Current environmental monitoring approaches focus primarily on chemical occurrence. However, based on concentration alone, it can be difficult to identify which compounds may be of toxicological concern and should be prioritized for further monitoring, in-depth testing, or management. This can be problematic because toxicological characterization is lacking for many emerging contaminants. New sources of high-throughput screening (HTS) data, such as the ToxCast database, which contains information for over 9000 compounds screened through up to 1100 bioassays, are now available. Integrated analysis of chemical occurrence data with HTS data offers new opportunities to prioritize chemicals, sites, or biological effects for further investigation based on concentrations detected in the environment linked to relative potencies in pathway-based bioassays. As a case study, chemical occurrence data from a 2012 study in the Great Lakes Basin along with the ToxCast effects database were used to calculate exposure-activity ratios (EARs) as a prioritization tool. Technical considerations of data processing and use of the ToxCast database are presented and discussed. EAR prioritization identified multiple sites, biological pathways, and chemicals that warrant further investigation. Prioritized bioactivities from the EAR analysis were linked to discrete adverse outcome pathways to identify potential adverse outcomes and biomarkers for use in subsequent monitoring efforts.

While chemical analysis of contaminant mixtures remains an essential component of environmental monitoring, bioactivity-based assessments using in vitro systems increasingly are used in the detection of biological effects. Historically, in vitro assessments focused on a few biological pathways, for example, aryl hydrocarbon receptor (AhR) or estrogen receptor (ER) activities. High-throughput screening (HTS) technologies have greatly increased the number of biological targets and processes that can be rapidly assessed. Here we screened extracts of surface waters from a nationwide survey of United States streams for bioactivities associated with 69 different end points using two multiplexed HTS assays. Bioactivity of extracts from 38 streams was evaluated and compared with concentrations of over 700 analytes to identify chemicals contributing to observed effects. Eleven primary biological end points were detected. Pregnane X receptor (PXR) and AhR-mediated activities were the most commonly detected. Measured chemicals did not completely account for AhR and PXR responses. Surface waters with AhR and PXR effects were associated with low intensity, developed land cover. Likewise, elevated bioactivities frequently associated with wastewater discharges included endocrine-related end points ER and glucocorticoid receptor. These results underscore the value of bioassay-based monitoring of environmental mixtures for detecting biological effects that could not be ascertained solely through chemical analyses.

In vitro assays are widely employed to obtain intrinsic clearance estimates used in toxicokinetic modeling efforts. However, the reliability of these methods is seldom reported. Here we describe the results of an international ring trial designed to evaluate two in vitro assays used to measure intrinsic clearance in rainbow trout. An important application of these assays is to predict the effect of biotransformation on chemical bioaccumulation. Six laboratories performed substrate depletion experiments with cyclohexyl salicylate, fenthion, 4-n-nonylphenol, deltamethrin, methoxychlor, and pyrene using cryopreserved hepatocytes and liver S9 fractions from trout. Variability within and among laboratories was characterized as the percent coefficient of variation (CV) in measured in vitro intrinsic clearance rates (CLIN VITRO, INT; ml/h/mg protein or 106 cells) for each chemical and test system. Mean intralaboratory CVs for each test chemical averaged 18.9% for hepatocytes and 14.1% for S9 fractions, whereas interlaboratory CVs (all chemicals and all tests) averaged 30.1% for hepatocytes and 22.4% for S9 fractions. When CLIN VITRO, INT values were extrapolated to in vivo intrinsic clearance estimates (CLIN VIVO, INT; l/d/kg fish), both assays yielded similar levels of activity (<4-fold difference for all chemicals). Hepatic clearance rates (CLH; l/d/kg fish) calculated using data from both assays exhibited even better agreement. These findings show that both assays are highly reliable and suggest that either may be used to inform chemical bioaccumulation assessments for fish. This study highlights several issues related to the demonstration of assay reliability and may provide a template for evaluating other in vitro biotransformation assays.

In response to various legislative mandates, the US Environmental Protection Agency (USEPA) formed its Endocrine Disruptor Screening Program (EDSP), which in turn, formed the basis of a tiered testing strategy to determine the potential of pesticides, commercial chemicals, and environmental contaminants to disrupt the endocrine system. The first tier of tests is intended to detect the potential for endocrine disruption mediated through estrogen, androgen, or thyroid pathways, whereas the second tier is intended to further characterize the effects on these pathways and to establish a dose-response relationship for adverse effects. One of these tier 2 tests, the Medaka Extended One Generation Reproduction Test (MEOGRT), was developed by the USEPA for the EDSP and, in collaboration with the Japanese Ministry of the Environment, for the Guidelines for the Testing of Chemicals of the Organisation for Economic Co-operation and Development (OECD). The MEOGRT protocol was iteratively modified based on knowledge gained after the successful completion of 9 tests with variations in test protocols. The present study describes both the final MEOGRT protocol that has been published by the USEPA and the OECD, and the iterations that provided valuable insights into nuances of the protocol. The various tests include exposure to 17β-estradiol, 4-t-octylphenol, o,p'- dichlorodiphenyltrichloroethane, 4-chloro-3-methylphenol, tamoxifen, 17β-trenbolone, vinclozolin, and prochloraz. Environ Toxicol Chem 2017;36:3387-3403. Published 2017 Wiley Periodicals Inc. on behalf of SETAC. This article is a US government work and, as such, is in the public domain in the United States of America.

Introduction JThis report summarizes the statistical analysis of serum chemistry, hematology, and thiodiglycol data collected in Phase lB of M1REF Task 94-33. This report should be considered a supplement to the Phase I B statistical report dated February 5, 1998. The conclusions drawn in the previous report remain valid. Statistical Methods Serum chemistry and hematology data for each animal on study days 0, 1, 3, and 7 were included I in the statistical analysis. Twice daily thiodiglycol readings from study days 0-7 were included in the analysis. For each parameter, the following analysis of variance (ANOVA) model was fitted to the data: yjI`+Ot+Tj+Ej where yij is the reading for animal j on study day i, g. is the overall mean, ai is a fixed effect for study day i, rj is a random effect for animal j, and Eij is a random error term. Appropriate contrasts were used to estimate the difference in means between study days. For the serum Schemistry and hematology parameters, all pairwise comparisons between study day means were estimated. For the thiodiglycol parameters, AM and PM means on each study day were compared to the pretreatment mean (study day 0, AM reading). For each parameter, a Bonferroni adjustment for multiple comparisons was applied to ensure that the probability of making at least one incorrect conclusion of significance is no higher than 0.05. The SAS (V6.12) MIXED procedure was used to fit the statistical models. Results Descriptive statistics and statistical comparisons are presented in Tables 1, 2, and 3, for the serum chemistry, hematology, and thiodiglycol parameters, respectively. Figures A-I through A-3 8 present the means and 95 percent confidence intervals for each parameter, in the order they are presented in Tables 1 to 3. Similarly, Figures B-1 through B-38 present the data for each animal. Statistical comparisons of serum chemistry and hematology data are summarized by the letters entered in the last row for each parameter in Tables 1 and 2. For these comparisons, study day means that were not significantly different (at an over all 0.05 level) share at least one letter and those that were significantly different do not have a common letter. When no significant differences were noted among the study days, the single letter A appears in each cell (see Aspartate Transaminase). For parameters where significant differences were noted, the results are summarized below, in addition to the table. D •. D-2 Serum Chemistry Parameters (Table 1) Alanine Transaminase: Means on days 1 and 3 were greater than that on day 7. No differences were noted in comparisons to the mean on day 0. Albumin: The mean on day 3 was greater than that on day 7. Alkaline Phosphatase: The mean on day 0 was greater than on days 1, 3, and 7. The mean on day 1 was greater than on days 3 and 7. The means on days 3 and 7 were not significantly different. Amylase: The mean on day 0 was greater than on days 1, 3, and 7. Blood Urea Nitrogen: The mean on day 0 was less than on days 1, 3, and 7. Calcium : The mean on day I was s...