Derivation of Noncancer Reference Values for Acrylonitrile

Dose‐response assessments were conducted for the noncancer effects of acrylonitrile (AN) for the purposes of deriving subchronic and chronic oral reference dose (RfD) and inhalation reference concentration (RfC) values. Based upon an evaluation of available toxicity data, the irritation and neurological effects of AN were determined to be appropriate bases for deriving reference values. A PBPK model, which describes the toxicokinetics of AN and its metabolite 2‐cyanoethylene oxide (CEO) in both rats and humans, was used to assess the dose‐response data in terms of an internal dose measure for the oral RfD values, but could not be used in deriving the inhalation RfC values. Benchmark dose (BMD) methods were used to derive all reference values. Where sufficient information was available, data‐derived uncertainty factors were applied to the points of departure determined by BMD methods. From this assessment, subchronic and chronic oral RfD values of 0.5 and 0.05 mg/kg/day, respectively, were derived. Similarly, subchronic and chronic inhalation RfC values of 0.1 and 0.06 mg/m³, respectively, were derived. Confidence in the reference values derived for AN was considered to be medium to high, based upon a consideration of the confidence in the key studies, the toxicity database, dosimetry, and dose‐response modeling.     

Evaluation of the benchmark dose for point of departure determination for a variety of chemical classes in applied regulatory settings

Repeated-dose studies received by the New Substances Assessment and Control Bureau (NSACB) of Health Canada are used to provide hazard information toward risk calculation. These studies provide a point of departure (POD), traditionally the NOAEL or LOAEL, which is used to extrapolate the quantity of substance above which adverse effects can be expected in humans. This project explored the use of benchmark dose (BMD) modeling as an alternative to this approach for studies with few dose groups. Continuous data from oral repeated-dose studies for chemicals previously assessed by NSACB were reanalyzed using U.S. EPA benchmark dose software (BMDS) to determine the BMD and BMD 95% lower confidence limit (BMDL(05) ) for each endpoint critical to NOAEL or LOAEL determination for each chemical. Endpoint-specific benchmark dose-response levels , indicative of adversity, were consistently applied. An overall BMD and BMDL(05) were calculated for each chemical using the geometric mean. The POD obtained from benchmark analysis was then compared with the traditional toxicity thresholds originally used for risk assessment. The BMD and BMDL(05) generally were higher than the NOAEL, but lower than the LOAEL. BMDL(05) was generally constant at 57% of the BMD. Benchmark provided a clear advantage in health risk assessment when a LOAEL was the only POD identified, or when dose groups were widely distributed. Although the benchmark method cannot always be applied, in the selected studies with few dose groups it provided a more accurate estimate of the real no-adverse-effect level of a substance.     

Benchmark Dose Calculation for Ordered Categorical Responses

The use of benchmark dose (BMD) calculations for dichotomous or continuous responses is well established in the risk assessment of cancer and noncancer endpoints. In some cases, responses to exposure are categorized in terms of ordinal severity effects such as none, mild, adverse, and severe. Such responses can be assessed using categorical regression (CATREG) analysis. However, while CATREG has been employed to compare the benchmark approach and the no‐adverse‐effect‐level (NOAEL) approach in determining a reference dose, the utility of CATREG for risk assessment remains unclear. This study proposes a CATREG model to extend the BMD approach to ordered categorical responses by modeling severity levels as censored interval limits of a standard normal distribution. The BMD is calculated as a weighted average of the BMDs obtained at dichotomous cutoffs for each adverse severity level above the critical effect, with the weights being proportional to the reciprocal of the expected loss at the cutoff under the normal probability model. This approach provides a link between the current BMD procedures for dichotomous and continuous data. We estimate the CATREG parameters using a Markov chain Monte Carlo simulation procedure. The proposed method is demonstrated using examples of aldicarb and urethane, each with several categories of severity levels. Simulation studies comparing the BMD and BMDL (lower confidence bound on the BMD) using the proposed method to the correspondent estimates using the existing methods for dichotomous and continuous data are quite compatible; the difference is mainly dependent on the choice of cutoffs for the severity levels.     

A Simulation Study of the Influence of Study Design on the Estimation of Benchmark Doses for Developmental Toxicity1

The benchmark dose (BMD)⁴ approach is emerging as replacement to determination of the No Observed Adverse Effect Level (NOAEL) in noncancer risk assessment. This possibility raises the issue as to whether current study designs for endpoints such as developmental toxicity, optimized for detecting pair wise comparisons, could be improved for the purpose of calculating BMDs. In this paper, we examine various aspects of study design (number of dose groups, dose spacing, dose placement, and sample size per dose group) on BMDs for two endpoints of developmental toxicity (the incidence of abnormalities and of reduced fetal weight). Design performance was judged by the mean-squared error (reflective of the variance and bias) of the maximum likelihood estimate (MLE) from the log-logistic model of the 5% added risk level (the likely target risk for a benchmark calculation), as well as by the length of its 95% confidence interval (the lower value of which is the BMD). We found that of the designs evaluated, the best results were obtained when two dose levels had response rates above the background level, one of which was near the ED₀₅, were present. This situation is more likely to occur with more, rather than fewer dose levels per experiment. In this instance, there was virtually no advantage in increasing the sample size from 10 to 20 litters per dose group. If neither of the two dose groups with response rates above the background level was near the ED₀₅, satisfactory results were also obtained, but the BMDs tended to be more conservative (i.e., lower). If only one dose level with a response rate above the background level was present, and it was near the ED₀₅, reasonable results for the MLE and BMD were obtained, but here we observed benefits of larger dose group sizes. The poorest results were obtained when only a single group with an elevated response rate was present, and the response rate was much greater than the ED₀₅. The results indicate that while the benchmark dose approach is readily applicable to the standard study designs and generally observed dose-responses in developmental assays, some minor design modifications would increase the accuracy and precision of the BMD.     

Bootstrap Estimation of Benchmark Doses and Confidence Limits with Clustered Quantal Data

The benchmark dose (BMD) is an exposure level that would induce a small risk increase (BMR level) above the background. The BMD approach to deriving a reference dose for risk assessment of noncancer effects is advantageous in that the estimate of BMD is not restricted to experimental doses and utilizes most available dose-response information. To quantify statistical uncertainty of a BMD estimate, we often calculate and report its lower confidence limit (i.e., BMDL), and may even consider it as a more conservative alternative to BMD itself. Computation of BMDL may involve normal confidence limits to BMD in conjunction with the delta method. Therefore, factors, such as small sample size and nonlinearity in model parameters, can affect the performance of the delta method BMDL, and alternative methods are useful. In this article, we propose a bootstrap method to estimate BMDL utilizing a scheme that consists of a resampling of residuals after model fitting and a one-step formula for parameter estimation. We illustrate the method with clustered binary data from developmental toxicity experiments. Our analysis shows that with moderately elevated dose-response data, the distribution of BMD estimator tends to be left-skewed and bootstrap BMDL s are smaller than the delta method BMDL s on average, hence quantifying risk more conservatively. Statistically, the bootstrap BMDL quantifies the uncertainty of the true BMD more honestly than the delta method BMDL as its coverage probability is closer to the nominal level than that of delta method BMDL. We find that BMD and BMDL estimates are generally insensitive to model choices provided that the models fit the data comparably well near the region of BMD. Our analysis also suggests that, in the presence of a significant and moderately strong dose-response relationship, the developmental toxicity experiments under the standard protocol support dose-response assessment at 5% BMR for BMD and 95% confidence level for BMDL.     

Aldrin and Dieldrin: A Reevaluation of the Cancer and Noncancer Dose‐Response Assessments

The dose‐response analyses of cancer and noncancer health effects of aldrin and dieldrin were evaluated using current methodology, including benchmark dose analysis and the current U.S. Environmental Protection Agency (U.S. EPA) guidance on body weight scaling and uncertainty factors. A literature review was performed to determine the most appropriate adverse effect endpoints. Using current methodology and information, the estimated reference dose values were 0.0001 and 0.00008 mg/kg‐day for aldrin and dieldrin, respectively. The estimated cancer slope factors for aldrin and dieldrin were 3.4 and 7.0 (mg/kg‐day)^?1, respectively (i.e., about 5‐ and 2.3‐fold lower risk than the 1987 U.S. EPA assessments). Because aldrin and dieldrin are no longer used as pesticides in the United States, they are presumed to be a low priority for additional review by the U.S. EPA. However, because they are persistent and still detected in environmental samples, quantitative risk assessments based on the best available methods are required. Recent epidemiologic studies do not demonstrate a causal association between aldrin and dieldrin and human cancer risk. The proposed reevaluations suggest that these two compounds pose a lower human health risk than currently reported by the U.S. EPA.     

Potential Uncertainty Reduction in Model‐Averaged Benchmark Dose Estimates Informed by an Additional Dose Study

A methodology is presented for assessing the information value of an additional dosage experiment in existing bioassay studies. The analysis demonstrates the potential reduction in the uncertainty of toxicity metrics derived from expanded studies, providing insights for future studies. Bayesian methods are used to fit alternative dose‐response models using Markov chain Monte Carlo (MCMC) simulation for parameter estimation and Bayesian model averaging (BMA) is used to compare and combine the alternative models. BMA predictions for benchmark dose (BMD) are developed, with uncertainty in these predictions used to derive the lower bound BMDL. The MCMC and BMA results provide a basis for a subsequent Monte Carlo analysis that backcasts the dosage where an additional test group would have been most beneficial in reducing the uncertainty in the BMD prediction, along with the magnitude of the expected uncertainty reduction. Uncertainty reductions are measured in terms of reduced interval widths of predicted BMD values and increases in BMDL values that occur as a result of this reduced uncertainty. The methodology is illustrated using two existing data sets for TCDD carcinogenicity, fitted with two alternative dose‐response models (logistic and quantal‐linear). The example shows that an additional dose at a relatively high value would have been most effective for reducing the uncertainty in BMA BMD estimates, with predicted reductions in the widths of uncertainty intervals of approximately 30%, and expected increases in BMDL values of 5–10%. The results demonstrate that dose selection for studies that subsequently inform dose‐response models can benefit from consideration of how these models will be fit, combined, and interpreted.     

Model Selection and Estimation with Quantal‐Response Data in Benchmark Risk Assessment

This article describes several approaches for estimating the benchmark dose (BMD) in a risk assessment study with quantal dose‐response data and when there are competing model classes for the dose‐response function. Strategies involving a two‐step approach, a model‐averaging approach, a focused‐inference approach, and a nonparametric approach based on a PAVA‐based estimator of the dose‐response function are described and compared. Attention is raised to the perils involved in data “double‐dipping” and the need to adjust for the model‐selection stage in the estimation procedure. Simulation results are presented comparing the performance of five model selectors and eight BMD estimators. An illustration using a real quantal‐response data set from a carcinogenecity study is provided.     

Influence of Distribution of Animals between Dose Groups on Estimated Benchmark Dose and Animal Welfare for Continuous Effects

The benchmark dose (BMD) approach is increasingly used as a preferred approach for dose–effect analysis, but standard experimental designs are generally not optimized for BMD analysis. The aim of this study was to evaluate how the use of unequally sized dose groups affects the quality of BMD estimates in toxicity testing, with special consideration of the total burden of animal distress. We generated continuous dose–effect data by Monte Carlo simulation using two dose–effect curves based on endpoints with different shape parameters. Eighty‐five designs, each with four dose groups of unequal size, were examined in scenarios ranging from low‐ to high‐dose placements and with a total number of animals set to 40, 80, or 200. For each simulation, a BMD value was estimated and compared with the “true” BMD. In general, redistribution of animals from higher to lower dose groups resulted in an improved precision of the calculated BMD value as long as dose placements were high enough to detect a significant trend in the dose–effect data with sufficient power. The improved BMD precision and the associated reduction of the number of animals exposed to the highest dose, where chemically induced distress is most likely to occur, are favorable for the reduction and refinement principles. The result thereby strengthen BMD‐aligned design of experiments as a means for more accurate hazard characterization along with animal welfare improvements.     

Using In Vitro High‐Throughput Screening Data for Predicting Benzo[k]Fluoranthene Human Health Hazards

Today there are more than 80,000 chemicals in commerce and the environment. The potential human health risks are unknown for the vast majority of these chemicals as they lack human health risk assessments, toxicity reference values, and risk screening values. We aim to use computational toxicology and quantitative high‐throughput screening (qHTS) technologies to fill these data gaps, and begin to prioritize these chemicals for additional assessment. In this pilot, we demonstrate how we were able to identify that benzo[k]fluoranthene may induce DNA damage and steatosis using qHTS data and two separate adverse outcome pathways (AOPs). We also demonstrate how bootstrap natural spline‐based meta‐regression can be used to integrate data across multiple assay replicates to generate a concentration–response curve. We used this analysis to calculate an in vitro point of departure of 0.751 μM and risk‐specific in vitro concentrations of 0.29 μM and 0.28 μM for 1:1,000 and 1:10,000 risk, respectively, for DNA damage. Based on the available evidence, and considering that only a single HSD17B4 assay is available, we have low overall confidence in the steatosis hazard identification. This case study suggests that coupling qHTS assays with AOPs and ontologies will facilitate hazard identification. Combining this with quantitative evidence integration methods, such as bootstrap meta‐regression, may allow risk assessors to identify points of departure and risk‐specific internal/in vitro concentrations. These results are sufficient to prioritize the chemicals; however, in the longer term we will need to estimate external doses for risk screening purposes, such as through margin of exposure methods.     

Assessing human health response in life cycle assessment using ED10s and DALYs: part 2--Noncancer effects.

In Part 1 of this article we developed an approach for the calculation of cancer effect measures for life cycle assessment (LCA). In this article, we propose and evaluate the method for the screening of noncancer toxicological health effects. This approach draws on the noncancer health risk assessment concept of benchmark dose, while noting important differences with regulatory applications in the objectives of an LCA study. We adopt the centraltendency estimate of the toxicological effect dose inducing a 10% response over background, ED10, to provide a consistent point of departure for default linear low-dose response estimates (betaED10). This explicit estimation of low-dose risks, while necessary in LCA, is in marked contrast to many traditional procedures for noncancer assessments. For pragmatic reasons, mechanistic thresholds and nonlinear low-dose response curves were not implemented in the presented framework. In essence, for the comparative needs of LCA, we propose that one initially screens alternative activities or products on the degree to which the associated chemical emissions erode their margins of exposure, which may or may not be manifested as increases in disease incidence. We illustrate the method here by deriving the betaED10 slope factors from bioassay data for 12 chemicals and outline some of the possibilities for extrapolation from other more readily available measures, such as the no observable adverse effect levels (NOAEL), avoiding uncertainty factors that lead to inconsistent degrees of conservatism from chemical to chemical. These extrapolations facilitated the initial calculation of slope factors for an additional 403 compounds; ranging from 10(-6) to 10(3) (risk per mg/kg-day dose). The potential consequences of the effects are taken into account in a preliminary approach by combining the betaED10 with the severity measure disability adjusted life years (DALY), providing a screening-level estimate of the potential consequences associated with exposures, integrated over time and space, to a given mass of chemical released into the environment for use in LCA.     

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.     

Influence of Distribution of Animals between Dose Groups on Estimated Benchmark Dose and Animal Distress for Quantal Responses

Increasingly, dose‐response data are being evaluated with the benchmark dose (BMD) approach rather than by the less precise no‐observed‐adverse‐effect‐level (NOAEL) approach. However, the basis for designing animal experiments, using equally sized dose groups, is still primed for the NOAEL approach. The major objective here was to assess the impact of using dose groups of unequal size on both the quality of the BMD and overall animal distress. We examined study designs with a total number of 200 animals distributed in four dose groups employing quantal data generated by Monte Carlo simulations. Placing more animals at doses close to the targeted BMD provided an estimate of BMD that was slightly better than the standard design with equally sized dose groups. In situations involving a clear dose‐response, this translates into fewer animals receiving high doses and thus less overall animal distress. Accordingly, in connection with risk and safety assessment, animal distress can potentially be reduced by distributing the animals appropriately between dose groups without decreasing the quality of the information obtained.     

Implications of Developmental Toxicity Study Design for Quantitative Risk Assessment

Standard experimental designs for conducting developmental toxicity studies typically include three- or four-dose levels in addition to a control group. Some researchers have suggested that designs with more exposure groups would improve dose-response characterization and risk estimation. Such proposals have not, however, been supported by the results of simulation studies, which instead back the use of fewer dose levels. This discrepancy is partly due to using a known dose–response pattern to generate data, making model choice obvious. While the carcinogenicity literature has explored implications of different study designs, little attention has been given to the role of design in developmental toxicity risk assessment (or noncancer toxicology in general). In this research, we explore the implications of various experimental designs for developmental toxicity by resampling data from a large study of 2,4,5-trichlorophenoxyacetic acid in mice. We compare the properties of benchmark dose (BMD) estimation for different design strategies by randomly selecting animals within particular dose groups from the entire 2,4,5-T database of over 77,000 birth outcomes to create smaller "pseudo-studies" that are representative of standard bioassay sample sizes. Our results show that experimental designs which include more dose levels have advantages in terms of risk characterization and estimation.     

Benchmark Calculations in Risk Assessment Using Continuous Dose-Response Information: The Influence of Variance and the Determination of a Cut-Off Value

A benchmark dose (BMD) is the dose of a chemical that corresponds to a predetermined increase in the response (the benchmark response, BMR) of a health effect. In this article, a method (the hybrid approach) for benchmark calculations from continuous dose-response information is investigated. In the formulation of the methodology, a cut-off value for an adverse health effect has to be determined. It is shown that the influence of variance on the hybrid model depends on the choice of determination of the cut-off point. If the cut-off value is determined as corresponding to a specified tail proportion of the control distribution, P(0), the BMD becomes biased upward when the variance is biased upward. On the contrary, if the cut-off value is directly determined to some level of the continuous response variable, the BMD becomes biased upward when the variance is biased downward. A simulation study was also performed in which the accuracy and precision of the BMD was compared for the two ways of determining the cut-off value. In general, considering BMRs of 1, 5, and 10% (additional risk) the precision of the BMD became higher when the cut-off value was estimated by specifying P(0), relative to the case with a direct determination. Use of the square-root of the maximum-likelihood estimator of the variance in BMD estimation may provide a bias that is reflected by the cut-off formulation (downward bias if specifying P(0), and upward bias if specifying the cut-off, c, directly). This feature may be reduced if an unbiased estimator of the standard deviation is used in the calculations.     

A Review of Recent Advances in Benchmark Dose Methodology

In this review, recent methodological developments for the benchmark dose (BMD) methodology are summarized. Specifically, we introduce the advances for the main steps in BMD derivation: selecting the procedure for defining a BMD from a predefined benchmark response (BMR), setting a BMR, selecting a dose–response model, and estimating the corresponding BMD lower limit (BMDL). Although the last decade has shown major progress in the development of BMD methodology, there is still room for improvement. Remaining challenges are the implementation of new statistical methods in user‐friendly software and the lack of consensus about how to derive the BMDL.     

Part I––Comparing Noncancer Chronic Human Health Reference Values: An Analysis of Science Policy Choices

The goal of this study was to systematically evaluate the choices made in deriving a chronic oral noncancer human health reference value (HHRV) for a given chemical by different organizations, specifically those from the U.S. Environmental Protection Agency, Health Canada, RIVM (the Netherlands), and the U.S. Agency for Toxic Substances and Disease Registry. This analysis presents a methodological approach for comparing both the HHRVs and the specific choices made in the process of deriving an HHRV across these organizations. Overall, across the 96 unique chemicals and 171 two‐way organizational comparisons, the HHRV agreed approximately 26% of the time. A qualitative method for identifying the primary factors influencing these HHRV differences was also developed, using arrays of HHRVs across organizations for the same chemical. The primary factors identified were disagreement on the critical or principal study and differential application of the total uncertainty factor across organizations. Of the cases where the total UF was the primary factor influencing HHRV disagreement, the database UF had the greatest influence.     

Empirical Scaling of Single Oral Lethal Doses Across Mammalian Species Based on a Large Database

The scaling of administered doses to achieve equal degrees of toxic effect in different species has been relatively poorly examined for noncancer toxicity, either empirically or theoretically. We investigate empirical patterns in the correspondence of single oral dose LD, values across several mammalian species for a large number of chemicals based on data reported in the RTECSQ database maintained by the National Institute for Occupational Safety and Health. We find a good correspondence of LD, values across species when the dose levels are expressed in terms of mgadministered per kg of body mass. Our findings contrast with earlier analyses that support scaling doses by the 3/4-power of body mass to achieve equal subacute toxicity of antineoplastic agents. We suggest that, especially for severe toxicity, single- and repeated-dosing regimes may have different cross-species scaling properties, as they may depend on standing levels of defenses and rate of regeneration of defenses, respectively.     

Characterizing Dose-Response I: Critical Assessment of the Benchmark Dose Concept

We present a critical assessment of the benchmark dose (BMD) method introduced by Crump⁽¹⁾ as an alternative method for setting a characteristic dose level for toxicant risk assessment. The no-observed-adverse-effect-level (NOAEL) method has been criticized because it does not use all of the data and because the characteristic dose level obtained depends on the dose levels and the statistical precision (sample sizes) of the study design. Defining the BMD in terms of a confidence bound on a point estimate results in a characteristic dose that also varies with the statistical precision and still depends on the study dose levels.⁽²⁾ Indiscriminate choice of benchmark response level may result in a BMD that reflects little about the dose-response behavior available from using all of the data. Another concern is that the definition of the BMD for the quantal response case is different for the continuous response case. Specifically, defining the BMD for continuous data using a ratio of increased effect divided by the background response results in an arbitrary dependence on the natural background for the endpoint being studied, making comparison among endpoints less meaningful and standards more arbitrary. We define a modified benchmark dose as a point estimate using the ratio of increased effect divided by the full adverse response range which enables consistent placement of the benchmark response level and provides a BMD with a more consistent relationship to the dose-response curve shape.     

On Determining the BMD from Multiple Outcomes in Developmental Toxicity Studies when One Outcome is Intentionally Missing

Public health concerns over the occurrence of developmental abnormalities that can occur as a result of prenatal exposure to drugs, chemicals, and other environmental factors has led to a number of developmental toxicity studies and the use of the benchmark dose (BMD) for risk assessment. To characterize risk from multiple sources, more recent analytic methods involve a joint modeling approach, accounting for multiple dichotomous and continuous outcomes. For some continuous outcomes, evaluating all subjects may not be feasible, and only a subset may be evaluated due to limited resources. The subset can be selected according to a prespecified probability model and the unobserved data can be viewed as intentionally missing in the sense that subset selection results in missingness that is experimentally planned. We describe a subset selection model that allows for sampling pups with malformations and healthy pups at different rates, and includes the well‐known simple random sample (SRS) as a special case. We were interested in understanding how sampling rates that are selected beforehand influence the precision of the BMD. Using simulations we show how improvements over the SRS can be obtained by oversampling malformations, and how some sampling rates can yield precision that is substantially worse than the SRS. We also illustrate the potential for cost saving with oversampling. Simulations are based on a joint mixed effects model, and to account for subset selection, use of case weights to obtain valid dose‐response estimates.