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.     

Models of Neurotoxicity: Extrapolation of Benchmark Doses in Vitro

In risk assessment, no observed exposure level (NOAEL) and benchmark dose (BMD) are usually derived either from epidemiological studies in humans or from animal experiments. In many in vitro studies, concentration-effect/response curves have been analyzed using different mathematical models finalized to the identification of EC50. In the present article, we propose a model to fit dose-response curves in vitro. The BMD approach has been used to compare the cell viability (MIT assay) of different rat (C6 and PC12, glial and neuronal, respectively) and human cell lines (D384 and SK-N-MC, glial and neuronal, respectively) after 24-hour exposure to the following neurotoxic substances: manganese chloride (MnCl2), methyl-mercury (Me-Hg), and the enantiomers of styrene oxide (SO). For all rat and human cell lines, the potency of the examined compounds was: MnCl2 < S-SO < R-SO < Me-Hg. A preliminary comparison with in vivo toxicity data for these substances gave rise to consistent results. Whereas a reasonable agreement between in vitro and in vivo data has been found for Mn and styrene oxide, a wide scatter of LOAEL has been reported for Me-Hg and these appear to be either much higher or lower than the BMD for the MIT assay we observed in vitro.     

Relation Between Benchmark Dose and No-Observed-Adverse-Effect Level in Clinical Research: Effects of Daily Alcohol Intake on Blood Pressure in Japanese Salesmen

The benchmark dose (BMD) is defined as the dose that corresponds to a specific change in an adverse response compared to the response in unexposed subjects, and the lower 95% confidence limit is termed the benchmark dose level (BMDL). In this study, the threshold of daily ethanol intake affecting blood pressure was calculated by both the BMD approach and multiple logistic regression analysis to clarify the relation between the BMDL and no-observed-adverse-effect level (NOAEL). Systolic and diastolic blood pressures (SBP and DBP) and daily ethanol intake were explored in 1,100 Japanese salesmen. The SBP and DBP were positively related to daily ethanol intake (p < 0.001) when adjusting for possible confounders such as age, body mass index, and smoking status. The adjusted risk for hypertension (SBP >or= 140 mmHg or DBP >or= 90 mmHg) increased significantly when daily ethanol intake exceeded 60 g/day, and the categorical dose of interest was 60.1-90 g/day. The BMDL and BMD of ethanol intake for increased SBP and DBP were estimated to be approximately 60 and 75 g/day, respectively. These findings suggest that the BMDL and BMD correspond to the NOAEL and lowest-observed-adverse-effect level, respectively, if the sample number of clinical data is large enough to confirm the dose-response association.     

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.     

Calculation of Benchmark Doses from Continuous Data

A benchmark dose (BMD) is the dose of a substance that corresponds to a prescribed increase in the response (called the benchmark response or BMR) of a health effect. A statistical lower bound on the benchmark dose (BMDL) has been proposed as a replacement for the no-observed-adverse-effect-level (NOAEL) in setting acceptable human exposure levels. A method is developed in this paper for calculating BMDs and BMDLs from continuous data in a manner that is consistent with those calculated from quantal data. The method involves defining an abnormal response, either directly by specifying a cutoff x₀ that separates continuous responses into normal and abnormal categories, or indirectly by specifying the proportion P₀ of abnormal responses expected among unexposed subjects. The method does not involve actually dichotomizing individual continuous responses into quantal responses, and in certain cases can be applied to continuous data in summarized form (e.g., means and standard deviations of continuous responses among subjects in discrete dose groups). In addition to specifying the BMR and either x₀ or P₀ , the method requires specification of the distribution of continuous responses, including specification of the dose-response θ(d) for a measure of central tendency. A method is illustrated for selecting θ(d) to make the probability of an abnormal response any desired dose-response function. This enables the same dose-response model (Weibull, log-logistic, etc.) to be used for the probability of an abnormal response, regardless of whether the underlying data are continuous or quantal. Whenever the continuous responses are normally distributed with standard deviation σ (independent of dose), the method is equivalent to defining the BMD as the dose corresponding to a prescribed change in the mean response relative to σ.     

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.     

Benchmark Dose of Lead Inducing Anemia at the Workplace

To estimate the critical dose of lead inducing anemia in humans, the effects of lead on hemoglobin (Hb) and hematocrit (Hct) levels and red blood cell (RBC) count were examined in 388 male lead-exposed workers with blood lead (BPb) levels of 0.05-5.5 (mean 1.3) micromol/L by using the benchmark dose (BMD) approach. The BPb level was significantly related to Hb (regression coefficient beta=-0.276), RBC (beta=-11.35), and Hct (beta=-0.563) among the workers (p < 0.001) when controlling for age and working status. The average BPb levels were significantly higher in the workers with anemia (1.85 micromol/L), based on the WHO criteria, than in those without anemia (1.26 micromol/L). The benchmark dose levels of BPb (i.e., lower 95% confidence limits of BMD), calculated from the K-power model set at an abnormal probability of 5% in unexposed workers and an excess risk of 5% in exposed workers were estimated to be 0.94 micromol/L (19.5 microg/dl) for Hb, 0.94 micromol/L (19.4 microg/dl) for RBC, and 1.43 micromol/L (29.6 microg/dl) for Hct. These findings suggest that reduction in hematopoietic indicators may be initiated at BPbs below the level currently considered without effect.     

A Simulation Study of Quantitative Risk Assessment for Bivariate Continuous Outcomes

The neurotoxic effects of chemical agents are often investigated in controlled studies on rodents, with binary and continuous multiple endpoints routinely collected. One goal is to conduct quantitative risk assessment to determine safe dose levels. Yu and Catalano (2005) describe a method for quantitative risk assessment for bivariate continuous outcomes by extending a univariate method of percentile regression. The model is likelihood based and allows for separate dose‐response models for each outcome while accounting for the bivariate correlation. The approach to benchmark dose (BMD) estimation is analogous to that for quantal data without having to specify arbitrary cutoff values. In this article, we evaluate the behavior of the BMD relative to background rates, sample size, level of bivariate correlation, dose‐response trend, and distributional assumptions. Using simulations, we explore the effects of these factors on the resulting BMD and BMDL distributions. In addition, we illustrate our method with data from a neurotoxicity study of parathion exposure in rats.     

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.     

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.     

Percentiles of the Product of Uncertainty Factors for Establishing Probabilistic Reference Doses

Exposure guidelines for potentially toxic substances are often based on a reference dose (RfD) that is determined by dividing a no-observed-adverse-effect-level (NOAEL), lowest-observed-adverse-effect-level (LOAEL), or benchmark dose (BD) corresponding to a low level of risk, by a product of uncertainty factors. The uncertainty factors for animal to human extrapolation, variable sensitivities among humans, extrapolation from measured subchronic effects to unknown results for chronic exposures, and extrapolation from a LOAEL to a NOAEL can be thought of as random variables that vary from chemical to chemical. Selected databases are examined that provide distributions across chemicals of inter- and intraspecies effects, ratios of LOAELs to NOAELs, and differences in acute and chronic effects, to illustrate the determination of percentiles for uncertainty factors. The distributions of uncertainty factors tend to be approximately lognormally distributed. The logarithm of the product of independent uncertainty factors is approximately distributed as the sum of normally distributed variables, making it possible to estimate percentiles for the product. Hence, the size of the products of uncertainty factors can be selected to provide adequate safety for a large percentage (e.g., approximately 95%) of RfDs. For the databases used to describe the distributions of uncertainty factors, using values of 10 appear to be reasonable and conservative. For the databases examined the following simple "Rule of 3s" is suggested that exceeds the estimated 95th percentile of the product of uncertainty factors: If only a single uncertainty factor is required use 33, for any two uncertainty factors use 3 x 33 approximately 100, for any three uncertainty factors use a combined factor of 3 x 100 = 300, and if all four uncertainty factors are needed use a total factor of 3 x 300 = 900. If near the 99th percentile is desired use another factor of 3. An additional factor may be needed for inadequate data or a modifying factor for other uncertainties (e.g., different routes of exposure) not covered above.     

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.     

Benchmark Dose Risk Assessment for Formaldehyde Using Airflow Modeling and a Single-Compartment, DNA-Protein Cross-Link Dosimetry Model to Estimate Human Equivalent Doses

Formaldehyde induced squamous-cell carcinomas in the nasal passages of F344 rats in two inhalation bioassays at exposure levels of 6 ppm and above. Increases in rates of cell proliferation were measured by T. M. Monticello and colleagues at exposure levels of 0.7 ppm and above in the same tissues from which tumors arose. A risk assessment for formaldehyde was conducted at the CIIT Centers for Health Research, in collaboration with investigators from Toxicological Excellence in Risk Assessment (TERA) and the U.S. Environmental Protection Agency (U.S. EPA) in 1999. Two methods for dose-response assessment were used: a full biologically based modeling approach and a statistically oriented analysis by benchmark dose (BMD) method. This article presents the later approach, the purpose of which is to combine BMD and pharmacokinetic modeling to estimate human cancer risks from formaldehyde exposure. BMD analysis was used to identify points of departure (exposure levels) for low-dose extrapolation in rats for both tumor and the cell proliferation endpoints. The benchmark concentrations for induced cell proliferation were lower than for tumors. These concentrations were extrapolated to humans using two mechanistic models. One model used computational fluid dynamics (CFD) alone to determine rates of delivery of inhaled formaldehyde to the nasal lining. The second model combined the CFD method with a pharmacokinetic model to predict tissue dose with formaldehyde-induced DNA-protein cross-links (DPX) as a dose metric. Both extrapolation methods gave similar results, and the predicted cancer risk in humans at low exposure levels was found to be similar to that from a risk assessment conducted by the U.S. EPA in 1991. Use of the mechanistically based extrapolation models lends greater certainty to these risk estimates than previous approaches and also identifies the uncertainty in the measured dose-response relationship for cell proliferation at low exposure levels, the dose-response relationship for DPX in monkeys, and the choice between linear and nonlinear methods of extrapolation as key remaining sources of uncertainty.     

Benchmark Dose Analysis via Nonparametric Regression Modeling

Estimation of benchmark doses (BMDs) in quantitative risk assessment traditionally is based upon parametric dose‐response modeling. It is a well‐known concern, however, that if the chosen parametric model is uncertain and/or misspecified, inaccurate and possibly unsafe low‐dose inferences can result. We describe a nonparametric approach for estimating BMDs with quantal‐response data based on an isotonic regression method, and also study use of corresponding, nonparametric, bootstrap‐based confidence limits for the BMD. We explore the confidence limits’ small‐sample properties via a simulation study, and illustrate the calculations with an example from cancer risk assessment. It is seen that this nonparametric approach can provide a useful alternative for BMD estimation when faced with the problem of parametric model uncertainty.     

Bayesian Quantile Impairment Threshold Benchmark Dose Estimation for Continuous Endpoints

Quantitative risk assessment often begins with an estimate of the exposure or dose associated with a particular risk level from which exposure levels posing low risk to populations can be extrapolated. For continuous exposures, this value, the benchmark dose, is often defined by a specified increase (or decrease) from the median or mean response at no exposure. This method of calculating the benchmark dose does not take into account the response distribution and, consequently, cannot be interpreted based upon probability statements of the target population. We investigate quantile regression as an alternative to the use of the median or mean regression. By defining the dose–response quantile relationship and an impairment threshold, we specify a benchmark dose as the dose associated with a specified probability that the population will have a response equal to or more extreme than the specified impairment threshold. In addition, in an effort to minimize model uncertainty, we use Bayesian monotonic semiparametric regression to define the exposure–response quantile relationship, which gives the model flexibility to estimate the quantal dose–response function. We describe this methodology and apply it to both epidemiology and toxicology data.     

Using Physiologically-Based Pharmacokinetic Modeling to Address Nonlinear Kinetics and Changes in Rodent Physiology and Metabolism Due to Aging and Adaptation in Deriving Reference Values for Propylene Glycol Methyl Ether and Propylene Glycol Methyl Ether Acetate

Reference values, including an oral reference dose (RfD) and an inhalation reference concentration (RfC), were derived for propylene glycol methyl ether (PGME), and an oral RfD was derived for its acetate (PGMEA). These values were based on transient sedation observed in F344 rats and B6C3F1 mice during a two‐year inhalation study. The dose‐response relationship for sedation was characterized using internal dose measures as predicted by a physiologically‐based pharmacokinetic (PBPK) model for PGME and its acetate. PBPK modeling was used to account for changes in rodent physiology and metabolism due to aging and adaptation, based on data collected during Weeks 1, 2, 26, 52, and 78 of a chronic inhalation study. The peak concentration of PGME in richly perfused tissues (i.e., brain) was selected as the most appropriate internal dose measure based on a consideration of the mode of action for sedation and similarities in tissue partitioning between brain and other richly perfused tissues. Internal doses (peak tissue concentrations of PGME) were designated as either no‐observed‐adverse‐effect levels (NOAELs) or lowest‐observed‐adverse‐effect levels (LOAELs) based on the presence or the absence of sedation at each time point, species, and sex in the two‐year study. Distributions of the NOAEL and LOAEL values expressed in terms of internal dose were characterized using an arithmetic mean and standard deviation, with the mean internal NOAEL serving as the basis for the reference values, which was then divided by appropriate uncertainty factors. Where data were permitting, chemical‐specific adjustment factors were derived to replace default uncertainty factor values of 10. Nonlinear kinetics, which was predicted by the model in all species at PGME concentrations exceeding 100 ppm, complicate interspecies, and low‐dose extrapolations. To address this complication, reference values were derived using two approaches that differ with respect to the order in which these extrapolations were performed: (1) default approach of interspecies extrapolation to determine the human equivalent concentration (PBPK modeling) followed by uncertainty factor application, and (2) uncertainty factor application followed by interspecies extrapolation (PBPK modeling). The resulting reference values for these two approaches are substantially different, with values from the latter approach being seven‐fold higher than those from the former approach. Such a striking difference between the two approaches reveals an underlying issue that has received little attention in the literature regarding the application of uncertainty factors and interspecies extrapolations to compounds where saturable kinetics occur in the range of the NOAEL. Until such discussions have taken place, reference values based on the former approach are recommended for risk assessments involving human exposures to PGME and PGMEA.     

Correlation of Noncancer Benchmark Doses in Short‐ and Long‐Term Rodent Bioassays

This study investigated whether, in the absence of chronic noncancer toxicity data, short‐term noncancer toxicity data can be used to predict chronic toxicity effect levels by focusing on the dose–response relationship instead of a critical effect. Data from National Toxicology Program (NTP) technical reports have been extracted and modeled using the Environmental Protection Agency's Benchmark Dose Software. Best‐fit, minimum benchmark dose (BMD), and benchmark dose lower limits (BMDLs) have been modeled for all NTP pathologist identified significant nonneoplastic lesions, final mean body weight, and mean organ weight of 41 chemicals tested by NTP between 2000 and 2012. Models were then developed at the chemical level using orthogonal regression techniques to predict chronic (two years) noncancer health effect levels using the results of the short‐term (three months) toxicity data. The findings indicate that short‐term animal studies may reasonably provide a quantitative estimate of a chronic BMD or BMDL. This can allow for faster development of human health toxicity values for risk assessment for chemicals that lack chronic toxicity data.     

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.     

Properties of Model-Averaged BMDLs: A Study of Model Averaging in Dichotomous Response Risk Estimation

Model averaging (MA) has been proposed as a method of accounting for model uncertainty in benchmark dose (BMD) estimation. The technique has been used to average BMD dose estimates derived from dichotomous dose-response experiments, microbial dose-response experiments, as well as observational epidemiological studies. While MA is a promising tool for the risk assessor, a previous study suggested that the simple strategy of averaging individual models' BMD lower limits did not yield interval estimators that met nominal coverage levels in certain situations, and this performance was very sensitive to the underlying model space chosen. We present a different, more computationally intensive, approach in which the BMD is estimated using the average dose-response model and the corresponding benchmark dose lower bound (BMDL) is computed by bootstrapping. This method is illustrated with TiO(2) dose-response rat lung cancer data, and then systematically studied through an extensive Monte Carlo simulation. The results of this study suggest that the MA-BMD, estimated using this technique, performs better, in terms of bias and coverage, than the previous MA methodology. Further, the MA-BMDL achieves nominal coverage in most cases, and is superior to picking the "best fitting model" when estimating the benchmark dose. Although these results show utility of MA for benchmark dose risk estimation, they continue to highlight the importance of choosing an adequate model space as well as proper model fit diagnostics.     

Estimation of Benchmark Dose as the Threshold Amount of Alcohol Consumption for Blood Pressure in Japanese Workers

In order to determine the threshold amount of alcohol consumption for blood pressure, we calculated the benchmark dose (BMD) of alcohol consumption and its 95% lower confidence interval (BMDL) in Japanese workers. The subjects consisted of 4,383 males and 387 females in a Japanese steel company. The target variables were systolic, diastolic, and mean arterial pressures. The effects of other potential covariates such as age and body mass index were adjusted by including these covariates in the multiple linear regression models. In male workers, BMD/BMDL for alcohol consumption (g/week) at which the probability of an adverse response was estimated to increase by 5% relative to no alcohol consumption, were 396/315 (systolic blood pressure), 321/265 (diastolic blood pressure), and 326/269 (mean arterial pressures). These values were based on significant regression coefficients of alcohol consumption. In female workers, BMD/BMDL for alcohol consumption based on insignificant regression coefficients were 693/134 (systolic blood pressure), 199/90 (diastolic blood pressure), and 267/77 (mean arterial pressure). Therefore, BMDs/BMDLs in males were more informative than those in females as there was no significant relationship between alcohol and blood pressure in females. The threshold amount of alcohol consumption determined in this study provides valuable information for preventing alcohol-induced hypertension.