Background: In quantitative chemical risk assessment, a reference value is an estimate of an exposure to a chemical that is "likely to be without appreciable risk." Because current "deterministic" approaches do not quantitatively characterize the likelihood or severity of harm, the National Academies has recommended using reference values derived from a risk-specific dose that are treated as random variables, with probability distributions characterizing uncertainty and variability. Objectives: In order to build familiarity and address issues needed for routine and standardized derivation of probabilistic risk-specific dose distributions, a case example applying the unified probabilistic framework presented in Chiu and Slob (2015) is developed for acrolein. This case study is based on an updated systematic evidence map of literature (Keshava et al., 2020) identifying nasal lesions reported in Dorman et al. (2008) as the most appropriate endpoint and study for reference value derivation. Methods: The probability distribution was calculated for the risk-specific dose, which in this implementation of the approach was calculated for the dose at which 1% of the human population is estimated to experience minimal lesions, and a probabilistic reference value was computed as the 5th percentile of this distribution. A deterministic reference value was also derived for comparison, and a sensitivity analysis of the probabilistic reference value was conducted investigating alternative assumptions for the point of departure type and exposure duration. Results: The probabilistic reference value of 6 x 10(-4) mg/m(3) was slightly lower than the deterministic reference value of 8 x 10(-4) mg/m(3), and the risk-specific dose distribution had an uncertainty spanning a factor of 137 (95th-5th percentile ratio). Sensitivity analysis yielded slightly higher probabilistic reference values ranging between 9 x 10(-4) mg/m(3) and 2 x 10(-3) mg/m(3). Conclusions: Using a probabilistic approach for deriving a reference value allows quantitative characterization of the severity, incidence, and uncertainty of effects at a given dose. The results can be used to inform risk management decisions and improve risk communication.

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

Background: Biomonitoring studies indicate a trend towards increased human exposure to diisobutyl phthalate (DIBP), a replacement for dibutyl phthalate (DBP). Recent reviews have found DIBP to be a male reproductive toxicant, but have not evaluated other hazards of DIBP exposure. Objective: To inform chemical risk assessment, we performed a systematic review to identify and characterize outcomes within six broad hazard categories (male reproductive, female reproductive, developmental, liver, kidney, and cancer) following exposure of nonhuman mammalian animals to DIBP or the primary metabolite, monoisobutyl phthalate (MIBP). Methods: A literature search was conducted in four online scientific databases [PubMed, Web of Science, Toxline, and Toxic Substances Control Act Test Submissions 2.0 (TSCATS2)], and augmented by review of regulatory sources as well as forward and backward searches. Studies were identified for inclusion based on defined PECO (Population, Exposure, Comparator, Outcome) criteria. Studies were evaluated using criteria defined a priori for reporting quality, risk of bias, and sensitivity using a domain-based approach. Evidence was synthesized by outcome and life stage of exposure, and strength of evidence was summarized into categories of robust, moderate, slight, indeterminate, or compelling evidence of no effect, using a structured framework. Results: Nineteen toxicological studies in rats or mice met the inclusion criteria. There was robust evidence that DIBP causes male reproductive toxicity. Male rats and mice exposed to DIBP during gestation had decreased testosterone and adverse effects on sperm or testicular histology, with additional phthalate syndrome effects observed in male rats. There was also evidence of androgen-dependent and -independent male reproductive effects in rats and mice following peripubertal or young adult exposure to DIBP or MIBP, but confidence was reduced because of concerns over risk of bias and sensitivity in the available studies. There was also robust evidence that DIBP causes developmental toxicity; specifically, increased post-implantation loss and decreased pre- and postnatal growth. For other hazards, evidence was limited by the small number of studies, experimental designs that were suboptimal for evaluating outcomes, and study evaluation concerns such as incomplete reporting of methods and results. There was slight evidence for female reproductive toxicity and effects on liver, and indeterminate evidence for effects on kidney and cancer. Conclusion: Results support DIBP as a children's health concern and indicate that male reproductive and developmental toxicities are hazards of DIBP exposure, with some evidence for female reproductive and liver toxicity. Data gaps include the need for more studies on male reproductive effects following postnatal and adult exposure, and studies to characterize potential hormonal mechanisms in females.

Background: The phthalate syndrome (PS) is a collection of related male reproductive developmental effects, ranging in severity, that have been observed in rats after gestational exposure to developmentally-toxic phthalates. For statistical purposes, the PS is defined as a single endpoint and one dose-response analysis is conducted, rather than conducting multiple analyses on each individual endpoint. Objective: To improve dose-response modeling approaches for the PS and other syndromes of effects by accounting for differing severity levels among the endpoints. Methods: Ordinal dose-response modeling was performed on PS data from a published study of diisobutyl phthalate (DIBP) gestational exposure to male Sprague-Dawley rats. To incorporate PS endpoint severity, the endpoints were categorized into ordinal levels based on the expected impact of male developmental endpoint's on fertility. Then, a benchmark dose was estimated for each ordinal level. A bootstrap procedure was used to account for the nested nature of the data, and a sensitivity analysis was performed to assess the bootstrap results. A comparison of the estimates between the ordinal and the dichotomous model was performed. Results: The ordinal version of the log-logistic model applied to the data categorized by PS endpoint severity level provided benchmark dose estimates that were closer to each other in value and had lower variability than the traditional dichotomous application. The sensitivity analysis confirmed the validity of the bootstrap results. Conclusion: The ordinal dose-response modeling method accounts for severity differences among dichotomous PS endpoints, can be expanded in the future to include more severity levels, and can be used in both single and cumulative phthalate risk assessments.

The use of human biomonitoring data to characterize exposure to environmental contaminants in epidemiology studies has expanded greatly in recent years. Substantial variability in effect measures may arise when using different exposure metrics for a given contaminant, and it is often not clear which metric is the best surrogate for the 'causal' or 'true' exposure. Here we evaluated variability and potential bias in epidemiologic associations resulting from the use of different phthalate exposure metrics in the 2009-2010 National Health and Nutrition Examination Survey (NHANES). We examined associations between urinary phthalate metabolites and the outcomes of body mass index (BMI) and waist circumference (WC). We examined each of the following NHANES-derived exposure metrics for metabolites of individual phthalates: molar excretion rate (nmol/min), molar amount (nmol), molar concentration (nmol/mL, with and without additional model adjustment for creatinine), creatinine corrected molar concentration (nmol/g creatinine), and reconstructed daily phthalate intake (nmol/kg/day). In order to investigate potential biasing effect of each metric, we first assumed that daily intake of the parent phthalate is the causal exposure. We then constructed a simulated population based on the 2009-2010 NHANES, and randomly assigned each individual a di-2-ethylhexyl phthalate (DEHP) intake dose based on a published distribution, but independent of any other factor. Accordingly, all associations between these randomly assigned intake doses and individuals' BMI and WC should be null. Next, demographic data in the NHANES were incorporated into a pharmacokinetic model to predict urinary molar excretions of five DEHP metabolites based on the randomly assigned DEHP intake. The predicted molar excretions were then used to calculate the same exposure metrics listed above. Three exposure metrics (randomly generated intake, excretion rate, urine concentration) showed no significant associations with BMI, which supports the null hypothesis stated above. In contrast, metrics adjusted for creatinine showed a significant negative correlation, and reconstructed daily intake showed a significant positive correlation, indicating the introduction of bias away from the true (i.e., null) association. Interestingly, trends in the simulation analysis were similar to those seen in the observed NHANES data. Our findings show that, at least in this example case, the choice of exposure metric can introduce significant bias of varying magnitude and direction into the calculation of epidemiologic associations.