdens than in single exposure, except for the liver. Using dietary intake data to simulate lifetime exposures to DLC mixtures, the model demonstrated that the relative contributions of individual congeners to blood or tissue toxic equivalency (TEQ) values are markedly different than those to intake TEQ. In summary, we developed a mixture PBPK modeling framework for DLCs that may be utilized upon further improvement as a quantitative tool to estimate tissue dosimetry and health risks of DLC mixtures. Full article
(This article belongs to the Special Issue Computational Toxicology: Expanding Frontiers in Risk Assessment)
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19 pages, 1985 KiB  
Article
Exposure Prioritization (Ex Priori): A Screening-Level High-Throughput Chemical Prioritization Tool
by Heidi F. Hubbard, Caroline L. Ring, Tao Hong, Cara C. Henning, Daniel A. Vallero, Peter P. Egeghy and Michael-Rock Goldsmith
Toxics 2022, 10(10), 569; https://doi.org/10.3390/toxics10100569 - 28 Sep 2022
Cited by 1 | Viewed by 2176
Abstract
To estimate potential chemical risk, tools are needed to prioritize potential exposures for chemicals with minimal data. Consumer product exposures are a key pathway, and variability in consumer use patterns is an important factor. We designed Ex Priori, a flexible dashboard-type screening-level [...] Read more.
To estimate potential chemical risk, tools are needed to prioritize potential exposures for chemicals with minimal data. Consumer product exposures are a key pathway, and variability in consumer use patterns is an important factor. We designed Ex Priori, a flexible dashboard-type screening-level exposure model, to rapidly visualize exposure rankings from consumer product use. Ex Priori is Excel-based. Currently, it is parameterized for seven routes of exposure for 1108 chemicals present in 228 consumer product types. It includes toxicokinetics considerations to estimate body burden. It includes a simple framework for rapid modeling of broad changes in consumer use patterns by product category. Ex Priori rapidly models changes in consumer user patterns during the COVID-19 pandemic and instantly shows resulting changes in chemical exposure rankings by body burden. Sensitivity analysis indicates that the model is sensitive to the air emissions rate of chemicals from products. Ex Priori’s simple dashboard facilitates dynamic exploration of the effects of varying consumer product use patterns on prioritization of chemicals based on potential exposures. Ex Priori can be a useful modeling and visualization tool to both novice and experienced exposure modelers and complement more computationally intensive population-based exposure models. Full article
(This article belongs to the Special Issue Computational Toxicology: Expanding Frontiers in Risk Assessment)
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19 pages, 2774 KiB  
Article
Evaluation of Metabolism of a Defined Pesticide Mixture through Multiple In Vitro Liver Models
by Alan Valdiviezo, Yuki Kato, Erin S. Baker, Weihsueh A. Chiu and Ivan Rusyn
Toxics 2022, 10(10), 566; https://doi.org/10.3390/toxics10100566 - 27 Sep 2022
Cited by 5 | Viewed by 2600
Abstract
The evaluation of exposure to multiple contaminants in a mixture presents a number of challenges. For example, the characterization of chemical metabolism in a mixture setting remains a research area with critical knowledge gaps. Studies of chemical metabolism typically utilize suspension cultures of [...] Read more.
The evaluation of exposure to multiple contaminants in a mixture presents a number of challenges. For example, the characterization of chemical metabolism in a mixture setting remains a research area with critical knowledge gaps. Studies of chemical metabolism typically utilize suspension cultures of primary human hepatocytes; however, this model is not suitable for studies of more extended exposures and donor-to-donor variability in a metabolic capacity is unavoidable. To address this issue, we utilized several in vitro models based on human-induced pluripotent stem cell (iPSC)-derived hepatocytes (iHep) to characterize the metabolism of an equimolar (1 or 5 µM) mixture of 20 pesticides. We used iHep suspensions and 2D sandwich cultures, and a microphysiological system OrganoPlate® 2-lane 96 (MimetasTM) that also included endothelial cells and THP-1 cell-derived macrophages. When cell culture media were evaluated using gas and liquid chromatography coupled to tandem mass spectrometry methods, we found that the parent molecule concentrations diminished, consistent with metabolic activity. This effect was most pronounced in iHep suspensions with a 1 µM mixture, and was lowest in OrganoPlate® 2-lane 96 for both mixtures. Additionally, we used ion mobility spectrometry–mass spectrometry (IMS-MS) to screen for metabolite formation in these cultures. These analyses revealed the presence of five primary metabolites that allowed for a more comprehensive evaluation of chemical metabolism in vitro. These findings suggest that iHep-based suspension assays maintain higher metabolic activity compared to 2D sandwich and OrganoPlate® 2-lane 96 model. Moreover, this study illustrates that IMS-MS can characterize in vitro metabolite formation following exposure to mixtures of environmental contaminants. Full article
(This article belongs to the Special Issue Computational Toxicology: Expanding Frontiers in Risk Assessment)
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20 pages, 3922 KiB  
Article
Cumulative Risk Meets Inter-Individual Variability: Probabilistic Concentration Addition of Complex Mixture Exposures in a Population-Based Human In Vitro Model
by Suji Jang, Lucie C. Ford, Ivan Rusyn and Weihsueh A. Chiu
Toxics 2022, 10(10), 549; https://doi.org/10.3390/toxics10100549 - 20 Sep 2022
Cited by 4 | Viewed by 1691
Abstract
Although humans are continuously exposed to complex chemical mixtures in the environment, it has been extremely challenging to investigate the resulting cumulative risks and impacts. Recent studies proposed the use of “new approach methods,” in particular in vitro assays, for hazard and dose–response [...] Read more.
Although humans are continuously exposed to complex chemical mixtures in the environment, it has been extremely challenging to investigate the resulting cumulative risks and impacts. Recent studies proposed the use of “new approach methods,” in particular in vitro assays, for hazard and dose–response evaluation of mixtures. We previously found, using five human cell-based assays, that concentration addition (CA), the usual default approach to calculate cumulative risk, is mostly accurate to within an order of magnitude. Here, we extend these findings to further investigate how cell-based data can be used to quantify inter-individual variability in CA. Utilizing data from testing 42 Superfund priority chemicals separately and in 8 defined mixtures in a human cell-based population-wide in vitro model, we applied CA to predict effective concentrations for cytotoxicity for each individual, for “typical” (median) and “sensitive” (first percentile) members of the population, and for the median-to-sensitive individual ratio (defined as the toxicodynamic variability factor, TDVF). We quantified the accuracy of CA with the Loewe Additivity Index (LAI). We found that LAI varies more between different mixtures than between different individuals, and that predictions of the population median are generally more accurate than predictions for the “sensitive” individual or the TDVF. Moreover, LAI values were generally <1, indicating that the mixtures were more potent than predicted by CA. Together with our previous studies, we posit that new approach methods data from human cell-based in vitro assays, including multiple phenotypes in diverse cell types and studies in a population-wide model, can fill critical data gaps in cumulative risk assessment, but more sophisticated models of in vitro mixture additivity and bioavailability may be needed. In the meantime, because simple CA models may underestimate potency by an order of magnitude or more, either whole-mixture testing in vitro or, alternatively, more stringent benchmarks of cumulative risk indices (e.g., lower hazard index) may be needed to ensure public health protection. Full article
(This article belongs to the Special Issue Computational Toxicology: Expanding Frontiers in Risk Assessment)
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20 pages, 5506 KiB  
Article
A Population-Based Human In Vitro Approach to Quantify Inter-Individual Variability in Responses to Chemical Mixtures
by Lucie C. Ford, Suji Jang, Zunwei Chen, Yi-Hui Zhou, Paul J. Gallins, Fred A. Wright, Weihsueh A. Chiu and Ivan Rusyn
Toxics 2022, 10(8), 441; https://doi.org/10.3390/toxics10080441 - 01 Aug 2022
Cited by 6 | Viewed by 2396
Abstract
Human cell-based population-wide in vitro models have been proposed as a strategy to derive chemical-specific estimates of inter-individual variability; however, the utility of this approach has not yet been tested for cumulative exposures in mixtures. This study aimed to test defined mixtures and [...] Read more.
Human cell-based population-wide in vitro models have been proposed as a strategy to derive chemical-specific estimates of inter-individual variability; however, the utility of this approach has not yet been tested for cumulative exposures in mixtures. This study aimed to test defined mixtures and their individual components and determine whether adverse effects of the mixtures were likely to be more variable in a population than those of the individual chemicals. The in vitro model comprised 146 human lymphoblastoid cell lines from four diverse subpopulations of European and African descent. Cells were exposed, in concentration–response, to 42 chemicals from diverse classes of environmental pollutants; in addition, eight defined mixtures were prepared from these chemicals using several exposure- or hazard-based scenarios. Points of departure for cytotoxicity were derived using Bayesian concentration–response modeling and population variability was quantified in the form of a toxicodynamic variability factor (TDVF). We found that 28 chemicals and all mixtures exhibited concentration–response cytotoxicity, enabling calculation of the TDVF. The median TDVF across test substances, for both individual chemicals or defined mixtures, ranged from a default assumption (101/2) of toxicodynamic variability in human population to >10. The data also provide a proof of principle for single-variant genome-wide association mapping for toxicity of the chemicals and mixtures, although replication would be necessary due to statistical power limitations with the current sample size. This study demonstrates the feasibility of using a set of human lymphoblastoid cell lines as an in vitro model to quantify the extent of inter-individual variability in hazardous properties of both individual chemicals and mixtures. The data show that population variability of the mixtures is unlikely to exceed that of the most variable component, and that similarity in genome-wide associations among components may be used to accrue additional evidence for grouping of constituents in a mixture for cumulative assessments. Full article
(This article belongs to the Special Issue Computational Toxicology: Expanding Frontiers in Risk Assessment)
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19 pages, 1456 KiB  
Article
Use of Biomarker Data and Relative Potencies of Mutagenic Metabolites to Support Derivation of Cancer Unit Risk Values for 1,3-Butadiene from Rodent Tumor Data
by Christopher R. Kirman and Sean M. Hays
Toxics 2022, 10(7), 394; https://doi.org/10.3390/toxics10070394 - 15 Jul 2022
Viewed by 1263
Abstract
Unit Risk (UR) values were derived for 1,3-butadiene (BD) based upon its ability to cause tumors in laboratory mice and rats. Metabolism has been established as the significant molecular initiating event of BD’s carcinogenicity. The large quantitative species differences in the metabolism of [...] Read more.
Unit Risk (UR) values were derived for 1,3-butadiene (BD) based upon its ability to cause tumors in laboratory mice and rats. Metabolism has been established as the significant molecular initiating event of BD’s carcinogenicity. The large quantitative species differences in the metabolism of BD and potency of critical BD epoxide metabolites must be accounted for when rodent toxicity responses are extrapolated to humans. Previously published methods were extended and applied to cancer risk assessments to account for species differences in metabolism, as well as differences in mutagenic potency of BD metabolites within the context of data-derived adjustment factors (DDEFs). This approach made use of biomarker data (hemoglobin adducts) to quantify species differences in the internal doses of BD metabolites experienced in mice, rats, and humans. Using these methods, the dose–response relationships in mice and rats exhibit improved concordance, and result in upper bound UR values ranging from 2.1 × 10−5 to 1.2 × 10−3 ppm−1 for BD. Confidence in these UR values was considered high based on high confidence in the key studies, medium-to-high confidence in the toxicity database, high confidence in the estimates of internal dose, and high confidence in the dose–response modeling. Full article
(This article belongs to the Special Issue Computational Toxicology: Expanding Frontiers in Risk Assessment)
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24 pages, 5007 KiB  
Article
The DevTox Germ Layer Reporter Platform: An Assay Adaptation of the Human Pluripotent Stem Cell Test
by John T. Gamble, Kristen Hopperstad and Chad Deisenroth
Toxics 2022, 10(7), 392; https://doi.org/10.3390/toxics10070392 - 13 Jul 2022
Cited by 1 | Viewed by 2411
Abstract
Environmental chemical exposures are a contributing factor to birth defects affecting infant morbidity and mortality. The USA EPA is committed to developing new approach methods (NAMs) to detect chemical risks to susceptible populations, including pregnant women. NAM-based coverage for cellular mechanisms associated with [...] Read more.
Environmental chemical exposures are a contributing factor to birth defects affecting infant morbidity and mortality. The USA EPA is committed to developing new approach methods (NAMs) to detect chemical risks to susceptible populations, including pregnant women. NAM-based coverage for cellular mechanisms associated with early human development could enhance identification of potential developmental toxicants (DevTox) for new and existing data-poor chemicals. The human pluripotent stem cell test (hPST) is an in vitro test method for rapidly identifying potential human developmental toxicants that employs directed differentiation of embryonic stem cells to measure reductions in SOX17 biomarker expression and nuclear localization. The objective of this study was to expand on the hPST principles to develop a model platform (DevTox GLR) that utilizes the transgenic RUES2-GLR cell line expressing fluorescent reporter fusion protein biomarkers for SOX17 (endoderm marker), BRA (mesoderm marker), and SOX2 (ectoderm and pluripotency marker). Initial assay adaption to definitive endoderm (DevTox GLR-Endo) was performed to emulate the hPST SOX17 endpoint and enable comparative evaluation of concordant chemical effects. Assay duration was reduced to two days and screening throughput scaled to 384-well format for enhanced speed and efficiency. Assay performance for 66 chemicals derived from reference and training set data resulted in a balanced accuracy of 72% (79% sensitivity and 65% specificity). The DevTox GLR-Endo assay demonstrates successful adaptation of the hPST concept with increased throughput, shorter assay duration, and minimal endpoint processing. The DevTox GLR model platform expands the in vitro NAM toolbox to rapidly identify potential developmental hazards and mechanistically characterize toxicant effects on pathways and processes associated with early human development. Full article
(This article belongs to the Special Issue Computational Toxicology: Expanding Frontiers in Risk Assessment)
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18 pages, 1321 KiB  
Article
Update and Evaluation of a High-Throughput In Vitro Mass Balance Distribution Model: IV-MBM EQP v2.0
by James M. Armitage, Alessandro Sangion, Rohan Parmar, Alexandra B. Looky and Jon A. Arnot
Toxics 2021, 9(11), 315; https://doi.org/10.3390/toxics9110315 - 20 Nov 2021
Cited by 14 | Viewed by 2753
Abstract
This study demonstrates the utility of an updated mass balance model for predicting the distribution of organic chemicals in in vitro test systems (IV-MBM EQP v2.0) and evaluates its performance with empirical data. The IV-MBM EQP v2.0 tool was parameterized and applied to [...] Read more.
This study demonstrates the utility of an updated mass balance model for predicting the distribution of organic chemicals in in vitro test systems (IV-MBM EQP v2.0) and evaluates its performance with empirical data. The IV-MBM EQP v2.0 tool was parameterized and applied to four independent data sets with measured ratios of bulk medium or freely-dissolved to initial nominal concentrations (e.g., C24/C0 where C24 is the measured concentration after 24 h of exposure and C0 is the initial nominal concentration). Model performance varied depending on the data set, chemical properties (e.g., “volatiles” vs. “non-volatiles”, neutral vs. ionizable organics), and model assumptions but overall is deemed acceptable. For example, the r2 was greater than 0.8 and the mean absolute error (MAE) in the predictions was less than a factor of two for most neutral organics included. Model performance was not as good for the ionizable organic chemicals included but the r2 was still greater than 0.7 and the MAE less than a factor of three. The IV-MBM EQP v2.0 model was subsequently applied to several hundred chemicals on Canada’s Domestic Substances List (DSL) with nominal effects data (AC50s) reported for two in vitro assays. We report the frequency of chemicals with AC50s corresponding to predicted cell membrane concentrations in the baseline toxicity range (i.e., >20–60 mM) and tabulate the number of chemicals with “volatility issues” (majority of chemical in headspace) and “solubility issues” (freely-dissolved concentration greater than water solubility after distribution). In addition, the predicted “equivalent EQP blood concentrations” (i.e., blood concentration at equilibrium with predicted cellular concentration) were compared to the AC50s as a function of hydrophobicity (log octanol-water partition or distribution ratio). The predicted equivalent EQP blood concentrations exceed the AC50 by up to a factor of 100 depending on hydrophobicity and assay conditions. The implications of using AC50s as direct surrogates for human blood concentrations when estimating the oral equivalent doses using a toxicokinetic model (i.e., reverse dosimetry) are then briefly discussed. Full article
(This article belongs to the Special Issue Computational Toxicology: Expanding Frontiers in Risk Assessment)
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15 pages, 2461 KiB  
Article
Human Chemical Exposure from Background Emissions in the United States and the Implication for Quantifying Risks from Marginal Emission Increase
by Dingsheng Li and Li Li
Toxics 2021, 9(11), 308; https://doi.org/10.3390/toxics9110308 - 15 Nov 2021
Cited by 1 | Viewed by 2914
Abstract
The linear dose–response relationship has long been assumed in assessments of health risk from an incremental chemical emission relative to background emissions. In this study, we systematically examine the relevancy of such an assumption with real-world data. We used the reported emission data, [...] Read more.
The linear dose–response relationship has long been assumed in assessments of health risk from an incremental chemical emission relative to background emissions. In this study, we systematically examine the relevancy of such an assumption with real-world data. We used the reported emission data, as background emissions, from the 2017 U.S. National Emission Inventory for 95 organic chemicals to estimate the central tendencies of exposures of the general U.S. population. Previously published nonlinear dose–response relationships for chemicals were used to estimate health risk from exposure. We also explored and identified four intervals of exposure in which the nonlinear dose–response relationship may be linearly approximated with fixed slopes. Predicted rates of exposure to these 95 chemicals are all within the lowest of the four intervals and associated with low health risk. The health risk may be overestimated if a slope on the dose–response relationship extrapolated from toxicological assays based on high response rates is used for a marginal increase in emission not substantially higher than background emissions. To improve the confidence of human health risk estimates for chemicals, future efforts should focus on deriving a more accurate dose–response relationship at lower response rates and interface it with exposure assessments. Full article
(This article belongs to the Special Issue Computational Toxicology: Expanding Frontiers in Risk Assessment)
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17 pages, 1914 KiB  
Article
The Residential Population Generator (RPGen): Parameterization of Residential, Demographic, and Physiological Data to Model Intraindividual Exposure, Dose, and Risk
by Alexander East, Daniel Dawson, Graham Glen, Kristin Isaacs, Kathie Dionisio, Paul S. Price, Elaine A. Cohen Hubal and Daniel A. Vallero
Toxics 2021, 9(11), 303; https://doi.org/10.3390/toxics9110303 - 12 Nov 2021
Viewed by 2186
Abstract
Exposure to chemicals is influenced by associations between the individual’s location and activities as well as demographic and physiological characteristics. Currently, many exposure models simulate individuals by drawing distributions from population-level data or use exposure factors for single individuals. The Residential Population Generator [...] Read more.
Exposure to chemicals is influenced by associations between the individual’s location and activities as well as demographic and physiological characteristics. Currently, many exposure models simulate individuals by drawing distributions from population-level data or use exposure factors for single individuals. The Residential Population Generator (RPGen) binds US surveys of individuals and households and combines the population with physiological characteristics to create a synthetic population. In general, the model must be supported by internal consistency; i.e., values that could have come from a single individual. In addition, intraindividual variation must be representative of the variation present in the modeled population. This is performed by linking individuals and similar households across income, location, family type, and house type. Physiological data are generated by linking census data to National Health and Nutrition Examination Survey data with a model of interindividual variation of parameters used in toxicokinetic modeling. The final modeled population data parameters include characteristics of the individual’s community (region, state, urban or rural), residence (size of property, size of home, number of rooms), demographics (age, ethnicity, income, gender), and physiology (body weight, skin surface area, breathing rate, cardiac output, blood volume, and volumes for body compartments and organs). RPGen output is used to support user-developed chemical exposure models that estimate intraindividual exposure in a desired population. By creating profiles and characteristics that determine exposure, synthetic populations produced by RPGen increases the ability of modelers to identify subgroups potentially vulnerable to chemical exposures. To demonstrate application, RPGen is used to estimate exposure to Toluene in an exposure modeling case example. Full article
(This article belongs to the Special Issue Computational Toxicology: Expanding Frontiers in Risk Assessment)
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Review

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24 pages, 1324 KiB  
Review
An Overview of Physiologically-Based Pharmacokinetic Models for Forensic Science
by Kiara Fairman, Me-Kyoung Choi, Pavani Gonnabathula, Annie Lumen, Andrew Worth, Alicia Paini and Miao Li
Toxics 2023, 11(2), 126; https://doi.org/10.3390/toxics11020126 - 28 Jan 2023
Cited by 4 | Viewed by 3076
Abstract
A physiologically-based pharmacokinetic (PBPK) model represents the structural components of the body with physiologically relevant compartments connected via blood flow rates described by mathematical equations to determine drug disposition. PBPK models are used in the pharmaceutical sector for drug development, precision medicine, and [...] Read more.
A physiologically-based pharmacokinetic (PBPK) model represents the structural components of the body with physiologically relevant compartments connected via blood flow rates described by mathematical equations to determine drug disposition. PBPK models are used in the pharmaceutical sector for drug development, precision medicine, and the chemical industry to predict safe levels of exposure during the registration of chemical substances. However, one area of application where PBPK models have been scarcely used is forensic science. In this review, we give an overview of PBPK models successfully developed for several illicit drugs and environmental chemicals that could be applied for forensic interpretation, highlighting the gaps, uncertainties, and limitations. Full article
(This article belongs to the Special Issue Computational Toxicology: Expanding Frontiers in Risk Assessment)
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38 pages, 1950 KiB  
Review
IVIVE: Facilitating the Use of In Vitro Toxicity Data in Risk Assessment and Decision Making
by Xiaoqing Chang, Yu-Mei Tan, David G. Allen, Shannon Bell, Paul C. Brown, Lauren Browning, Patricia Ceger, Jeffery Gearhart, Pertti J. Hakkinen, Shruti V. Kabadi, Nicole C. Kleinstreuer, Annie Lumen, Joanna Matheson, Alicia Paini, Heather A. Pangburn, Elijah J. Petersen, Emily N. Reinke, Alexandre J. S. Ribeiro, Nisha Sipes, Lisa M. Sweeney, John F. Wambaugh, Ronald Wange, Barbara A. Wetmore and Moiz Mumtazadd Show full author list remove Hide full author list
Toxics 2022, 10(5), 232; https://doi.org/10.3390/toxics10050232 - 01 May 2022
Cited by 34 | Viewed by 10961
Abstract
During the past few decades, the science of toxicology has been undergoing a transformation from observational to predictive science. New approach methodologies (NAMs), including in vitro assays, in silico models, read-across, and in vitro to in vivo extrapolation (IVIVE), are being developed to [...] Read more.
During the past few decades, the science of toxicology has been undergoing a transformation from observational to predictive science. New approach methodologies (NAMs), including in vitro assays, in silico models, read-across, and in vitro to in vivo extrapolation (IVIVE), are being developed to reduce, refine, or replace whole animal testing, encouraging the judicious use of time and resources. Some of these methods have advanced past the exploratory research stage and are beginning to gain acceptance for the risk assessment of chemicals. A review of the recent literature reveals a burst of IVIVE publications over the past decade. In this review, we propose operational definitions for IVIVE, present literature examples for several common toxicity endpoints, and highlight their implications in decision-making processes across various federal agencies, as well as international organizations, including those in the European Union (EU). The current challenges and future needs are also summarized for IVIVE. In addition to refining and reducing the number of animals in traditional toxicity testing protocols and being used for prioritizing chemical testing, the goal to use IVIVE to facilitate the replacement of animal models can be achieved through their continued evolution and development, including a strategic plan to qualify IVIVE methods for regulatory acceptance. Full article
(This article belongs to the Special Issue Computational Toxicology: Expanding Frontiers in Risk Assessment)
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25 pages, 4117 KiB  
Conference Report
Advancing New Approach Methodologies (NAMs) for Tobacco Harm Reduction: Synopsis from the 2021 CORESTA SSPT—NAMs Symposium
by Kyeonghee Monica Lee, Richard Corley, Annie M. Jarabek, Nicole Kleinstreuer, Alicia Paini, Andreas O. Stucki and Shannon Bell
Toxics 2022, 10(12), 760; https://doi.org/10.3390/toxics10120760 - 06 Dec 2022
Viewed by 2899
Abstract
New approach methodologies (NAMs) are emerging chemical safety assessment tools consisting of in vitro and in silico (computational) methodologies intended to reduce, refine, or replace (3R) various in vivo animal testing methods traditionally used for risk assessment. Significant progress has been made toward [...] Read more.
New approach methodologies (NAMs) are emerging chemical safety assessment tools consisting of in vitro and in silico (computational) methodologies intended to reduce, refine, or replace (3R) various in vivo animal testing methods traditionally used for risk assessment. Significant progress has been made toward the adoption of NAMs for human health and environmental toxicity assessment. However, additional efforts are needed to expand their development and their use in regulatory decision making. A virtual symposium was held during the 2021 Cooperation Centre for Scientific Research Relative to Tobacco (CORESTA) Smoke Science and Product Technology (SSPT) conference (titled “Advancing New Alternative Methods for Tobacco Harm Reduction”), with the goals of introducing the concepts and potential application of NAMs in the evaluation of potentially reduced-risk (PRR) tobacco products. At the symposium, experts from regulatory agencies, research organizations, and NGOs shared insights on the status of available tools, strengths, limitations, and opportunities in the application of NAMs using case examples from safety assessments of chemicals and tobacco products. Following seven presentations providing background and application of NAMs, a discussion was held where the presenters and audience discussed the outlook for extending the NAMs toxicological applications for tobacco products. The symposium, endorsed by the CORESTA In Vitro Tox Subgroup, Biomarker Subgroup, and NextG Tox Task Force, illustrated common ground and interest in science-based engagement across the scientific community and stakeholders in support of tobacco regulatory science. Highlights of the symposium are summarized in this paper. Full article
(This article belongs to the Special Issue Computational Toxicology: Expanding Frontiers in Risk Assessment)
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10 pages, 259 KiB  
Commentary
Integrating Multiscale Geospatial Environmental Data into Large Population Health Studies: Challenges and Opportunities
by Yuxia Cui, Kristin M. Eccles, Richard K. Kwok, Bonnie R. Joubert, Kyle P. Messier and David M. Balshaw
Toxics 2022, 10(7), 403; https://doi.org/10.3390/toxics10070403 - 20 Jul 2022
Cited by 4 | Viewed by 2065
Abstract
Quantifying the exposome is key to understanding how the environment impacts human health and disease. However, accurately, and cost-effectively quantifying exposure in large population health studies remains a major challenge. Geospatial technologies offer one mechanism to integrate high-dimensional environmental data into epidemiology studies, [...] Read more.
Quantifying the exposome is key to understanding how the environment impacts human health and disease. However, accurately, and cost-effectively quantifying exposure in large population health studies remains a major challenge. Geospatial technologies offer one mechanism to integrate high-dimensional environmental data into epidemiology studies, but can present several challenges. In June 2021, the National Institute of Environmental Health Sciences (NIEHS) held a workshop bringing together experts in exposure science, geospatial technologies, data science and population health to address the need for integrating multiscale geospatial environmental data into large population health studies. The primary objectives of the workshop were to highlight recent applications of geospatial technologies to examine the relationships between environmental exposures and health outcomes; identify research gaps and discuss future directions for exposure modeling, data integration and data analysis strategies; and facilitate communications and collaborations across geospatial and population health experts. This commentary provides a high-level overview of the scientific topics covered by the workshop and themes that emerged as areas for future work, including reducing measurement errors and uncertainty in exposure estimates, and improving data accessibility, data interoperability, and computational approaches for more effective multiscale and multi-source data integration, along with potential solutions. Full article
(This article belongs to the Special Issue Computational Toxicology: Expanding Frontiers in Risk Assessment)
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