Introduction to benchmark dose methods and U.S. EPA's benchmark dose software (BMDS) version 2.1.1

Davis, Jerry; Gift, Jeffrey S.; Zhao, Qianqian

doi:10.1016/j.taap.2010.10.016

Cited by 227 publications

(200 citation statements)

References 14 publications

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“…A series of models were fit using the BMD Software (U.S. Environmental Protection Agency or EPA) including the Hill, exponential 1, exponential 2, exponential 3, exponential 4, exponential 5, polynomial 2 , power, and a linear model (Supplementary Table 1). Model fit and statistical assumptions were assessed to ensure adherence to recommendations outlined by the EPA (Davis et al, 2011). All data were logtransformed to achieve a normal distribution and equal variance across dose groups prior to statistical analyses.…”

Section: Statistical Modeling Of the Individual Drrsmentioning

confidence: 99%

“…The BMD software was then used to identify the best fitting model for the low-dose region of the DRRs. In all instances, statistical assumptions and model fit were assessed via the previously mentioned recommendations of the EPA (Davis et al, 2011). All coefficients acquired from the BMD software were validated with maximum-likelihood estimation using the bbmle package in R (Bolker, 2010;R Core Team, 2015).…”

Section: Statistical Modeling Of the Population Drrsmentioning

confidence: 99%

See 1 more Smart Citation

The Influence of Human Interindividual Variability on the Low-Dose Region of Dose-Response Curve Induced by 2,3,7,8-Tetrachlorodibenzo-p-Dioxin in Primary B Cells

Dornbos¹,

Crawford²,

Kaminski³

et al. 2016

Toxicol. Sci.

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The influence of interindividual variability is not typically assessed in traditional toxicological studies. Given that chemical exposures occur in heterogeneous populations, this knowledge gap has the potential to cause undue harm within the realms of public health and industrial and municipal finances. A recent report from the National Research Council (NRC) suggests that when accounting for interindividual variation in responses, traditionally assumed nonlinear dose-response relationships (DRRs) for noncancer-causing endpoints would better be explained with a linear relationship within the lowdose region. To address this knowledge gap and directly test the NRC's assumption, this study focused on assessing the DRR between 2,3,7,8-tetracholorodibenzo-p-dioxin (TCDD) exposure and immune suppression in a cohort of unique human donors. Human B cells were isolated from 51 individual donors and treated with logarithmically increasing concentrations of TCDD (0-30 nM TCDD). Two endpoints sensitive to TCDD were assessed: (1) number of IgM-secreting B cells and (2) quantity of IgM secreted. The results show that TCDD significantly suppressed both the number of IgM-secreting B cells and the quantity of IgM secreted (P < .05). Statistical model comparisons indicate that the low-dose region of the two DRRs is best explained with a nonlinear relationship. Rather than assuming low-dose linearity for all noncancer-causing DRRs, our study suggests the need to consider the specific mode of action of toxicants and pharmaceuticals during risk-management decision making.Key words: TCDD; AHR; human B cells; dose response; risk assessment; genetic variability.Understanding of the dose-response relationship (DRR) for any given chemical lies at the heart of risk assessment. Traditionally, data derived from lab-based toxicology studies are extrapolated in assessing the potential adverse health effects of chemicals in the human population. As traditional laboratory-based toxicology studies are performed on a small number of individuals or inbred mouse strains and as chemical exposures occur in a heterogeneous population of individuals, there lies a disconnect between lab-based toxicology and population-level risk assessment. Little is known regarding the degree of variability in a DRR within a heterogeneous population (Rhomberg, 2011).A recent report published by the National Research Council (NRC) suggested that interindividual variability within the human population would effectively linearize the low-dose region of DRRs for all noncancer-causing chemicals (NRC, 2009).

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Section: Statistical Modeling Of the Individual Drrsmentioning

confidence: 99%

Section: Statistical Modeling Of the Population Drrsmentioning

confidence: 99%

The Influence of Human Interindividual Variability on the Low-Dose Region of Dose-Response Curve Induced by 2,3,7,8-Tetrachlorodibenzo-p-Dioxin in Primary B Cells

Dornbos¹,

Crawford²,

Kaminski³

et al. 2016

Toxicol. Sci.

View full text Add to dashboard Cite

show abstract

“…sample sizes, exposure durations, routes of exposure, endpoints, and whether the study followed laboratory quality control procedures (Davis et al 2011). relevance of the animal species/strain used, route of exposure, number of dose groups, number of animals per dose group, number and range of endpoints, completeness of data reporting, statistical analysis, and characterization of dose-response (Stern et al 2007).…”

Section: International Journal Of Environmental Health Research 11mentioning

confidence: 99%

“…For example, Bayesian networks depend on the quality of the data-set used to parameterize the network (Jaworska et al 2010), probabilistic inference cannot compensate for biases in the underlying data-sets, and the accuracy of threshold values derived from SSDs are influenced by the quality of data used to generate them (Sánchez-Bayo & Goka 2012; Qin et al 2013). Similarly, input data quality greatly influences the accuracy of BMD results (Stern et al 2007;Dor et al 2009;Davis et al 2011), and specific quality requirements apply to data used in such modeling (Davis et al 2011): Dichotomous data should be reported qualitatively, with mention of specific information about incidence or percentage of response. Continuous data should be reported as a mean measure of biological effect, along with a measure of variability and the number of animals at each dose level (or individual animal data can be used, if available).…”

Section: Maximmentioning

confidence: 99%

A systematic review of methods of uncertainty analysis and their applications in the assessment of chemical exposures, effects, and risks

Maxim¹

2014

International Journal of Environmental Health Research

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Methods of uncertainty analysis are being included increasingly in regulatory chemical risk assessment. Although best practices have been established by several safety agencies in Europe and the United States, they exist only in the grey literature - there has been no comprehensive analysis of the scientific, peer-reviewed literature on these methods. We therefore conducted a systematic review of the recent peer-reviewed literature (2007-2013) on uncertainty analysis relevant to chemical risks. The main objective was to determine whether current methods are robust enough for regulatory use, because the methods used to protect public health must meet the most stringent scientific standards. Based on 297 papers, we concluded that the peer-reviewed literature is much more critical about the disadvantages of those methods, compared to the grey literature. Furthermore, uncertainty analyses can be influenced significantly by subjective expert judgment. As a suggested improvement, we developed guidelines for transparent reporting of uncertainty assessment results.

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“…Nonnormal models such as the gamma (Banga et al, 2002), lognormal (Slob, 2002), or beta-normal (Razzaghi, 2009), along with heteroscedastic normal models , have also been studied. Many of these have made their way into widely available software such as the U.S. EPA's BMDS program (Davis et al, 2012) or the Dutch RIVM's PROAST package (RIVM, 2008). In all these cases, however, only a single-exposure variable was considered.…”

Section: Introduction: Benchmark Analysismentioning

confidence: 99%

Benchmark dose profiles for joint‐action continuous data in quantitative risk assessment

Deutsch

Piegorsch

2013

Biometrical J

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Benchmark analysis is a widely used tool in biomedical and environmental risk assessment. Therein, estimation of minimum exposure levels, called benchmark doses (BMDs), that induce a prespecified benchmark response (BMR) is well understood for the case of an adverse response to a single stimulus. For cases where two agents are studied in tandem, however, the benchmark approach is far less developed. This paper demonstrates how the benchmark modeling paradigm can be expanded from the single-agent setting to joint-action, two-agent studies. Focus is on continuous response outcomes. Extending the single-exposure setting, representations of risk are based on a joint-action dose-response model involving both agents. Based on such a model, the concept of a benchmark profile-a two-dimensional analog of the single-dose BMD at which both agents achieve the specified BMR-is defined for use in quantitative risk characterization and assessment.

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Introduction to benchmark dose methods and U.S. EPA's benchmark dose software (BMDS) version 2.1.1

Cited by 227 publications

References 14 publications

The Influence of Human Interindividual Variability on the Low-Dose Region of Dose-Response Curve Induced by 2,3,7,8-Tetrachlorodibenzo-p-Dioxin in Primary B Cells

The Influence of Human Interindividual Variability on the Low-Dose Region of Dose-Response Curve Induced by 2,3,7,8-Tetrachlorodibenzo-p-Dioxin in Primary B Cells

A systematic review of methods of uncertainty analysis and their applications in the assessment of chemical exposures, effects, and risks

Benchmark dose profiles for joint‐action continuous data in quantitative risk assessment

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