Unveiling first report on in silico modeling of aquatic toxicity of organic chemicals to Labeo rohita (Rohu) employing QSAR and q-RASAR

Labeo rohita, a fish species within the Carp family, holds significant dietary and aquacultural importance in South Asian countries. However, the habitats of L. rohita often face exposure to various harmful pesticides and organic compounds originating from industrial and agricultural runoff. It is challenging to individually investigate the effects of each potentially harmful compound. In such cases, in silico techniques like Quantitative Structure-Activity Relationship (QSAR) and quantitative Read-Across Structure-Activity Relationship (q-RASAR) can be employed to construct algorithmic models capable of simultaneously assessing the toxicity of numerous compounds. We utilized the US EPA's ToxValDB database to curate data regarding acute median lethal concentration (LC50) toxicity for L. rohita. The experimental variables included study type (mortality), study duration (ranging from 0.25 h to 4 h), exposure route (static, flowthrough, and renewal), exposure method (drinking water), and types of chemicals (industrial chemicals and pharmaceuticals). Using this dataset, we developed regression-based QSAR and q-RASAR models to predict chemical toxicity to L. rohita based on chemical descriptors. The key descriptors for predicting the toxicity of L. rohita in the regression-based QSAR model include F05[S-Cl], SpMax_EA(ri), s4_relPathLength_2, and SpDiam_AEA(ed). These descriptors can be employed to estimate the toxicity of untested compounds and aid in the development of compounds with lower toxicity based on the presence or absence of these descriptors. Both the QSAR and q-RASAR models serve as valuable tools for understanding the chemicals' structural features responsible for toxicity and for filling gaps in aquatic toxicity data by predicting the toxicity of newly untested compounds in relation to L. rohita. Finally, the developed best model was employed to predict 297 external chemicals, the most toxic substances to L. rohita were identified as cyhalothrin, isobornyl thiocyanatoacetate, and paclobutrzol, while the least toxic ones included ethyl acetate, ethylthiourea, and n-butyric acid.

First report on chemometric modeling of tilapia fish aquatic toxicity to organic chemicals: Toxicity data gap filling

The Toxic Substances Control Act (TSCA) mandates the Environmental Protection Agency (EPA) to document chemicals entering the US. Due to the vast range of toxicity endpoints, experimental toxicological study for all chemicals is impossible to conduct. To address this, in silico methods like QSAR and read-across are strategically used to prioritize testing for chemicals lacking ecotoxicity data. Aquatic toxicity is one of the most critical endpoints directly related to aquatic species, mainly fish, followed by direct to indirect effects on humans through drinking water and fish as food, respectively. Therefore, we have employed the ToxValDB database to curate acute LC50 toxicity data for three Tilapia species covering two different genera, an ideal species for aquatic toxicity testing. Employing the curated dataset, we have developed multiple robust and predictive QSAR and quantitative read-across structure-activity relationship (q-RASAR) models for Tilapia zillii, Oreochromis niloticus, and Oreochromis mossambicus which helped to understand the toxicological mode of action (MoA) of the modeled chemicals and predict the aquatic toxicity of new untested chemicals followed by toxicity data gap filling. The best three QSAR models showed encouraging statistical quality in terms of determination coefficient R2 (0.94, 0.74, and 0.77), cross-validated leave-one-out Q2 (0.90, 0.67 and 0.70), and predictive capability in terms of R2pred (0.95, 0.77, and 0.74) for T. zillii, O. niloticus, and O. mossambicus datasets, respectively. The developed best mathematical models were used for the prediction of aquatic toxicity in terms of pLC50 for 297 untested organic chemicals across three major Tilapia species ranging from 1.841 to 8.561 M in terms of environmental risk assessment.

q-RASTR modelling for prediction of diverse toxic chemicals towards T. pyriformis

A series of diverse organic compounds impose serious detrimental effects on the health of living organisms and the environment. Determination of the structural aspects of compounds that impart toxicity and evaluation of the same is crucial before public usage. The present study aims to determine the structural characteristics of compounds for Tetrahymena pyriformis toxicity using the q-RASTR (Quantitative Read Across Structure-Toxicity Relationship) model. It was developed using RASTR and 2-D descriptors for a dataset of 1792 compounds with defined endpoint (pIGC50) against a model organism, T. pyriformis. For the current study, the whole dataset was divided based on activity/property into the training and test sets, and the q-RASTR model was developed employing six descriptors (three latent variables) having r2, Q2F1 and Q2 values of 0.739, 0.767, and 0.735, respectively. The generated model was thoroughly validated using internationally recognized internal and external validation criteria to assess the model's dependability and predictability. It was highlighted that high molecular weight, aromatic hydroxyls, nitrogen, double bonds, and hydrophobicity increase the toxicity of organic compounds. The current study demonstrates the applicability of the RASTR algorithm in QSTR model development for the prediction of toxic chemicals (pIGC50) towards T. pyriformis.

Read-across-based intelligent learning: development of a global q-RASAR model for the efficient quantitative predictions of skin sensitization potential of diverse organic chemicals

Environmental chemicals and contaminants cause a wide array of harmful implications to terrestrial and aquatic life which ranges from skin sensitization to acute oral toxicity. The current study aims to assess the quantitative skin sensitization potential of a large set of industrial and environmental chemicals acting through different mechanisms using the novel quantitative Read-Across Structure-Activity Relationship (q-RASAR) approach. Based on the identified important set of structural and physicochemical features, Read-Across-based hyperparameters were optimized using the training set compounds followed by the calculation of similarity and error-based RASAR descriptors. Data fusion, further feature selection, and removal of prediction confidence outliers were performed to generate a partial least squares (PLS) q-RASAR model, followed by the application of various Machine Learning (ML) tools to check the quality of predictions. The PLS model was found to be the best among different models. A simple user-friendly Java-based software tool was developed based on the PLS model, which efficiently predicts the toxicity value(s) of query compound(s) along with their status of Applicability Domain (AD) in terms of leverage values. This model has been developed using structurally diverse compounds and is expected to predict efficiently and quantitatively the skin sensitization potential of environmental chemicals to estimate their occupational and health hazards.

Determining the toxicity of organic compounds to the nematode Caenorhabditis elegans based on aqueous concentrations

Caenorhabditis elegans is used for assessing the toxicity of chemicals in aqueous medium. However, chemicals can absorb to the bacterial food, which reduces the freely dissolved concentrations of the tested compounds. Thus, based on total or nominal concentrations, toxicity is underestimated, resulting in misleading assumptions on toxicity mechanisms or comparisons to other test organisms. As the verification of freely dissolved exposure concentrations (Cfree) is challenging in small test systems, simple partitioning models might by a good option for estimating Cfree. Therefore, C. elegans was exposed to seven differently acting organic chemicals with varying hydrophobicities, thus also different affinities to bind to the food of C. elegans. Measured concentrations of the dissolved aqueous and the bacterial-bound fraction allowed the calculation of binding constants (Kb). Experimental Kb were comparable to literature data of hydrophobic chemicals and correlated well with their hydrophobicity, expressed as log KOW. The chronic toxicity of the various compounds on C. elegans' reproduction, based on their aqueous concentration, was weakly related to their log KOW. Toxicity expressed based on chemical activity and comparisons with a baseline toxicity model, nevertheless, suggested a narcotic mode of action for most hydrophobic compounds (except methylisothiazolinone and trichlorocarbanilide). Although revealing a similar toxicity ranking than Daphnia magna, C. elegans was less sensitive, probably due to its ability to reduce its internal concentrations by means of its very impermeable cuticle or by efficient detoxification mechanisms. It could be shown that measured aqueous concentrations in the nematode test system corresponded well with freely dissolved concentrations that were modeled using simple mass-balance models from nominal concentrations. This offers the possibility to estimate freely dissolved concentrations of chemicals from nominal concentrations, making routine testing of chemicals and their comparison to other species more accurate.

Data driven toxicity assessment of organic chemicals against Gammarus species using QSAR approach

Nowadays, organic chemicals play an essential role in almost all walks of life and have become indispensable to modern society. However, the continually synthesized chemicals and the numerous potential adverse endpoints against living organisms increasingly promote the regulators regarding the computational approach as a crucial supplement and an alternative to the traditional animal tests in chemical risk assessment. In this present research, we evaluated the ecotoxicity of chemicals against four typical Gammarus species, which constituted a critical element in detritus cycle and also the recommended species for water monitoring. We first screened the molecular descriptors based on the Genetic Algorithm and then developed the Quantitative Structure-Activity Relationship models using the Multiple Linear Regression method. The statistical results from various validation metrics suggested that the obtained models were internally robust and externally predictive. The application domain analysis based on the leverage approach and standardized residual method demonstrated the broad application range of each model. The interpretation of molecular descriptors in each model suggested that the chemicals with higher polarity and hydrophilicity tend to be less toxic, whereas the lipophilic moieties would enhance the chemical toxicity. Meanwhile, the other selected descriptors, such as Chi-cluster, heterocyclic, and distance matrix descriptors, manifested that the chemical toxicity was also affected by molecular branching, connectivity, electrotopological state, and other various properties. In summary, the present work proposed well-performed QSAR models and clarified the possible toxic mechanism of chemicals against Gammarus species. The obtained models could help predict the toxicity data and conduct a preliminary risk assessment, thus guiding the subsequent animal tests and reducing the assessment cost.

Application of a developed triple-classification machine learning model for carcinogenic prediction of hazardous organic chemicals to the US, EU, and WHO based on Chinese database

Cancer, the second largest human disease, has become a major public health problem. The prediction of chemicals' carcinogenicity before their synthesis is crucial. In this paper, seven machine learning algorithms (i.e., Random Forest (RF), Logistic Regression (LR), Support Vector Machines (SVM), Complement Naive Bayes (CNB), K-Nearest Neighbor (KNN), XGBoost, and Multilayer Perceptron (MLP)) were used to construct the carcinogenicity triple classification prediction (TCP) model (i.e., 1A, 1B, Category 2). A total of 1444 descriptors of 118 hazardous organic chemicals were calculated by Discovery Studio 2020, Sybyl X-2.0 and PaDEL-Descriptor software. The constructed carcinogenicity TCP model was evaluated through five model evaluation indicators (i.e., Accuracy, Precision, Recall, F1 Score and AUC). The model evaluation results show that Accuracy, Precision, Recall, F1 Score and AUC evaluation indicators meet requirements (greater than 0.6). The accuracy of RF, LR, XGBoost, and MLP models for predicting carcinogenicity of Category 2 is 91.67%, 79.17%, 100%, and 100%, respectively. In addition, the constructed machine learning model in this study has potential for error correction. Taking XGBoost model as an example, the predicted carcinogenicity level of 1,2,3-Trichloropropane (96-18-4) is Category 2, but the actual carcinogenicity level is 1B. But the difference between Category 2 and 1B is only 0.004, indicating that the XGBoost is one optimum model of the seven constructed machine learning models. Besides, results showed that functional groups like chlorine and benzene ring might influence the prediction of carcinogenic classification. Therefore, considering functional group characteristics of chemicals before constructing the carcinogenicity prediction model of organic chemicals is recommended. The predicted carcinogenicity of the organic chemicals using the optimum machine leaning model (i.e., XGBoost) was also evaluated and verified by the toxicokinetics. The RF and XGBoost TCP models constructed in this paper can be used for carcinogenicity detection before synthesizing new organic substances. It also provides technical support for the subsequent management of organic chemicals.

Influence of water exchange rates on toxicity and bioaccumulation of hydrophobic organic chemicals in sediment toxicity tests

In standardized sediment toxicity tests, the applied water exchange methods range from static to flow-through conditions and vary between protocols and laboratories even for the same test species. This variation potentially results in variable chemical exposure, hampering the interpretation of toxicity and bioaccumulation. To address these issues, we performed sediment toxicity tests with a mixture of three polycyclic aromatic hydrocarbons (PAHs) and the freshwater epibenthic amphipod Hyalella azteca as model chemicals and organism, respectively. Five standardized water exchange methods were applied: static, semi-static, or flow-through conditions. By measuring total (C_diss) and freely dissolved concentrations (C_free) of PAHs with water sampling and direct immersion solid-phase microextraction methods, respectively, we found that C_diss in overlying water differed by a factor of up to 107 among water exchange conditions, whereas both C_diss and C_free in pore water did not differ by more than a factor of 2.6. Similar survival rates, growth rates, and bioaccumulation of PAHs between water exchange methods suggest that H. azteca was predominantly exposed to pore water rather than overlying water. By applying mechanistic kinetic modeling to simulate spatiotemporal concentration profiles in sediment toxicity tests, we discuss the importance of the water exchange rates and resulting temporal and spatial exposure variability for the extrapolation of laboratory sediment toxicity to field conditions, particularly for chemicals with relatively low hydrophobicity and sediments with low organic carbon content.

Ensemble multiclassification model for aquatic toxicity of organic compounds

With environmental pollution becoming increasingly serious, organic compounds have become the main hazard of environmental pollution and exert substantial negative impacts on aquatic organisms. In research pertaining to the acute toxicity of organic compounds, traditional biological experimental methods are time-consuming and expensive. In addition, computer-aided binary classification models cannot accurately classify acute toxicity. Therefore, the multiclassication model is necessary for more accurate classification of acute toxicity. In this study, median lethal concentrations of 373 organic compounds in the environmental toxicology datasets ECOTOX and EAT5 were used. These chemicals were classified into four categories based on the European Economic Community criteria. Then the random forest, support vector machine, extreme gradient boosting, adaptive gradient boosting, and C5.0 decision tree algorithms and eight molecular fingerprints were used to build a multiclassification base model for the acute toxicity of organic compounds. The base models were repeated 100 times with fivefold cross-validation and external validation. The ensemble model was obtained by the voting method. The best base classifier was ExtendFP-C5.0, which had an accuracy, sensitivity and specificity values of 87.30%, 87.32% and 95.76% for external validation, and the voting ensemble model performance of 96.92%, 96.93% and 98.97%, respectively. The ensemble model achieved a higher accuracy than previously reported studies. Our study will help to further classify the acute toxicity of organic compounds to aquatic organisms and predict the hazard classes of organic compounds.

Quasi-SMILES: Self-consistent models for toxicity of organic chemicals to tadpoles

Simplified molecular input-line entry systems (SMILES) are the representation of the molecular structure that can be used to establish quantitative structure-property/activity relationships (QSPRs/QSARs) for various endpoints expressed as mathematical functions of the molecular architecture. Quasi-SMILES is extending the traditional SMILES by means of additional symbols that reflect experimental conditions. Using the quasi-SMILES models of toxicity to tadpoles gives the possibility to build up models by taking into account the time of exposure. Toxic effects of experimental situations expressed via 188 quasi-SMILES (the negative logarithm of molar concentrations which lead to lethal 50% tadpoles effected during 12 h, 24 h, 48 h, 72 h, and 96 h) were modelled with good results (the average determination coefficient for the validation sets is about 0.97). In this way, we developed new models for this amphibian endpoint, which is poorly studied.

Mixture Risk Assessment of Complex Real-Life Mixtures-The PANORAMIX Project

Humans are involuntarily exposed to hundreds of chemicals that either contaminate our environment and food or are added intentionally to our daily products. These complex mixtures of chemicals may pose a risk to human health. One of the goals of the European Union's Green Deal and zero-pollution ambition for a toxic-free environment is to tackle the existent gaps in chemical mixture risk assessment by providing scientific grounds that support the implementation of adequate regulatory measures within the EU. We suggest dealing with this challenge by: (1) characterising 'real-life' chemical mixtures and determining to what extent they are transferred from the environment to humans via food and water, and from the mother to the foetus; (2) establishing a high-throughput whole-mixture-based in vitro strategy for screening of real-life complex mixtures of organic chemicals extracted from humans using integrated chemical profiling (suspect screening) together with effect-directed analysis; (3) evaluating which human blood levels of chemical mixtures might be of concern for children's development; and (4) developing a web-based, ready-to-use interface that integrates hazard and exposure data to enable component-based mixture risk estimation. These concepts form the basis of the Green Deal project PANORAMIX, whose ultimate goal is to progress mixture risk assessment of chemicals.

Linking electron ionization mass spectra of organic chemicals to toxicity endpoints through machine learning and experimentation

Quantitative structure-activity relationship (QSAR) modeling has been widely used to predict the potential harm of chemicals, in which the prediction heavily relies on the accurate annotation of chemical structures. However, it is difficult to determine the accurate structure of an unknown compound in many cases, such as in complex water environments. Here, we solved the above problem by linking electron ionization mass spectra (EI-MS) of organic chemicals to toxicity endpoints through various machine learning methods. The proposed method was verified by predicting 50% growth inhibition of Tetrahymena pyriformis (T. pyriformis) and liver toxicity. The optimal model performance obtained an R² > 0.7 or balanced accuracy > 0.72 for both the training set and test set. External experimentation further verified the application potential of our proposed method in the toxicity prediction of unknown chemicals. Feature importance analysis allowed us to identify critical spectral features that were responsible for chemical-induced toxicity. Our approach has the potential for toxicity prediction in such fields that it is difficult to determine accurate chemical structures.

Pesticide toxicity towards microalgae increases with environmental mixture complexity

Effect-directed analysis (EDA) aims at identifying the compound(s) responsible for toxicity in a complex environmental sample where several dozens of contaminants can be present. In this study, we used an environmental mixture extracted from the Polar Organic Chemical Integrative Sampler (POCIS) previously immersed downstream a landfill (River Ponteils, South West France), to perform an EDA approach using a microalgal bioassay based on the photosynthetic capacities of diatom (Nitzschia palea) cultures. Adverse effects on photosynthetic capacities were recorded when algae were exposed to the entire POCIS extract (> 85% inhibition at the highest concentration tested). This result was coherent with the detection of diuron and isoproturon, which were the 2 most concentrated herbicides in the extract. However, the EDA process did not allow pointing out the specific compound(s) responsible for the observed toxicity but rather suggested that multiple compounds were involved in the overall toxicity and caused mixture effects.

Comparison of Species Sensitivity Distributions for Sediment-Associated Nonionic Organic Chemicals Through Equilibrium Partitioning Theory and Spiked-Sediment Toxicity Tests with Invertebrates

Equilibrium partitioning (EqP) theory and spiked-sediment toxicity tests are useful methods to develop sediment quality benchmarks. However, neither approach has been directly compared based on species sensitivity distributions (SSDs) to date. In the present study, we compared SSDs for 10 nonionic hydrophobic chemicals (e.g., pyrethroid insecticides, other insecticides, and polycyclic aromatic hydrocarbons) based on 10-14-day spiked-sediment toxicity test data with those based on EqP theory using acute water-only tests. Because the exposure periods were different between the two tests, effective concentrations (i.e., median effective/lethal concentration) were corrected to compare SSDs. Accordingly, we found that hazardous concentrations for 50% and 5% of species (HC50 and HC5, respectively) differed by up to a factor of 100 and 129 between the two approaches, respectively. However, when five or more species were used for SSD estimation, their differences were reduced to a factor of 1.7 and 5.1 for HC50 and HC5, respectively, and the 95% confidence intervals of HC50 values overlapped considerably between the two approaches. These results suggest that when the number of test species is adequate, SSDs based on EqP theory and spiked-sediment tests are comparable in sediment risk assessments. Environ Toxicol Chem 2022;41:462-473. © 2021 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Development and Evaluation of a Holistic and Mechanistic Modeling Framework for Chemical Emissions, Fate, Exposure, and Risk

BACKGROUND

Large numbers of chemicals require evaluation to determine if their production and use pose potential risks to ecological and human health. For most chemicals, the inadequacy and uncertainty of chemical-specific data severely limit the application of exposure- and risk-based methods for screening-level assessments, priority setting, and effective management.

OBJECTIVE

We developed and evaluated a holistic, mechanistic modeling framework for ecological and human health assessments to support the safe and sustainable production, use, and disposal of organic chemicals.

METHODS

We consolidated various models for simulating the PROduction-To-EXposure (PROTEX) continuum with empirical data sets and models for predicting chemical property and use function information to enable high-throughput (HT) exposure and risk estimation. The new PROTEX-HT framework calculates exposure and risk by integrating mechanistic computational modules describing chemical behavior and fate in the socioeconomic system (i.e., life cycle emissions), natural and indoor environments, various ecological receptors, and humans. PROTEX-HT requires only molecular structure and chemical tonnage (i.e., annual production or consumption volume) as input information. We evaluated the PROTEX-HT framework using 95 organic chemicals commercialized in the United States and demonstrated its application in various exposure and risk assessment contexts.

RESULTS

Seventy-nine percent and 97% of the PROTEX-HT human exposure predictions were within one and two orders of magnitude, respectively, of independent human exposure estimates inferred from biomonitoring data. PROTEX-HT supported screening and ranking chemicals based on various exposure and risk metrics, setting chemical-specific maximum allowable tonnage based on user-defined toxicological thresholds, and identifying the most relevant emission sources, environmental media, and exposure routes of concern in the PROTEX continuum. The case study shows that high chemical tonnage did not necessarily result in high exposure or health risks.

CONCLUSION

Requiring only two chemical-specific pieces of information, PROTEX-HT enables efficient screening-level evaluations of existing and premanufacture chemicals in various exposure- and risk-based contexts. https://doi.org/10.1289/EHP9372.

Microglia Reduce Herpes Simplex Virus 1 Lethality of Mice with Decreased T Cell and Interferon Responses in Brains

Herpes simplex virus 1 (HSV-1) infects the majority of the human population and can induce encephalitis, which is the most common cause of sporadic, fatal encephalitis. An increase of microglia is detected in the brains of encephalitis patients. The issues regarding whether and how microglia protect the host and neurons from HSV-1 infection remain elusive. Using a murine infection model, we showed that HSV-1 infection on corneas increased the number of microglia to outnumber those of infiltrating leukocytes (macrophages, neutrophils, and T cells) and enhanced microglia activation in brains. HSV-1 antigens were detected in brain neurons, which were surrounded by microglia. Microglia depletion increased HSV-1 lethality of mice with elevated brain levels of viral loads, infected neurons, neuron loss, CD4 T cells, CD8 T cells, neutrophils, interferon (IFN)-β, and IFN-γ. In vitro studies demonstrated that microglia from infected mice reduced virus infectivity. Moreover, microglia induced IFN-β and the signaling pathway of signal transducer and activator of transcription (STAT) 1 to inhibit viral replication and damage of neurons. Our study reveals how microglia protect the host and neurons from HSV-1 infection.

Toxicokinetics of hydrophobic organic compounds in oligochaeta: A critical review

Toxicokinetic studies appertain to the fundamental research of soil bioavailability. However, the research outcomes of aspects influencing uptake and elimination of hydrophobic organic compounds have not been summarized so far. In our review, a recapitulation of available toxicokinetic data (i.e. experimental conditions, if the steady state was reached, uptake and elimination rate constants, and bioaccumulation factors) is presented in well-arranged tables. Further, toxicokinetic models are overviewed in the schematic form. In the review, the required information could be quickly found and/or the experimental gaps easily identified. Generally a little is known about the effects of soil properties other than soil organic matter. Limited or no data are available about soil treatment, food supply during laboratory exposure, and metabolization in oligochaeta. The impact of these factors might be important especially for arable soils with typically low organic matter content but high consequences on humans. Besides these circumstances, other uncertainties between published studies have been found. Firstly, the scientific results are provided in heterogenous units: bioaccumulation factors as well as the rate constants are reported in dry or wet weight of soil and earthworms. The steady state is another critical factor because the time to reach the equilibrium is influenced not only by soil and compound characteristics but for example also by aging. Nevertheless, toxicokinetic studies bring irreplaceable information about the real situation in soil and our review help to define missing knowledge and estimate the scientific priorities.

Application of the Target Lipid Model to Assess Toxicity of Heterocyclic Aromatic Compounds to Aquatic Organisms

Heterocyclic aromatic compounds can be found in crude oil and coal and often co-exist in environmental samples with their homocyclic aromatic counterparts. The target lipid model (TLM) is a modeling framework that relates aquatic toxicity to the octanol-water partition coefficient (K_OW ) that has been calibrated and validated for hydrocarbons. A systematic analysis of the applicability of the TLM to heterocyclic aromatic compounds has not been performed. The objective of the present study was to compile reliable toxicity data for heterocycles and determine whether observed toxicity could be successfully described by the TLM. Results indicated that the TLM could be applied to this compound class by adopting an empirically derived coefficient that accounts for partitioning between water and lipid. This coefficient was larger than previously reported for aromatic hydrocarbons, indicating that these heterocyclic compounds exhibit higher affinity to target lipid and toxicity. A mechanistic evaluation confirmed that the hydrogen bonding accepting moieties of the heteroatoms helped explain differences in partitioning behavior. Given the TLM chemical class coefficient reported in the present study, heterocyclic aromatics can now be explicitly incorporated in TLM-based risk assessments of petroleum substances, other products, or environmental media containing these compounds. Environ Toxicol Chem 2021;40:3000-3009. © 2021 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Generating accurate in silico predictions of acute aquatic toxicity for a range of organic chemicals: Towards similarity-based machine learning methods

There has been an increase in the use of non-animal approaches, such as in silico and/or in vitro methods, for assessing the risks of hazardous chemicals. A number of machine learning algorithms link molecular descriptors that interpret chemical structural properties with their biological activity. These computer-aided methods encounter several challenges, the most significant being the heterogeneity of datasets; more efficient and inclusive computational methods that are able to process large and heterogeneous chemical datasets are needed. In this context, this study verifies the utility of similarity-based machine learning methods in predicting the acute aquatic toxicity of diverse organic chemicals on Daphnia magna and Oryzias latipes. Two similarity-based methods were tested that employ a limited training dataset, most similar to a given fitting point, instead of using the entire dataset that encompasses a wide range of chemicals. The kernel-weighted local polynomial approach had a number of advantages over the distance-weighted k-nearest neighbor (k-NN) algorithm. The results highlight the importance of lipophilicity, electrophilic reactivity, molecular polarizability, and size in determining acute toxicity. The rigorous model validation ensures that this approach is an important tool for estimating toxicity in new or untested chemicals.

Cardiotoxicity Hazard and Risk Characterization of ToxCast Chemicals Using Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes from Multiple Donors

Heart disease remains a significant human health burden worldwide with a significant fraction of morbidity attributable to environmental exposures. However, the extent to which the thousands of chemicals in commerce and the environment may contribute to heart disease morbidity is largely unknown, because in contrast to pharmaceuticals, environmental chemicals are seldom tested for potential cardiotoxicity. Human induced pluripotent stem cell (iPSC)-derived cardiomyocytes have become an informative in vitro model for cardiotoxicity testing of drugs with the availability of cells from multiple individuals allowing in vitro testing of population variability. In this study, we hypothesized that a panel of iPSC-derived cardiomyocytes from healthy human donors can be used to screen for the potential cardiotoxicity hazard and risk of environmental chemicals. We conducted concentration-response testing of 1029 chemicals (drugs, pesticides, flame retardants, polycyclic aromatic hydrocarbons (PAHs), plasticizers, industrial chemicals, food/flavor/fragrance agents, etc.) in iPSC-derived cardiomyocytes from 5 donors. We used kinetic calcium flux and high-content imaging to derive quantitative measures as inputs into Bayesian population concentration-response modeling of the effects of each chemical. We found that many environmental chemicals pose a hazard to human cardiomyocytes in vitro with more than half of all chemicals eliciting positive or negative chronotropic or arrhythmogenic effects. However, most of the tested environmental chemicals for which human exposure and high-throughput toxicokinetics data were available had wide margins of exposure and, thus, do not appear to pose a significant human health risk in a general population. Still, relatively narrow margins of exposure (<100) were estimated for some perfuoroalkyl substances and phthalates, raising concerns that cumulative exposures may pose a cardiotoxicity risk. Collectively, this study demonstrated the value of using a population-based human in vitro model for rapid, high-throughput hazard and risk characterization of chemicals for which little to no cardiotoxicity data are available from guideline studies in animals.

Mutagenicity, health risk, and disease burden of exposure to organic micropollutants in water from a drinking water treatment plant in the Yangtze River Delta, China

A wide variety of organic micropollutants in drinking water pose a serious threat to human health. This study was aimed to reveal the characteristics of organic micropollution profiles in water from a drinking water treatment plant (DWTP) in the Yangtze River Delta, China and investigate the mutagenicity, health risk and disease burden through mixed exposure to micropollutants in water. The presence of organic micropollutants in seven categories in organic extracts (OEs) of water from the DWTP was determined, and Ames test was conducted to test the mutagenic effect of OEs. Meanwhile, health risk of exposure to organic micropollutants in finished water through three exposure routes (ingestion, dermal absorption and inhalation) was assessed with the method proposed by U.S. EPA, and disability-adjusted life years (DALYs) were combined to estimate the disease burden of cancer based on the carcinogenic risk (CR) assessment. The results showed that 28 organic micropollutants were detected in the raw and finished water at total concentrations of 967.28 ng/L and 1073.45 ng/L, respectively, of which phthalate esters (PAEs) were the dominant category (95.79% in the raw water and 96.61% in the finished water). Although the results of the Ames test for OEs were negative and the non-carcinogenic hazard index of the organic micropollutants in the finished water was less than 1 in all age groups, the total CR was 2.17 × 10^-5, higher than the negligible risk level (1.00 × 10^-6). The total DALYs caused by the organic micropollutants in the finished water was 2945.59 person-years, and the average individual DALYs was 2.21 × 10^-6 per person-year (ppy), which was 2.21 times the reference risk level (1.00 × 10^-6 ppy) defined by the WHO. Exposure to nitrosamines (NAms) was the major contributor to the total CR (92.06%) and average individual DALYs (94.58%). This study demonstrated that despite the negative result of the mutagenicity test with TA98 and TA100 strains, the health risk of exposure to organic micropollutants in drinking water should not be neglected.

Environmental risks related to organic compounds from the combustion of paper briquettes in domestic boilers

Environmental risks connected with the combustion of paper/cardboard briquettes are still not sufficiently known. This paper aims to bring attention to the risks related to the utilisation of paper briquettes in local boilers and to characterise these risks by means of the identification of organic compounds in deposits from exhaust flues. The identification of the chemical compounds was performed by pyrolysis gas chromatography with mass spectrometric detection. Paper/cardboard briquettes contain 119 compounds of biogenic origin derived from major biomass components and 53 additives. Additives are used both for improving the properties of paper and in printing inks. By burning the paper briquettes, the same 53 compounds from the additive group were caught in the deposits from the flue gas pathway, occurring in the range of 1-10% of the concentration of individual compounds (additives) contained in the input fuel. Compounds that are very stable during the combustion process have an enrichment factor (EF) >30, which corresponded to approximately 3% of the additive capture in deposits. The highest values were found for plasticisers (phthalates). Many of the primary organic compounds contained in the input raw material do not decompose during combustion and can have adverse effects on human health.

Cytotoxic Evaluation and Determination of Organic and Inorganic Eluates from Restorative Materials

Over the last years, diverse commercial resin-based composites have dominated as dental filling materials. The purpose of the present study was to determine organic and inorganic eluates from five restorative materials using GC/MS and ICP–OES and to compare the effect on cell survival of human gingival fibroblasts of a conventional and a bioactive resin. Five commercially available restorative materials were employed for this study: Activa^TM Bioactive Restorative, ENA HRi, Enamel plus HRi Biofunction, Fuji II LC Capsule, and Fuji IX Capsule. Disks that were polymerized with a curing LED light or left to set were immersed in: 1 mL methanol or artificial saliva for GC/MS analysis, 5mL deionized water for ICP–OES, and 5mL of culture medium for cell viability. Cell viability was investigated with a modified staining sulforhodamine B assay.The following organic substances were detected: ACP, BHT, BPA, 1,4-BDDMA, CQ, DBP, DMABEE, HEMA, MCE, MeHQ, MOPA, MS, TMPTMA, and TPSb and the ions silicon, aluminum, calcium, sodium, and barium. Activa Bioactive Restorative was found to be biocompatible. Elution of organic substances depended on material’s composition, the nature of the solvent and the storage time. Ions’ release depended on material’s composition and storage time. The newly introduced bioactive restorative was found to be more biocompatible.

QSARINS-Chem standalone version: A new platform-independent software to profile chemicals for physico-chemical properties, fate, and toxicity

The new software QSARINS-Chem standalone version is a multiplatform tool, freely downloadable, for the in silico profiling of multiple properties and activities of organic chemicals. This software, which is based on the concept of the QSARINS-chem module embedded in the QSARINS software, has been fully redesigned and redeveloped in the Java™ language. In addition to a selection of models included in the old module, the new software predicts biotransformation rates and aquatic toxicities of pharmaceuticals and personal care products in multiple organisms, and offers a suite of tools for the analysis of predictions. Furthermore, a comprehensive and transparent database of molecular structures is provided. The new QSARINS-Chem standalone version is an informative and solid tool, which is useful to support the assessment of the potential hazard and risks related to organic chemicals and is dedicated to users which are interested in the application of QSARs to generate reliable predictions.

Aquatic toxicity (Pre)screening strategy for structurally diverse chemicals: global or local classification tree models?

With an ever-increasing number of synthetic chemicals being manufactured, it is unrealistic to expect that they will all be subjected to comprehensive and effective risk assessment. A shift from conventional animal testing to computer-aided methods is therefore an important step towards advancing the environmental risk assessments of chemicals. The aims of this study are two-fold: firstly, it examines the relationships between structural and physicochemical features of a diverse set of organic chemicals, and their acute aquatic toxicity towards Daphnia magna and Oryzias latipes using a classification tree approach. Secondly, it compares the efficiency and accuracy of the predictions of two modeling schemes: local models that are inherently restricted to a smaller subset of structurally-related substances, and a global model that covers a wider chemical space and a number of modes of toxic action. The classification tree-based models differentiate the organic chemicals into either 'highly toxic' or 'low to non-toxic' classes, based on internal and external validation criteria. These mechanistically-driven models, which demonstrate good performance, reveal that the key factors driving acute aquatic toxicity are lipophilicity, electrophilic reactivity, molecular polarizability and size. A comparative analysis of the performance of the two modeling schemes indicates that the local models, trained on homogeneous data sets, are less error prone, and therefore superior to the global model. Although the global models showed worse performance metrics compared to the local ones, their applicability domain is much wider, thereby significantly increasing their usefulness in practical applications for regulatory purposes. This demonstrates their advantage over local models and shows they are an invaluable tool for modeling heterogeneous chemical data sets.

QSPR modeling of octanol-water partition coefficient and organic carbon normalized sorption coefficient of diverse organic chemicals using Extended Topochemical Atom (ETA) indices

Octanol-water partition coefficient (logK_ow) and soil organic carbon content normalized sorption coefficient (logK_oc) values are two important physicochemical properties in the context of bioaccumulation and environmental fate of organic compounds and their environmental risk assessment. Simple, interpretable and easy-to-derive extended topochemical atom (ETA) indices obtained from 2D structural representation of compounds were used for quantitative structure-property relationship (QSPR) modeling of these two endpoints. Linear regression based models developed using only ETA indices show encouraging statistical and validation results. Based on the information obtained from developed QSPR models, we may conclude that molecular volume, branching pattern, presence of hydrophobic Cl atoms, cyclicity/fusion, polar environment, electron density, unsaturation content, hydrogen bonding propensity or hydrogen bond donor atoms, local topology, presence of heteroatoms and aromaticity are crucial factors in controlling the logK_ow and logK_oc values of the compounds. The suggested explanatory features for different classes of chemicals or the whole diverse set can help in safer designing of chemicals, which is one of the primary agenda of the "Green Chemistry" program.

Applicability of Equilibrium Sampling in Informing Tissue Residues and Dietary Risks of Legacy and Current-Use Organic Chemicals in Aquaculture

Equilibrium sampling based on silicone polydimethylsiloxane (PDMS) has been used to determine the concentrations of freely dissolved hydrophobic organic compounds (HOCs) and assess the thermodynamic potentials for bioaccumulation of these compounds in the aquatic environment. This allows the use of PDMS-based sampling techniques in assisting conventional sampling and extraction methods for the determination of the concentrations of HOCs in aquaculture products. The present study is an ex situ demonstration of how well PDMS can inform the tissue residues and dietary risks of legacy or current-use organic chemicals in aquaculture species from farm ponds in eastern China. For legacy contaminants such as polybrominated diphenyl ethers (PBDEs, n = 10), good agreement between the predicted concentrations based on PDMS and the measured lipid-normalized concentrations was observed for 60% of the studied biota, including both pelagic and benthic species. For pesticides currently used, such as pyrethroid (PE) (n = 4) and organophosphate pesticides (OPPs, n = 7), the measured tissue residues were consistently higher than those predicted by PDMS, possibly caused by the continuous input from the surroundings. For the organochlorine pesticides (OCPs, n = 5), the only detected chemical was also underestimated. Adjusted by ingestion rates of aquaculture products and toxicology data, the target hazard quotients of these chemicals predicted from PDMS were generally comparable to those derived from measured concentrations in tissue because of the predominance of PBDEs. Overall, PDMS-based equilibrium sampling offered an alternative approach for the prediction of tissue residues and dietary risks of PBDEs. Moreover, it should be applied with caution for PEs, OPPs, and OCPs. Improving the application of PDMS for these chemicals in farm ponds warrants future study. Environ Toxicol Chem 2021;40:79-87. © 2020 SETAC.

Lung disorders induced by respirable organic chemicals

Respirable organic chemicals were originally thought to cause allergic respiratory diseases, such as bronchial asthma and hypersensitivity pneumonitis, and believed not to cause lung disorders derived from inflammatory or fibrotic processes such as pulmonary fibrosis and interstitial pneumonitis. It has recently been reported, however, that exposure to organic chemicals can cause interstitial lung diseases. In this review, we discuss the clinical features of occupational asthma and hypersensitivity pneumonitis, as well as other lung disorders, including interstitial pneumonitis, caused by humidifier disinfectants in Korea and by a cross-linked acrylic acid-based polymer (CL-PAA) in Japan.

Microglia influence host defense, disease, and repair following murine coronavirus infection of the central nervous system

The present study examines functional contributions of microglia in host defense, demyelination, and remyelination following infection of susceptible mice with a neurotropic coronavirus. Treatment with PLX5622, an inhibitor of colony stimulating factor 1 receptor (CSF1R) that efficiently depletes microglia, prior to infection of the central nervous system (CNS) with the neurotropic JHM strain of mouse hepatitis virus (JHMV) resulted in increased mortality compared with control mice that correlated with impaired control of viral replication. Single cell RNA sequencing (scRNASeq) of CD45+ cells isolated from the CNS revealed that PLX5622 treatment resulted in muted CD4+ T cell activation profile that was associated with decreased expression of transcripts encoding MHC class II and CD86 in macrophages but not dendritic cells. Evaluation of spinal cord demyelination revealed a marked increase in white matter damage in PLX5622-treated mice that corresponded with elevated expression of transcripts encoding disease-associated proteins Osteopontin (Spp1), Apolipoprotein E (Apoe), and Triggering receptor expressed on myeloid cells 2 (Trem2) that were enriched within macrophages. In addition, PLX5622 treatment dampened expression of Cystatin F (Cst7), Insulin growth factor 1 (Igf1), and lipoprotein lipase (Lpl) within macrophage populations which have been implicated in promoting repair of damaged nerve tissue and this was associated with impaired remyelination. Collectively, these findings argue that microglia tailor the CNS microenvironment to enhance control of coronavirus replication as well as dampen the severity of demyelination and influence repair.

Norm index-based QSAR models for acute toxicity of organic compounds toward zebrafish embryo

Zebrafish embryos are highly sensitive to toxicant exposure and have been used to evaluate the potential eco-toxicity caused by organic pollutants in the aquatic environment. This study was to develop four quantitative structure-activity relationship (QSAR) models based on norm descriptors for acute toxicity of different exposure times toward zebrafish embryo of organic compounds with various structures. Norm descriptors were obtained by calculating the norm index of the atomic distribution matrix, which was composed of atomic spatial distribution and atomic properties. These norm index-based QSAR models presented satisfactory results with R² of 0.8549, 0.9162, 0.8335 and 0.8119 for 48, 96, 120 and 132 h, respectively. Validation results including cross validation, external validation, Y-randomized test and applicability domain analysis indicated that the proposed models were stable, robust and reliable. Accordingly, these norm descriptors might be effective in predicting the acute toxicity of various organics to zebrafish embryos, which might be useful for evaluating the potential hazards of organic pollutants to aquatic environment.

Chemical identity and cardiovascular toxicity of hydrophobic organic components in PM2.5

Numerous experimental and epidemiological studies have demonstrated that exposure to PM_2.5 may result in pathogenesis of several major cardiovascular diseases (CVDs), which can be attributed to the combined adverse effects induced by the complicated components of PM_2.5. Organic materials, which are major components of PM_2.5, contain thousands of chemicals, and most of them are environmental hazards. However, the contamination profile and contribution to overall toxicity of PM_2.5-bound organic components (OCs) have not been thoroughly evaluated yet. Herein, we aim to provide an overview of the literature on PM_2.5-bound hydrophobic OCs, with an emphasis on the chemical identity and reported impairments on the cardiovascular system, including the potential exposure routes and mechanisms. We first provide an update on the worldwide mass concentration and composition data of PM_2.5, and then, review the contamination profile of PM_2.5-bound hydrophobic OCs, including constitution, concentration, distribution, formation, source, and identification. In particular, the link between exposure to PM_2.5-bound hydrophobic OCs and CVDs and its possible underlying mechanisms are discussed to evaluate the possible risks of PM_2.5-bound hydrophobic OCs on the cardiovascular system and to provide suggestions for future studies.

Predicting oxidative stress induced by organic chemicals by using quantitative Structure-Activity relationship methods

Cellular exposure to xenobiotic human-made products will lead to oxidative stress that gives rise to DNA damage, as well as chemical or mechanical damage. Distinguishing the chemicals that will induce oxidative stress and predicting their toxicity is necessary. In the present study, 4270 compounds in the ARE-bla assay were investigated to predict active and inactive compounds by using simple algorithms, namely, recursive partitioning (RP) and binomial logistic regression, and to develop the quantitative structure-activity relationship (QSAR) models of chemicals that activate the ARE pathway to induce oxidative stress and exert toxic effects on cells. A decision tree based on scaffold-based fragments obtained through RP analysis showed the best identification accuracy. However, the overall identification accuracy of this model for active compounds was unsatisfactory due to limited fragments. Furthermore, a binomial logistic regression model was developed from 638 active compounds and 3632 inactive chemicals. The model with a cutoff of 0.15 could predict chemicals that were active or inactive with the prediction accuracy of 69.1%. Its area under the receiver operating characteristic (ROC) curve metric (AUROC) was 0.762, which indicated the acceptable predictive ability of this model. The parameters nBM (number of multiple bonds) and H% (percentage of H atom) played dominant roles in the prediction of the activity (inactive or active) of chemicals. A global QSAR model was developed to predict the toxicity of active chemicals. However, the model displayed an unsatisfactory result with R² = 0.316 and R²_ext = 0.090. Active chemicals were then classified on the basis of structure. A total of 79 compounds with carbon chains could be predicted with acceptable performance by using a QSAR model with six descriptors (R² = 0.722, R²_ext = 0.798, Q²_Loo = 0.654, Q²_Boot = 0.755, Q²_ext = 0.721). The simple models established here contribute to efforts on identification compounds inducing oxidative stress and provide the scientific basis for risk assessment to organisms in the environment.

Effect of the structural factors of organic compounds on the acute toxicity toward Daphnia magna

The acute toxicity of organic compounds towards Daphina magna was subjected to QSAR analysis. The two-dimensional simplex representation of molecular structure (2D SiRMS) and the support vector machine (SVM), gradient boosting (GBM) methods were used to develop QSAR models. Adequate regression QSAR models were developed for incubation of 24 h. Their interpretation allowed us to quantitatively describe and rank the well-known toxicophores, to refine their molecular surroundings, and to distinguish the structural derivatives of the fragments that significantly contribute to the acute toxicity (LC₅₀) of organic compounds towards D. magna. Based on the results of the interpretation of the regression models, a molecular design (modification) of highly toxic compounds was performed in order to reduce their hazard. In addition, acceptable classification QSAR models were developed to reliably predict the following mode of action (MOA): specific and non-specific toxicity of organic compounds towards D. magna. When interpreting these models, we were able to determine the structural fragments and the physicochemical characteristics of molecules that are responsible for the manifestation of one of the modes of action. The on-line version of the OCHEM expert system (https://ochem.eu), HYBOT descriptors, and the random forest and SVM methods were used for a comparative QSAR investigation.

Plasmon-enabled degradation of organic micropollutants in water by visible-light illumination of Janus gold nanorods

The development of sustainable methods for the degradation of pollutants in water is an ongoing critical challenge. Anthropogenic organic micropollutants such as pharmaceuticals, present in our water supplies in trace quantities, are currently not remediated by conventional treatment processes. Here, we report an initial demonstration of the oxidative degradation of organic micropollutants using specially designed nanoparticles and visible-wavelength sunlight. Gold "Janus" nanorods (Au JNRs), partially coated with silica to enhance their colloidal stability in aqueous solutions while also maintaining a partially uncoated Au surface to facilitate photocatalysis, were synthesized. Au JNRs were dispersed in an aqueous solution containing peroxydisulfate (PDS), where oxidative degradation of both simulant and actual organic micropollutants was observed. Photothermal heating, light-induced hot electron-driven charge transfer, and direct electron shuttling under dark conditions all contribute to the observed oxidation chemistry. This work not only provides an ideal platform for studying plasmonic photochemistry in aqueous medium but also opens the door for nanoengineered, solar-based methods to remediate recalcitrant micropollutants in water supplies.

Quantitative structure-toxicity relationships of organic chemicals against Pseudokirchneriella subcapitata

Predicting the toxicity of organic toxicants to aquatic life through chemometric approach is challenging area. In this paper, a six-descriptor quantitative structure-activity/toxicity relationship (QSAR/QSTR) model was successfully developed for the toxicity pEC₁₀ of organic chemicals against Pseudokirchneriella subcapitata, by applying support vector machine (SVM) together with genetic algorithm. A sufficiently large data set consisting of 334 organic chemicals was randomly divided into a training set (167 compounds) and a test set (167 compounds) with a ratio of 1:1. The optimal SVM model possesses coefficient of determination R² of 0.76 and mean absolute error (MAE) of 0.60 for the training set and R² of 0.75 and MAE of 0.61 for the test set. Compared with other models reported in the literature, our SVM model for the toxicity pEC₁₀ shows significant statistical quality and satisfactory predictive ability, although it has fewer molecular descriptors and more samples in the test set. A QSTR model for pEC₅₀ of organic chemicals against Pseudokirchneriella subcapitata was also developed with the same subsets and molecular descriptors.

Mitochondrial response and resilience to anthropogenic chemicals during embryonic development

Mitochondria are integral to maintaining cellular homeostasis. Optimum mitochondrial function is critical during embryonic development, as they play a key role in early signaling cascades and epigenetic programming, in addition to sustaining an adequate energy production. Mitochondria are sensitive targets of environmental toxins, potentially even at levels considered safe under current regulatory limits. Most mitochondrial analyses have focused only on chemical exposure effects in vitro or in isolated mitochondria. However, comparatively little is known about mitochondrial effects of chemical exposure during vertebrate embryogenesis, especially during the recovery phase following a chemical insult. Here, we used the zebrafish (Danio rerio), in a 96-well plate system, to examine mitochondrial effects of 24 chemicals including pharmaceuticals, industrial chemicals, and agrochemicals. We used oxygen consumption rate (OCR) during embryogenesis as a proxy for mitochondrial function. Embryonic OCR (eOCR) was measured in clean egg water immediately following 24 h of chemical exposure and subsequently for an additional 8 h. Each chemical, dependent upon the concentration, resulted in a unique eOCR response profile. While some eOCR effects were persistent or recoverable over time, some effects were only detected several hours after being removed from the exposure. Non-monotonic dose response effects as well as mitochondrial hormesis were also detected following exposure to some chemicals. Collectively, our study shows that mitochondrial response to chemicals are highly dynamic and warrant careful consideration when determining mitochondrial toxicity of a given chemical.

Effects of chemical fractions from an oil sands end-pit lake on reproduction of fathead minnows

Oil sands process-affected water (OSPW) is a byproduct of bitumen extraction in the surface-mining oil sands industry in Alberta, Canada. Organic compounds in OSPW can be acutely or chronically toxic to aquatic organisms, so part of a long-term strategy for remediation of OSPW is ageing of water in artificial lakes, termed end-pit lakes. BaseMine Lake (BML) is the first oil sands end-pit lake, commissioned in 2012. At the time of its establishment, an effects-directed analysis of BML-OSPW showed that naphthenic acids and polar organic chemical species containing sulfur or nitrogen contributed to its acute lethality. However, the chronic toxicity of these same chemical fractions has not yet been investigated. In this work, the short-term fathead minnow reproductive bioassay was used to assess endocrine-system effects of two fractions of BML-OSPW collected in 2015. One of the fractions (F1) contained predominantly naphthenic acids, while the other (F2) contained non-acidic polar organic chemical species. Exposure of minnows to F1 or F2 at concentrations equivalent to 25% (v/v) of the 2015 BML-OSPW sample (5-15% of the 2012 BML-OSPW sample) did not alter reproductive performance, fertilization success, or concentrations of sex steroids in female or male minnows. Additionally, there were no significant differences in fertility, hatching success, or incidence of morphological indices of embryos collected on day 7 or 14 from exposed breeding trios. However, exposure of male fathead minnow to 25% (v/v) intact 2015 BML-OSPW resulted in a significantly greater hepatosomatic index. Exposure of fathead minnow to refined fractions of dissolved organic chemicals in 2015 BML-OSPW, or a 25% (v/v) of the intact mixture did not affect fertility or fecundity as measured by use of the 21-day reproductive bioassay. These data will be useful in setting future threshold criteria for OSPW reclamation and treatment.

Biosynthesis of depsipeptides with a 3-hydroxybenzoate moiety and selective anticancer activities involves a chorismatase

Neoantimycins are anticancer compounds of 15-membered ring antimycin-type depsipeptides. They are biosynthesized by a hybrid multimodular protein complex of nonribosomal peptide synthetase (NRPS) and polyketide synthase (PKS), typically from the starting precursor 3-formamidosalicylate. Examining fermentation extracts of Streptomyces conglobatus, here we discovered four new neoantimycin analogs, unantimycins B-E, in which 3-formamidosalicylates are replaced by an unusual 3-hydroxybenzoate (3-HBA) moiety. Unantimycins B-E exhibited levels of anticancer activities similar to those of the chemotherapeutic drug cisplatin in human lung cancer, colorectal cancer, and melanoma cells. Notably, they mostly displayed no significant toxicity toward noncancerous cells, unlike the serious toxicities generally reported for antimycin-type natural products. Using site-directed mutagenesis and heterologous expression, we found that unantimycin productions are correlated with the activity of a chorismatase homolog, the nat-hyg5 gene, from a type I PKS gene cluster. Biochemical analysis confirmed that the catalytic activity of Nat-hyg5 generates 3-HBA from chorismate. Finally, we achieved selective production of unantimycins B and C by engineering a chassis host. On the basis of these findings, we propose that unantimycin biosynthesis is directed by the neoantimycin-producing NRPS-PKS complex and initiated with the starter unit of 3-HBA. The elucidation of the biosynthetic unantimycin pathway reported here paves the way to improve the yield of these compounds for evaluation in oncotherapeutic applications.

Transcriptome analyses unravel CYP1A1 and CYP1B1 as novel biomarkers for disinfection by-products (DBPs) derived from chlorinated algal organic matter

Disinfection by-products (DBPs) are generated during chlorination of drinking water. Previous studies demonstrate that DBPs are cytotoxic, genotoxic and associated with an increased risk of human cancer. However, the molecular basis of DBPs-induced toxic effects remains unclear. Here, we chlorinated samples of algal-derived organic matter (AOM) and sediment organic matter (SOM) from a local drinking water reservoir. Chemical properties, toxicities and transcriptomic profiles of human Caco-2 cell exposed to AOM and SOM were compared before and after chlorination. We analyzed chlorination-caused distinct gene expression patterns between AOM and SOM, and identified a set of 22 differentially expressed genes under chlorination of AOM that are different from chlorinated SOM. Consequent network analysis indicates that differential CYP1A1, CYP1B1, ID1 and ID2 are common targets of the upstream regulators predicted in the AOM group, but not the SOM group. Through experimental validation and data integration from previous reports related to DBPs or environmental stressors, we found that CYP1A1 and CYP1B1 are specifically up-regulated after chlorinating AOM. Our study demonstrates that the two CYP1 genes likely act as novel biomarkers of AOM derived DBPs, and this would be helpful for testing drinking water DBPs toxicity and further monitoring drinking water safety.

Enhanced marine monitoring and toxicity study of oil spill dispersants including Corexit EC9500A in the presence of diluted bitumen

Observations made for the analysis of the oil spill dispersant tracer dioctyl sulfosuccinate (DOSS) during LC50 toxicity testing, highlighted a stability issue for this tracer compound in seawater. A liquid chromatography high-resolution quadrupole time-of-flight mass spectrometry (LC/QToF) was used to confirm monooctyl sulfosuccinate (MOSS) as the only significant DOSS breakdown product, and not the related isomer, 4-(2-ethylhexyl) 2-sulfobutanedioate. Combined analysis of DOSS and MOSS was shown to be applicable to monitoring of spill dispersants Corexit® EC9500A, Finasol OSR52, Slickgone NS, and Slickgone EW. The unassisted conversion of DOSS to MOSS occurred in all four oil spill dispersants solubilized in seawater, although differences were noted in the rate of MOSS formation. A marine microcosm study of Corexit EC9500A, the formulation most rapid to form MOSS, provided further evidence of the stoichiometric conversion of DOSS to MOSS under conditions relevant to real world dilbit spill. Results supported combined DOSS and MOSS analysis for the monitoring of spill dispersant in a marine environment, with a significant extension of sample collection time by 10 days or longer in cooler conditions. Implications of the unassisted formation of MOSS and combined DOSS:MOSS analysis are discussed in relation to improving dispersant LC50 toxicity studies.

Burkholderia: An Untapped but Promising Bacterial Genus for the Conversion of Aromatic Compounds

Burkholderia, a bacterial genus comprising more than 120 species, is typically reported to inhabit soil and water environments. These Gram-negative bacteria harbor a variety of aromatic catabolic pathways and are thus potential organisms for bioremediation of sites contaminated with aromatic pollutants. However, there are still substantial gaps in our knowledge of these catabolic processes that must be filled before these pathways and organisms can be harnessed for biotechnological applications. This review presents recent discoveries on the catabolism of monoaromatic and polycyclic aromatic hydrocarbons, as well as of heterocyclic compounds, by a diversity of Burkholderia strains. We also present a perspective on the beneficial features of Burkholderia spp. and future directions for their potential utilization in the bioremediation and bioconversion of aromatic compounds.

Predictive Models for Human Organ Toxicity Based on In Vitro Bioactivity Data and Chemical Structure

Traditional toxicity testing reliant on animal models is costly and low throughput, posing a significant challenge with the increasing numbers of chemicals that humans are exposed to in the environment. The purpose of this investigation was to build optimal prediction models for various human in vivo/organ-level toxicity end points (extracted from ChemIDPlus) using chemical structure and Tox21 in vitro quantitative high-throughput screening (qHTS) bioactivity assay data. Several supervised machine learning algorithms were applied to model 14 human toxicity end points pertaining to vascular, kidney, ureter and bladder, and liver organ systems. Three metrics were used to evaluate model performance: area under the receiver operating characteristic curve (AUC-ROC), balanced accuracy (BA), and Matthews correlation coefficient (MCC). The top four models, with AUC-ROC values >0.8, were derived for endocrine (0.90 ± 0.00), musculoskeletal (0.88 ± 0.02), peripheral nerve and sensation (0.85 ± 0.01), and brain and coverings (0.83 ± 0.02) toxicities, whereas the best model AUC-ROC values were >0.7 for the remaining 10 toxicities. Model performance was found to be dependent on the specific data set, model type, and feature selection method used. In addition, chemical structure and assay data showed different levels of contribution to the prediction of different toxicity end points. Although in vitro assay data, when combined with chemical structure, slightly improved the predictive accuracy for most end points (11 out of 14), a noteworthy finding was the near equal success of the structure-only models, which do not require Tox21 qHTS screening data, and the relatively poor performance of assay-only models. Thus, the top-performing structure-only models from this study could be applied for hazard screening of large sets of chemicals for potential human toxicity, whereas the largest assay contributions to models (i.e., cellular targets) could be used, along with the top-contributing structural features, to provide insight into toxicity mechanisms.

Synthesis of AgNPs coated with secondary metabolites of Acacia nilotica: An efficient antimicrobial and detoxification agent for environmental toxic organic pollutants

This study concentrates on biosynthesis of Silver Nanoparticles (AgNPs) from stem extract of Acacia nilotica (A. nilotica). The reaction was completed at a temperature ~40-45 °C and time duration of 5 h. AgNPs were thoroughly investigated via advanced characterization techniques such as UV-Vis spectrophotometry (UV-Vis), Fourier Transform Infrared spectroscopy (FTIR), X-ray Diffractometry (XRD), Field Emission Scanning Electron Microscopy (FESEM), High Resolution Transmission Electron Microscopy (HRTEM), X-ray Photoelectron Spectroscopy (XPS), Thermo Gravimetric Analysis (TGA), Diffuse Reflectance Spectroscopy (DRS), Brunner-Emmett-Teller (BET), Dynamic Light Scattering (DLS), and Zeta potential analysis. AgNPs with average size below 50 nm were revealed by all the measuring techniques. Maximum surface area ~5.69 m²/g was reported for the as synthesized NPs with total pore volume ~0.0191 mL/g and average pore size ~1.13 nm. Physical properties such as size and shape have changed the surface plasmon resonance peak in UV-visible spectrum. Antimicrobial activity was reported due to denaturation of microbial ribosome's sulphur and phosphorus bond by silver ions against bacterium Methicillin Resistant Staphylococcus aureus (MRSA) and fungus Candida Albican (CA). Furthermore, AgNPs degraded toxic pollutants such as 4-nitrophenol (4-NP), 2-nitrophenol (2-NP) and various hazardous dyes such as Congo Red (CR), Methylene Blue (MB) and Methyl Orange (MO) up to 95%. The present work provided low cost, green and an effective way for synthesis of AgNPs which were utilized as potential antimicrobial agents as well as effective catalyst for detoxification of various pollutants and dyes.

New in silico models to predict in vitro micronucleus induction as marker of genotoxicity

The evaluation of genotoxicity is a fundamental part of the safety assessment of chemicals due to the relevance of the potential health effects of genotoxicants. Among the testing methods available, the in vitro micronucleus assay with mammalian cells is one of the most used and required by regulations targeting several industrial sectors such as the cosmetic industry and food-related sectors. As an alternative to the testing methods, in recent years, lots in silico methods were developed to predict the genotoxicity of chemicals, including models for the Ames mutagenicity test, the in vitro chromosomal aberrations and the in vivo micronucleus assay. We developed several in silico models for the prediction of genotoxicity as reflected by the in vitro micronucleus assay. The resulting models include both statistical and knowledge-based models. The most promising model is the one based on fragments extracted with the SARpy platform. More than 100 structural alerts were extracted, including also fragments associated with the non-genotoxic activity. The model is characterized by high accuracy and the lowest false negative rate, making this tool suitable for chemical screening according to the regulators' needs. The SARpy model will be implemented on the VEGA platform (https://www.vegahub.eu) and will be freely available.

Prediction of chemical toxicity to Tetrahymena pyriformis with four-descriptor models

A quantitative structure-toxicity relationship (QSTR) model based on four descriptors was successfully developed for 1163 chemical toxicants against Tetrahymena pyriformis by applying general regression neural network (GRNN). The training set consisting of 600 organic compounds was used to train GRNN models that were evaluated with the test set of 563 compounds. For the optimal GRNN model, the training set possesses the coefficient of determination R² of 0.86 and root mean square (rms) error of 0.41, and the test set has R² of 0.80 and rms of 0.41. Investigated results indicate that the optimal GRNN model is accurate, although the GRNN model has only four descriptor and more samples in the test set.

Toxicity of fine and quasi-ultrafine particles: Focus on the effects of organic extractable and non-extractable matter fractions

To date no study has been able to clearly attribute the observed toxicological effects of atmospheric particles (PM) to a specific class of components. The toxicity of both the organic extractable matter (OEM_2.5-0.3) and non-extractable matter (NEM_2.5-0.3) of fine particles (PM_2.5-0.3) was compared to that of PM_2.5-0.3 in its entirety on normal human epithelial bronchial BEAS-2B cells in culture. The specific effect of the quasi-ultrafine fraction (PM_0.3) was assessed, by comparing the responses of cells exposed to the PM_2.5-0.3 and PM_0.3 organic extractable matter, OEM_2.5-0.3 and OEM_0.3 respectively. Chemically, PAH, O-PAH, and N-PAH were respectively 43, 17, and 4 times more concentrated in PM_0.3 than in PM_2.5-0.3, suggesting thereby a predominant influence of anthropogenic activities and combustion sources. BEAS-2B cells exposed to PM_2.5-0.3, NEM_2.5-0.3, EOM_2.5-0.3 and OEM_0.3 lead to different profiles of expression of selected genes and proteins involved in the metabolic activation of PAH, O-PAH, and N-PAH, and in the genotoxicity pathways. Specifically, OEM_0.3 was the most inducer for phase I and phase II enzymes implicated in the metabolic activation of PAH (AHR, AHRR, ARNT, CYP1A1, CYP1B1, EPHX-1, GSTA-4) thereby producing the highest DNA damage, felt by ATR and, thereafter, a cascade of protein phosphorylation (CHK1/CHK2/MDM2) closely related to the cell cycle arrest (P21 and P53 induction). This study underlined the crucial role played by the organic chemicals present in PM_0.3. These results should be considered in any future study looking for the main chemical determinants responsible for the toxicity of ambient fine PM.

Recommendations for Improving Methods and Models for Aquatic Hazard Assessment of Ionizable Organic Chemicals

Ionizable organic chemicals (IOCs) such as organic acids and bases are an important substance class requiring aquatic hazard evaluation. Although the aquatic toxicity of IOCs is highly dependent on the water pH, many toxicity studies in the literature cannot be interpreted because pH was not reported or not kept constant during the experiment, calling for an adaptation and improvement of testing guidelines. The modulating influence of pH on toxicity is mainly caused by pH-dependent uptake and bioaccumulation of IOCs, which can be described by ion-trapping and toxicokinetic models. The internal effect concentrations of IOCs were found to be independent of the external pH because of organisms' and cells' ability to maintain a stable internal pH milieu. If the external pH is close to the internal pH, existing quantitative structure-activity relationships (QSARs) for neutral organics can be adapted by substituting the octanol-water partition coefficient by the ionization-corrected liposome-water distribution ratio as the hydrophobicity descriptor, demonstrated by modification of the target lipid model. Charged, zwitterionic and neutral species of an IOC can all contribute to observed toxicity, either through concentration-additive mixture effects or by interaction of different species, as is the case for uncoupling of mitochondrial respiration. For specifically acting IOCs, we recommend a 2-step screening procedure with ion-trapping/QSAR models used to predict the baseline toxicity, followed by adjustment using the toxic ratio derived from in vitro systems. Receptor- or plasma-binding models also show promise for elucidating IOC toxicity. The present review is intended to help demystify the ecotoxicity of IOCs and provide recommendations for their hazard and risk assessment. Environ Toxicol Chem 2020;39:269-286. © 2019 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals, Inc. on behalf of SETAC.

Effect of temperature on toxicity and biodegradability of dissolved organic nitrogen formed during hydrothermal liquefaction of biomass

This study investigated the nutrient content and reuse potential of wastewater generated during hydrothermal liquefaction of microalgal biomass. The hydrothermal liquefaction reaction was tested at 270, 300, 330, and 345 °C to determine the effect of temperature on the formation of non-biodegradable dissolved organic nitrogen (nbDON). Total nitrogen, ammonium, color, and toxicity were selected as key characteristics for the reuse of hydrothermal liquefaction wastewater. Results indicated that a higher concentration of nbDON₅ (nbDON defined with a 5 day growth assay) and more diverse heterocyclic N-containing organic compounds were associated with greater toxicity as measured by a growth rate assay. For the tested temperature ranges, the total nitrogen content of the hydrothermal liquefaction wastewater slightly decreased from 5020 ± 690 mg L^-1 to 4160 ± 120 mg L^-1, but the % nbDON₅ fraction increased from 57 ± 3 %DON to 96 ± 5 %DON. The temperature of hydrothermal liquefaction reactions can be optimized to maximize carbon conversion and nitrogen recovery.