Pollutants in human milk are critical for evaluating maternal internal exposure and infant external exposure. However, most studies have focused on a limited range of pollutants. Here, 15 pooled samples (prepared from 467 individual samples) of human milk from three areas of the Yangtze River Delta (YRD) in China were analyzed by gas chromatography quadrupole time-of-flight mass spectrometry. In total, 171 compounds of nine types were preliminarily identified. Among these, 16 compounds, including 2,5-di-tert-butylhydroquinone and 2-tert-butyl-1,4-benzoquinone, were detected in human milk for the first time. Partial least-squares discriminant analysis identified ten area-specific pollutants, including 2-naphthylamine, 9-fluorenone, 2-isopropylthianthrone, and benzo[a]pyrene, among pooled human milk samples from Shanghai (n = 3), Jiangsu Province (n = 6), and Zhejiang Province (n = 6). Risk index (RI) values were calculated and indicated that legacy polycyclic aromatic hydrocarbons (PAHs) contributed only 20% of the total RIs for the identified PAHs and derivatives, indicating that more attention should be paid to PAHs with various functional groups. Nine priority pollutants in human milk from the YRD were identified. The most important were 4-tert-amylphenol, caffeine, and 2,6-di-tert-butyl-p-benzoquinone, which are associated with apoptosis, oxidative stress, and other health hazards. The results improve our ability to assess the health risks posed by pollutants in human milk.

This article describes a range of high-dimensional data visualization strategies that we have explored for their ability to complement machine learning algorithm predictions derived from MultiFlow® assay results. For this exercise, we focused on seven biomarker responses resulting from the exposure of TK6 cells to each of 126 diverse chemicals over a range of concentrations. Obviously, challenges associated with visualizing seven biomarker responses were further complicated whenever there was a desire to represent the entire 126 chemical data set as opposed to results from a single chemical. Scatter plots, spider plots, parallel coordinate plots, hierarchical clustering, principal component analysis, toxicological prioritization index, multidimensional scaling, t-distributed stochastic neighbor embedding, and uniform manifold approximation and projection are each considered in turn. Our report provides a comparative analysis of these techniques. In an era where multiplexed assays and machine learning algorithms are becoming the norm, stakeholders should find some of these visualization strategies useful for efficiently and effectively interpreting their high-dimensional data.

Difficult to test substances, including poorly soluble, mildly irritating, or UVCBs (unknown or variable composition complex reaction products or biological materials), producing weak or borderline in vivo results, face additional challenges in in vitro assays that often necessitate data integration in a weight of evidence (WOE) approach to inform skin sensitization potential. Here we present several case studies on difficult to test substances and highlight the utility of the toxicological priority index (ToxPi) as a data visualization tool to compare skin sensitization biological activity. The case study test substances represent two poorly soluble substances, tetrakis (2-ethylbutyl) orthosilicate and decyl palmitate, and two UVCB substances, alkylated anisole and hydrazinecarboximidamide, 2-[(2-hydroxyphenyl)methylene]-, reaction products with 2 undecanone. Data from key events within the skin sensitization adverse outcome pathway were gathered from publicly available sources or specifically generated. Incorporating the data for these case study test substances as well as data on chemicals of a known sensitization class (sensitizer, irritating non-sensitizer, and non-sensitizer) into ToxPi produced biological activity profiles which were grouped using unsupervised hierarchical clustering. Three of the case study test substances concluded to lack skin sensitization potential by traditional WOE produced biological activity profiles most consistent with non-sensi­tizing substances, whereas the prediction was less definitive for a substance considered positive by traditional WOE. Visualizing the data using bioactivity profiles can provide further support for WOE conclusions in certain circumstances but is unlikely to replace WOE as a stand-alone prediction due to limitations of the method including the impact of missing data points.
Non-animal test methods to detect chemicals that cause skin allergies are accepted alternatives to animal testing for this purpose. However, some chemicals are difficult to test using these methods, e.g., substances that cause skin irritation, are not water soluble or are mixtures of different compo­nents. We compiled existing and new data on how four such chemicals activate key elements of the biological pathway leading to allergic skin reactions and compared the resulting patterns with respective patterns of many chemicals confirmed to cause skin allergy, skin irritation or neither. The patterns were visualized and analyzed with a computer software tool. The tool confirmed that three substances were non-sensitizers but did not confirm that the fourth substance was a skin sensitizer as predicted by the standard assessment. This approach, which incorporates all available data types into the assessment of difficult to test chemicals, may further reduce unnecessary animal testing.

Groundwater contamination remains a global threat due to its toxic effects to humans and the environment. The remediation of contaminated groundwater sites can be costly, thus, identifying the priority areas of concern is important to reduce money spent on resources. In this study, we aimed to identify and rank the priority groundwater sites in a contaminated petrochemical district by combining alternative, non-animal approaches - chemical analysis, cell-based high throughput screening (HTS), and Toxicological Priority Index (ToxPi) computational toxicology tool. Groundwater samples collected from ten different sites in a contaminated district showed pollutant levels below the detection limit, however, hepatotoxic bioactivity was demonstrated in human hepatoma HepaRG cells. Integrating the pollutants information (i.e., pollutant characteristics and concentration data) with the bioactivity data of the groundwater samples, an evidence-based ranking of the groundwater sites for future remediation was established using ToxPi analysis. The currently presented combinatorial approach of screening groundwater sites for remediation purposes can further be refined by including relevant parameters, which can boost the utility of this approach for groundwater screening and future remediation.

Stress from mixtures of synthetic chemicals is among the key issues that have significant adverse impacts on the marine ecosystems. A robust screening workflow integrating toxicological-based ranking schemes is still deficient for comprehensive investigation on the main constituents in chemical mixtures that contribute to the ecological risks. In this study, the presence and compositions of a collection of priority pollutants were monitored by suspect screening analysis of seawater and estuarine water samples from the semiclosed Bohai Sea. In total, 108 organic pollutants in nine use categories were identified. Pesticides, intermediates, plastic additives, and per- and polyfluoroalkyl substances were the extensively detected chemical groups. Varied distribution patterns of the pollutants were illustrated intuitively in distinctive sampling areas by hierarchical cluster analysis, which were mainly influenced by run-off inputs, ocean currents, and chemical use history. Ecological risks of chemicals with quantified residue levels were first assessed by the toxicity-weighted concentration ranking scheme, and pentachlorophenol was found as the main contributor in the investigating areas. By optimization of multiple alternative variables (e.g., instrumental response and detection frequency), extended ranking of all the identified pollutants was plausible under the toxicological priority index framework. Similarity in toxicological endpoints of the prioritized pollutants could further been screened by ToxAlerts. Aromatic amine was highlighted as the most frequently detected structural alert (SA) for genotoxic carcinogenicity and mutagenicity. These findings fully demonstrate rationality of the ranking schemes integrated into the suspect screening analysis for profiling contamination characteristics, assessing ecological risk potentials, and prioritizing SAs.

Humans are daily exposed to multiple residues of pesticides with agricultural workers representing a subpopulation at higher risk. In this context, the cumulative risk assessment of pesticide mixtures is an urgent issue. The present study evaluated, as a case study, the toxicological profiles of thirteen pesticide mixtures used for grapevine protection, including ten active compounds (sulfur, potassium phosphonate, metrafenone, zoxamide, cyflufenamid, quinoxyfen, mancozeb, folpet, penconazole and dimethomorph), at concentrations used on field. A battery of in vitro tests for cell viability and oxidative stress endpoints (cytotoxicity, apoptosis, necrosis, ROS production, mitochondrial membrane potential, gene expression of markers for apoptosis and oxidative stress) was performed on two cellular models representative of main target organs of workers\' and population exposure: pulmonary A549 and hepatic HepG2 cell lines. All the endpoints provided evidence for effects also at the lower concentrations used. The overall data were integrated into the ToxPI tool obtaining a toxicity ranking of the mixtures, allowing to prioritize effects also among similarly composed blends. The clustering of the toxicological profiles further provided evidence of common and different modes of action of the mixtures. The approach demonstrated to be suitable for the purpose and it could be applied also in other contexts.

The use of systems science methodologies to understand complex environmental and human health relationships is increasing. Requirements for advanced datasets, models, and expertise limit current application of these approaches by many environmental and public health practitioners.
A conceptual system-of-systems model was applied for children in North Carolina counties that includes example indicators of children\'s physical environment (home age, Brownfield sites, Superfund sites), social environment (caregiver\'s income, education, insurance), and health (low birthweight, asthma, blood lead levels). The web-based Toxicological Prioritization Index (ToxPi) tool was used to normalize the data, rank the resulting vulnerability index, and visualize impacts from each indicator in a county. Hierarchical clustering was used to sort the 100 North Carolina counties into groups based on similar ToxPi model results. The ToxPi charts for each county were also superimposed over a map of percentage county population under age 5 to visualize spatial distribution of vulnerability clusters across the state.
Data driven clustering for this systems model suggests 5 groups of counties. One group includes 6 counties with the highest vulnerability scores showing strong influences from all three categories of indicators (social environment, physical environment, and health). A second group contains 15 counties with high vulnerability scores driven by strong influences from home age in the physical environment and poverty in the social environment. A third group is driven by data on Superfund sites in the physical environment.
This analysis demonstrated how systems science principles can be used to synthesize holistic insights for decision making using publicly available data and computational tools, focusing on a children\'s environmental health example. Where more traditional reductionist approaches can elucidate individual relationships between environmental variables and health, the study of collective, system-wide interactions can enable insights into the factors that contribute to regional vulnerabilities and interventions that better address complex real-world conditions.

Landfills in the United States are a significant source of pollution to ground and surface water. Current environmental regulations require detection and/or monitoring assessments of landfill leachate for contaminants that have been deemed particularly harmful. However, the lists of contaminants to be monitored are not comprehensive. Further, landfill leachate composition varies over space and time, and thus the contaminants, and their corresponding toxicity, are not consistent across or within landfills. One of the main objectives of this study was to prioritize contaminants found in landfill leachate using a systematic, toxicity-based prioritization scheme. A literature review was conducted, and from it, 484 landfill leachate contaminants with available CAS numbers were identified. In vitro, in vivo, and predicted human toxicity data were collected from ToxCast, ECOTOX, and CTV Predictor, respectively. These data were integrated using the Toxicological Priority Index (ToxPi) for the 322 contaminants which had available toxicity data from at least two of the databases. Four modifications to this general prioritization scheme were developed to demonstrate the flexibility of this scheme for addressing varied research and applied objectives. The general scheme served as a basis for comparison of the results from the modified schemes, and allowed for identification of contaminants uniquely prioritized in each of the schemes. The schemes outlined here can be used to identify the most harmful contaminants in environmental media in order to design the most relevant mitigation strategies and monitoring plans. Finally, future research directions involving the combination of these prioritization schemes and non-target global metabolomic profiling are discussed.

The prevalence of pharmaceuticals and personal care products (PPCPs) in municipal wastewater has led to increased concerns about their impact on both human health and ecosystem. The constructed wetlands have been recognized as one of the cost-effective and green mitigation approaches to remove the PPCPs in the municipal wastewater. In this study, the effectiveness of a full scale constructed wetlands treatment system (CCWTs) in removing the 36 PPCPs was investigated. The load mass of PPCPs discharged by the wastewater treatment plant into the CCWTs was calculated. Removal efficiencies of PPCPs were evaluated based on physico-chemical properties such as octanol-water partition coefficient (Log kow), molecular weight (MW, g mol-1) and the acid dissociation constant (pKa). The CCWTs are especially efficient in removing azithromycin, sertraline, tolfenamic acid, and diphenhydramine with removing efficiency >88%. However, the removal efficiencies of PPCPs in CCWTs exhibit a large variability, depending on physical and chemical properties of the molecules, with 4.7-96.7% for antibiotics, 5-86% for antidepressant and antiseizure drugs, 3.5-88% for NSAIDs, 29-77% for β-blockers and statins and 5.5-94% for other types of PPCPs. In addition, the environmental risk assessment showed that majority of the PPCPs (excluding sulfamethoxazole) in the effluent yielded low aquatic risk (risk quotient, RQ ≤ 0.1) due to the efficiency of CCWTs. The toxicity index scores were calculated by integration of the predicted and available toxicological hazard data into the prioritization ranking algorithm through Toxicological Prioritization Index (ToxPi).

Traditional methods for cancer risk assessment are retrospective, resource-intensive, and not feasible for the vast majority of environmental chemicals. In earlier studies, we used a set of six biomarkers to accurately identify liver tumorigens in transcript profiles derived from chemically-treated rats using either a Toxicological Priority Index (ToxPi) approach or using derived biomarker thresholds for cancer. The biomarkers consisting of 7-113 genes are used to predict the most common liver cancer molecular initiating events: genotoxicity, cytotoxicity and activation of the xenobiotic receptors AhR, CAR, ER, and PPARα. In the present study, we apply and evaluate the performance of these methods for cancer prediction in an independent rat liver study of 44 chemicals (6 h-7d exposures) examined by Affymetrix arrays. In the first approach, ToxPi ranking of biomarker scores consistently gave the highest scores to tumorigenic chemical-dose pairs; balanced accuracies for identification of liver tumorigenic chemicals were up to 89 %. The second approach used tumorigenic thresholds derived in the present study or from our earlier study that were set at the maximum value for chemical-dose exposures without detectable liver tumor outcomes. Using these thresholds, balanced accuracies were up to 90 %. Both approaches identified all tumorigenic chemicals. Almost all of the tumorigenic chemicals activated more than one MIE. We also compared biomarker responses between two types of profiling platforms (Affymetrix full-genome array, TempO-Seq 1500+ array containing ∼2600 genes) and found that the lack of the full set of biomarker genes on the 1500+ array resulted in decreased ability to identify chemicals that activate the MIEs. Overall, these results demonstrate that predictive approaches based on the 6 biomarkers could be used in short-term assays to identify chemicals and their doses that induce liver tumors, the most common endpoint in rodent bioassays.