Identification of Optimal Machine Learning Algorithms and Molecular Fingerprints for Explainable Toxicity Prediction Models Using ToxCast/Tox21 Bioassay Data.

Recent studies have primarily focused on introducing novel frameworks to enhance the predictive power of toxicity prediction models by refining molecular representation methods and algorithms. However, these methods are inherently complex and often pose challenges in understanding and explaining, leading to barriers in their regulatory adoption and validation. Therefore, it is necessary to select the optimal model, considering not only model performance but also interpretability. This study aimed to identify the optimal combination of molecular fingerprints (pattern-based versus algorithm-based) and machine learning algorithms (simple versus complex) for developing explainable toxicity prediction models through an comprehensive investigation of the ToxCast/Tox21 bioassay data set. For 1092 ToxCast/Tox21 assays, five molecular fingerprints (MACCS, Morgan, RDKit, Layered, and Patterned) and six algorithms (MLP, GBT, Random Forest, kNN, Logistic Regression, and Naïve Bayes) were used to train the models. Results showed that 35 models revealed acceptable performance (F1 score or accuracy is 0.8 or higher). Among the combinations, either MACCS or Morgan, paired with Random Forest, demonstrated robust performance compared with other molecular fingerprints and algorithms. MACCS and Random Forest are valuable, even when prioritizing interpretability. Consequently, the MACCS-Random Forest combination model based on four assays, targeting G protein-coupled receptor and kinase, were identified and they can be used to discern specific structural features or patterns in chemical compounds, offering explainable insights into toxicity-related chemical structures. This study indicates the importance of not disregarding the utilization of simple models when assessing both predictivity and interpretability within the context of chemical feature-based Tox21 data analysis.

Bridging the Gap Between Human Toxicology and Ecotoxicology Under One Health Perspective by a Cross-Species Adverse Outcome Pathway Network for Reproductive Toxicity.

Although ecotoxicological and toxicological risk assessments are performed separately from each other, recent efforts have been made in both disciplines to reduce animal testing and develop predictive approaches instead, for example, via conserved molecular markers, and in vitro and in silico approaches. Among them, adverse outcome pathways (AOPs) have been proposed to facilitate the prediction of molecular toxic effects at larger biological scales. Thus, more toxicological data are used to inform on ecotoxicological risks and vice versa. An AOP has been previously developed to predict reproductive toxicity of silver nanoparticles via oxidative stress on the nematode Caenorhabditis elegans (AOPwiki ID 207). Following this previous study, our present study aims to extend the biologically plausible taxonomic domain of applicability (tDOA) of AOP 207. Various types of data, including in vitro human cells, in vivo, and molecular to individual, from previous studies have been collected and structured into a cross-species AOP network that can inform both human toxicology and ecotoxicology risk assessments. The first step was the collection and analysis of literature data to fit the AOP criteria and build a first AOP network. Then, key event relationships were assessed using a Bayesian network modeling approach, which gave more confidence in our overall AOP network. Finally, the biologically plausible tDOA was extended using in silico approaches (Genes-to-Pathways Species Conservation Analysis and Sequence Alignment to Predict Across Species Susceptibility), which led to the extrapolation of our AOP network across over 100 taxonomic groups. Our approach shows that various types of data can be integrated into an AOP framework, and thus facilitates access to knowledge and prediction of toxic mechanisms without the need for further animal testing. Environ Toxicol Chem 2024;00:1-14. © 2024 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Integrating aggregate exposure pathway and adverse outcome pathway for micro/nanoplastics: A review on exposure, toxicokinetics, and toxicity studies.

Micro/nanoplastics (MNPs) have emerged as a significant environmental concern due to their widespread distribution and potential adverse effects on human health and the environment. In this study, to integrate exposure and toxicity pathways of MNPs, a comprehensive review of the occurrence, toxicokinetics (absorption, distribution, and excretion [ADE]), and toxicity of MNPs were investigated using the aggregate exposure pathway (AEP) and adverse outcome pathway (AOP) frameworks. Eighty-five papers were selected: 34 papers were on detecting MNPs in environmental samples, 38 papers were on the ADE of MNPs in humans and fish, and 36 papers were related to MNPs toxicity using experimental models. This review not only summarizes individual studies but also presents a preliminary AEP-AOP framework. This framework offers a comprehensive overview of pathways, enabling a clearer visualization of intricate processes spanning from environmental media, absorption, distribution, and molecular effects to adverse outcomes. Overall, this review emphasizes the importance of integrating exposure and toxicity pathways of MNPs by utilizing AEP-AOP to comprehensively understand their impacts on human and ecological organisms. The findings contribute to highlighting the need for further research to fill the existing knowledge gaps in this field and the development of more effective strategies for the safe management of MNPs.

Exploring the potential of ToxCast™ data for mechanism-based prioritization of chemicals in regulatory context: Case study with priority existing chemicals (PECs) under K-REACH.

Recent studies have highlighted the potential of the ToxCast™ database for mechanism-based prioritization of chemicals. To explore the applicability of ToxCast data in the context of regulatory inventory chemicals, we screened 510 priority existing chemicals (PECs) regulated under the Act on the Registration and Evaluation, etc. of Chemical Substances (K-REACH) using ToxCast bioassays. In our analysis, a hit-call data matrix containing 298984 chemical-gene interactions was computed for 949 bioassays with the intended target genes, which enabled the identification of the putative toxicity mechanisms. Based on the reactivity to the chemicals, we analyzed 412 bioassays whose intended target gene families were cytochrome P450, oxidoreductase, transporter, nuclear receptor, steroid hormone, and DNA-binding. We also identified 141 chemicals based on their reactivity in the bioassays. These chemicals are mainly in consumer products including colorants, preservatives, air fresheners, and detergents. Our analysis revealed that in vitro bioactivities were involved in the relevant mechanisms inducing in vivo toxicity; however, this was not sufficient to predict more hazardous chemicals. Overall, the current results point to a potential and limitation in using ToxCast data for chemical prioritization in regulatory context in the absence of suitable in vivo data.

Identification of molecular initiating events (MIE) using chemical database analysis and nuclear receptor activity assays for screening potential inhalation toxicants.

An adverse outcome pathway (AOP) framework can facilitate the use of alternative assays in chemical regulations by providing scientific evidence. Previously, an AOP, peroxisome proliferative-activating receptor gamma (PPARγ) antagonism that leads to pulmonary fibrosis, was developed. Based on a literature search, PPARγ inactivation has been proposed as a molecular initiating event (MIE). In addition, a list of candidate chemicals that could be used in the experimental validation was proposed using toxicity database and deep learning models. In this study, the screening of environmental chemicals for MIE was conducted using in silico and in vitro tests to maximize the applicability of this AOP for screening inhalation toxicants. Initially, potential inhalation exposure chemicals that are active in three or more key events were selected, and in silico molecular docking was performed. Among the chemicals with low binding energy to PPARγ, nine chemicals were selected for validation of the AOP using in vitro PPARγ activity assay. As a result, rotenone, triorthocresyl phosphate, and castor oil were proposed as PPARγ antagonists and stressor chemicals of the AOP. Overall, the proposed tiered approach of the database-in silico-in vitro can help identify the regulatory applicability and assist in the development and experimental validation of AOP.

Integrative Data Mining Approach: Case Study with Adverse Outcome Pathway Network Leading to Pulmonary Fibrosis.

An adverse outcome pathway (AOP) framework can be applied as an efficient tool for the rapid screening of environmental chemicals. For the development of an AOP, a database mining approach can support an expert derivation approach by gathering a wider range of evidence than in a literature review. In this study, data from various databases were integrated and analyzed to supplement the AOP leading to pulmonary fibrosis by analyzing additional evidence using a data mining approach and establishing an application domain for chemicals. First, we collected chemicals, genes, and phenotypes that were studied and related to pulmonary fibrosis through the Comparative Toxicogenomics Database (CTD). CGPD-tetramers constructed by linking each related chemical, gene, phenotype, and disease can provide the basic components for the assembly of putative AOPs. Next, an AOP network was established by connecting eight existing AOPs for pulmonary fibrosis developed by expert derivation from the AOP Wiki. Finally, the pulmonary fibrosis AOP network was proposed by integrating the AOP network from AOP Wiki and the CGPD-tetramers from the CTD. To prioritize potential chemical stressors in the AOP network, 61 chemicals were ranked using the relevance of the chemical to the AOP and chemical exposure information from the CompTox Chemicals Dashboard. The approach proposed in this study can guide the utilization of available evidence from various databases as well as the literature in constructing AOP networks related to specific diseases.

Effects of Class Imbalance and Data Scarcity on the Performance of Binary Classification Machine Learning Models Developed Based on ToxCast/Tox21 Assay Data.

The development of toxicity classification models using the ToxCast database has been extensively studied. Machine learning approaches are effective in identifying the bioactivity of untested chemicals. However, ToxCast assays differ in the amount of data and degree of class imbalance (CI). Therefore, the resampling algorithm employed should vary depending on the data distribution to achieve optimal classification performance. In this study, the effects of CI and data scarcity (DS) on the performance of binary classification models were investigated using ToxCast bioassay data. An assay matrix based on CI and DS was prepared for 335 assays with biologically intended target information, and 28 CI assays and 3 DS assays were selected. Thirty models established by combining five molecular fingerprints (i.e., Morgan, MACCS, RDKit, Pattern, and Layered) and six algorithms [i.e., gradient boosting tree, random forest (RF), multi-layered perceptron, k-nearest neighbor, logistic regression, and naive Bayes] were trained using the selected assay data set. Of the 30 trained models, MACCS-RF showed the best performance and thus was selected for analyses of the effects of CI and DS. Results showed that recall and F1 were significantly lower when training with the CI assays than with the DS assays. In addition, hyperparameter tuning of the RF algorithm significantly improved F1 on CI assays. This study provided a basis for developing a toxicity classification model with improved performance by evaluating the effects of data set characteristics. This study also emphasized the importance of using appropriate evaluation metrics and tuning hyperparameters in model development.

Quantitative adverse outcome pathway (qAOP) using bayesian network model on comparative toxicity of multi-walled carbon nanotubes (MWCNTs): safe-by-design approach.

While the various physicochemical properties of engineered nanomaterials influence their toxicities, their understanding is still incomplete. A predictive framework is required to develop safe nanomaterials, and a Bayesian network (BN) model based on adverse outcome pathway (AOP) can be utilized for this purpose. In this study, to explore the applicability of the AOP-based BN model in the development of safe nanomaterials, a comparative study was conducted on the change in the probability of toxicity pathways in response to changes in the dimensions and surface functionalization of multi-walled carbon nanotubes (MWCNTs). Based on the results of our previous study, we developed an AOP leading to cell death, and the experimental results were collected in human liver cells (HepG2) and bronchial epithelium cells (Beas-2B). The BN model was trained on these data to identify probabilistic causal relationships between key events. The results indicated that dimensions were the main influencing factor for lung cells, whereas -OH or -COOH surface functionalization and aspect ratio were the main influencing factors for liver cells. Endoplasmic reticulum stress was found to be a more sensitive pathway for dimensional changes, and oxidative stress was a more sensitive pathway for surface functionalization. Overall, our results suggest that the AOP-based BN model can be used to provide a scientific basis for the development of safe nanomaterials.

Application of ToxCast/Tox21 data for toxicity mechanism-based evaluation and prioritization of environmental chemicals: Perspective and limitations.

In response to the need to minimize the use of experimental animals, new approach methodologies (NAMs) using advanced technology have emerged in the 21st century. ToxCast/Tox21 aims to evaluate the adverse effects of chemicals quickly and efficiently using a high-throughput screening and to transform the paradigm of toxicity assessment into mechanism-based toxicity prediction. The ToxCast/Tox21 database, which contains extensive data from over 1400 assays with numerous biological targets and activity data for over 9000 chemicals, can be used for various purposes in the field of chemical prioritization and toxicity prediction. In this study, an overview of the database was explored to aid mechanism-based chemical prioritization and toxicity prediction. Implications for the utilization of the ToxCast/Tox21 database in chemical prioritization and toxicity prediction were derived. The research trends in ToxCast/Tox21 assay data were reviewed in the context of toxicity mechanism identification, chemical priority, environmental monitoring, assay development, and toxicity prediction. Finally, the potential applications and limitations of using ToxCast/Tox21 assay data in chemical risk assessment were discussed. The analysis of the toxicity mechanism-based assays of ToxCast/Tox21 will help in chemical prioritization and regulatory applications without the use of laboratory animals.

Artificial Intelligence-Based Toxicity Prediction of Environmental Chemicals: Future Directions for Chemical Management Applications.

Recently, research on the development of artificial intelligence (AI)-based computational toxicology models that predict toxicity without the use of animal testing has emerged because of the rapid development of computer technology. Various computational toxicology techniques that predict toxicity based on the structure of chemical substances are gaining attention, including the quantitative structure-activity relationship. To understand the recent development of these models, we analyzed the databases, molecular descriptors, fingerprints, and algorithms considered in recent studies. Based on a selection of 96 papers published since 2014, we found that AI models have been developed to predict approximately 30 different toxicity end points using more than 20 toxicity databases. For model development, molecular access system and extended-connectivity fingerprints are the most commonly used molecular descriptors. The most used algorithm among the machine learning techniques is the random forest, while the most used algorithm among the deep learning techniques is a deep neural network. The use of AI technology in the development of toxicity prediction models is a new concept that will aid in achieving a scientific accord and meet regulatory applications. The comprehensive overview provided in this study will provide a useful guide for the further development and application of toxicity prediction models.

Advancing the Adverse Outcome Pathway for PPARγ Inactivation Leading to Pulmonary Fibrosis Using Bradford-Hill Consideration and the Comparative Toxicogenomics Database.

Pulmonary fibrosis is regulated by transforming growth factor-ß (TGF-ß) and peroxisome proliferator-activated receptor-gamma (PPARγ). An adverse outcome pathway (AOP) for PPARγ inactivation leading to pulmonary fibrosis has been previously developed. To advance the development of this AOP, the confidence of the overall AOP was assessed using the Bradford-Hill considerations as per the recommendations from the Organisation for Economic Co-operation and Development (OECD) Users' Handbook. Overall, the essentiality of key events (KEs) and the biological plausibility of key event relationships (KERs) were rated high. In contrast, the empirical support of KERs was found to be moderate. To experimentally evaluate the KERs from the molecular initiating event (MIE) and KE1, PPARγ (MIE) and TGF-ß (KE1) inhibitors were used to examine the effects of downstream events following inhibition of their upstream events. PPARγ inhibition (MIE) led to TGF-ß activation (KE1), upregulation in vimentin expression (KE3), and an increase in the fibronectin level (KE4). Similarly, activated TGF-ß (KE1) led to an increase in vimentin (KE3) and fibronectin expression (KE4). In the database analysis using the Comparative Toxicogenomics Database, 31 genes related to each KE were found to be highly correlated with pulmonary fibrosis, and the top 21 potential stressors were suggested. The AOP for pulmonary fibrosis evaluated in this study will be the basis for the screening of inhaled toxic substances in the environment.

Physical analysis reveals distinct responses of human bronchial epithelial cells to guanidine and isothiazolinone biocides.

Changes in the physical state of the cells can serve as important indicators of stress responses because they are closely linked with the changes in the pathophysiological functions of the cells. Physical traits can be conveniently assessed by analyzing the morphological features and the stresses at the cell-matrix and cell-cell adhesions in both single-cell and monolayer model systems in 2D. In this study, we investigated the mechano-stress responses of human bronchial epithelial cells, BEAS-2B, to two functionally distinct groups of biocides identified during the humidifier disinfectant accident, namely, guanidine (PHMG) and isothiazolinone (CMIT/MIT). We analyzed the physical traits, including cell area, nuclear area, and nuclear shape. While the results showed inconsistent average responses to the biocides, the degree of dispersion in the data set, measured by standard deviation, was remarkably higher in CMIT/MIT treated cells for all traits. As mechano-stress endpoints, traction and intercellular stresses were also measured, and the cytoskeletal actin structures were analyzed using immunofluorescence. This study demonstrates the versatility of the real-time imaging-based biomechanical analysis, which will contribute to identifying the temporally sensitive cellular behaviors as well as the emergence of heterogeneity in response to exogenously imposed stress factors. This study will also shed light on a comparative understanding of less studied substance, CMIT/MIT, in relation to a more studied substance, PHMG, which will further contribute to more strategic planning for proper risk management of the ingredients involved in toxicological accidents.

Identification of toxicity pathway of diesel particulate matter using AOP of PPARγ inactivation leading to pulmonary fibrosis.

Diesel particulate matter (DPM), a major subset of urban fine particulate matter (PM2.5), raises huge concerns for human health and has therefore been classified as a group 1 carcinogen by the International Agency for Research on Cancer (IARC). However, as DPM is a complex mixture of various chemicals, understanding of DPM's toxicity mechanism remains limited. As the major exposure route of DPM is through inhalation, we herein investigated its toxicity mechanism based on the Adverse Outcome Pathway (AOP) of pulmonary fibrosis, which we previously submitted to AOPWiki as AOP ID 206 (AOP206). We first screened whether individual chemicals in DPM have the potential to exert their toxicity through AOP206 by using the ToxCast database and deep learning models approach, then confirmed this by examining whether DPM as a mixture alters the expression of the molecular initiating event (MIE) and key events (KEs) of AOP206. For identifying the activeness of the component chemicals of DPM, we used 24 ToxCast assays potentially related to AOP206 and deep learning models based on these assays, which were identified and developed in our previous study. Of the 100 individual chemicals in DPM, 34 were active in PPARγ (MIE)-related assay, of which 17 were active in one or more KEs. To further identify whether individual chemicals in DPM are related to the MIE of AOP206, we performed molecular docking simulation on PPARγ for the chemicals showing activeness. Benzo[e]pyrene, benzo[a]pyrene and other related chemicals were the most likely to bind to PPARγ. In in vitro experiments, PPARγ activity increased with exposure of the DPM mixture, and the protein expression of PPARγ (MIE), and fibronectin (AO) also tended to be increased. Overall, we have demonstrated that AOP206 can be applied to identify the toxicity pathway of DPM. Further, we suggest that applying the AOP approach using ToxCast and deep learning models is useful for identifying potential toxicity pathways of chemical mixtures, such as DPM, by determining the activity of individual chemicals.

Inhalation toxicity of polystyrene micro(nano)plastics using modified OECD TG 412.

Recently, there have been reports that many microplastics are found in the air, which has raised concerns about their toxicity. To date, however, only limited research has investigated the effects of micro(nano)plastics on human health, and even less the potential for inhalation toxicity. To fill this research gap, we investigated the potential inhalation toxicity of micro(nano)plastics using a modified OECD Guideline for Testing of Chemicals No. 412 '28-Day (subacute) inhalation toxicity study' using a whole-body inhalation system. Sprague-Dawley rats were exposed to three different exposure concentrations of polystyrene micro(nano)plastics (PSMPs), as well as control, for 14 days of inhalation exposure. After 14 days, alterations were observed on sevral endpoints in physiological, serum biochemical, hematological, and respiratory function markers measured on the samples exposed to PSMPs. However, no concentration-response relationships were observed, suggesting that these effects may not be definitively linked to exposure of PSMPs. On the other hand, the expression of inflammatory proteins (TGF-ß and TNF-α) increased in the lung tissue in an exposure concentration-dependent manner. The overall results indicate that 14-day inhalation exposure of PSMPs to rats has a more pronounced effect at the molecular level than at the organismal one. These results suggest that if the exposure sustained, alterations at the molecular level may lead to subsequent alterations at the higher levels, and consequently, the health risks of inhalation exposed micro(nano)plastics should not be neglected.

Development of AOP relevant to microplastics based on toxicity mechanisms of chemical additives using ToxCast™ and deep learning models combined approach.

Various additives are used in plastic products to improve the properties and the durability of the plastics. Their possible elution from the plastics when plastics are fragmented into micro- and nano-size in the environment is suspected to one of the major contributors to environmental and human toxicity of microplastics. In this context, to better understand the hazardous effect of microplastics, the toxicity of chemical additives was investigated. Fifty most common chemicals presented in plastics were selected as target additives. Their toxicity was systematically identified using apical and molecular toxicity databases, such as ChemIDplus and ToxCast™. Among the vast ToxCast assays, those having intended gene targets were selected for identification of the mechanism of toxicity of plastic additives. Deep learning artificial neural network models were further developed based on the ToxCast assays for the chemicals not tested in the ToxCast program. Using both the ToxCast database and deep learning models, active chemicals on each ToxCast assays were identified. Through correlation analysis between molecular targets from ToxCast and mammalian toxicity results from ChemIDplus, we identified the fifteen most relevant mechanisms of toxicity for the understanding mechanism of toxicity of plastic additives. They are neurotoxicity, inflammation, lipid metabolism, and cancer pathways. Based on these, along with, previously conducted systemic review on the mechanism of toxicity of microplastics, here we have proposed potential adverse outcome pathways (AOPs) relevant to microplastics pollution. This study also suggests in vivo and in vitro toxicity database and deep learning model combined approach is appropriate to provide insight into the toxicity mechanism of the broad range of environmental chemicals, such as plastic additives.

Identification of adverse outcome pathway related to high-density polyethylene microplastics exposure: Caenorhabditis elegans transcription factor RNAi screening and zebrafish study.

To gain insight into the human health implications of microplastics, in this study, we investigated the possible mechanisms affecting the toxicity of high-density polyethylene (HDPE) in the nematode Caenorhabditis elegans using RNAi screening and a bioinformatics-based unbiased approach. The candidate pathways identified from C. elegans study were also confirmed using vertebrate model, zebrafish, Danio rerio and human relevance was then inferred using Comparative Toxicogenomics Database (CTD) analysis. Prior to evaluating the toxicity, label-free Raman mapping was conducted to investigate whether or not the organisms could uptake HDPE. C. elegans transcription factor RNAi screening results showed that the nucleotide excision repair (NER) and transforming growth factor-beta (TGF-ß) signaling pathways were significantly associated with HDPE exposure, which was also confirmed in zebrafish model. Gene-disease interaction analysis using the CTD revealed the possible human health implications of microplastics. Finally, based on this finding, related AOPs were identified from AOP Wiki (http://aopwiki.org), which are "Peroxisome proliferator-activated receptors γ inactivation leading to lung fibrosis" and "AFB1: Mutagenic Mode-of-Action leading to Hepatocellular Carcinoma". Further studies are needed for the validation of these AOPs with various microplastics.

Cross-sectional and longitudinal associations between global DNA (hydroxy) methylation and exposure biomarkers of the Hebei Spirit oil spill cohort in Taean, Korea.

The Hebei Spirit oil spill (HSOS) occurred on the west coast of South Korea (Taean county) on December 7, 2007, and studies revealed that exposure to the oil spill was associated with various adverse health issues in the inhabiting population. However, no studies evaluated the association between crude-oil exposure and epigenetic changes. This study aimed to investigate the HSOS exposure-associated longitudinal and cross-sectional variations in global DNA methylation (5-mc) and/or hydroxymethylation (5-hmc) and expression profiles of related genes in Taean cohort participants from 2009 (AH-baseline) and 2014 (AH-follow-up) relative to the reference group (AL). We measured global DNA 5-mc and 5-hmc levels and related gene expression levels in whole blood. We identified significant associations between HSOS exposure and AH-baseline-5-mc, AH-baseline-5-hmc, and AH-follow-up-5-hmc. HSOS exposure was associated with lower %5-mc content and higher %5-hmc content in the same individuals from both the cross-sectional and longitudinal studies. In addition, we found a strong correlation between 5-mc and DNMT3B expression, and between 5-hmc and TET1 expression. Our findings suggested that epigenetic changes are important biomarkers for HSOS exposure and that 5-hmc is likely to be more sensitive for environmental epidemiological studies.

Activation of the nucleotide excision repair pathway by crude oil exposure: A translational study from model organisms to the Hebei Spirit Oil Spill Cohort.

In order to gain insight into the human health implications of the Hebei Spirit Oil Spill (HSOS), the mechanism of toxicity of the Iranian heavy crude (IHC), the main oil component in the HSOS was investigated in Caenorhabditis elegans and zebrafish. The identified mechanism was translated to humans using blood samples from Taean residents, who experienced HSOS with different levels of exposure to the spill. C. elegans TF RNAi screening with IHC oil revealed the nucleotide excision repair (NER) pathway as being significantly involved by oil exposure. To identify the main toxicity contributors within the chemical mixture of the crude oil, further studies were conducted on C. elegans by exposure to C3-naphthalene, an alkylated polycyclic aromatic hydrocarbon (PAH), which constitutes one of the major components of IHC oil. Increased expression of NER pathway genes was observed following exposure to the IHC oil, C3-naphthalene enriched fraction and C3-naphthalene. As the NER pathway is conserved in fish and humans, the same experiment was conducted in zebrafish, and the data were similar to what was seen in C. elegans. Increased expression of NER pathway genes was observed in human samples from the high exposure group, which suggests the involvement of the NER pathway in IHC oil exposure. Overall, the study suggests that IHC oil may cause bulk damage to DNA and activation of the NER system and Alkylated PAHs are the major contributor to DNA damage. Our study provides an innovative approach for studying translational toxicity testing from model organisms to human health.

High-throughput COPAS assay for screening of developmental and reproductive toxicity of nanoparticles using the nematode Caenorhabditis elegans.

With the rapid advancement and numerous applications of engineered nanomaterials (ENMs) in science and technology, their effects on animal health, environment and safety should be considered carefully. However, quick assessment of their effects on developmental and reproductive health and an understanding of how they cause such adverse toxic effects remain challenging, because of the fast-growing number of ENMs and the limitations of the different toxicity assays currently in use as well as lack of suitable animal model systems. In this study, we performed a high-throughput complex object parametric analyzer and sorter (COPAS) assay for assessing the developmental and reproductive toxicity of ENMs using Caenorhabditis elegans and provide descriptions of the data and their subsequent analysis. The results showed significant reproductive and developmental toxicity potential of different ENMs. We assessed the usefulness of this method in terms of error-free data, user-friendliness and results being consistent with those of visual, molecular and cellular studies. Moreover, the COPAS Biosort system could be used on a larger scale to screen thousands of chemicals, drugs, pharmaceuticals and ENMs. This study also indicates that the COPAS-based high-throughput screening system is highly reliable for the assessment of toxicity and health risks of NMs.

Adverse outcome pathways potentially related to hazard identification of microplastics based on toxicity mechanisms.

Increasing concern over microplastics has recently brought increased attention to studies on microplastic toxicity. Here, we conduct a systematic review on toxicity of microplastics that focuses on identifying data gaps in the mechanisms of microplastic toxicity. We observe that microplastic toxicology research thus far has focused on ecotoxicity using apical endpoints and only a few studies deal with toxicity mechanisms. Based on this review, we propose putative Adverse Outcome Pathways (AOPs) applicable to microplastic management to understand microplastic toxicity. We matched toxicity mechanisms and apical endpoints to a key event (KE) and adverse outcome (AO) information from the AOP Wiki. Overall, our results suggest that the molecular initiating event (MIE) was reactive oxygen species (ROS) formation and the AO was increased mortality, decreased growth and feeding, and reproduction failure. However, there are a limited number of studies on toxicity mechanisms of microplastics and, therefore, evidence concerning the relationship between KEs is not sufficient. Clearly, more studies on toxicity mechanisms are required to fill these gaps in data. This study also suggests that the AOP framework is a suitable tool to integrate existing data from various literature sources and can identify data gaps in microplastic toxicity mechanisms.