Advancing chemical risk assessment decision-making with population variability data: challenges and opportunities

Next generation risk assessment of hair dye HC yellow no. 13: Ensuring protection from liver steatogenic effects

This study employs animal-free Next Generation Risk Assessment (NGRA) principles to evaluate the safety of repeated dermal exposure to 2.5% (w/w) HC Yellow No. 13 (HCY13) hair dye. As multiple in silico tools consistently flagged hepatotoxic potential, likely due to HCY13's trifluoromethyl group, which is known to interfere with hepatic lipid metabolism, liver steatosis was chosen as the primary mode of action for evaluation. AOP-guided in vitro tests were conducted, exposing human stem cell-derived hepatic cells to varying HCY13 concentrations over 72 h. The expression of 11 lipid metabolism-related marker genes (AHR, PPARA, LXRA, APOB, ACOX1, CPT1A, FASN, SCD1, DGAT2, CD36, and PPARG) and triglyceride accumulation, a phenotypic hallmark of steatosis, were measured. PROAST software was used to calculate in vitro Points of Departure (PoDNAM) for each biomarker. Using GastroPlus 9.9, physiologically-based pharmacokinetic (PBPK) models estimated internal liver concentrations (Cmax liver) of HCY13, ranging from 4 to 20 pM. All PoDNAM values significantly exceeded the predicted Cmax liver, indicating that HCY13 at 2.5% (w/w) is unlikely to induce liver steatosis under the assumed conditions. This research demonstrates the utility of NGRA, integrating AOP-based in vitro assays and computational models to protect human health and support regulatory decision-making without animal testing.

Biomonitoring-Based Risk Assessment of Pyrethroid Exposure in the U.S. Population: Application of High-Throughput and Physiologically Based Kinetic Models

Pyrethroid insecticides have been extensively utilized in agriculture and residential areas in the United States. This study evaluated the exposure risk by age using available biomonitoring data. We analyzed pyrethroid metabolite concentrations in urine using the National Health and Nutrition Examination Survey (NHANES) data. Reverse dosimetry was conducted with a high-throughput model and a physiologically based kinetic (PBK) model integrated with a Bayesian inference framework. We further derived Benchmark Dose (BMD) values and systemic points of departure in rats using Bayesian BMD and PBK models. Margins of exposure (MOE) were calculated to assess neurotoxic risk based on estimated daily oral intake and dose metrics in plasma and brain. Results from both models indicated that young children have higher pyrethroid exposure compared to other age groups. All estimated risk values were within acceptable levels of acute neurotoxic effect. Additionally, MOEs calculated from oral doses were lower than those derived from internal doses, highlighting that traditional external exposure assessments tend to overestimate risk compared to advanced internal dose-based techniques. In conclusion, combining high-throughput and PBK approaches enhances the understanding of human health risks associated with pyrethroid exposures, demonstrating their potential for future applications in exposure tracking and health risk assessment.

Ethics, One Health approaches, and SDGs: conference lessons for an emerging field

One Health ethics is an emerging field that has gained traction since its origin in approximately 2015. This article builds upon the insights shared during a panel discussion on One Health, Sustainable Development Goals (SDGs), and ethical conflicts at the 28th Annual International Sustainable Development Research Society Conference. The conference, themed Sustainable Development and Courage: Culture, Art, and Human Rights, aimed to advance and expand recent knowledge in the field. Key themes discussed during the conference panel included interdisciplinarity and multidisciplinary, risk, resilience, wicked problems with no readily available solutions, and praxis. A conclusion is that ethics should become more prominent within One Health discussions. Four aspects emerged from this discussion: (1) Ethics is needed to solve wicked problems within One Health approaches. (2) Aspects of multi-, inter-, and transdisciplinarity need to be considered together with their implications for ethics. (3) Two crucial concepts, risk and resilience, need to be addressed. (4) Ethical decision models are called for and need to be developed.

Data-Driven Characterization of Genetic Variability in Disease Pathways and Pesticide-Induced Nervous System Disease in the United States Population

BACKGROUND

Genetic susceptibility to chemicals is incompletely characterized. However, nervous system disease development following pesticide exposure can vary in a population, implying some individuals may have higher genetic susceptibility to pesticide-induced nervous system disease.

OBJECTIVES

We aimed to build a computational approach to characterize single-nucleotide polymorphisms (SNPs) implicated in chemically induced adverse outcomes and used this framework to assess the link between differential population susceptibility to pesticides and human nervous system disease.

METHODS

We integrated publicly available datasets of Chemical-Gene, Gene-Pathway, and SNP-Disease associations to build Chemical-Pathway-Gene-SNP-Disease linkages for humans. As a case study, we integrated these linkages with spatialized pesticide application data for the US from 1992 to 2018 and spatialized nervous system disease rates for 2018. Through this, we characterized SNPs that may be important in states with high disease occurrence based on the pesticides used there.

RESULTS

We found that the number of SNP hits per pesticide in US states positively correlated with disease incidence and prevalence for Alzheimer's disease, Parkinson disease, and multiple sclerosis. We performed frequent itemset mining to differentiate pesticides used over time in states with high and low disease occurrence and found that only 19% of pesticide sets overlapped between 10 states with high disease occurrence and 10 states with low disease occurrence rates, and more SNPs were implicated in pathways in high disease occurrence states. Through a cross-validation of subsets of five high and low disease occurrence states, we characterized SNPs, genes, pathways, and pesticides more frequently implicated in high disease occurrence states.

DISCUSSION

Our findings support that pesticides contribute to nervous system disease, and we developed priority lists of SNPs, pesticides, and pathways for further study. This data-driven approach can be adapted to other chemicals, diseases, and locations to characterize differential population susceptibility to chemical exposures. https://doi.org/10.1289/EHP14108.

Advancing probabilistic risk assessment by integrating human biomonitoring, new approach methods, and Bayesian modeling: A case study with the mycotoxin deoxynivalenol

Deoxynivalenol (DON) is a mycotoxin frequently observed in cereals and cereal-based foods, with reported toxicological effects including reduced body weight, immunotoxicity and reproductive defects. The European Food Safety Authority used traditional risk assessment approaches to derive a deterministic Tolerable Daily Intake (TDI) of 1 μg/kg-day, however data from human biomarkers studies indicate widespread and variable exposure worldwide, necessitating more sophisticated and advanced methods to quantify population risk. The World Health Organization/International Programme on Chemical Safety (WHO/IPCS) has previously used DON as a case example in replacing the TDI with a probabilistic toxicity value, using default uncertainty and variability distributions to derive the Human Dose corresponding to an effect size M in the Ith percentile of the population (HDMI) for M = 5 % decrease in body weight and I = 1 %. In this study, we extend this case study by incorporating (1) Bayesian modeling approaches, (2) using both in vivo data and in vitro population new approach methods to replace default distributions for interspecies toxicokinetic (TK) differences and intraspecies TK and toxicodynamic (TD) variability, and (3) integrating biomonitoring data and probabilistic dose-response functions to characterize population risk distributions. We first derive an HDMI of 5.5 [1.4-24] μg/kg-day, also using TK modeling to converted the HDMI to Biomonitoring Equivalents, BEMI for comparison with biomonitoring data, with a blood BEMI of 0.53 [0.17-1.6] μg/L and a urinary excretion BEMI of 3.9 [1.0-16] μg/kg-day. We then illustrate how this integrative approach can advance quantitative risk characterization using two human biomonitoring datasets, estimating both the fraction of population with an effect size M ≥ 5 % as well as the distribution of effect sizes. Overall, we demonstrate that integration of Bayesian modeling, human biomonitoring data, and in vitro population-based TD data within the WHO/IPCS probabilistic framework yields more accurate, precise, and comprehensive risk characterization.

Current practice and recommendations for advancing how human variability and susceptibility are considered in chemical risk assessment

A key element of risk assessment is accounting for the full range of variability in response to environmental exposures. Default dose-response methods typically assume a 10-fold difference in response to chemical exposures between average (healthy) and susceptible humans, despite evidence of wider variability. Experts and authoritative bodies support using advanced techniques to better account for human variability due to factors such as in utero or early life exposure and exposure to multiple environmental, social, and economic stressors.This review describes: 1) sources of human variability and susceptibility in dose-response assessment, 2) existing US frameworks for addressing response variability in risk assessment; 3) key scientific inadequacies necessitating updated methods; 4) improved approaches and opportunities for better use of science; and 5) specific and quantitative recommendations to address evidence and policy needs.Current default adjustment factors do not sufficiently capture human variability in dose-response and thus are inadequate to protect the entire population. Susceptible groups are not appropriately protected under current regulatory guidelines. Emerging tools and data sources that better account for human variability and susceptibility include probabilistic methods, genetically diverse in vivo and in vitro models, and the use of human data to capture underlying risk and/or assess combined effects from chemical and non-chemical stressors.We recommend using updated methods and data to improve consideration of human variability and susceptibility in risk assessment, including the use of increased default human variability factors and separate adjustment factors for capturing age/life stage of development and exposure to multiple chemical and non-chemical stressors. Updated methods would result in greater transparency and protection for susceptible groups, including children, infants, people who are pregnant or nursing, people with disabilities, and those burdened by additional environmental exposures and/or social factors such as poverty and racism.

Cumulative Risk Meets Inter-Individual Variability: Probabilistic Concentration Addition of Complex Mixture Exposures in a Population-Based Human In Vitro Model

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.

A high-throughput 384-well CometChip platform reveals a role for 3-methyladenine in the cellular response to etoposide-induced DNA damage

The Comet or single-cell gel electrophoresis assay is a highly sensitive method to measure cellular, nuclear genome damage. However, low throughput can limit its application for large-scale studies. To overcome these limitations, a 96-well CometChip platform was recently developed that increases throughput and reduces variation due to simultaneous processing and automated analysis of 96 samples. To advance throughput further, we developed a 384-well CometChip platform that allows analysis of ∼100 cells per well. The 384-well CometChip extends the capacity by 4-fold as compared to the 96-well system, enhancing application for larger DNA damage analysis studies. The overall sensitivity of the 384-well CometChip is consistent with that of the 96-well system, sensitive to genotoxin exposure and to loss of DNA repair capacity. We then applied the 384-well platform to screen a library of protein kinase inhibitors to probe each as enhancers of etoposide induced DNA damage. Here, we found that 3-methyladenine significantly increased levels of etoposide-induced DNA damage. Our results suggest that a 384-well CometChip is useful for large-scale DNA damage analyses, which may have increased potential in the evaluation of chemotherapy efficacy, compound library screens, population-based analyses of genome damage and evaluating the impact of environmental genotoxins on genome integrity.

A Population-Based Human In Vitro Approach to Quantify Inter-Individual Variability in Responses to Chemical Mixtures

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.

Beyond pharmaceuticals: Fit-for-purpose new approach methodologies for environmental cardiotoxicity testing

Environmental factors play a substantial role in determining cardiovascular health, but data informing the risks presented by environmental toxicants is insufficient. In vitro new approach methodologies (NAMs) offer a promising approach with which to address the limitations of traditional in vivo and in vitro assays for assessing cardiotoxicity. Driven largely by the needs of pharmaceutical toxicity testing, considerable progress in developing NAMs for cardiotoxicity analysis has already been made. As the scientific and regulatory interest in NAMs for environmental chemicals continues to grow, a thorough understanding of the unique features of environmental cardiotoxicants and their associated cardiotoxicities is needed. Here, we review the key characteristics of as well as important regulatory and biological considerations for fit-for-purpose NAMs for environmental cardiotoxicity. By emphasizing the challenges and opportunities presented by NAMs for environmental cardiotoxicity we hope to accelerate their development, acceptance, and application.

A tiered approach to population-based in vitro testing for cardiotoxicity: Balancing estimates of potency and variability

Population-wide in vitro studies for characterization of cardiotoxicity hazard, risk, and population variability show that human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CM) are a powerful and high-throughput testing platform for drugs and environmental chemicals alike. However, studies in multiple donor-derived hiPSC-CMs, across large libraries of chemicals tested in concentration-response are technically complex, and study design optimization is needed to determine sufficient and fit-for-purpose population size considerations. Therefore, we tested a hypothesis that a computational down-sampling analysis based on the data from hiPSC-CM screening of 136 diverse compounds in a population of 43 non-diseased donors, including multiple replicates of the "standard" donor hiPSC-CMs, will inform optimal study designs depending on the decision context (hazard, risk and/or inter-individual variability in cardiotoxicity). Through 50 independent random subsamples of 5, 10, or 20 donors, we estimated accuracy and precision for quantifying potency, inter-individual variability, and QT prolongation risk; the results were compared to the full 43-donor cohort. We found that for potency and clinical risk of QT prolongation, a cohort of 5 randomly-selected unique donors provides accurate and precise estimates. Larger cohort sizes afforded marginal improvements, and 5 replicates of a single donor performed worse. For estimating inter-individual variability, cohorts of at least 20 donors are needed, with smaller populations on average showing bias towards underestimation in population variance. Collectively, this study shows that a variable-size hiPSC-CM-based population-wide in vitro model can be used in a number of decision scenarios for identifying cardiotoxic hazards of drugs and environmental chemicals in the population context.

A new approach method for characterizing inter-species toxicodynamic variability

Inter-species differences in toxicodynamics are often a critical source of uncertainty in safety evaluations and typically dealt with using default adjustment factors. In vitro studies that use cells from different species demonstrated some success for estimating the relationships between life span and/or body weight and sensitivity to cytotoxicity; however, no apparent investigation evaluated the utility of these models for risk assessment. It was hypothesized that an in vitro model using dermal fibroblasts derived from diverse species and individuals might be utilized to inform the extent of inter-species and inter-individual variability in toxicodynamics. To test this hypothesis and characterize both inter-species and inter-individual variability in cytotoxicity, concentration-response cytotoxicity screening of 40 chemicals in primary dermal fibroblasts from 68 individuals of 54 diverse species was conducted. Chemicals examined included drugs, environmental pollutants, and food/flavor/fragrance agents; most of these were previously assessed either in vivo or in vitro for inter-species or inter-individual variation. Species included humans, the typical preclinical species and representatives from other orders of mammals and birds. Data demonstrated that both inter-species and inter-individual components of variability contribute to the observed differences in sensitivity to cell death. Further, it was found that the magnitude of the observed inter-species and inter-individual differences was chemical-dependent. This study contributes to the paradigm shift in risk assessment from reliance on in vivo toxicity testing to higher-throughput in vitro or alternative approaches, extending the strategy to replace use of default adjustment factors with experimental characterization of toxicodynamic inter-individual variability and to also address toxicodynamic inter-species variability.

Intra- and Inter-Species Variability in Urinary N7-(1-Hydroxy-3-buten-2-yl)guanine Adducts Following Inhalation Exposure to 1,3-Butadiene

1,3-Butadiene is a known carcinogen primarily targeting lymphoid tissues, lung, and liver. Cytochrome P450 activates butadiene to epoxides which form covalent DNA adducts that are thought to be a key mechanistic event in cancer. Previous studies suggested that inter-species, -tissue, and -individual susceptibility to adverse health effects of butadiene exposure may be due to differences in metabolism and other mechanisms. In this study, we aimed to examine the extent of inter-individual and inter-species variability in the urinary N7-(1-hydroxy-3-buten-2-yl)guanine (EB-GII) DNA adduct, a well-known biomarker of exposure to butadiene. For a population variability study in mice, we used the collaborative cross model. Female and male mice from five strains were exposed to filtered air or butadiene (590 ppm, 6 h/day, 5 days/week for 2 weeks) by inhalation. Urine samples were collected, and the metabolic activation of butadiene by DNA-reactive species was quantified as urinary EB-GII adducts. We quantified the degree of EB-GII variation across mouse strains and sexes; then, we compared this variation with the data from rats (exposed to 62.5 or 200 ppm butadiene) and humans (0.004-2.2 ppm butadiene). We show that sex and strain are significant contributors to the variability in urinary EB-GII levels in mice. In addition, we find that the degree of variability in urinary EB-GII in collaborative cross mice, when expressed as an uncertainty factor for the inter-individual variability (UF_H), is relatively modest (≤threefold) possibly due to metabolic saturation. By contrast, the variability in urinary EB-GII (adjusted for exposure) observed in humans, while larger than the default value of 10-fold, is largely consistent with UF_H estimates for other chemicals based on human data for non-cancer endpoints. Overall, these data demonstrate that urinary EB-GII levels, particularly from human studies, may be useful for quantitative characterization of human variability in cancer risks to butadiene.

CRISPR approach in environmental chemical screening focusing on population variability

A significant barrier to include population variability in risk assessment is our incomplete understanding of inter-individual variability and the differential susceptibility to environmental exposures induced adverse outcomes. By combining genome editing tools with the population diversity model, this article intended to highlight a potential strategy to identify and characterize the inter-individual variability factors, the determinant gene anchoring to a particular phenotype. The goal could be achieved by integrating the perturbed CRISPR-based unbiased functional genomics screening, genome-wide or a focused subset of genes, in a population-based in vitro model system (such as the lymphoblastoid cell lines, LCL, available from HapMap and 1000 Genomes project). Then data can be translated to genetic variability and individual (or subpopulation) susceptibility by incorporating ethnicity and corresponding genome-wide association studies (GWAS) with functional genomics screening results. This approach can provide complementary data for next-generation risk assessment, in particular, for environmental stressors. The current paper outlined the previous work conducted with a population-based in vitro model system, perturbed CRISPR-based functional toxicogenomic screening of environmental chemicals, and finally, the potential strategies to combine these two platforms with their opportunities and challenges to achieve a mechanistic understanding of population variability.

Human-relevant mechanisms and risk factors for TAK-875-Induced liver injury identified via a gene pathway-based approach in Collaborative Cross mice

Development of TAK-875 was discontinued when a small number of serious drug-induced liver injury (DILI) cases were observed in Phase 3 clinical trials. Subsequent studies have identified hepatocellular oxidative stress, mitochondrial dysfunction, altered bile acid homeostasis, and immune response as mechanisms of TAK-875 DILI and the contribution of genetic risk factors in oxidative response and mitochondrial pathways to the toxicity susceptibility observed in patients. We tested the hypothesis that a novel preclinical approach based on gene pathway analysis in the livers of Collaborative Cross mice could be used to identify human-relevant mechanisms of toxicity and genetic risk factors at the level of the hepatocyte as reported in a human genome-wide association study. Eight (8) male mice (4 matched pairs) from each of 45 Collaborative Cross lines were treated with a single oral (gavage) dose of either vehicle or 600 mg/kg TAK-875. As expected, liver injury was not detected histologically and few changes in plasma biomarkers of hepatotoxicity were observed. However, gene expression profiling in the liver identified hundreds of transcripts responsive to TAK-875 treatment across all strains reflecting alterations in immune response and bile acid homeostasis and the interaction of treatment and strain reflecting oxidative stress and mitochondrial dysfunction. Fold-change expression values were then used to develop pathway-based phenotypes for genetic mapping which identified candidate risk factor genes for TAK-875 toxicity susceptibility at the level of the hepatocyte. Taken together, these findings support our hypothesis that a gene pathway-based approach using Collaborative Cross mice could inform sensitive strains, human-relevant mechanisms of toxicity, and genetic risk factors for TAK-875 DILI. This novel preclinical approach may be helpful in understanding, predicting, and ultimately preventing clinical DILI for other drugs.

Distinct genetic regions are associated with differential population susceptibility to chemical exposures

Interactions between environmental factors and genetics underlie the majority of chronic human diseases. Chemical exposures are likely an underestimated contributor, yet gene-environment (GxE) interaction studies rarely assess their modifying effects. Here, we describe a novel method to profile the human genome and identify regions associated with differential population susceptibility to chemical exposures. Single nucleotide polymorphisms (SNPs) implicated in enriched chemical-disease intersections were identified and validated for three chemical classes with expected GxE interaction potential (neuroactive, hepatoactive, and cardioactive compounds). The same approach was then used to characterize consumer product classes with unknown risk for GxE interactions (washing products, cosmetics, and adhesives). Additionally, high-risk variant sets that may confer differential population susceptibility were identified for these consumer product groups through frequent itemset mining and pathway analysis. A dataset of 2454 consumer product chemical-disease linkages, with risk values, SNPs, and pathways for each association was developed, describing the interplay between environmental factors and genetics in human disease progression. We found that genetic hotspots implicated in GxE interactions differ across chemical classes (e.g., washing products had high-risk SNPs implicated in nervous system disease) and illustrate how this approach can discover new associations (e.g., washing product n-butoxyethanol implicated SNPs in the PI3K-Akt signaling pathway for Alzheimer's disease). Hence, our approach can predict high-risk genetic regions for differential population susceptibility to chemical exposures and characterize chemical modifying factors in specific diseases. These methods show promise for describing how chemical exposures can lead to varied health outcomes in a population and for incorporating inter-individual variability into chemical risk assessment.

Recent Advances in Probabilistic Dose-Response Assessment to Inform Risk-Based Decision Making

Paradoxically, risk assessments for the majority of chemicals lack any quantitative characterization as to the likelihood, incidence, or severity of the risks involved. The relatively few cases where "risk" is truly quantified are based on either epidemiologic data or extrapolation of experimental animal cancer bioassay data. The paucity of chemicals and health endpoints for which such data are available severely limits the ability of decisionmakers to account for the impacts of chemical exposures on human health. The development by the World Health Organization International Programme on Chemical Safety (WHO/IPCS) in 2014 of a comprehensive framework for probabilistic dose-response assessment has opened the door to a myriad of potential advances to better support decision making. Building on the pioneering work of Evans, Hattis, and Slob from the 1990s, the WHO/IPCS framework provides both a firm conceptual foundation as well as practical implementation tools to simultaneously assess uncertainty, variability, and severity of effect as a function of exposure. Moreover, such approaches do not depend on the availability of epidemiologic data, nor are they limited to cancer endpoints. Recent work has demonstrated the broad feasibility of such approaches in order to estimate the functional relationship between exposure level and the incidence or severity of health effects. While challenges remain, such as better characterization of the relationship between endpoints observed in experimental animal or in vitro studies and human health effects, the WHO/IPCS framework provides a strong basis for expanding the breadth of risk management decision contexts supported by chemical risk assessment.

Rapid hazard characterization of environmental chemicals using a compendium of human cell lines from different organs

The lack of adequate toxicity data for the vast majority of chemicals in the environment has spurred the development of new approach methodologies (NAMs). This study aimed to develop a practical high-throughput in vitro model for rapidly evaluating potential hazards of chemicals using a small number of human cells. Forty-two compounds were tested using human induced pluripotent stem cell (iPSC)-derived cells (hepatocytes, neurons, cardiomyocytes and endothelial cells), and a primary endothelial cell line. Both functional and cytotoxicity endpoints were evaluated using high-content imaging. Concentration-response was used to derive points-of-departure (POD). PODs were integrated with ToxPi and used as surrogate NAM-based PODs for risk characterization. We found chemical class-specific similarity among the chemicals tested; metal salts exhibited the highest overall bioactivity. We also observed cell type-specific patterns among classes of chemicals, indicating the ability of the proposed in vitro model to recognize effects on different cell types. Compared to available NAM datasets, such as ToxCast/Tox21 and chemical structure-based descriptors, we found that the data from the five-cell-type model was as good or even better in assigning compounds to chemical classes. Additionally, the PODs from this model performed well as a conservative surrogate for regulatory in vivo PODs and were less likely to underestimate in vivo potency and potential risk compared to other NAM-based PODs. In summary, we demonstrate the potential of this in vitro screening model to inform rapid risk-based decision-making through ranking, clustering, and assessment of both hazard and risks of diverse environmental chemicals.

PBPK modeling of impact of nonalcoholic fatty liver disease on toxicokinetics of perchloroethylene in mice

BACKGROUND

Nonalcoholic fatty liver disease (NAFLD), a major cause of chronic liver disease in the Western countries with increasing prevalence worldwide, may substantially affect chemical toxicokinetics and thereby modulate chemical toxicity.

OBJECTIVES

This study aims to use physiologically-based pharmacokinetic (PBPK) modeling to characterize the impact of NAFLD on toxicokinetics of perchloroethylene (perc).

METHODS

Quantitative measures of physiological and biochemical changes associated with the presence of NAFLD induced by high-fat or methionine/choline-deficient diets in C57B1/6 J mice are incorporated into a previously developed PBPK model for perc and its oxidative and conjugative metabolites. Impacts on liver fat and volume, as well as blood:air and liver:air partition coefficients, are incorporated into the model. Hierarchical Bayesian population analysis using Markov chain Monte Carlo simulation is conducted to characterize uncertainty, as well as disease-induced variability in toxicokinetics.

RESULTS

NAFLD has a major effect on toxicokinetics of perc, with greater oxidative and lower conjugative metabolism as compared to healthy mice. The NAFLD-updated PBPK model accurately predicts in vivo metabolism of perc through oxidative and conjugative pathways in all tissues across disease states and strains, but underestimated parent compound concentrations in blood and liver of NAFLD mice.

CONCLUSIONS

We demonstrate the application of PBPK modeling to predict the effects of pre-existing disease conditions as a variability factor in perc metabolism. These results suggest that non-genetic factors such as diet and pre-existing disease can be as influential as genetic factors in altering toxicokinetics of perc, and thus are likely contribute substantially to population variation in its adverse effects.

Population-Based Analysis of DNA Damage and Epigenetic Effects of 1,3-Butadiene in the Mouse

Metabolism of 1,3-butadiene, a known human and rodent carcinogen, results in formation of reactive epoxides, a key event in its carcinogenicity. Although mice exposed to 1,3-butadiene present DNA adducts in all tested tissues, carcinogenicity is limited to liver, lung, and lymphoid tissues. Previous studies demonstrated that strain- and tissue-specific epigenetic effects in response to 1,3-butadiene exposure may influence susceptibly to DNA damage and serve as a potential mechanism of tissue-specific carcinogenicity. This study aimed to investigate interindividual variability in the effects of 1,3-butadiene using a population-based mouse model. Male mice from 20 Collaborative Cross strains were exposed to 0 or 635 ppm 1,3-butadiene by inhalation (6 h/day, 5 days/week) for 2 weeks. We evaluated DNA damage and epigenetic effects in target (lung and liver) and nontarget (kidney) tissues of 1,3-butadiene-induced carcinogenesis. DNA damage was assessed by measuring N-7-(2,3,4-trihydroxybut-1-yl)-guanine (THB-Gua) adducts. To investigate global histone modification alterations, we evaluated the trimethylation and acetylation of histones H3 and H4 across tissues. Changes in global cytosine DNA methylation were evaluated from the levels of methylation of LINE-1 and SINE B1 retrotransposons. We quantified the degree of variation across strains, deriving a chemical-specific human variability factor to address population variability in carcinogenic risk, which is largely ignored in current cancer risk assessment practice. Quantitative trait locus mapping identified four candidate genes related to chromatin remodeling whose variation was associated with interstrain susceptibility. Overall, this study uses 1,3-butadiene to demonstrate how the Collaborative Cross mouse population can be used to identify the mechanisms for and quantify the degree of interindividual variability in tissue-specific effects that are relevant to chemically induced carcinogenesis.

Initial Assessment of Variability of Responses to Toxicants in Donor-Specific Endothelial Colony Forming Cells

There is increased interest in using high throughput in vitro assays to characterize human population variability in response to toxicants and drugs. Utilizing primary human endothelial colony-forming cells (ECFCs) isolated from blood would be highly useful for this purpose because these cells are involved in neonatal and adult vasculogenesis. We characterized the cytotoxicity of four known toxic chemicals (NaAsO2, CdCl2, tributyltin [TBT], and menadione) and their four relatively nontoxic counterparts (Na2HAsO4, ZnCl2, SnCl2, and phytonadione, respectively) in eight ECFC clones representing four neonatal donors (2 male and 2 female donors, 2 clones per donor). ECFCs were exposed to 9 concentrations of each chemical in duplicate; cell viability was evaluated 48 h later using the fluorescent vital dye fluorescent dye 5-Carboxyfluorescein Diacetate (CFDA), yielding concentration-effect curves from each experiment. Technical (day-to-day) variability of the assay, assessed from three independent experiments, was low: p-values for the differences of results were 0.74 and 0.64 for the comparison of day 2 vs. day 1 and day 3 vs. day 1, respectively. The statistical analysis used to compare the entire concentration-effect curves has revealed significant differences in levels of cytotoxicity induced by the toxic and relatively nontoxic chemical counterparts, demonstrating that donor-specific ECFCs can clearly differentiate between these two groups of chemicals. Partitioning of the total variance in the nested design assessed the contributions of between-clone and between-donor variability for different levels of cytotoxicity. Individual ECFC clones demonstrated highly reproducible responses to the chemicals. The most toxic chemical was TBT, followed by NaAsO2, CdCl2, and Menadione. Nontoxic counterparts exhibited low cytotoxicity at the higher end of concentration ranges tested. Low variability was observed between ECFC clones obtained from the same donor or different donors for CdCl2, NaAsO2, and TBT, but for menadione, the between-donor variability was much greater than the between-clone variability. The low between-clone variability indicates that an ECFC clone may represent an individual donor in cell-based assays, although this finding must be confirmed using a larger number of donors. Such confirmation would demonstrate that an in vitro ECFC-based testing platform can be used to characterize the inter-individual variability of neonatal ECFCs exposed to drugs and/or environmental toxicants.

Mouse Population-Based Approaches to Investigate Adverse Drug Reactions

Genetic variation is now recognized as a key factor in the toxicity of pharmaceutical agents. However, genetic diversity is not present in standard nonclinical toxicology models, and small clinical studies (phase I/II) may not include enough subjects to identify toxicity liabilities associated with less common susceptibility factors. As a result, many drugs pass through preclinical and early clinical studies before safety concerns are realized. Furthermore, when adverse drug reactions are idiosyncratic in nature, suggesting a role for rare genetic variants in the toxicity susceptibility, even large clinical studies (phase III) are often underpowered (due to low population frequency and/or small effect size of the risk factor) to identify associations that may be used for precision medicine risk mitigation strategies. Genetically diverse mouse populations can be used to help overcome the limitations of standard nonclinical and clinical studies and to model toxicity responses that require genetic susceptibility factors. Furthermore, mouse population-based approaches can be used to: 1) identify sensitive strains that can serve as a screening tool for next-in-class compounds, 2) identify genetic susceptibility factors that can be used for risk mitigation strategies, and 3) study mechanisms underlying drug toxicity. This review describes genetically diverse mouse populations and provides examples of their utility in investigating adverse drug response. It also explores recent efforts to adapt mouse population-based approaches to in vitro platforms, thereby enabling the incorporation of genetic diversity and the identification of genetic risk factors and mechanisms associated with drug toxicity susceptibility at all stages of drug development.

Bridging the Data Gap From in vitro Toxicity Testing to Chemical Safety Assessment Through Computational Modeling

Chemical toxicity testing is moving steadily toward a human cell and organoid-based in vitro approach for reasons including scientific relevancy, efficiency, cost, and ethical rightfulness. Inferring human health risk from chemical exposure based on in vitro testing data is a challenging task, facing various data gaps along the way. This review identifies these gaps and makes a case for the in silico approach of computational dose-response and extrapolation modeling to address many of the challenges. Mathematical models that can mechanistically describe chemical toxicokinetics (TK) and toxicodynamics (TD), for both in vitro and in vivo conditions, are the founding pieces in this regard. Identifying toxicity pathways and in vitro point of departure (PoD) associated with adverse health outcomes requires an understanding of the molecular key events in the interacting transcriptome, proteome, and metabolome. Such an understanding will in turn help determine the sets of sensitive biomarkers to be measured in vitro and the scope of toxicity pathways to be modeled in silico. In vitro data reporting both pathway perturbation and chemical biokinetics in the culture medium serve to calibrate the toxicity pathway and virtual tissue models, which can then help predict PoDs in response to chemical dosimetry experienced by cells in vivo. Two types of in vitro to in vivo extrapolation (IVIVE) are needed. (1) For toxic effects involving systemic regulations, such as endocrine disruption, organism-level adverse outcome pathway (AOP) models are needed to extrapolate in vitro toxicity pathway perturbation to in vivo PoD. (2) Physiologically-based toxicokinetic (PBTK) modeling is needed to extrapolate in vitro PoD dose metrics into external doses for expected exposure scenarios. Linked PBTK and TD models can explore the parameter space to recapitulate human population variability in response to chemical insults. While challenges remain for applying these modeling tools to support in vitro toxicity testing, they open the door toward population-stratified and personalized risk assessment.

Incorporation of the glutathione conjugation pathway in an updated physiologically-based pharmacokinetic model for perchloroethylene in mice

BACKGROUND

Perchloroethylene (perc) induced target organ toxicity has been associated with tissue-specific metabolic pathways. Previous physiologically-based pharmacokinetic (PBPK) modeling of perc accurately predicted oxidative metabolites but suggested the need to better characterize glutathione (GSH) conjugation as well as toxicokinetic uncertainty and variability.

OBJECTIVES

We updated the previously published "harmonized" perc PBPK model in mice to better characterize GSH conjugation metabolism as well as the uncertainty and variability of perc toxicokinetics.

METHODS

The updated PBPK model includes expanded models for perc and its oxidative metabolite trichloroacetic acid (TCA), and physiologically-based sub-models for conjugative metabolites. Previously compiled mouse kinetic data in B6C3F1 and Swiss-Webster mice were augmented to include data from a recent study in male C57BL/6J mice that measured perc and metabolites in serum and multiple tissues. Hierarchical Bayesian population analysis using Markov chain Monte Carlo was conducted to characterize uncertainty and inter-strain variability in perc metabolism.

RESULTS

The updated model fit the data as well or better than the previously published "harmonized" PBPK model. Tissue dosimetry for both oxidative and conjugative metabolites was successfully predicted across the three strains of mice, with estimated residuals errors of 2-fold for majority of data. Inter-strain variability across three strains was evident for oxidative metabolism; GSH conjugation data were only available for one strain.

CONCLUSIONS

This updated PBPK model fills a critical data gap in quantitative risk assessment by predicting the internal dosimetry of perc and its oxidative and GSH conjugation metabolites and lays the groundwork for future studies to better characterize toxicokinetic variability.

A human population-based organotypic in vitro model for cardiotoxicity screening

Assessing inter-individual variability in responses to xenobiotics remains a substantial challenge, both in drug development with respect to pharmaceuticals and in public health with respect to environmental chemicals. Although approaches exist to characterize pharmacokinetic variability, there are no methods to routinely address pharmacodynamic variability. In this study, we aimed to demonstrate the feasibility of characterizing inter-individual variability in a human in vitro model. Specifically, we hypothesized that genetic variability across a population of iPSC-derived cardiomyocytes translates into reproducible variability in both baseline phenotypes and drug responses. We measured baseline and drug-related effects in iPSC-derived cardiomyocytes from 27 healthy donors on kinetic Ca²⁺ flux and high-content live cell imaging. Cells were treated in concentration-response with cardiotoxic drugs: isoproterenol (β-adrenergic receptor agonist/positive inotrope), propranolol (β-adrenergic receptor antagonist/negative inotrope), and cisapride (hERG channel inhibitor/QT prolongation). Cells from four of the 27 donors were further evaluated in terms of baseline and treatment-related gene expression. Reproducibility of phenotypic responses was evaluated across batches and time. iPSC-derived cardiomyocytes exhibited reproducible donor-specific differences in baseline function and drug-induced effects. We demonstrate the feasibility of using a panel of population-based organotypic cells from healthy donors as an animal replacement experimental model. This model can be used to rapidly screen drugs and chemicals for inter-individual variability in cardiotoxicity. This approach demonstrates the feasibility of quantifying inter-individual variability in xenobiotic responses and can be expanded to other cell types for which in vitro populations can be derived from iPSCs.

Beyond the RfD: Broad Application of a Probabilistic Approach to Improve Chemical Dose-Response Assessments for Noncancer Effects

BACKGROUND

The National Academies recommended risk assessments redefine the traditional noncancer Reference Dose (RfD) as a probabilistically derived risk-specific dose, a framework for which was recently developed by the World Health Organization (WHO).

OBJECTIVES

Our aim was to assess the feasibility and implications of replacing traditional RfDs with probabilistic estimates of the human dose associated with an effect magnitude M and population incidence I (HD_MI).

METHODS

We created a comprehensive, curated database of RfDs derived from animal data and developed a standardized, automated, web-accessible probabilistic dose-response workflow implementing the WHO framework.

RESULTS

We identified 1,464 RfDs and associated endpoints, representing 608 chemicals across many types of effects. Applying our standardized workflow resulted in 1,522 HD_MI values. Traditional RfDs are generally within an order of magnitude of the HD_MI lower confidence bound for I=1% and M values commonly used for benchmark doses. The greatest contributor to uncertainty was lack of benchmark dose estimates, followed by uncertainty in the extent of human variability. Exposure at the traditional RfD frequently implies an upper 95% confidence bound of several percent of the population affected. Whether such incidences are considered acceptable is likely to vary by chemical and risk context, especially given the wide range of severity of the associated effects, from clinical chemistry to mortality.

CONCLUSIONS

Overall, replacing RfDs with HD_MI estimates can provide a more consistent, scientifically rigorous, and transparent basis for risk management decisions, as well as support additional decision contexts such as economic benefit-cost analysis, risk-risk tradeoffs, life-cycle impact analysis, and emergency response. https://doi.org/10.1289/EHP3368.

Population-based dose-response analysis of liver transcriptional response to trichloroethylene in mouse

Studies of gene expression are common in toxicology and provide important clues to mechanistic understanding of adverse effects of chemicals. Most prior studies have been performed in a single strain or cell line; however, gene expression is heavily influenced by the genetic background, and these genotype-expression differences may be key drivers of inter-individual variation in response to chemical toxicity. In this study, we hypothesized that the genetically diverse Collaborative Cross mouse population can be used to gain insight and suggest mechanistic hypotheses for the dose- and genetic background-dependent effects of chemical exposure. This hypothesis was tested using a model liver toxicant trichloroethylene (TCE). Liver transcriptional responses to TCE exposure were evaluated 24 h after dosing. Transcriptomic dose-responses were examined for both TCE and its major oxidative metabolite trichloroacetic acid (TCA). As expected, peroxisome- and fatty acid metabolism-related pathways were among the most dose-responsive enriched pathways in all strains. However, nearly half of the TCE-induced liver transcriptional perturbation was strain-dependent, with abundant evidence of strain/dose interaction, including in the peroxisomal signaling-associated pathways. These effects were highly concordant between the administered TCE dose and liver levels of TCA. Dose-response analysis of gene expression at the pathway level yielded points of departure similar to those derived from the traditional toxicology studies for both non-cancer and cancer effects. Mapping of expression-genotype-dose relationships revealed some significant associations; however, the effects of TCE on gene expression in liver appear to be highly polygenic traits that are challenging to positionally map. This study highlights the usefulness of mouse population-based studies in assessing inter-individual variation in toxicological responses, but cautions that genetic mapping may be challenging because of the complexity in gene exposure-dose relationships.