Development and demonstration of a data visualization platform of short-term guidelines for ambient air levels of benzene during disaster response in Houston, Texas.

Industrial disasters have caused hazardous air pollution and public health impacts. Response officials have developed limited exposure guidelines to direct them during the event; often, guidelines are outdated and may not represent relevant elevated-exposure periods. The 2019 Intercontinental Terminals Company (ITC) fire in Houston, Texas led to large-scale releases of benzene and presented a public health threat. This incident highlights the need for effective response and nimble, rapid public health communication. We developed a data-driven visualization tool to store, display, and interpret ambient benzene concentrations to assist health officials during environmental emergencies. Guidance values to interpret risk from acute exposure to benzene were updated using recent literature that also considers exposure periodicity. The visualization platform can process data from different sampling instruments and air monitors automatically, and displays information publicly in real time, along with the associated risk information and action recommendations. The protocol was validated by applying it retrospectively to the ITC event. The new guidance values are 6-30 times lower than those derived by the Texas regulatory agency. Fixed-site monitoring data, assessed using the protocol and revised thresholds, indicated that eight shelter-in-place and 17 air-quality alerts may have been considered. At least one of these shelter-in-place alerts corresponded to prolonged, elevated benzene concentrations (~1000 ppb). This new tool addresses essential gaps in the timely communication of air pollution measurements, provides context to understand potential health risks from exposure to benzene, and provides a clear protocol for local officials in responding to industrial air releases of benzene. Integr Environ Assess Manag 2024;20:533-546. © 2023 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

A comprehensive analysis of the animal carcinogenicity data for glyphosate from chronic exposure rodent carcinogenicity studies.

Since the introduction of glyphosate-tolerant genetically-modified plants, the global use of glyphosate has increased dramatically making it the most widely used pesticide on the planet. There is considerable controversy concerning the carcinogenicity of glyphosate with scientists and regulatory authorities involved in the review of glyphosate having markedly different opinions. One key aspect of these opinions is the degree to which glyphosate causes cancer in laboratory animals after lifetime exposure. In this review, twenty-one chronic exposure animal carcinogenicity studies of glyphosate are identified from regulatory documents and reviews; 13 studies are of sufficient quality and detail to be reanalyzed in this review using trend tests, historical control tests and pooled analyses. The analyses identify 37 significant tumor findings in these studies and demonstrate consistency across studies in the same sex/species/strain for many of these tumors. Considering analyses of the individual studies, the consistency of the data across studies, the pooled analyses, the historical control data, non-neoplastic lesions, mechanistic evidence and the associated scientific literature, the tumor increases seen in this review are categorized as to the strength of the evidence that glyphosate causes these cancers. The strongest evidence shows that glyphosate causes hemangiosarcomas, kidney tumors and malignant lymphomas in male CD-1 mice, hemangiomas and malignant lymphomas in female CD-1 mice, hemangiomas in female Swiss albino mice, kidney adenomas, liver adenomas, skin keratoacanthomas and skin basal cell tumors in male Sprague-Dawley rats, adrenal cortical carcinomas in female Sprague-Dawley rats and hepatocellular adenomas and skin keratocanthomas in male Wistar rats.

Concordance between sites of tumor development in humans and in experimental animals for 111 agents that are carcinogenic to humans.

Since the inception of the IARC Monographs Programme in the early 1970s, this Programme has developed 119 Monograph Volumes on more than 1000 agents for which there exists some evidence of cancer risk to humans. Of these, 120 agents were found to meet the criteria for classification as carcinogenic to humans (Group 1). Volume 100 of the IARC Monographs, compiled in 2008-2009 and published in 2012, provided a review and update of the 107 Group 1 agents identified as of 2009. These agents were divided into six broad categories: (I) pharmaceuticals; (II) biological agents; (III) arsenic, metals, fibers and dusts; (IV) radiation; (V) personal habits and indoor combustions; and (VI) chemical agents and related occupations. The Group I agents reviewed in Volume 100, as well as five additional Group 1 agents defined in subsequent Volumes of the Monographs, were used to assess the degree of concordance between sites where tumors originate in humans and experimental animals including mice, rats, hamsters, dogs, and non-human primates using an anatomically based tumor nomenclature system, representing 39 tumor sites and 14 organ and tissue systems. This evaluation identified 91 Group 1 agents with sufficient evidence (82 agents) or limited evidence (9 agents) of carcinogenicity in animals. The most common tumors observed in both humans and animals were those of the respiratory system including larynx, lung, and lower respiratory tract. In humans, respiratory system tumors were noted for 31 of the 111 distinct Group 1 carcinogens identified up to and including Volume 109 of the IARC Monographs, comprising predominantly 14 chemical agents and related occupations in category VI; seven arsenic, metals, fibers, and dusts in category III, and five personal habits and indoor combustions in category V. Subsequent to respiratory system tumors, those in lymphoid and hematopoietic tissues (26 agents), the urothelium (18 agents), and the upper aerodigestive tract (16 agents) were most often seen in humans, while tumors in digestive organs (19 agents), skin (18 agents), and connective tissues (17 agents) were frequently seen in animals. Exposures to radiation, particularly X- and γ-radiation, and tobacco smoke were associated with tumors at multiple sites in humans. Although the IARC Monographs did not emphasize tumor site concordance between animals and humans, substantial concordance was detected for several organ and tissue systems, even under the stringent criteria for sufficient evidence of carcinogenicity used by IARC. Of the 60 agents for which at least one tumor site was identified in both humans and animals, 52 (87%) exhibited tumors in at least one of the same organ and tissue systems in humans and animals. It should be noted that some caution is needed in interpreting concordance at sites where sample size is particularly small. Although perfect (100%) concordance was noted for agents that induce tumors of the mesothelium, only two Group 1 agents that met the criteria for inclusion in the concordance analysis caused tumors at this site. Although the present analysis demonstrates good concordance between animals and humans for many, but not all, tumor sites, limitations of available data may result in underestimation of concordance.

The Use of Signal-Transduction and Metabolic Pathways to Predict Human Disease Targets from Electric and Magnetic Fields Using in vitro Data in Human Cell Lines.

Using in vitro data in human cell lines, several research groups have investigated changes in gene expression in cellular systems following exposure to extremely low frequency (ELF) and radiofrequency (RF) electromagnetic fields (EMF). For ELF EMF, we obtained five studies with complete microarray data and three studies with only lists of significantly altered genes. Likewise, for RF EMF, we obtained 13 complete microarray datasets and 5 limited datasets. Plausible linkages between exposure to ELF and RF EMF and human diseases were identified using a three-step process: (a) linking genes associated with classes of human diseases to molecular pathways, (b) linking pathways to ELF and RF EMF microarray data, and (c) identifying associations between human disease and EMF exposures where the pathways are significantly similar. A total of 60 pathways were associated with human diseases, mostly focused on basic cellular functions like JAK-STAT signaling or metabolic functions like xenobiotic metabolism by cytochrome P450 enzymes. ELF EMF datasets were sporadically linked to human diseases, but no clear pattern emerged. Individual datasets showed some linkage to cancer, chemical dependency, metabolic disorders, and neurological disorders. RF EMF datasets were not strongly linked to any disorders but strongly linked to changes in several pathways. Based on these analyses, the most promising area for further research would be to focus on EMF and neurological function and disorders.

Key Characteristics of Carcinogens as a Basis for Organizing Data on Mechanisms of Carcinogenesis.

BACKGROUND: A recent review by the International Agency for Research on Cancer (IARC) updated the assessments of the > 100 agents classified as Group 1, carcinogenic to humans (IARC Monographs Volume 100, parts A-F). This exercise was complicated by the absence of a broadly accepted, systematic method for evaluating mechanistic data to support conclusions regarding human hazard from exposure to carcinogens. OBJECTIVES AND METHODS: IARC therefore convened two workshops in which an international Working Group of experts identified 10 key characteristics, one or more of which are commonly exhibited by established human carcinogens. DISCUSSION: These characteristics provide the basis for an objective approach to identifying and organizing results from pertinent mechanistic studies. The 10 characteristics are the abilities of an agent to 1) act as an electrophile either directly or after metabolic activation; 2) be genotoxic; 3) alter DNA repair or cause genomic instability; 4) induce epigenetic alterations; 5) induce oxidative stress; 6) induce chronic inflammation; 7) be immunosuppressive; 8) modulate receptor-mediated effects; 9) cause immortalization; and 10) alter cell proliferation, cell death, or nutrient supply. CONCLUSION: We describe the use of the 10 key characteristics to conduct a systematic literature search focused on relevant end points and construct a graphical representation of the identified mechanistic information. Next, we use benzene and polychlorinated biphenyls as examples to illustrate how this approach may work in practice. The approach described is similar in many respects to those currently being implemented by the U.S. EPA's Integrated Risk Information System Program and the U.S. National Toxicology Program. CITATION: Smith MT, Guyton KZ, Gibbons CF, Fritz JM, Portier CJ, Rusyn I, DeMarini DM, Caldwell JC, Kavlock RJ, Lambert P, Hecht SS, Bucher JR, Stewart BW, Baan R, Cogliano VJ, Straif K. 2016. Key characteristics of carcinogens as a basis for organizing data on mechanisms of carcinogenesis. Environ Health Perspect 124:713-721; http://dx.doi.org/10.1289/ehp.1509912.

A simple procedure for estimating pseudo risk ratios from exposure to non-carcinogenic chemical mixtures.

Non-cancer risk assessment traditionally assumes a threshold of effect, below which there is a negligible risk of an adverse effect. The Agency for Toxic Substances and Disease Registry derives health-based guidance values known as Minimal Risk Levels (MRLs) as estimates of the toxicity threshold for non-carcinogens. Although the definition of an MRL, as well as EPA reference dose values (RfD and RfC), is a level that corresponds to "negligible risk," they represent daily exposure doses or concentrations, not risks. We present a new approach to calculate the risk at exposure to specific doses for chemical mixtures, the assumption in this approach is to assign de minimis risk at the MRL. The assigned risk enables the estimation of parameters in an exponential model, providing a complete dose-response curve for each compound from the chosen point of departure to zero. We estimated parameters for 27 chemicals. The value of k, which determines the shape of the dose-response curve, was moderately insensitive to the choice of the risk at the MRL. The approach presented here allows for the calculation of a risk from a single substance or the combined risk from multiple chemical exposures in a community. The methodology is applicable from point of departure data derived from quantal data, such as data from benchmark dose analyses or from data that can be transformed into probabilities, such as lowest-observed-adverse-effect level. The individual risks are used to calculate risk ratios that can facilitate comparison and cost-benefit analyses of environmental contamination control strategies.

Environmental Predictors of US County Mortality Patterns on a National Basis.

A growing body of evidence has found that mortality rates are positively correlated with social inequalities, air pollution, elevated ambient temperature, availability of medical care and other factors. This study develops a model to predict the mortality rates for different diseases by county across the US. The model is applied to predict changes in mortality caused by changing environmental factors. A total of 3,110 counties in the US, excluding Alaska and Hawaii, were studied. A subset of 519 counties from the 3,110 counties was chosen by using systematic random sampling and these samples were used to validate the model. Step-wise and linear regression analyses were used to estimate the ability of environmental pollutants, socio-economic factors and other factors to explain variations in county-specific mortality rates for cardiovascular diseases, cancers, chronic obstructive pulmonary disease (COPD), all causes combined and lifespan across five population density groups. The estimated models fit adequately for all mortality outcomes for all population density groups and, adequately predicted risks for the 519 validation counties. This study suggests that, at local county levels, average ozone (0.07 ppm) is the most important environmental predictor of mortality. The analysis also illustrates the complex inter-relationships of multiple factors that influence mortality and lifespan, and suggests the need for a better understanding of the pathways through which these factors, mortality, and lifespan are related at the community level.

Biological networks for predicting chemical hepatocarcinogenicity using gene expression data from treated mice and relevance across human and rat species.

BACKGROUND: Several groups have employed genomic data from subchronic chemical toxicity studies in rodents (90 days) to derive gene-centric predictors of chronic toxicity and carcinogenicity. Genes are annotated to belong to biological processes or molecular pathways that are mechanistically well understood and are described in public databases. OBJECTIVES: To develop a molecular pathway-based prediction model of long term hepatocarcinogenicity using 90-day gene expression data and to evaluate the performance of this model with respect to both intra-species, dose-dependent and cross-species predictions. METHODS: Genome-wide hepatic mRNA expression was retrospectively measured in B6C3F1 mice following subchronic exposure to twenty-six (26) chemicals (10 were positive, 2 equivocal and 14 negative for liver tumors) previously studied by the US National Toxicology Program. Using these data, a pathway-based predictor model for long-term liver cancer risk was derived using random forests. The prediction model was independently validated on test sets associated with liver cancer risk obtained from mice, rats and humans. RESULTS: Using 5-fold cross validation, the developed prediction model had reasonable predictive performance with the area under receiver-operator curve (AUC) equal to 0.66. The developed prediction model was then used to extrapolate the results to data associated with rat and human liver cancer. The extrapolated model worked well for both extrapolated species (AUC value of 0.74 for rats and 0.91 for humans). The prediction models implied a balanced interplay between all pathway responses leading to carcinogenicity predictions. CONCLUSIONS: Pathway-based prediction models estimated from sub-chronic data hold promise for predicting long-term carcinogenicity and also for its ability to extrapolate results across multiple species.

Evaluation of biomonitoring data from the CDC National Exposure Report in a risk assessment context: perspectives across chemicals.

BACKGROUND: Biomonitoring data reported in the National Report on Human Exposure to Environmental Chemicals [NER; Centers for Disease Control and Prevention (2012)] provide information on the presence and concentrations of > 400 chemicals in human blood and urine. Biomonitoring Equivalents (BEs) and other risk assessment-based values now allow interpretation of these biomonitoring data in a public health risk context. OBJECTIVES: We compared the measured biomarker concentrations in the NER with BEs and similar risk assessment values to provide an across-chemical risk assessment perspective on the measured levels for approximately 130 analytes in the NER. METHODS: We identified available risk assessment-based biomarker screening values, including BEs and Human Biomonitoring-I (HBM-I) values from the German Human Biomonitoring Commission. Geometric mean and 95th percentile population biomarker concentrations from the NER were compared to the available screening values to generate chemical-specific hazard quotients (HQs) or cancer risk estimates. CONCLUSIONS: Most analytes in the NER show HQ values of < 1; however, some (including acrylamide, dioxin-like chemicals, benzene, xylene, several metals, di-2(ethylhexyl)phthalate, and some legacy organochlorine pesticides) approach or exceed HQ values of 1 or cancer risks of > 1 × 10-4 at the geometric mean or 95th percentile, suggesting exposure levels may exceed published human health benchmarks. This analysis provides for the first time a means for examining population biomonitoring data for multiple environmental chemicals in the context of the risk assessments for those chemicals. The results of these comparisons can be used to focus more detailed chemical-specific examination of the data and inform priorities for chemical risk management and research.

A signal-to-noise crossover dose as the point of departure for health risk assessment.

BACKGROUND: The U.S. National Toxicology Program (NTP) cancer bioassay database provides an opportunity to compare both existing and new approaches to determining points of departure (PoDs) for establishing reference doses (RfDs). OBJECTIVES: The aims of this study were a) to investigate the risk associated with the traditional PoD used in human health risk assessment [the no observed adverse effect level (NOAEL)]; b) to present a new approach based on the signal-to-noise crossover dose (SNCD); and c) to compare the SNCD and SNCD-based RfD with PoDs and RfDs based on the NOAEL and benchmark dose (BMD) approaches. METHODS: The complete NTP database was used as the basis for these analyses, which were performed using the Hill model. We determined NOAELs and estimated corresponding extra risks. Lower 95% confidence bounds on the BMD (BMDLs) corresponding to extra risks of 1%, 5%, and 10% (BMDL01, BMDL05, and BMDL10, respectively) were also estimated. We introduce the SNCD as a new PoD, defined as the dose where the additional risk is equal to the "background noise" (the difference between the upper and lower bounds of the two-sided 90% confidence interval on absolute risk) or a specified fraction thereof. RESULTS: The median risk at the NOAEL was approximately 10%, and the default uncertainty factor (UF = 100) was considered most applicable to the BMDL10. Therefore, we chose a target risk of 1/1,000 (0.1/100) to derive an SNCD-based RfD by linear extrapolation. At the median, this approach provided the same RfD as the BMDL10 divided by the default UF. CONCLUSIONS: Under a standard BMD approach, the BMDL10 is considered to be the most appropriate PoD. The SNCD approach, which is based on the lowest dose at which the signal can be reliably detected, warrants further development as a PoD for human health risk assessment.

Global gene expression profiling of a population exposed to a range of benzene levels.

BACKGROUND: Benzene, an established cause of acute myeloid leukemia (AML), may also cause one or more lymphoid malignancies in humans. Previously, we identified genes and pathways associated with exposure to high (> 10 ppm) levels of benzene through transcriptomic analyses of blood cells from a small number of occupationally exposed workers. OBJECTIVES: The goals of this study were to identify potential biomarkers of benzene exposure and/or early effects and to elucidate mechanisms relevant to risk of hematotoxicity, leukemia, and lymphoid malignancy in occupationally exposed individuals, many of whom were exposed to benzene levels < 1 ppm, the current U.S. occupational standard. METHODS: We analyzed global gene expression in the peripheral blood mononuclear cells of 125 workers exposed to benzene levels ranging from < 1 ppm to > 10 ppm. Study design and analysis with a mixed-effects model minimized potential confounding and experimental variability. RESULTS: We observed highly significant widespread perturbation of gene expression at all exposure levels. The AML pathway was among the pathways most significantly associated with benzene exposure. Immune response pathways were associated with most exposure levels, potentially providing biological plausibility for an association between lymphoma and benzene exposure. We identified a 16-gene expression signature associated with all levels of benzene exposure. CONCLUSIONS: Our findings suggest that chronic benzene exposure, even at levels below the current U.S. occupational standard, perturbs many genes, biological processes, and pathways. These findings expand our understanding of the mechanisms by which benzene may induce hematotoxicity, leukemia, and lymphoma and reveal relevant potential biomarkers associated with a range of exposures.

Expression and function of 5-hydroxytryptamine 4 receptors in smooth muscle preparations from the duodenum, ileum, and pelvic flexure of horses without gastrointestinal tract disease.

OBJECTIVE: To evaluate the expression of the 5-hydroxytryptamine 4 (5-HT4) receptor subtype and investigate the modulating function of those receptors on contractility in intestinal tissues obtained from horses without gastrointestinal tract disease. SAMPLE POPULATION: Smooth muscle preparations from the duodenum, ileum, and pelvic flexure collected immediately after slaughter of 24 horses with no history or signs of gastrointestinal tract disease. PROCEDURES: In isometric organ baths, the contractile activities of smooth muscle preparations in response to 5-hydroxytryptamine and electric field stimulation were assessed; the effect of tegaserod alone or in combination with 5-hydroxytryptamine on contractility of intestinal specimens was also investigated. Presence and distribution of 5-HT4 receptors in intestinal tissues and localization on interstitial cells of Cajal were examined by use of an immunofluorescence technique. RESULTS: Widespread 5-HT4 receptor immunoreactivity was observed in all intestinal smooth muscle layers; 5-HT4 receptors were absent from the myenteric plexus and interstitial cells of Cajal. In electrical field-stimulated tissue preparations of duodenum and pelvic flexure, tegaserod increased the amplitude of smooth muscle contractions in a concentration-dependent manner. Preincubation with tegaserod significantly decreased the basal tone of the 5-HT-evoked contractility in small intestine specimens, compared with the effect of 5-HT alone, thereby confirming that tegaserod was acting as a partial agonist. CONCLUSIONS AND CLINICAL RELEVANCE: In horses, 5-HT4 receptors on smooth muscle cells appear to be involved in the contractile response of the intestinal tract to 5-hydroxytryptamine. Results suggest that tegaserod may be useful for treatment of reduced gastrointestinal tract motility in horses.

What role for biologically based dose-response models in estimating low-dose risk?

BACKGROUND: Biologically based dose-response (BBDR) models can incorporate data on biological processes at the cellular and molecular level to link external exposure to an adverse effect. OBJECTIVES: Our goal was to examine the utility of BBDR models in estimating low-dose risk. METHODS: We reviewed the utility of BBDR models in risk assessment. RESULTS: BBDR models have been used profitably to evaluate proposed mechanisms of toxicity and identify data gaps. However, these models have not improved the reliability of quantitative predictions of low-dose human risk. In this commentary we identify serious impediments to developing BBDR models for this purpose. BBDR models do not eliminate the need for empirical modeling of the relationship between dose and effect, but only move it from the whole organism to a lower level of biological organization. However, in doing this, BBDR models introduce significant new sources of uncertainty. Quantitative inferences are limited by inter- and intraindividual heterogeneity that cannot be eliminated with available or reasonably anticipated experimental techniques. BBDR modeling does not avoid uncertainties in the mechanisms of toxicity relevant to low-level human exposures. Although implementation of BBDR models for low-dose risk estimation have thus far been limited mainly to cancer modeled using a two-stage clonal expansion framework, these problems are expected to be present in all attempts at BBDR modeling. CONCLUSIONS: The problems discussed here appear so intractable that we conclude that BBDR models are unlikely to be fruitful in reducing uncertainty in quantitative estimates of human risk from low-level exposures in the foreseeable future. Use of in vitro data from recent advances in molecular toxicology in BBDR models is not likely to remove these problems and will introduce new issues regarding extrapolation of data from in vitro systems.

Choosing the right path: enhancement of biologically relevant sets of genes or proteins using pathway structure.

A method is proposed that finds enriched pathways relevant to a studied condition using the measured molecular data and also the structural information of the pathway viewed as a network of nodes and edges. Tests are performed using simulated data and genomic data sets and the method is compared to two existing approaches. The analysis provided demonstrates the method proposed is very competitive with the current approaches and also provides biologically relevant results.

Uncertainties in biologically-based modeling of formaldehyde-induced respiratory cancer risk: identification of key issues.

In a series of articles and a health-risk assessment report, scientists at the CIIT Hamner Institutes developed a model (CIIT model) for estimating respiratory cancer risk due to inhaled formaldehyde within a conceptual framework incorporating extensive mechanistic information and advanced computational methods at the toxicokinetic and toxicodynamic levels. Several regulatory bodies have utilized predictions from this model; on the other hand, upon detailed evaluation the California EPA has decided against doing so. In this article, we study the CIIT model to identify key biological and statistical uncertainties that need careful evaluation if such two-stage clonal expansion models are to be used for extrapolation of cancer risk from animal bioassays to human exposure. Broadly, these issues pertain to the use and interpretation of experimental labeling index and tumor data, the evaluation and biological interpretation of estimated parameters, and uncertainties in model specification, in particular that of initiated cells. We also identify key uncertainties in the scale-up of the CIIT model to humans, focusing on assumptions underlying model parameters for cell replication rates and formaldehyde-induced mutation. We discuss uncertainties in identifying parameter values in the model used to estimate and extrapolate DNA protein cross-link levels. The authors of the CIIT modeling endeavor characterized their human risk estimates as "conservative in the face of modeling uncertainties." The uncertainties discussed in this article indicate that such a claim is premature.