Benchmark Dose Methodology Research Articles

Benchmark dose calculations for PFAS exposure based on two data sets on immunotoxic effects

BackgroundExposure to perfluorinated alkylate substances (PFAS) is associated with harmful effects on human health, including developmental immunotoxicity. This outcome was chosen as the critical effect by the European Food Safety Authority (EFSA), which calculated a new joint reference dose for four PFAS using a Benchmark Dose (BMD) analysis of a study of 1-year old children. However, the U.S. Environmental Protection Agency (EPA) recently proposed much lower exposure limits.MethodsWe explored the BMD methodology for summary and individual data and compared the results with and without grouping for two data sets available. We compared the performance of different dose-response models including a hockey-stick model and a piecewise linear model. We considered different ways of testing the assumption of equal weight-based toxicity of the four PFAS and evaluated more flexible models with exposure indices allowing for differences in toxicity.ResultsResults relying on full and decile-based data were in good accordance. However, BMD results for the larger study were lower than observed by EFSA for the smaller study. EFSA derived a lower confidence limit for the BMD of 17.5 ng/mL for the sum of serum-PFAS concentration, while similar calculations in the larger cohort yielded values of about 1.5 ng/mL. As the assumption of equal weight-based toxicity of the four PFAS seems questionable, we confirmed dose-dependencies that allowed potency differences between PFAS. We also found that models linear in the parameters for the BMD analysis showed superior coverage probabilities. In particular, we found the piecewise linear model to be useful for Benchmark analysis.ConclusionsBoth data sets considered could be analyzed on a decile basis without important bias or loss of power. The larger study showed substantially lower BMD results, both for individual PFAS and for joint exposures. Overall, EFSA’s proposed tolerable exposure limit appears too high, while the EPA proposal is in better accordance with the results.

Statistical analysis of avian reproduction studies

Avian reproduction studies for regulatory risk assessment are undergoing review by regulatory authorities, often leading to requests for statistical re-analysis of older studies using newer methods, sometimes with older study data that do not support these newer methods. We propose detailed statistical protocols with updated statistical methodology for use with both new and older studies and recommend improvements in experimental study design to set up future studies for robust statistical analyses. There is increased regulatory and industry attention to the potential use of benchmark dose (BMD) methodology to derive the endpoint to be used in avian reproduction studies for regulatory risk assessment. We present benefits and limitations of this BMD approach for older studies being re-evaluated and for new studies designed for with BMD analysis anticipated. Model averaging is recommended as preferable to model selection for BMD analysis. Even for a new study following the modified experimental design analyses, with BMD methodology will only be possible for a restricted set of response variables. The judicious use of historical control data, identification of outlier data points, increased use of distributions more consistent with the nature of the data collected as opposed to forcing normality-based methods, and trend-based hypothesis tests are shown to be effective for many studies, but limitations on their applicability are also recognized and explained. Updated statistical methodologies are illustrated with case studies conducted under existing regulatory guidelines that have been submitted for product registrations. Through the adoption of alternative avian reproduction study design elements combined with the suggested revised statistical methodologies the conduct, analyses, and utility of avian reproduction studies for avian risk assessments can be improved.

Meta-analysis of transcriptomic datasets using benchmark dose modeling shows value in supporting radiation risk assessment

Purpose Benchmark dose (BMD) modeling is used to determine the dose of a stressor at which a predefined increase in any biological effect above background occurs (e.g. 10% increase from control values). BMD analytical tools have the capacity to model transcriptional dose-response data to derive BMDs for genes, pathways and gene ontologies. We recently demonstrated the value of this approach to support various areas of radiation research using predominately ‘in-house’ generated datasets. Materials and methods As a continuation of this work, transcriptomic studies of relevance to ionizing radiation were retrieved through the Gene Expression Omnibus (GEO). The datasets were compiled and filtered, then analyzed using BMDExpress. The objective was to determine the reproducibility of BMD values in relation to pathways and genes across different exposure scenarios and compare to those derived using cytogenetic endpoints. A number of graphic visualization approaches were used to determine if BMD outputs could be correlated to parameters such as dose-rate, radiation quality and cell type. Results Curated studies were diverse and derived from experiments with varied design and intent. Despite this, common genes and pathways were identified with low and high dose thresholds. The higher BMD values were associated with immune response and cell death, while transcripts with lower BMD values were generally related to the classic DNA damage response/repair processes, centered on TP53 signaling. Analysis of datasets with relatively similar dose-ranges under comparable experimental conditions showed a bi-modal distribution with a high degree of consistency in BMD values across shared genes and pathways, particularly for those below the 25th percentile of total distribution by dose. The median BMD values were noted to be approximately 0.5 Gy for genes/pathways that comprised mode 1. Furthermore, transcriptional BMD values derived from a subset of genes using in vivo and in vitro datasets were in accord to those using cytogenetic endpoints. Conclusion Overall, the results from this work highlight the value of the BMD methodology to derive meaningful outputs that are consistent across different models, provided the studies are conducted using a similar dose-range.

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.

Quantitative ecotoxicity analysis for pesticide mixtures using benchmark dose methodology

Pesticide mixtures can often be found on crops and in the natural environment due to the usage of multiple pesticides in crop production. However, the toxicity of pesticides is mostly evaluated individually but not jointly. Many studies have pointed out that pesticide mixture may have elevated toxicity compared with its individual counterpart, therefore, it is important to quantify the change in toxicity. Such quantification can provide invaluable information for environmental and ecological risk assessment, and further support risk management to develop appropriate means to mitigate the risk.The objective of this study is to quantify the ecotoxicity of pesticide mixtures composed of different combinations of four pesticides (i.e., Acetemiprid, Carbendazim, Chlorpyrifos, Cyhalothrin) to (1) understand if the co-presence of multiple pesticides will affect the toxicity and (2) to quantitatively approximate the change in toxicity. We first conducted acute toxicity testing and avoidance response testing using earthworms to obtain dose-response data for two different endpoints; then the benchmark dose (BMD) methodology was applied to estimate the toxicity values for the active ingredients of these four pesticides and their mixtures. The BMD analysis results suggest that the ecotoxicity of the active ingredients of the pesticides is very likely to increase when two or more pesticides are used simultaneously, highlighting the importance to consider toxicity of mixtures in the regulatory decision making process. This study demonstrates that the benchmark dose methodology can be a useful tool to quantify the toxicity of chemical mixtures and support cumulative risk assessment accordingly.

Modeling the emetic potencies of food-borne trichothecenes by benchmark dose methodology

Trichothecene mycotoxins commonly co-contaminate cereal products. They cause immunosuppression, anorexia, and emesis in multiple species. Dietary exposure to such toxins often occurs in mixtures. Hence, if it were possible to determine their relative toxicities and assign toxic equivalency factors (TEFs) to each trichothecene, risk management and regulation of these mycotoxins could become more comprehensive and simple. We used a mink emesis model to compare the toxicities of deoxynivalenol, 3-acetyldeoxynivalenol, 15-acetyldeoxynivalenol, nivalenol, fusarenon-X, HT-2 toxin, and T-2 toxin. These toxins were administered to mink via gavage and intraperitoneal injection. The United States Environmental Protection Agency (EPA) benchmark dose software was used to determine benchmark doses for each trichothecene. The relative potencies of each of these toxins were calculated as the ratios of their benchmark doses to that of DON. Our results showed that mink were more sensitive to orally administered toxins than to toxins administered by IP. T-2 and HT-2 toxins caused the greatest emetic responses, followed by FX, and then by DON, its acetylated derivatives, and NIV. Although these results provide key information on comparative toxicities, there is still a need for more animal based studies focusing on various endpoints and combined effects of trichothecenes before TEFs can be established.

Risk Assessment of Negative Sensory Effects in Product Development Using the Benchmark Dose (BMD) Methodology for Continuous Data

AbstractThe Benchmark Dose (BMD) methodology is a statistical tool for quantitative risk assessment. It has been developed as an alternative to the approach of “no observed adverse effect level” or “lowest observed adverse effect level” during the last decades in many scientific fields for risk assessment. Because risk is various and ubiquitous, the BMD methodology has potential applications in the food science field for risk assessment of negative sensory effects in product development and sensory and consumer research. This paper discusses the BMD analysis in a product development effort using ratings data and Thurstonian discriminal d′ data.Practical ApplicationsRisk assessment of negative sensory effects in product development using the Benchmark Dose (BMD) methodology has two major objectives. One objective is to determine an appropriate change of ingredient for a product at an allowed risk. The second objective is to assess the risk of negative sensory effects associated with a particular change. The well‐developed U.S. Environmental Protection Agency's (EPA) Benchmark Dose Modeling Software (BMDS) and the R/S‐Plus codes developed and provided in the paper can be used for BMD analysis.

Epidemiology-based risk assessment using the benchmark dose/margin of exposure approach: the example of ethanol and liver cirrhosis

A novel approach to derive a threshold dose with respect to alcohol-related harm, the benchmark dose (BMD) methodology, is introduced to provide a basis for evidence-based drinking guidelines. This study is the first to calculate a BMD for alcohol exposure using epidemiological cohort data. With this BMD we will be able to calculate the margin of exposure (MOE) for alcohol consumption, which can be used for comparative risk assessment and applied to setting public health policy. Benchmark dose-response modelling of epidemiological data gathered during a recent systematic review and meta-analysis of alcohol consumption as a risk factor for liver cirrhosis morbidity and mortality. For a benchmark response (BMR) of 1.5%, the resulting BMD values were 30.9 g/day for males and 29.7 g/day for females; the corresponding lower one-sided confidence values were 25.7 and 27.2 g/day, respectively. The intake scenario for the Canadian population resulted in an MOE of 1.23. Intake scenarios for individuals as based on the Canadian drinking guidelines led to MOE values between 0.96 and 1.91. Using an uncertainty factor of 10, the acceptable daily intake for alcohol would be 2.6 g/day. The BMD approach was feasible in developing evidence-based guidelines for low-risk drinking. As our calculated MOEs result around unity (i.e. 1) for moderate drinking, it is evident that the current guidelines correspond very well to low risk on the dose-response curve. The BMD methodology therefore validates current guidelines. The results again highlight the health risk associated with alcohol consumption.

Inhalation of an essential metal: Development of reference exposure levels for manganese

Exposures to high levels of manganese by ingestion or inhalation can damage the central nervous system. However, the capacity of environmental manganese to cause neurotoxicity is of most concern following inhalation exposure. Reference exposure levels (RELs) are values developed by California EPA’s Office of Environmental Health Hazard Assessment (OEHHA) to protect the general public from periodic and continual exposures to airborne toxicants. The recently revised guidelines for the development of noncancer RELs encourage the use of benchmark dose methodology where appropriate, and explicitly address the potential susceptibilities associated with early-life exposures (OEHHA, 2008). This paper describes the application of those guidelines to the derivation of RELs to protect the general public from routine 8h and chronic exposures to airborne manganese. The data were amenable to benchmark analysis and the RELs derived reflect the mounting evidence that children represent a population that is differentially susceptible to manganese toxicity.

Replace the NOAEL and LOAEL with the BMDL01 and BMDL10

Although benchmark-dose methodology has existed for more than 20 years, benchmark doses (BMDs) still have not fully supplanted the no-observed-adverse-effect level (NOAEL) and lowest-observed-adverse-effect level (LOAEL) as points of departure from the experimental dose–response range for setting acceptable exposure levels of toxic substances. Among the issues involved in replacing the NOAEL (LOAEL) with a BMD are (1) which added risk level(s) above background risk should be targeted as benchmark responses (BMRs), (2) whether to apply the BMD methodology to both carcinogenic and noncarcinogenic toxic effects, and (3) how to model continuous health effects that aren’t observed in a natural risk-based context like dichotomous health effects. This paper addresses these issues and recommends specific BMDs to replace the NOAEL and LOAEL.

Experimental Design and Statistical Analysis of Studies to Demonstrate a Threshold in Genetic Toxicology: A Mini-review

A mechanistic understanding of genotoxicity is important for the risk assessment of the exposure of human populations to chemicals. The nature of the dose response relationship at low doses is valuable information in the evaluation of the biological importance of such exposures. A range of mathematical and statistical approaches have been used to try to characterize responses at these low doses. Methods include mathematical models which do or do not include thresholds and statistical methods which try to identify No-observable effect levels (NOELs). It is important to appreciate that determination of an NOEL is not evidence for a threshold. There is an increasing appreciation of the potential to identify ‘pragmatic’ thresholds using experimental systems with a range of biomarkers. The accurate characterization and estimation of these dose-response relationships requires careful experimental design which can improve the accuracy of the estimates of the response while avoiding the introduction of artifactual effects. Statistical approaches such as Design of Experiment (DoE) methodology, which builds on the traditional factorial design, can provide efficient approaches for the description and estimation of dose-response relationships of both individual and combinations of agents. Estimation approaches such as the benchmark dose methodology and the concept of thresholds of toxicological concern provide practical methods for addressing the threshold problem.

The role of the benchmark dose in a regulatory context

The use of no observed adverse effect levels (NOAELs) as a way of interpreting toxicology studies carries a number of problems, and the benchmark dose (BMD), or its lower confidence limit have been proposed as potential replacements. In practice, the theoretical advantages of the BMD approach are often outweighed by the practical disadvantages posed in a regulatory context. Attempts to seek consensus for the routine use of BMD methodology tend to involve diluting its potential advantages as much as they address the disadvantages, resulting in a relatively complex interpolation tool that delivers little more than the NOAEL. It is time to recognise that the BMD will never entirely replace the NOAEL. The two methods can have complementary roles. The NOAEL is well suited as a routine simple summary of effects in toxicology studies, whilst the BMD can be a higher tier approach for the interpretation of the most critical studies in a regulatory data package.

An evaluation of benchmark dose methodology for non-cancer continuous-data health effects in animals due to exposures to dioxin (TCDD)

The U.S. Environmental Protection Agency (EPA) has conducted extensive reviews and analyses of health effects associated with exposures to 2,3,7,8-tetrachlorodibenzo- p-dioxin (TCDD) and related compounds. Because the carcinogenicity of TCDD has received considerable attention from EPA and others, this paper focuses on animal data for non-cancer health effects that sometimes appear to be almost as sensitive as cancer to TCDD exposures. Benchmark dose (BMD) methodology can be used to identify point-of-departure (POD) estimates for use in derivation of reference doses or evaluation of margins of exposure. However, selection of an appropriate BMD methodology for assessment of non-cancer data, which are usually continuous (non-quantal), needs to be considered. One option available for a benchmark dose is to use a small percentage change in the mean response relative to the estimated maximum effect of TCDD at large doses. The benchmark based on a change estimated to equal 1% of the estimated maximum change from background to the asymptotic response at large doses (denoted as the relative ED 01) was used by EPA in a reassessment of TCDD health risks. A lower confidence limit (LED 01) could serve as a point of departure for setting a reference dose (RfD). This is a somewhat arbitrary effect level, generally within the background range of variation among unexposed animals, with an unknown risk. An alternative approach is recommended in which the risk of abnormal levels can be estimated. For continuous-data effects, a low and/or high percentile (e.g., 1st and/or 99th) in unexposed control animals can be used to define abnormal (not necessarily adverse) levels. From a dose–response curve and the standard deviation, it is possible to estimate the excess risk (proportion) of animals with abnormal levels as a function of dose for normally distributed levels. With this approach, the risk-based benchmark dose (BMD 01) represents the dose with an estimated excess risk of 1% of the animals in the abnormal range rather than an arbitrary change in the value of the measured endpoint. Values for the relative and risk-based benchmark doses are computed from published data for a variety of non-cancer health effects associated with exposure to TCDD. For the 30 cases investigated, the BMD 01 tended to vary around the lowest experimental dose tested, whereas the relative ED 01 tended to be about a factor of three below the lowest dose, and the BMD 01 was more precisely estimated than the ED 01 as reflected by narrower confidence intervals. The BMDL 01 values were on average more than fivefold higher than the corresponding LED 01 values. However, these values still provide a conservative assessment for POD assessment, because the BMDL 01 tends to be about an order of magnitude lower (more conservative) than the no-observed-adverse-effect level. This analysis demonstrates the potential impact of alternative choices in benchmark dose methodology. In combination with selection of appropriate adverse health effect endpoint(s) and studies, use of the risk-based BMD results in identification of more valid and meaningful POD estimates for non-cancer effects compared to the use of the relative ED approach.

An evaluation of benchmark dose methodology for non-cancer continuous-data health effects in animals due to exposures to dioxin (TCDD)

The U.S. Environmental Protection Agency (EPA) has conducted extensive reviews and analyses of health effects associated with exposures to 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) and related compounds. Because the carcinogenicity of TCDD has received considerable attention from EPA and others, this paper focuses on animal data for non-cancer health effects that sometimes appear to be almost as sensitive as cancer to TCDD exposures. Benchmark dose (BMD) methodology can be used to identify point-of-departure (POD) estimates for use in derivation of reference doses or evaluation of margins of exposure. However, selection of an appropriate BMD methodology for assessment of non-cancer data, which are usually continuous (non-quantal), needs to be considered. One option available for a benchmark dose is to use a small percentage change in the mean response relative to the estimated maximum effect of TCDD at large doses. The benchmark based on a change estimated to equal 1% of the estimated maximum change from background to the asymptotic response at large doses (denoted as the relative ED01) was used by EPA in a reassessment of TCDD health risks. A lower confidence limit (LED01) could serve as a point of departure for setting a reference dose (RfD). This is a somewhat arbitrary effect level, generally within the background range of variation among unexposed animals, with an unknown risk. An alternative approach is recommended in which the risk of abnormal levels can be estimated. For continuous-data effects, a low and/or high percentile (e.g., 1st and/or 99th) in unexposed control animals can be used to define abnormal (not necessarily adverse) levels. From a dose–response curve and the standard deviation, it is possible to estimate the excess risk (proportion) of animals with abnormal levels as a function of dose for normally distributed levels. With this approach, the risk-based benchmark dose (BMD01) represents the dose with an estimated excess risk of 1% of the animals in the abnormal range rather than an arbitrary change in the value of the measured endpoint. Values for the relative and risk-based benchmark doses are computed from published data for a variety of non-cancer health effects associated with exposure to TCDD. For the 30 cases investigated, the BMD01 tended to vary around the lowest experimental dose tested, whereas the relative ED01 tended to be about a factor of three below the lowest dose, and the BMD01 was more precisely estimated than the ED01 as reflected by narrower confidence intervals. The BMDL01 values were on average more than fivefold higher than the corresponding LED01 values. However, these values still provide a conservative assessment for POD assessment, because the BMDL01 tends to be about an order of magnitude lower (more conservative) than the no-observed-adverse-effect level. This analysis demonstrates the potential impact of alternative choices in benchmark dose methodology. In combination with selection of appropriate adverse health effect endpoint(s) and studies, use of the risk-based BMD results in identification of more valid and meaningful POD estimates for non-cancer effects compared to the use of the relative ED approach.

Quantitative risk analyses for RCF: survey and synthesis

Refractory ceramic fiber (RCF) is a high-temperature insulating fiber used principally in industrial applications. Epidemiological studies on occupationally exposed cohorts have not indicated that exposure leads to fibrosis, increased lung cancer, or mesothelioma. However, inhalation bioassays with rats and hamsters have shown that these animals can develop each of these endpoints when exposed to high levels of RCF-particle mixtures. This work summarizes relevant quantitative risk analyses based upon analysis of the rat bioassay studies, which lead to predicted unit risks that range nearly three orders of magnitude. Additionally, we identify key assumptions that affect the risk estimates and provide additional estimates using the benchmark dose methodology favored by the U.S. EPA in cases where mechanistic models are inadequate or not available. We show that a key determinant of risk is how lung burdens are normalized (e.g., in terms of the number of fibers per square centimeter of lung surface or the number of fibers per milligram dry lung) for species conversion. Plausible values of unit potency/risk range from 1.4 × 10 −4 to 7.2 × 10 −4, neglecting any allowance for the effects of particulate material in the RCF tested in the bioassay experiments.

A proposed inhalation reference concentration for methanol

A biologically based approach was taken to developing an inhalation Reference Concentration (RfC) for methanol, a high production volume chemical with many commercial applications, including use as an alternative fuel for motor vehicles and as a hydrogen source for fuel cells. Benchmark Dose methodology was applied to the most sensitive toxic endpoint for assessing potential health risks in humans, cervical rib malformation data obtained using CD-1 mice. The concentration of methanol in circulating blood was employed as the dose metric, and the maximum likelihood estimate of the blood methanol increment causing a 10% extra risk of these malformations, was 215.4 mg/L, with a lower 95% confidence bound of 97.4 mg/L. A “Reference Increment” for blood methanol was then determined by dividing this value by a 3-fold factor for residual pharmacodynamic uncertainty between species and a 10-fold factor for interindividual variation in human sensitivity to methanol. The resulting Reference Increment in blood methanol was then converted to an equivalent inhalation Reference Concentration with a physiologically based pharmacokinetic model evaluated for continuous exposure conditions. The resulting maximum likelihood estimate for the inhalation RfC was 298 mg/m 3, with a 95% lower confidence bound of 135 mg/m 3.

Evaluation of Subchronic Toxicity Data Using the Benchmark Dose Approach

We used the benchmark dose (BMD) methodology devised by Crump (Fundam. Appl. Toxicol.4, 854–871, 1984) to estimate BMDs for 90-day toxicological data and several fabricated data sets. From a toxicological perspective, dose–response modeling offers certain advantages over using a point estimate, such as the currently used no-observable-adverse-effect level (NOAEL) approach. However, there are many variables associated with the BMD that could be set to produce unreasonable BMD estimates. Some of these variables and decisions are examined in this study. BMDs were calculated for discrete and continuous endpoints using a variety of different variables (e.g., maximum likelihood estimates [MLEs], lower-confidence limits [LCLs], and different risk levels). In addition, the fabricated data sets were manipulated (i.e., dose groups eliminated) and the BMDs recalculated. This process tested how the BMD estimates varied using different forms of the data. For the 90-day toxicological studies, the BMDs were typically within an order of magnitude of the NOAEL for discrete endpoints. For the discrete endpoints, the MLEs were typically greater than the NOAEL and the LCLs were typically less than the NOAEL. The BMD was insensitive to changes in the data points one to two dose groups beyond the NOAEL/LOAEL. With the continuous data, the ratios of MLEs and LCLs to the NOAEL were highly variable, and no general trend could be determined. The BMD methodology offers potential improvements in the risk assessment process since dose–response characteristics are used to calculate the BMD. Depending upon how the BMD is defined, i.e., the form of the dose–response model, and how the BMD is used in the risk assessment process, BMD estimates may produce reference doses/concentrations that are more or less conservative than the NOAEL approach. Active involvement in discussions with regulatory agencies is needed to ensure that inappropriate models and unreasonable BMDs are not used. In addition, further discussions on how BMDs should be used in the risk assessment process are needed.

The use of benchmark dose methodology with acute inhalation lethality data.

Benchmark dose methodology has been proposed as a refinement to the no observed adverse effect level (NOAEL) methods currently used for health risk assessments. We compared log-normal probit and quantal Weibull benchmark concentration (BMC) estimates using 1, 5, and 10% response incidences with inhalation toxicity NOAELs and LOAELs from 120 acute lethality data sets. These studies yielded relatively steep dose-response slopes, which in turn influenced the suitability of selecting response incidences. The mean magnitude of difference between the 95% lower confidence limits (LCLs) for 1, 5, or 10% BMCs and corresponding NOAELs was less than twofold using the probit model and less than fourfold using the Weibull model. BMC estimates at the 10% response exceeded the observed LOAEL in some cases. Maximum likelihood estimates for doses with 1, 5, or 10% responses frequently exceeded LOAELs. The probit model repeatedly gave a better fit for the data compared with the Weibull model, resulting in improved goodness of fit tests and reduced 95% confidence intervals. The 95% LCL appears to be necessary at the 1, 5, or 10% response levels in order to safely estimate a concentration below that resulting in a LOAEL.

Review of noncancer risk assessment: Applications of benchmark dose methods

The overall goal of this project is to evaluate and compare risk assessment methods traditionally used for noncancer health risks and to compare them with new approaches. These methods include the no observed adverse effect level (NOAEL), lowest observed adverse effect level (LOAEL), and the more recently proposed benchmark dose (BMD), in which a dose is identified using a curve‐fitting procedure and a prespecified effect level. Applications of the BMD method are reviewed for developmental toxicity, reproductive toxicity, neurotoxicity, ecological toxicity, carcinogenicity, and other biological impacts. Evaluations have shown that the benchmark method is generally no more conservative than the NOAEL approach and confers several significant advantages for safety assessment. In addition, traditional safety factor approaches used for RfD calculation based on LOAEL values are overly conservative. Studies show that exposures at NOAELs are not “risk free”; but may represent effect levels ranging from 3% up to 21%. An important advantage of BMD approaches is that regulatory limits can be consistently set at a given response level rather than being dictated by study design. BMD methodology also allows for easy transition to biologically based dose response modeling as such models are developed.

The application of benchmark dose methodology to data from prenatal developmental toxicity studies

The benchmark dose (BMD) concept was applied to 246 prenatal-developmental toxicity (DT) datasets from government, industry and commercial laboratories. Five modeling approaches were used, 2 generic and 3 specific to DT models. BMDs for both quantal and continuous data were compared with statistically derived no observed adverse effect levels (NOAELs) to determine similarities. Quantal (Q) endpoints included litter responses (e.g., one or more dead or malformed implants), and QBMDs were calculated using a Q Weibull (QW) model. Two types of continuous (C) data were modeled, the proportion of implants affected per litter, and the change in fetal weight (both mean and distribution); continuous power (CP) and DT models were used to calculate CBMDs. QBMDs for a 5% change in response (QBMD 05,) were 6-fold lower, on average, than the corresponding NOAEL. CBMD 05s on average were similar to the corresponding NOAELs, and CBMD 05s from different models were similar to each other. Including litter size but not threshold improved the fit of the DT models. For fetal weight data, specific cutoff values were used to calculate BMDs that were similar on average to the corresponding NOAELs: (1) changes from the control mean (5% of the mean, 25th percentile of the control distribution, or a decrease of 0.5 standard deviation), and (2) a 5 or 10% decrease in the proportion of fetuses below the 5th or 10th percentile, respectively, of the control distribution. These results support the use of BMDs as providing a more consistent basis for risk assessment than do NOAELs.