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.

Quantitative Risk Assessment: Developing a Bayesian Approach to Dichotomous Dose-Response Uncertainty.

Model averaging for dichotomous dose-response estimation is preferred to estimate the benchmark dose (BMD) from a single model, but challenges remain regarding implementing these methods for general analyses before model averaging is feasible to use in many risk assessment applications, and there is little work on Bayesian methods that include informative prior information for both the models and the parameters of the constituent models. This article introduces a novel approach that addresses many of the challenges seen while providing a fully Bayesian framework. Furthermore, in contrast to methods that use Monte Carlo Markov Chain, we approximate the posterior density using maximum a posteriori estimation. The approximation allows for an accurate and reproducible estimate while maintaining the speed of maximum likelihood, which is crucial in many applications such as processing massive high throughput data sets. We assess this method by applying it to empirical laboratory dose-response data and measuring the coverage of confidence limits for the BMD. We compare the coverage of this method to that of other approaches using the same set of models. Through the simulation study, the method is shown to be markedly superior to the traditional approach of selecting a single preferred model (e.g., from the U.S. EPA BMD software) for the analysis of dichotomous data and is comparable or superior to the other approaches.

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.

Applying comprehensive environmental assessment to research planning for multiwalled carbon nanotubes: Refinements to inform future stakeholder engagement.

Risk assessments and risk management efforts to protect human health and the environment can benefit from early, coordinated research planning by researchers, risk assessors, and risk managers. However, approaches for engaging these and other stakeholders in research planning have not received much attention in the environmental scientific literature. The Comprehensive Environmental Assessment (CEA) approach under development by the United States Environmental Protection Agency (USEPA) is a means to manage complex information and input from diverse stakeholder perspectives on research planning that will ultimately support environmental and human health decision making. The objectives of this article are to 1) describe the outcomes of applying lessons learned from previous CEA applications to planning research on engineered nanomaterial, multiwalled carbon nanotubes (MWCNTs) and 2) discuss new insights and refinements for future efforts to engage stakeholders in research planning for risk assessment and risk management of environmental issues. Although framed in terms of MWCNTs, this discussion is intended to enhance research planning to support assessments for other environmental issues as well. Key insights for research planning include the potential benefits of 1) ensuring that participants have research, risk assessment, and risk management expertise in addition to diverse disciplinary backgrounds; 2) including an early scoping step before rounds of formal ratings; 3) using a familiar numeric scale (e.g., US dollars) versus ordinal rating scales of "importance"; 4) applying virtual communication tools to supplement face-to-face interaction between participants; and 5) refining criteria to guide development of specific, actionable research questions.

Model Uncertainty and Bayesian Model Averaged Benchmark Dose Estimation for Continuous Data.

The benchmark dose (BMD) approach has gained acceptance as a valuable risk assessment tool, but risk assessors still face significant challenges associated with selecting an appropriate BMD/BMDL estimate from the results of a set of acceptable dose-response models. Current approaches do not explicitly address model uncertainty, and there is an existing need to more fully inform health risk assessors in this regard. In this study, a Bayesian model averaging (BMA) BMD estimation method taking model uncertainty into account is proposed as an alternative to current BMD estimation approaches for continuous data. Using the "hybrid" method proposed by Crump, two strategies of BMA, including both "maximum likelihood estimation based" and "Markov Chain Monte Carlo based" methods, are first applied as a demonstration to calculate model averaged BMD estimates from real continuous dose-response data. The outcomes from the example data sets examined suggest that the BMA BMD estimates have higher reliability than the estimates from the individual models with highest posterior weight in terms of higher BMDL and smaller 90th percentile intervals. In addition, a simulation study is performed to evaluate the accuracy of the BMA BMD estimator. The results from the simulation study recommend that the BMA BMD estimates have smaller bias than the BMDs selected using other criteria. To further validate the BMA method, some technical issues, including the selection of models and the use of bootstrap methods for BMDL derivation, need further investigation over a more extensive, representative set of dose-response data.

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.

Is the assumption of normality or log-normality for continuous response data critical for benchmark dose estimation?

Continuous responses (e.g. body weight) are widely used in risk assessment for determining the benchmark dose (BMD) which is used to derive a U.S. EPA reference dose. One critical question that is not often addressed in dose-response assessments is whether to model the continuous data as normally or log-normally distributed. Additionally, if lognormality is assumed, and only summarized response data (i.e., mean±standard deviation) are available as is usual in the peer-reviewed literature, the BMD can only be approximated. In this study, using the "hybrid" method and relative deviation approach, we first evaluate six representative continuous dose-response datasets reporting individual animal responses to investigate the impact on BMD/BMDL estimates of (1) the distribution assumption and (2) the use of summarized versus individual animal data when a log-normal distribution is assumed. We also conduct simulation studies evaluating model fits to various known distributions to investigate whether the distribution assumption has influence on BMD/BMDL estimates. Our results indicate that BMDs estimated using the hybrid method are more sensitive to the distribution assumption than counterpart BMDs estimated using the relative deviation approach. The choice of distribution assumption has limited impact on the BMD/BMDL estimates when the within dose-group variance is small, while the lognormality assumption is a better choice for relative deviation method when data are more skewed because of its appropriateness in describing the relationship between mean and standard deviation. Additionally, the results suggest that the use of summarized data versus individual response data to characterize log-normal distributions has minimal impact on BMD estimates.

Approaches to cancer assessment in EPA's Integrated Risk Information System.

The U.S. Environmental Protection Agency's (EPA) Integrated Risk Information System (IRIS) Program develops assessments of health effects that may result from chronic exposure to chemicals in the environment. The IRIS database contains more than 540 assessments. When supported by available data, IRIS assessments provide quantitative analyses of carcinogenic effects. Since publication of EPA's 2005 Guidelines for Carcinogen Risk Assessment, IRIS cancer assessments have implemented new approaches recommended in these guidelines and expanded the use of complex scientific methods to perform quantitative dose-response assessments. Two case studies of the application of the mode of action framework from the 2005 Cancer Guidelines are presented in this paper. The first is a case study of 1,2,3-trichloropropane, as an example of a chemical with a mutagenic mode of carcinogenic action thus warranting the application of age-dependent adjustment factors for early-life exposure; the second is a case study of ethylene glycol monobutyl ether, as an example of a chemical with a carcinogenic action consistent with a nonlinear extrapolation approach. The use of physiologically based pharmacokinetic (PBPK) modeling to quantify interindividual variability and account for human parameter uncertainty as part of a quantitative cancer assessment is illustrated using a case study involving probabilistic PBPK modeling for dichloromethane. We also discuss statistical issues in assessing trends and model fit for tumor dose-response data, analysis of the combined risk from multiple types of tumors, and application of life-table methods for using human data to derive cancer risk estimates. These issues reflect the complexity and challenges faced in assessing the carcinogenic risks from exposure to environmental chemicals, and provide a view of the current trends in IRIS carcinogenicity 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.

Health assessment of phosgene: approaches for derivation of reference concentration.

This paper describes the derivation of the chronic reference concentration (RfC) for human inhalation of phosgene that was recently added to the Environmental Protection Agency's (EPA) Integrated Risk Information System (IRIS) data base (U.S. EPA, 2005. Toxicological Review of Phosgene: In Support of Summary Information on the Integrated Risk Information System (IRIS). Available online at: ). The RfC is an estimate of daily phosgene exposure to the human population that is likely to be without appreciable risk of deleterious effects during a lifetime. [For this and other definitions relevant to EPA risk assessments refer to the glossary of terms in the US EPA IRIS website (http://www.epa.gov/IRIS).] Phosgene is a potential environmental pollutant that is primarily used as a catalyst in the polyurethane industry. It is a gas at room temperature, and in aqueous solution it rapidly hydrolyzes to CO2 and HCl. In the absence of chronic human health effects information and lifetime animal cancer bioassays, the RfC is based on two 12-week inhalation studies in F344 rats which measured immune response and pulmonary effects, respectively. The immune response study showed impaired clearance of bacteria that was administered into the lungs of rats immediately after exposure to phosgene at concentrations of 0.1, 0.2 and 0.5 ppm. It also showed that the immune response in uninfected rats was stimulated by phosgene exposure at all concentrations. The pulmonary effects study showed a progressive concentration-related thickening and inflammation in the bronchiolar regions of the lung that was mild at 0.1 ppm and severe at 1.0 ppm. An increase in collagen content, as observed with histological collagen stains, was observed at 0.2 ppm and above. Though there is considerable uncertainty associated with the species and exposure duration employed, this endpoint is considered an indication of chronic lung injury of potential relevance to humans. Three different approaches for RfC derivation were taken in analyzing these studies: (1) the traditional NOAEL/LOAEL method; (2) the benchmark dose (BMD); and (3) the categorical regression for the analysis of severity-graded pulmonary damage data using the recently revised USEPA CatReg software. The BMD approach was selected as the method of choice to determine the RfC for phosgene because it has several advantages compared to the NOAEL/LOAEL: (1) it is not restricted to the set of doses used in the experiments; (2) the result is not dependent on sample size; (3) it incorporates information on statistical uncertainty. The CatReg approach allowed the incorporation of data on the severity of the pathological lesions, and therefore it complemented the other approaches. The BMD approach could not be applied to the immune response data because it was not possible to define an adverse effect level for bacterial resistance. However, NOAEL/LOAEL values for immune responses were consistent with benchmark dose levels derived from lung pathology data and used in the derivation of the RfC. The preferred RfC method and derivation involved dividing the benchmark dose from the collagen staining data (0.03 mg/m3) by a composite uncertainty factor of 100: RfC=0.03/100=3E-4 mg/m3.

Analyses of neurobehavioral screening data: benchmark dose estimation.

Zhu et al. (Zhu, Y., Wessel, M., Liu, T., Moser, V.C., 2005. Analyses of neurobehavioral screening data: dose-time-response modeling of continuous outcomes. Regul. Toxicol. Pharmacol. 41, 240-255) have recently applied dose-time-response models to longitudinal or time-course neurotoxicity data, and have illustrated the modeling process using continuous data from a functional observational battery (FOB). Following the work of these authors, the purpose of this paper is to show that the benchmark dose (BMD) method for single time point dose-response data can be generalized and applied to longitudinal data such as those generated in neurotoxicity studies. We propose a statistical procedure called bootstrap method for computing the lower confidence limits for the BMD. We demonstrate the method using three previously published FOB datasets of triethyltin (Moser, V.C., Becking, G.C., Cuomo, V., Frantik, E., Kulig, B., MacPhail, R.C., Tilson, H.A., Winneke, G., Brightwell, W.S., DeSalvia, M.A., Gill, M.W., Haggerty, G.C., Hornychova, M., Lammers, J., Larsson, J., McDaniel, K.L., Nelson, B.K., Ostergaard, G., 1997a. The IPCS study on neurobehavioral screening methods: results of chemical testing. Neurotoxicology 18, 969-1056.) and the models of Zhu et al. (Zhu, Y., Wessel, M., Liu, T., Moser, V.C., 2005. Analyses of neurobehavioral screening data: dose-time-response modeling of continuous outcomes. Regul. Toxicol. Pharmacol. 41, 240-255).