Comparative susceptibility of children and adults to neurological effects of inhaled manganese: A review of the published literature.

BACKGROUND: Manganese (Mn) is neurotoxic in adults and children. Current assessments are based on the more extensive adult epidemiological data, but the potential for greater childhood susceptibility remains a concern. To better understand potential lifestage-based variations, we compared susceptibilities to neurotoxicity in children and adults using Mn biomarker data. METHODS: We developed a literature search strategy based on a Population, Exposures, Comparators, and Outcomes statement focusing on inhalation exposures and neurological outcomes in humans. Screening was performed using DistillerSR. Hair biomarker studies were selected for evaluation because studies with air measurements were unavailable or considered inadequate for children. Studies were paired based on concordant Mn source, biomarker, and outcome. Comparisons were made based on reported dose-response slopes (children vs. adults). Study evaluation was conducted to understand the confidence in our comparisons. RESULTS: We identified five studies evaluating seven pairings of hair Mn and neurological outcomes (cognition and motor effects) in children and adults matched on sources of environmental Mn inhalation exposure. Two Brazilian studies of children and one of adults reported intelligent quotient (IQ) effects; effects in both comparisons were stronger in children (1.21 to 2.03-fold difference). In paired analyses of children and adults from the United States, children exhibited both stronger and weaker effects compared to adults (0.37 to 1.75-fold differences) on postural sway metrics. CONCLUSION: There is limited information on the comparative susceptibility of children and adults to inhaled Mn. We report that children may be 0.37 to 2.03 times as susceptible as adults to neurotoxic effects of Mn, thereby providing a quantitative estimate for some aspects of lifestage variation. Due to the limited number of paired studies available in the literature, this quantitative estimate should be interpreted with caution. Our analyses do not account for other sources of inter-individual variation. Additional studies of Mn-exposed children with direct air concentration measurements would improve the evidence base.

Use of study-specific MOE-like estimates to prioritize health effects from chemical exposure for analysis in human health assessments.

There are unique challenges in estimating dose-response with chemicals that are associated with multiple health outcomes and numerous studies. Some studies are more suitable than others for quantitative dose-response analyses. For such chemicals, an efficient method of screening studies and endpoints to identify suitable studies and potentially important health effects for dose-response modeling is valuable. Using inorganic arsenic as a test case, we developed a tiered approach that involves estimating study-specific margin of exposure (MOE)-like unitless ratios for two hypothetical scenarios. These study-specific unitless ratios are derived by dividing the exposure estimated to result in a 20% increase in relative risk over the background exposure (RRE20) by the background exposure, as estimated in two different ways. In our case study illustration, separate study-specific ratios are derived using estimates of United States population background exposure (RRB-US) and the mean study population reference group background exposure (RRB-SP). Systematic review methods were used to identify and evaluate epidemiologic studies, which were categorized based on study design (case-control, cohort, cross-sectional), various study quality criteria specific to dose-response analysis (number of dose groups, exposure ascertainment, exposure uncertainty), and availability of necessary dose-response data. Both case-control and cohort studies were included in the RRB analysis. The RRE20 estimates were derived by modeling effective counts of cases and controls estimated from study-reported adjusted odds ratios and relative risks. Using a broad (but not necessarily comprehensive) set of epidemiologic studies of multiple health outcomes selected for the purposes of illustrating the RRB approach, this test case analysis would suggest that diseases of the circulatory system, bladder cancer, and lung cancer may be arsenic health outcomes that warrant further analysis. This is suggested by the number of datasets from adequate dose-response studies demonstrating an effect with RRBs close to 1 (i.e., RRE20 values close to estimated background arsenic exposure levels).

Bayesian hierarchical dose-response meta-analysis of epidemiological studies: Modeling and target population prediction methods.

When assessing the human risks due to exposure to environmental chemicals, traditional dose-response analyses are not straightforward when there are numerous high-quality epidemiological studies of priority cancer and non-cancer health outcomes. Given this wealth of information, selecting a single "best" study on which to base dose-response analyses is difficult and would potentially ignore much of the available data. Therefore, systematic approaches are necessary for the analysis of these rich databases. Examples are meta-analysis (and further, meta-regression), which are well established methods that consider and incorporate information from multiple studies into the estimation of risks due to exposure to environmental contaminants. In this paper, we propose a hierarchical, Bayesian meta-analysis approach for the dose-response analysis of multiple epidemiological studies. This paper is the second of two papers detailing this approach; the first covered "pre-analysis" steps necessary to prepare the data for dose-response modeling. This paper focuses on the hierarchical Bayesian approach to dose-response modeling and extrapolation of risk to populations of interest using the association between bladder cancer and oral inorganic arsenic (iAs) exposure as an illustrative case study. In particular, this paper addresses the modeling of both case-control and cohort studies with a flexible, logistic model in a hierarchical Bayesian framework that estimates study-specific slopes, as well as a pooled slope across all studies. This approach is akin to a random effects model in which no assumption is made a priori that there is a single, common slope for all included studies. Further, this paper also details extrapolation of the estimates of logistic slope to extra risk in a target population using a lifetable analysis and basic assumptions about background iAs exposure levels. In this case, the target population was the general United States population and information on all-cause mortality and incidence and mortality from bladder cancer was used to perform the lifetable analysis. The methods herein were developed for general use in investigating the association between any pollutant and observed health-effects in epidemiological studies. In order to demonstrate these methods, inorganic arsenic was chosen as a case study given the large epidemiological database that exists for this contaminant.

Model averaging methods for the evaluation of dose-response model uncertainty when assessing the suitability of studies for estimating risk.

This paper describes the use of multiple models and model averaging for considering dose-response uncertainties when extrapolating low-dose risk from studies of populations with high levels of exposure. The model averaging approach we applied builds upon innovative methods developed by the U.S. Food and Drug Administration (FDA), principally through the relaxing of model constraints. The relaxing of model constraints allowed us to evaluate model uncertainty using a broader set of model forms and, within the context of model averaging, did not result in the extreme supralinearity that is the primary concern associated with the application of individual unconstrained models. A study of the relationship between inorganic arsenic exposure to a Taiwanese population and potential carcinogenic effects is used to illustrate the approach. We adjusted the reported number of cases from two published prospective cohort studies of bladder and lung cancer in a Taiwanese population to account for potential covariates and less-than-lifetime exposure (for estimating effects on lifetime cancer incidence), used bootstrap methods to estimate the uncertainty surrounding the µg/kg-day inorganic arsenic dose from drinking water and dietary intakes, and fit multiple models weighted by Bayesian Information Criterion to the adjusted incidence and dose data to generate dose-specific mean, 2.5th and 97.5th percentile risk estimates. Widely divergent results from adequate model fits for a broad set of constrained and unconstrained models applied individually and in a model averaging framework suggest that substantial model uncertainty exists in risk extrapolation from estimated doses in the Taiwanese studies to lower doses more relevant to countries like the U.S. that have proportionally lower arsenic intake levels.

Systematic dose-response of environmental epidemiologic studies: Dose and response pre-analysis.

Meta-analysis approaches can be used to assess the human risks due to exposure to environmental chemicals when there are numerous high-quality epidemiologic studies of priority outcomes in a database. However, methodological issues related to how different studies report effect measures and incorporate exposure into their analyses arise that complicate the pooled analysis of multiple studies. As such, there are "pre-analysis" steps that are often necessary to prepare summary data reported in epidemiologic studies for dose-response analysis. This paper uses epidemiologic studies of arsenic-induced health effects as a case example and addresses the issues surrounding the estimation of mean doses from censored dose- or exposure-intervals reported in the literature (e.g., estimation of mean doses from high exposures that are only reported as an open-ended interval), calculation of a common dose metric for use in a dose-response meta-analysis (one that takes into consideration inter-individual variability), and calculation of response "effective counts" that inherently account for confounders. The methods herein may be generalizable to 1) the analysis of other environmental contaminants with a suitable database of epidemiologic studies, and 2) any meta-analytic approach used to pool information across studies. A second companion paper detailing the use of "pre-analyzed" data in a hierarchical Bayesian dose-response model and techniques for extrapolating risks to target populations follows.

Framework for assessing causality of air pollution-related health effects for reviews of the National Ambient Air Quality Standards.

To inform regulatory decisions on the risk due to exposure to ambient air pollution, consistent and transparent communication of the scientific evidence is essential. The United States Environmental Protection Agency (U.S. EPA) develops the Integrated Science Assessment (ISA), which contains evaluations of the policy-relevant science on the effects of criteria air pollutants and conveys critical science judgments to inform decisions on the National Ambient Air Quality Standards. This article discusses the approach and causal framework used in the ISAs to evaluate and integrate various lines of scientific evidence and draw conclusions about the causal nature of air pollution-induced health effects. The framework has been applied to diverse pollutants and cancer and noncancer effects. To demonstrate its flexibility, we provide examples of causality judgments on relationships between health effects and pollutant exposures, drawing from recent ISAs for ozone, lead, carbon monoxide, and oxides of nitrogen. U.S. EPA's causal framework has increased transparency by establishing a structured process for evaluating and integrating various lines of evidence and uniform approach for determining causality. The framework brings consistency and specificity to the conclusions in the ISA, and the flexibility of the framework makes it relevant for evaluations of evidence across media and health effects.

Advancing alternatives analysis: The role of predictive toxicology in selecting safer chemical products and processes.

Alternatives analysis (AA) is a method used in regulation and product design to identify, assess, and evaluate the safety and viability of potential substitutes for hazardous chemicals. It requires toxicological data for the existing chemical and potential alternatives. Predictive toxicology uses in silico and in vitro approaches, computational models, and other tools to expedite toxicological data generation in a more cost-effective manner than traditional approaches. The present article briefly reviews the challenges associated with using predictive toxicology in regulatory AA, then presents 4 recommendations for its advancement. It recommends using case studies to advance the integration of predictive toxicology into AA, adopting a stepwise process to employing predictive toxicology in AA beginning with prioritization of chemicals of concern, leveraging existing resources to advance the integration of predictive toxicology into the practice of AA, and supporting transdisciplinary efforts. The further incorporation of predictive toxicology into AA would advance the ability of companies and regulators to select alternatives to harmful ingredients, and potentially increase the use of predictive toxicology in regulation more broadly. Integr Environ Assess Manag 2017;13:915-925. © 2017 SETAC.

Application of epigenetic data in human health risk assessment.

Despite the many recent advances in the field of epigenetics, application of this knowledge in environmental health risk assessment has been limited. In this paper, we identify opportunities for application of epigenetic data to support health risk assessment. We consider current applications and present a vision for the future.

The Next Generation of Risk Assessment Multi-Year Study-Highlights of Findings, Applications to Risk Assessment, and Future Directions.

BACKGROUND: The Next Generation (NexGen) of Risk Assessment effort is a multi-year collaboration among several organizations evaluating new, potentially more efficient molecular, computational, and systems biology approaches to risk assessment. This article summarizes our findings, suggests applications to risk assessment, and identifies strategic research directions. OBJECTIVE: Our specific objectives were to test whether advanced biological data and methods could better inform our understanding of public health risks posed by environmental exposures. METHODS: New data and methods were applied and evaluated for use in hazard identification and dose-response assessment. Biomarkers of exposure and effect, and risk characterization were also examined. Consideration was given to various decision contexts with increasing regulatory and public health impacts. Data types included transcriptomics, genomics, and proteomics. Methods included molecular epidemiology and clinical studies, bioinformatic knowledge mining, pathway and network analyses, short-duration in vivo and in vitro bioassays, and quantitative structure activity relationship modeling. DISCUSSION: NexGen has advanced our ability to apply new science by more rapidly identifying chemicals and exposures of potential concern, helping characterize mechanisms of action that influence conclusions about causality, exposure-response relationships, susceptibility and cumulative risk, and by elucidating new biomarkers of exposure and effects. Additionally, NexGen has fostered extensive discussion among risk scientists and managers and improved confidence in interpreting and applying new data streams. CONCLUSIONS: While considerable uncertainties remain, thoughtful application of new knowledge to risk assessment appears reasonable for augmenting major scope assessments, forming the basis for or augmenting limited scope assessments, and for prioritization and screening of very data limited chemicals. Citation: Cote I, Andersen ME, Ankley GT, Barone S, Birnbaum LS, Boekelheide K, Bois FY, Burgoon LD, Chiu WA, Crawford-Brown D, Crofton KM, DeVito M, Devlin RB, Edwards SW, Guyton KZ, Hattis D, Judson RS, Knight D, Krewski D, Lambert J, Maull EA, Mendrick D, Paoli GM, Patel CJ, Perkins EJ, Poje G, Portier CJ, Rusyn I, Schulte PA, Simeonov A, Smith MT, Thayer KA, Thomas RS, Thomas R, Tice RR, Vandenberg JJ, Villeneuve DL, Wesselkamper S, Whelan M, Whittaker C, White R, Xia M, Yauk C, Zeise L, Zhao J, DeWoskin RS. 2016. The Next Generation of Risk Assessment multiyear study-highlights of findings, applications to risk assessment, and future directions. Environ Health Perspect 124:1671-1682; http://dx.doi.org/10.1289/EHP233.

Systematic Omics Analysis Review (SOAR) tool to support risk assessment.

Environmental health risk assessors are challenged to understand and incorporate new data streams as the field of toxicology continues to adopt new molecular and systems biology technologies. Systematic screening reviews can help risk assessors and assessment teams determine which studies to consider for inclusion in a human health assessment. A tool for systematic reviews should be standardized and transparent in order to consistently determine which studies meet minimum quality criteria prior to performing in-depth analyses of the data. The Systematic Omics Analysis Review (SOAR) tool is focused on assisting risk assessment support teams in performing systematic reviews of transcriptomic studies. SOAR is a spreadsheet tool of 35 objective questions developed by domain experts, focused on transcriptomic microarray studies, and including four main topics: test system, test substance, experimental design, and microarray data. The tool will be used as a guide to identify studies that meet basic published quality criteria, such as those defined by the Minimum Information About a Microarray Experiment standard and the Toxicological Data Reliability Assessment Tool. Seven scientists were recruited to test the tool by using it to independently rate 15 published manuscripts that study chemical exposures with microarrays. Using their feedback, questions were weighted based on importance of the information and a suitability cutoff was set for each of the four topic sections. The final validation resulted in 100% agreement between the users on four separate manuscripts, showing that the SOAR tool may be used to facilitate the standardized and transparent screening of microarray literature for environmental human health risk assessment.

A framework for the next generation of risk science.

OBJECTIVES: In 2011, the U.S. Environmental Protection Agency initiated the NexGen project to develop a new paradigm for the next generation of risk science. METHODS: The NexGen framework was built on three cornerstones: the availability of new data on toxicity pathways made possible by fundamental advances in basic biology and toxicological science, the incorporation of a population health perspective that recognizes that most adverse health outcomes involve multiple determinants, and a renewed focus on new risk assessment methodologies designed to better inform risk management decision making. RESULTS: The NexGen framework has three phases. Phase I (objectives) focuses on problem formulation and scoping, taking into account the risk context and the range of available risk management decision-making options. Phase II (risk assessment) seeks to identify critical toxicity pathway perturbations using new toxicity testing tools and technologies, and to better characterize risks and uncertainties using advanced risk assessment methodologies. Phase III (risk management) involves the development of evidence-based population health risk management strategies of a regulatory, economic, advisory, community-based, or technological nature, using sound principles of risk management decision making. CONCLUSIONS: Analysis of a series of case study prototypes indicated that many aspects of the NexGen framework are already beginning to be adopted in practice.

Scientific considerations for evaluating cancer bioassays conducted by the Ramazzini Institute.

BACKGROUND: The Ramazzini Institute (RI) has completed nearly 400 cancer bioassays on > 200 compounds. The European Food Safety Authority (EFSA) and others have suggested that study design and protocol differences between the RI and other laboratories by may contribute to controversy regarding cancer hazard findings, principally findings on lymphoma/leukemia diagnoses. OBJECTIVE: We aimed to evaluate RI study design, protocol differences, and accuracy of tumor diagnoses for their impact on carcinogenic hazard characterization. METHODS: We analyzed the findings from a recent Pathology Working Group (PWG) review of RI procedures and tumor diagnoses, evaluated consistency of RI and other laboratory findings for chemicals identified by the RI as positive for lymphoma/leukemia, and examined evidence for a number of other issues raised regarding RI bioassays. The RI cancer bioassay design and protocols were evaluated in the context of relevant risk assessment guidance from international authorities. DISCUSSION: Although the PWG identified close agreement with RI diagnoses for most tumor types, it did not find close agreement for lymphoma/leukemia of the respiratory tract or for neoplasms of the inner ear and cranium. Here we discuss a) the implications of the PWG findings, particularly lymphoma diagnostic issues; b) differences between RI studies and those from other laboratories that are relevant to evaluating RI cancer bioassays; and c) future work that may help resolve some concerns. CONCLUSIONS: We concluded that a) issues related to respiratory tract infections have complicated diagnoses at that site (i.e., lymphoma/leukemia), as well as for neoplasms of the inner ear and cranium, and b) there is consistency and value in RI studies for identification of other chemical-related neoplasia.

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.

Advancing the next generation of health risk assessment.

BACKGROUND: Over the past 20 years, knowledge of the genome and its function has increased dramatically, but risk assessment methodologies using such knowledge have not advanced accordingly. OBJECTIVE: This commentary describes a collaborative effort among several federal and state agencies to advance the next generation of risk assessment. The objective of the NexGen program is to begin to incorporate recent progress in molecular and systems biology into risk assessment practice. The ultimate success of this program will be based on the incorporation of new practices that facilitate faster, cheaper, and/or more accurate assessments of public health risks. METHODS: We are developing prototype risk assessments that compare the results of traditional, data-rich risk assessments with insights gained from new types of molecular and systems biology data. In this manner, new approaches can be validated, traditional approaches improved, and the value of different types of new scientific information better understood. DISCUSSION AND CONCLUSIONS: We anticipate that these new approaches will have a variety of applications, such as assessment of new and existing chemicals in commerce and the design of chemical products and processes that reduce or eliminate the use or generation of hazardous substances. Additionally, results of the effort are likely to spur further research and test methods development. Full implementation of new approaches is likely to take 10-20 years.

Predicting later-life outcomes of early-life exposures.

BACKGROUND: In utero exposure of the fetus to a stressor can lead to disease in later life. Epigenetic mechanisms are likely mediators of later-life expression of early-life events. OBJECTIVES: We examined the current state of understanding of later-life diseases resulting from early-life exposures in order to identify in utero and postnatal indicators of later-life diseases, develop an agenda for future research, and consider the risk assessment implications of this emerging knowledge. METHODS: This review was developed based on our participation in a National Research Council workshop titled "Use of in Utero and Postnatal Indicators to Predict Health Outcomes Later in Life: State of the Science and Research Recommendations." We used a case study approach to highlight the later-life consequences of early-life malnutrition and arsenic exposure. DISCUSSION: The environmental sensitivity of the epigenome is viewed as an adaptive mechanism by which the developing organism adjusts its metabolic and homeostatic systems to suit the anticipated extrauterine environment. Inappropriate adaptation may produce a mismatch resulting in subsequent increased susceptibility to disease. A nutritional mismatch between the prenatal and postnatal environments, or early-life obesogen exposure, may explain at least some of the recent rapid increases in the rates of obesity, type 2 diabetes, and cardiovascular diseases. Early-life arsenic exposure is also associated with later-life diseases, including cardiovascular disease and cancer. CONCLUSIONS: With mounting evidence connecting early-life exposures and later-life disease, new strategies are needed to incorporate this emerging knowledge into health protective practices.

State-of-the-science workshop report: issues and approaches in low-dose-response extrapolation for environmental health risk assessment.

Low-dose extrapolation model selection for evaluating the health effects of environmental pollutants is a key component of the risk assessment process. At a workshop held in Baltimore, Maryland, on 23-24 April 2007, sponsored by U.S. Environmental Protection Agency and Johns Hopkins Risk Sciences and Public Policy Institute, a multidisciplinary group of experts reviewed the state of the science regarding low-dose extrapolation modeling and its application in environmental health risk assessments. Participants identified discussion topics based on a literature review, which included examples for which human responses to ambient exposures have been extensively characterized for cancer and/or noncancer outcomes. Topics included the need for formalized approaches and criteria to assess the evidence for mode of action (MOA), the use of human versus animal data, the use of MOA information in biologically based models, and the implications of interindividual variability, background disease processes, and background exposures in threshold versus nonthreshold model choice. Participants recommended approaches that differ from current practice for extrapolating high-dose animal data to low-dose human exposures, including categorical approaches for integrating information on MOA, statistical approaches such as model averaging, and inference-based models that explicitly consider uncertainty and interindividual variability.

U.S. air quality management: local, regional and global approaches.

The purpose of this article is to review approaches to air quality management (AQM) in the United States. To characterize AQM in the United States, four examples that addressed local, regional, and global scale air pollution are described. These examples include: (1) the Hazardous Air Pollutants (HAPs) program, (2) National Ambient Air Quality Standards (NAAQS) program, (3) "Cap & Trade" programs, and (4) U.S. global pollution control efforts. These four examples were chosen because each presents a different approach to AQM. This was not intended to be a comprehensive description of U.S. AQM programs, but rather representative of selected examples that highlight the themes of this program. Some general principles that are illustrated in the article and are considered important characteristics of U.S. AQM are: Ensure open access to information and transparency in decision making. Develop and sustain a well-trained workforce. Facilitate training, networking, and technology transfer among air quality managers. Integrate planning and coordination of efforts across jurisdictions (across federal, state, and local agencies). Educate and encourage participation of stakeholders. Balance of societal benefits and costs. Apply innovative approaches, where possible. Fund research to improve the scientific basis for problem identification and effective AQM strategy development.

Developing risk estimates for toxic air pollutants through probabilistic risk assessment

The environmental protection agency has recently accelerated its efforts to determine the need to regulate toxic air pollutants. A key input in determining the need for regulation is the characterization of estimated public health risk. this paper examines some aspects of the feasibility of using probabilistic methods for this purpose. The incorporation of expert judgment would be used to address limitations and uncertainties in the available scientific information.(AU)