A case study on the application of an expert-driven read-across approach in support of quantitative risk assessment of p,p'-dichlorodiphenyldichloroethane.

Deriving human health risk estimates for environmental chemicals has traditionally relied on in vivo toxicity databases to characterize potential adverse health effects and associated dose-response relationships. In the absence of in vivo toxicity information, new approach methods (NAMs) such as read-across have the potential to fill the required data gaps. This case study applied an expert-driven read-across approach to identify and evaluate analogues to fill non-cancer oral toxicity data gaps for p,p'-dichlorodiphenyldichloroethane (p,p'-DDD), an organochlorine contaminant known to occur at contaminated sites in the U.S. The source analogue p,p'-dichlorodiphenyltrichloroethane (DDT) and its no-observed-adverse-effect level of 0.05 mg/kg-day were proposed for the derivation of screening-level health reference values for the target chemical, p,p'-DDD. Among the primary similarity contexts (structure, toxicokinetics, and toxicodynamics), toxicokinetic considerations were instrumental in separating p,p'-DDT as the best source analogue from other potential candidates (p,p'-DDE and methoxychlor). In vitro high-throughput screening (HTS) assays from ToxCast were used to evaluate similarity in bioactivity profiles and make inferences toward plausible mechanisms of toxicity to build confidence in the read-across approach. This work demonstrated the value of NAMs such as read-across and in vitro HTS in human health risk assessment of environmental contaminants with the potential to inform regulatory decision-making.

Editor's Highlight: Application of Gene Set Enrichment Analysis for Identification of Chemically Induced, Biologically Relevant Transcriptomic Networks and Potential Utilization in Human Health Risk Assessment.

The rate of new chemical development in commerce combined with a paucity of toxicity data for legacy chemicals presents a unique challenge for human health risk assessment. There is a clear need to develop new technologies and incorporate novel data streams to more efficiently inform derivation of toxicity values. One avenue of exploitation lies in the field of transcriptomics and the application of gene expression analysis to characterize biological responses to chemical exposures. In this context, gene set enrichment analysis (GSEA) was employed to evaluate tissue-specific, dose-response gene expression data generated following exposure to multiple chemicals for various durations. Patterns of transcriptional enrichment were evident across time and with increasing dose, and coordinated enrichment plausibly linked to the etiology of the biological responses was observed. GSEA was able to capture both transient and sustained transcriptional enrichment events facilitating differentiation between adaptive versus longer term molecular responses. When combined with benchmark dose (BMD) modeling of gene expression data from key drivers of biological enrichment, GSEA facilitated characterization of dose ranges required for enrichment of biologically relevant molecular signaling pathways, and promoted comparison of the activation dose ranges required for individual pathways. Median transcriptional BMD values were calculated for the most sensitive enriched pathway as well as the overall median BMD value for key gene members of significantly enriched pathways, and both were observed to be good estimates of the most sensitive apical endpoint BMD value. Together, these efforts support the application of GSEA to qualitative and quantitative human health risk assessment.

The relationship between chemical-induced kidney weight increases and kidney histopathology in rats.

The kidney is a major site of chemical excretion, which results in its propensity to exhibit chemically-induced toxicological effects at a higher rate than most other organs. Although the kidneys are often weighed in animal toxicity studies, the manner in which these kidney weight measurements are interpreted and the value of this information in predicting renal damage remains controversial. In this study we sought to determine whether a relationship exists between chemically-induced kidney weight changes and renal histopathological alterations. We also examined the relative utility of absolute and relative (kidney-to-body weight ratio) kidney weight in the prediction of renal toxicity. For this, data extracted from oral chemical exposure studies in rats performed by the National Toxicology Program were qualitatively and quantitatively evaluated. Our analysis showed a statistically significant correlation between absolute, but not relative, kidney weight and renal histopathology in chemically-treated rats. This positive correlation between absolute kidney weight and histopathology was observed even with compounds that statistically decreased terminal body weight. Also, changes in absolute kidney weight, which occurred at subchronic exposures, were able to predict the presence or absence of kidney histopathology at both subchronic and chronic exposures. Furthermore, most increases in absolute kidney weight reaching statistical significance (irrespective of the magnitude of change) were found to be relevant for the prediction of histopathological changes. Hence, our findings demonstrate that the evaluation of absolute kidney weight is a useful method for identifying potential renal toxicants.

Using quantitative structure-activity relationship modeling to quantitatively predict the developmental toxicity of halogenated azole compounds.

Developmental toxicity is a relevant endpoint for the comprehensive assessment of human health risk from chemical exposure. However, animal developmental toxicity data remain unavailable for many environmental contaminants due to the complexity and cost of these types of analyses. Here we describe an approach that uses quantitative structure-activity relationship modeling as an alternative methodology to fill data gaps in the developmental toxicity profile of certain halogenated compounds. Chemical information was obtained and curated using the OECD Quantitative Structure-Activity Relationship Toolbox, version 3.0. Data from 35 curated compounds were analyzed via linear regression to build the predictive model, which has an R(2) of 0.79 and a Q(2) of 0.77. The applicability domain (AD) was defined by chemical category and structural similarity. Seven halogenated chemicals that fit the AD but are not part of the training set were employed for external validation purposes. Our model predicted lowest observed adverse effect level values with a maximal threefold deviation from the observed experimental values for all chemicals that fit the AD. The good predictability of our model suggests that this method may be applicable to the analysis of qualifying compounds whenever developmental toxicity information is lacking or incomplete for risk assessment considerations.

Incorporating new technologies into toxicity testing and risk assessment: moving from 21st century vision to a data-driven framework.

Based on existing data and previous work, a series of studies is proposed as a basis toward a pragmatic early step in transforming toxicity testing. These studies were assembled into a data-driven framework that invokes successive tiers of testing with margin of exposure (MOE) as the primary metric. The first tier of the framework integrates data from high-throughput in vitro assays, in vitro-to-in vivo extrapolation (IVIVE) pharmacokinetic modeling, and exposure modeling. The in vitro assays are used to separate chemicals based on their relative selectivity in interacting with biological targets and identify the concentration at which these interactions occur. The IVIVE modeling converts in vitro concentrations into external dose for calculation of the point of departure (POD) and comparisons to human exposure estimates to yield a MOE. The second tier involves short-term in vivo studies, expanded pharmacokinetic evaluations, and refined human exposure estimates. The results from the second tier studies provide more accurate estimates of the POD and the MOE. The third tier contains the traditional animal studies currently used to assess chemical safety. In each tier, the POD for selective chemicals is based primarily on endpoints associated with a proposed mode of action, whereas the POD for nonselective chemicals is based on potential biological perturbation. Based on the MOE, a significant percentage of chemicals evaluated in the first 2 tiers could be eliminated from further testing. The framework provides a risk-based and animal-sparing approach to evaluate chemical safety, drawing broadly from previous experience but incorporating technological advances to increase efficiency.

Temporal concordance between apical and transcriptional points of departure for chemical risk assessment.

The number of legacy chemicals without toxicity reference values combined with the rate of new chemical development is overwhelming the capacity of the traditional risk assessment paradigm. More efficient approaches are needed to quantitatively estimate chemical risks. In this study, rats were dosed orally with multiple doses of six chemicals for 5 days and 2, 4, and 13 weeks. Target organs were analyzed for traditional histological and organ weight changes and transcriptional changes using microarrays. Histological and organ weight changes in this study and the tumor incidences in the original cancer bioassays were analyzed using benchmark dose (BMD) methods to identify noncancer and cancer points of departure. The dose-response changes in gene expression were also analyzed using BMD methods and the responses grouped based on signaling pathways. A comparison of transcriptional BMD values for the most sensitive pathway with BMD values for the noncancer and cancer apical endpoints showed a high degree of correlation at all time points. When the analysis included data from an earlier study with eight additional chemicals, transcriptional BMD values for the most sensitive pathway were significantly correlated with noncancer (r = 0.827, p = 0.0031) and cancer-related (r = 0.940, p = 0.0002) BMD values at 13 weeks. The average ratio of apical-to-transcriptional BMD values was less than two, suggesting that for the current chemicals, transcriptional perturbation did not occur at significantly lower doses than apical responses. Based on our results, we propose a practical framework for application of transcriptomic data to chemical risk assessment.

Application of transcriptional benchmark dose values in quantitative cancer and noncancer risk assessment.

The traditional approach for estimating noncancer and cancer reference values in quantitative chemical risk assessment is time and resource intensive. The extent and nature of the studies required under the traditional approach has limited the number of chemicals with published risk assessments. In this study, female mice were exposed for 13 weeks to multiple concentrations of five chemicals that were positive in a 2-year cancer bioassay. Traditional histological and organ weight changes were evaluated, and gene expression microarray analysis was performed on the target tissues. The histological, organ weight changes, and the original tumor incidences in the original cancer bioassay were analyzed using standard benchmark dose (BMD) methods to identify noncancer and cancer points of departure, respectively. The dose-related changes in gene expression were also analyzed using a BMD approach and the responses grouped based on cellular biological processes. A comparison of the transcriptional BMD values with those for the traditional noncancer and cancer apical endpoints showed a high degree of correlation for specific cellular biological processes. For chemicals with human exposure data, the transcriptional BMD values were also used to calculate a margin of exposure. The margins of exposure ranged from 1900 to 54,000. Both the correlation between the BMD values for the transcriptional and apical endpoints and the margin of exposure analysis suggest that transcriptional BMD values may be used as potential points of departure for noncancer and cancer risk assessment.

Introduction to benchmark dose methods and U.S. EPA's benchmark dose software (BMDS) version 2.1.1.

Traditionally, the No-Observed-Adverse-Effect-Level (NOAEL) approach has been used to determine the point of departure (POD) from animal toxicology data for use in human health risk assessments. However, this approach is subject to substantial limitations that have been well defined, such as strict dependence on the dose selection, dose spacing, and sample size of the study from which the critical effect has been identified. Also, the NOAEL approach fails to take into consideration the shape of the dose-response curve and other related information. The benchmark dose (BMD) method, originally proposed as an alternative to the NOAEL methodology in the 1980s, addresses many of the limitations of the NOAEL method. It is less dependent on dose selection and spacing, and it takes into account the shape of the dose-response curve. In addition, the estimation of a BMD 95% lower bound confidence limit (BMDL) results in a POD that appropriately accounts for study quality (i.e., sample size). With the recent advent of user-friendly BMD software programs, including the U.S. Environmental Protection Agency's (U.S. EPA) Benchmark Dose Software (BMDS), BMD has become the method of choice for many health organizations world-wide. This paper discusses the BMD methods and corresponding software (i.e., BMDS version 2.1.1) that have been developed by the U.S. EPA, and includes a comparison with recently released European Food Safety Authority (EFSA) BMD guidance.

The application of non-default uncertainty factors in the U.S. EPA's Integrated Risk Information System (IRIS). Part I: UF(L), UF(S), and "other uncertainty factors".

The United States Environmental Protection Agency's Integrated Risk Information System (IRIS) includes hazard identification and dose-response assessment values developed by Agency scientists. Uncertainty factors (UFs) are used in the development of IRIS values to address the lack of information in five main areas. The standard UFs account for interspecies uncertainty (UF(A)) and intraspecies variability (UF(H)). The UF(A) addresses uncertainty related to the extrapolation of data from animals to humans, whereas the UF(H) addresses variability amongst individuals (i.e., intrahuman). Additional UFs have been employed to account for database incompleteness, extrapolations from a lowest-observed-adverse-effect level in the absence of a no-observed-adverse-effect level (UF(L)), and subchronic-to-chronic extrapolation (UF(S)). A sixth UF designated as "other uncertainty factors" (UF(O)) has also been applied in place of the UF(L) to account for uncertainty with the adversity of points of departure obtained using benchmark dose modeling. This review will discuss how UF(L), UF(S), and UF(O) have been applied in IRIS assessments, along with the rationale used to describe the choice of UF values that deviate from the standard default of 10.