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The present study evaluated whether in vitro measures of aromatase inhibition as inputs into a quantitative adverse outcome pathway (qAOP) construct could effectively predict in vivo effects on 17β-estradiol (E2) and vitellogenin (VTG) concentrations in female fathead minnows. Five chemicals identified as aromatase inhibitors in mammalian-based ToxCast assays were screened for their ability to inhibit fathead minnow aromatase in vitro. Female fathead minnows were then exposed to 3 of those chemicals: letrozole, epoxiconazole, and imazalil in concentration-response (5 concentrations plus control) for 24 h. Consistent with AOP-based expectations, all 3 chemicals caused significant reductions in plasma E2 and hepatic VTG transcription. Characteristic compensatory upregulation of aromatase and follicle-stimulating hormone receptor (fshr) transcripts in the ovary were observed for letrozole but not for the other 2 compounds. Considering the overall patterns of concentration-response and temporal concordance among endpoints, data from the in vivo experiments strengthen confidence in the qualitative relationships outlined by the AOP. Quantitatively, the qAOP model provided predictions that fell within the standard error of measured data for letrozole but not for imazalil and epoxiconazole. However, the inclusion of measured plasma concentrations of the test chemicals as inputs improved model predictions, with all predictions falling within the range of measured values. Results highlight both the utility and limitations of the qAOP and its potential use in 21st century ecotoxicology. Environ Toxicol Chem 2021;40:1155-1170. © 2020 SETAC. This article has been contributed to by US Government employees and their work is in the public domain in the USA.

Assessment of ecological risks of chemicals in the field usually involves complex mixtures of known and unknown compounds. We describe the use of pathway-based chemical and biological approaches to assess the risk of chemical mixtures in the Maumee River (OH, USA), which receives a variety of agricultural and urban inputs. Fathead minnows (Pimephales promelas) were deployed in cages for 4 d at a gradient of sites along the river and adjoining tributaries in 2012 and during 2 periods (April and June) in 2016, in conjunction with an automated system to collect composite water samples. More than 100 industrial chemicals, pharmaceuticals, and pesticides were detected in water at some of the study sites, with the greatest number typically found near domestic wastewater treatment plants. In 2016, there was an increase in concentrations of several herbicides from April to June at upstream agricultural sites. A comparison of chemical concentrations in site water with single chemical data from vitro high-throughput screening (HTS) assays suggested the potential for perturbation of multiple biological pathways, including several associated with induction or inhibition of different cytochrome P450 (CYP) isozymes. This was consistent with direct effects of water extracts in an HTS assay and induction of hepatic CYPs in caged fish. Targeted in vitro assays and measurements in the caged fish suggested minimal effects on endocrine function (e.g., estrogenicity). A nontargeted mass spectroscopy-based analysis suggested that hepatic endogenous metabolite profiles in caged fish covaried strongly with the occurrence of pesticides and pesticide degradates. These studies demonstrate the application of an integrated suite of measurements to help understand the effects of complex chemical mixtures in the field. Environ Toxicol Chem 2021;40:1098-1122. © 2020 SETAC. This article has been contributed to by US Government employees and their work is in the public domain in the USA.

Predictive approaches to assessing the toxicity of contaminant mixtures have been largely limited to chemicals that exert effects through the same biological molecular initiating event. However, by understanding specific pathways through which chemicals exert effects, it may be possible to identify shared "downstream" nodes as the basis for forecasting interactive effects of chemicals with different molecular initiating events. Adverse outcome pathway (AOP) networks conceptually support this type of analysis. We assessed the utility of a simple AOP network for predicting the effects of mixtures of an aromatase inhibitor (fadrozole) and an androgen receptor agonist (17β-trenbolone) on aspects of reproductive endocrine function in female fathead minnows. The fish were exposed to multiple concentrations of fadrozole and 17β-trenbolone individually or in combination for 48 or 96 h. Effects on 2 shared nodes in the AOP network, plasma 17β-estradiol (E2) concentration and vitellogenin (VTG) production (measured as hepatic vtg transcripts) responded as anticipated to fadrozole alone but were minimally impacted by 17β-trenbolone alone. Overall, there were indications that 17β-trenbolone enhanced decreases in E2 and vtg in fadrozole-exposed fish, as anticipated, but the results often were not statistically significant. Failure to consistently observe hypothesized interactions between fadrozole and 17β-trenbolone could be due to several factors, including lack of impact of 17β-trenbolone, inherent biological variability in the endpoints assessed, and/or an incomplete understanding of interactions (including feedback) between different pathways within the hypothalamic-pituitary-gonadal axis. Environ Toxicol Chem 2020;39:913-922. © 2020 SETAC.

To reduce the use of intact animals for chemical safety testing, while ensuring protection of ecosystems and human health, there is a demand for new approach methodologies (NAMs) that provide relevant scientific information at a quality equivalent to or better than traditional approaches. The present case study examined whether bioactivity and associated potency measured in an in vitro screening assay for aromatase inhibition could be used together with an adverse outcome pathway (AOP) and mechanistically based computational models to predict previously uncharacterized in vivo effects. Model simulations were used to inform designs of 60-h and 10-21-day in vivo exposures of adult fathead minnows (Pimephales promelas) to three or four test concentrations of the in vitro aromatase inhibitor imazalil ranging from 0.12 to 260 µg/L water. Consistent with an AOP linking aromatase inhibition to reproductive impairment in fish, exposure to the fungicide resulted in significant reductions in ex vivo production of 17β-estradiol (E2) by ovary tissue (≥165 µg imazalil/L), plasma E2 concentrations (≥74 µg imazalil/L), vitellogenin (Vtg) messenger RNA expression (≥165 µg imazalil/L), Vtg plasma concentrations (≥74 µg imazalil/L), uptake of Vtg into oocytes (≥260 µg imazalil/L), and overall reproductive output in terms of cumulative fecundity, number of spawning events, and eggs per spawning event (≥24 µg imazalil/L). Despite many potential sources of uncertainty in potency and efficacy estimates based on model simulations, observed magnitudes of apical effects were quite consistent with model predictions, and in vivo potency was within an order of magnitude of that predicted based on in vitro relative potency. Overall, our study suggests that NAMs and AOP-based approaches can support meaningful reduction and refinement of animal testing. Environ Toxicol Chem 2023;42:100-116. © 2022 SETAC. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

The androgen receptor (AR) is a target of interest for endocrine disruption research, as altered signaling can affect normal reproductive and neurological development for generations. In an effort to prioritize compounds with alternative methodologies, the U.S. Environmental Protection Agency (EPA) used in vitro data from 11 assays to construct models of AR agonist and antagonist signaling pathways. While these EPA ToxCast AR models require in vitro data to assign a bioactivity score, Bayesian machine learning methods can be used for prospective prediction from molecule structure alone. This approach was applied to multiple types of data corresponding to the EPA’s AR signaling pathway with proprietary software, Assay Central®. The training performance of all machine learning models, including six other algorithms, was evaluated by internal five-fold cross-validation statistics. Bayesian machine learning models were also evaluated with external predictions of reference chemicals to compare prediction accuracies to published results from the EPA. The machine learning model group selected for further studies of endocrine disruption consisted of continuous AC(50) data from the February 2019 release of ToxCast/Tox21. These efforts demonstrate how machine learning can be used to predict AR-mediated bioactivity and can also be applied to other targets of endocrine disruption.

The U.S. Environmental Protection Agency (EPA) periodically releases in vitro data across a variety of targets, including the estrogen receptor (ER). In 2015, the EPA used these data to construct mathematical models of ER agonist and antagonist pathways to prioritize chemicals for endocrine disruption testing. However, mathematical models require in vitro data prior to predicting estrogenic activity, but machine learning methods are capable of prospective prediction from the molecular structure alone. The current study describes the generation and evaluation of Bayesian machine learning models grouped by the EPA's ER agonist pathway model using multiple data types with proprietary software, Assay Central. External predictions with three test sets of in vitro and in vivo reference chemicals with agonist activity classifications were compared to previous mathematical model publications. Training data sets were subjected to additional machine learning algorithms and compared with rank normalized scores of internal five-fold cross-validation statistics. External predictions were found to be comparable or superior to previous studies published by the EPA. When assessing six additional algorithms for the training data sets, Assay Central performed similarly at a reduced computational cost. This study demonstrates that machine learning can prioritize chemicals for future in vitro and in vivo testing of ER agonism.

Quantitative adverse outcome pathways (qAOPs) describe quantitative response-response relationships that can predict the probability or severity of an adverse outcome for a given magnitude of chemical interaction with a molecular initiating event. However, the taxonomic domain of applicability for these predictions is largely untested. The present study began defining this applicability for a previously described qAOP for aromatase inhibition leading to decreased fecundity developed using data from fathead minnow (Pimephales promelas). This qAOP includes quantitative response-response relationships describing plasma 17β-estradiol (E2) as a function of plasma fadrozole, plasma vitellogenin (VTG) as a function of plasma E2, and fecundity as a function of plasma VTG. These quantitative response-response relationships simulated plasma E2, plasma VTG, and fecundity measured in female zebrafish (Danio rerio) exposed to fadrozole for 21 days but not these responses measured in female Japanese medaka (Oryzias lat...

Although endocrine disrupting compounds have been detected in wastewater and surface waters worldwide using a variety of in vitro effects-based screening tools, e.g. bioassays, few have examined potential attenuation of environmental contaminants by both natural (sorption, degradation, etc) and anthropogenic (water treatment practices) processes. This study used several bioassays and quantitative chemical analyses to assess residence-time weighted samples at six sites along a river in the northeastern United States beginning upstream from a wastewater treatment plant outfall and proceeding downstream along the stream reach to a drinking water treatment plant. Known steroidal estrogens were quantified and changes in signaling pathway molecular initiating events (activation of estrogen, androgen, glucocorticoid, peroxisome proliferator-activated, pregnane X receptor, and aryl hydrocarbon receptor signaling networks) were identified in water extracts. In initial multi-endpoint assays geographic and receptor-specific endocrine activity patterns in transcription factor signatures and nuclear receptor activation were discovered. In subsequent single endpoint receptor-specific bioassays, estrogen (16 of 18 samples; 0.01 to 28 ng estradiol equivalents [E2Eqs]/L) glucocorticoid (3 of 18 samples; 1.8 to 21 ng dexamethasone equivalents [DexEqs]/L), and androgen (2 of 18 samples; 0.95 to 2.1 ng dihydrotestosterone equivalents [DHTEqs]/L) receptor transcriptional activation occurred above respective assay method detection limits (0.04 ng E2Eqs/L, 1.2 ng DexEqs/L, and 0.77 ng DHTEqs/L) in multiple sampling events. Estrogen activity, the most often detected, correlated well with measured concentrations of known steroidal estrogens (r(2) = 0.890). Overall, activity indicative of multiple types of endocrine active compounds was highest in wastewater effluent samples, while activity downstream was progressively lower, and negligible in unfinished treated drinking water. Not only was estrogenic and glucocorticoid activity confirmed in the effluent by utilizing multiple methods concurrently, but other activated signaling networks that historically received less attention (i.e. peroxisome proliferator-activated receptor) were also detected.

Studies worldwide have demonstrated the occurrence of feminized male fish at sites impacted by human and animal wastes. A variety of chemicals could contribute to this phenomenon, but those receiving the greatest attention in terms of research and monitoring have been 17β-estradiol (β-E2) and 17α-ethinylestradiol, due both to their prevalence in the environment and strong estrogenic potency. A third steroid, estrone (E1), also can occur at high concentrations in surface waters but generally has been of lesser concern due to its relatively lower affinity for vertebrate estrogen receptors. In an initial experiment, male fathead minnow (Pimephales promelas) adults were exposed for 4-d to environmentally relevant levels of waterborne E1, which resulted in plasma β-E2 concentrations similar to those found in reproductively active females. In a second exposure we used 13C-labeled E1, together with liquid chromatography-tandem mass spectrometry, to demonstrate that elevated β-E2 measured in the plasma of the male fish was indeed derived from the external environment, most likely via a conversion catalyzed by one or more 17β-hydroxysteroid dehydrogenases. The results of our studies suggest that the potential impact of E1 as an environmental estrogen currently is underestimated.

The increasing use of pharmaceuticals has led to their subsequent input into and release from wastewater treatment plants, with corresponding discharge into surface waters that may subsequently exert adverse effects upon aquatic organisms. Although the distribution of pharmaceuticals in surface water has been extensively studied, the details of uptake, internal distribution, and kinetic processing of pharmaceuticals in exposed fish have received less attention. For this research, we investigated the uptake, disposition, and toxicokinetics of five pharmaceuticals (diclofenac, methocarbamol, rosuvastatin, sulfamethoxazole, and temazepam) in bluegill sunfish (Lepomis macrochirus) exposed to environmentally relevant concentrations (1000–4000 ng L–1) in a flow-through exposure system. Temazepam and methocarbamol were consistently detected in bluegill biological samples with the highest concentrations in bile of 4, 940, and 180 ng g–1, respectively, while sulfamethoxazole, diclofenac, and rosuvastatin were only...