John F. Fox

A systematic evaluation of the potential effects of trichloroethylene exposure on cardiac development.

The 2011 EPA trichloroethylene (TCE) IRIS assessment, used developmental cardiac defects from a controversial drinking water study in rats (Johnson et al. [51]), along with several other studies/endpoints to derive reference values. An updated literature search of TCE-related developmental cardiac defects was conducted. Study quality, strengths, and limitations were assessed. A putative adverse outcome pathway (AOP) construct was developed to explore key events for the most commonly observed cardiac dysmorphologies, particularly those involved with epithelial-mesenchymal transition (EMT) of endothelial origin (EndMT); several candidate pathways were identified. A hypothesis-driven weight-of-evidence analysis of epidemiological, toxicological, in vitro, in ovo, and mechanistic/AOP data concluded that TCE has the potential to cause cardiac defects in humans when exposure occurs at sufficient doses during a sensitive window of fetal development. The study by Johnson et al. [51] was reaffirmed as suitable for hazard characterization and reference value derivation, though acknowledging study limitations and uncertainties.

Nonparametric Bayesian Methods for Benchmark Dose Estimation

The article proposes and investigates the performance of two Bayesian nonparametric estimation procedures in the context of benchmark dose estimation in toxicological animal experiments. The methodology is illustrated using several existing animal dose-response data sets and is compared with traditional parametric methods available in standard benchmark dose estimation software (BMDS), as well as with a published model-averaging approach and a frequentist nonparametric approach. These comparisons together with simulation studies suggest that the nonparametric methods provide a lot of flexibility in terms of model fit and can be a very useful tool in benchmark dose estimation studies, especially when standard parametric models fail to fit to the data adequately.

Enhancing toxicity test performance by using a statistical criterion

Aquatic toxicity tests are laboratory experiments that measure the biological effect (e.g., growth, survival, reproduction) of effluents, receiving waters, or storm water on aquatic organisms. These toxicity tests must be performed using the best laboratory practices, and every effort must be made to enhance repeatability of the test method. We evaluated the generated reference toxicant test data for insurance of a level of quality assurance for tests over time within a laboratory and among laboratories. We recommend the reporting and evaluation of the percent minimum significant difference (PMSD) value for all toxicity test results. The minimum significant difference (MSD) represents the smallest difference between the control mean and a treatment mean that leads to the statistical rejection of the null hypothesis (i.e., no toxicity) at each concentration of the toxicity test dilution series. The MSD provides an indication of within-test variability, and smaller values of MSD are associated with increased power to detect a toxic effect. We recommend upper and lower PMSD bounds for each test method in order to minimize within-test variability and increase statistical power. To ensure that PMSD does not exceed an upper bound, testing laboratories may need to increase replication, decrease variability among replicates, or increase the control mean performance.

Parameters of a Dose‐Response Model Are on the Boundary: What Happens with BMDL?

It is well known that, under appropriate regularity conditions, the asymptotic distribution for the likelihood ratio statistic is chi(2). This result is used in EPAs benchmark dose software to obtain a lower confidence bound (BMDL) for the benchmark dose (BMD) by the profile likelihood method. Recently, based on work by Self and Liang, it has been demonstrated that the asymptotic distribution of the likelihood ratio remains the same if some of the regularity conditions are violated, that is, when true values of some nuisance parameters are on the boundary. That is often the situation for BMD analysis of cancer bioassay data. In this article, we study by simulation the coverage of one- and two-sided confidence intervals for BMD when some of the model parameters have true values on the boundary of a parameter space. Fortunately, because two-sided confidence intervals (size 1-2alpha) have coverage close to the nominal level when there are 50 animals in each group, the coverage of nominal 1-alpha one-sided intervals is bounded between roughly 1-2alpha and 1. In many of the simulation scenarios with a nominal one-sided confidence level of 95%, that is, alpha= 0.05, coverage of the BMDL was close to 1, but for some scenarios coverage was close to 90%, both for a group size of 50 animals and asymptotically (group size 100,000). Another important observation is that when the true parameter is below the boundary, as with the shape parameter of a log-logistic model, the coverage of BMDL in a constrained model (a case of model misspecification not uncommon in BMDS analyses) may be very small and even approach 0 asymptotically. We also discuss that whenever profile likelihood is used for one-sided tests, the Self and Liang methodology is needed to derive the correct asymptotic distribution.

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 Agencys 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.