Development of Adverse Outcome Pathway for PPARγ Antagonism Leading to Pulmonary Fibrosis and Chemical Selection for Its Validation: ToxCast Database and a Deep Learning Artificial Neural Network Model-Based Approach

Cyclosporin A toxicity on endothelial cells differentiated from induced pluripotent stem cells: Assembling an adverse outcome pathway

Cyclosporin A (CSA) is a potent immunosuppressive agent in pharmacologic studies. However, there is evidence for side effects, specifically regarding vascular dysfunction. Its mode of action inducing endothelial cell toxicity is partially unclear, and a connection with an adverse outcome pathway (AOP) is not established yet. Therefore, we designed this study to get deeper insights into the mechanistic toxicology of CSA on angiogenesis. Stem cells, especially induced pluripotent stem cells (iPSCs) with the ability of differentiation to all organs of the body, are considered a promising in vitro model to reduce animal experimentation. In this study, we differentiated iPSCs to endothelial cells (ECs) as one cell type that in other studies would allow to generate multi-cell type organoids from single donors. Flow cytometry and immunostaining confirmed our scalable differentiation protocol. Then dose and time course experiments assessing CSA cytotoxicity on iPS derived endothelial cells were performed. Transcriptomic data suggested CSA dependent induction of reactive oxygen species (ROS), mitochondrial dysfunction, and impaired angiogenesis via ROS induction which was confirmed by in vitro experiments. In order to put these data into a potential adverse outcome pathway (AOP) context, we performed a literature review for CSA-mediated endothelial cell toxicity and combined our experimental data with the publicly available knowledge. Such an AOP will help to design in vitro test batteries and to model events observed in human toxicity studies, as well in predictive toxicology.

Machine Learning-Based Toxicological Modeling for Screening Environmental Obesogens

The emerging presence of environmental obesogens, chemicals that disrupt energy balance and contribute to adipogenesis and obesity, has become a major public health challenge. Molecular initiating events (MIEs) describe biological outcomes resulting from chemical interactions with biomolecules. Machine learning models based on MIEs can predict complex toxic end points due to chemical exposure and improve the interpretability of models. In this study, a system was constructed that integrated six MIEs associated with adipogenesis and obesity. This system showed high accuracy in external validation, with an area under the receiver operating characteristic curve of 0.78. Molecular hydrophobicity (SlogP_VSA) and direct electrostatic interactions (PEOE_VSA) were identified as the two most critical molecular descriptors representing the obesogenic potential of chemicals. This system was further used to predict the obesogenic effects of chemicals on the candidate list of substances of very high concern (SVHCs). Results from 3T3-L1 adipogenesis assays verified that the system correctly predicted obesogenic or nonobesogenic effects of 10 of the 12 SVHCs tested, and identified four novel potential obesogens, including 2-benzotriazol-2-yl-4,6-ditert-butylphenol (UV-320), 4-(1,1,5-trimethylhexyl)phenol (p262-NP), 2-[4-(1,1,3,3-tetramethylbutyl)phenoxy]ethanol (OP1EO) and endosulfan. These validation data suggest that the screening system has good performance in adipogenic prediction.

Inhalation Toxicity Screening of Consumer Products Chemicals using OECD Test Guideline Data-based Machine Learning Models

This study aimed to screen the inhalation toxicity of chemicals found in consumer products such as air fresheners, fragrances, and anti-fogging agents submitted to K-REACH using machine learning models. We manually curated inhalation toxicity data based on OECD test guideline 403 (Acute inhalation), 412 (Sub-acute inhalation), and 413 (Sub-chronic inhalation) for 1709 chemicals from the OECD eChemPortal database. Machine learning models were trained using ten algorithms, along with four molecular fingerprints (MACCS, Morgan, Topo, RDKit) and molecular descriptors, achieving F1 scores ranging from 51 % to 91 % in test dataset. Leveraging the high-performing models, we conducted a virtual screening of chemicals, initially applying them to data-rich chemicals generally used in occupational settings to determine the prediction uncertainty. Results showed high sensitivity (75 %) but low specificity (23 %), suggesting that our models can contribute to conservative screening of chemicals. Subsequently, we applied the models to consumer product chemicals, identifying 79 as of high concern. Most of the prioritized chemicals lacked GHS classifications related to inhalation toxicity, even though they were predicted to be used in many consumer products. This study highlights a potential regulatory blind spot concerning the inhalation risk of consumer product chemicals while also indicating the potential of artificial intelligence (AI) models to aid in prioritizing chemicals at the screening level.

Application of machine learning in atmospheric pollution research: A state-of-art review

Machine learning (ML) is an artificial intelligence technology that has been used in atmospheric pollution research due to their powerful fitting ability. In this review, 105 articles related to ML and the atmospheric pollution research are critically reviewed. Applications of ML in the prediction of atmospheric pollution (mainly particulate matters) are systematically described, including the principle of prediction, influencing factors and improvement measures. Researchers can improve the accuracy of the prediction model through three main aspects, namely considering the geographical features of the study area into the model, introducing the physical characteristics of pollutants, matching and optimizing ML models. And by using interpretable ML tools, researchers are able to understand the mechanism of the model and gain in-depth information. Then, the state-of-art applications of ML in the source apportionment of atmospheric particulate matter and the effect of atmospheric pollutants on human health are also described. In addition, the advantages and disadvantages of the current applications of ML in atmospheric pollution research are summarized, and the application perspective of ML in this field is elucidated. Given the scarcity of source apportionment applications and human health research, standardized research methods and specialized ML methods are required in atmospheric pollution research to connect these two disciplines.

Machine Learning-Based Hazard-Driven Prioritization of Features in Nontarget Screening of Environmental High-Resolution Mass Spectrometry Data

Nontarget high-resolution mass spectrometry screening (NTS HRMS/MS) can detect thousands of organic substances in environmental samples. However, new strategies are needed to focus time-intensive identification efforts on features with the highest potential to cause adverse effects instead of the most abundant ones. To address this challenge, we developed MLinvitroTox, a machine learning framework that uses molecular fingerprints derived from fragmentation spectra (MS2) for a rapid classification of thousands of unidentified HRMS/MS features as toxic/nontoxic based on nearly 400 target-specific and over 100 cytotoxic endpoints from ToxCast/Tox21. Model development results demonstrated that using customized molecular fingerprints and models, over a quarter of toxic endpoints and the majority of the associated mechanistic targets could be accurately predicted with sensitivities exceeding 0.95. Notably, SIRIUS molecular fingerprints and xboost (Extreme Gradient Boosting) models with SMOTE (Synthetic Minority Oversampling Technique) for handling data imbalance were a universally successful and robust modeling configuration. Validation of MLinvitroTox on MassBank spectra showed that toxicity could be predicted from molecular fingerprints derived from MS2 with an average balanced accuracy of 0.75. By applying MLinvitroTox to environmental HRMS/MS data, we confirmed the experimental results obtained with target analysis and narrowed the analytical focus from tens of thousands of detected signals to 783 features linked to potential toxicity, including 109 spectral matches and 30 compounds with confirmed toxic activity.

Predicting chemicals' toxicity pathway of female reproductive disorders using AOP7 and deep neural networks

Experimental evidence shows that certain chemicals, particularly endocrine disrupting chemicals, may negatively affect the female reproductive system, thereby lowering women's fertility. However, humans are constantly exposed to a number of different chemicals with limited or no experimental data regarding their effect and the mechanism of action in the female reproductive system. To predict chemical hazards to the female reproductive system, we used a previously defined adverse outcome pathway (AOP) that links activation of the peroxisome proliferator-activated receptor γ to the reproductive toxicity in adult females (AOP7) and the Convolutional Deep Neural Network models that produce meaningful predictions when trained on a significant amount of data. The models trained using CompTox assays with intended molecular and biological targets corresponding to AOP7 achieved high performance (over 90% validation accuracy). The integration of AOP7 and Deep Neural Network identified chemicals that could negatively affect female reproduction through the mechanism described in AOP7. We provide a solution to quickly analyze the data and produce machine learning models to identify potentially active chemicals in the female reproductive system. Although we focused on the female reproductive system, this approach could be valid for a number of other chemicals and AOPs if the right data exist.

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 ToxCast™ database to 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 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.

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.

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.

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.

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.

Deploying elements of scoping review methods for adverse outcome pathway development: a space travel case example

PURPOSE

Health protection agencies require scientific information for evidence-based decision-making and guideline development. However, vetting and collating large quantities of published research to identify relevant high-quality studies is a challenge. One approach to address this issue is the use of adverse outcome pathways (AOPs) that provide a framework to assemble toxicological knowledge into causally linked chains of key events (KEs) across levels of biological organization to culminate in an adverse health outcome of significance to regulatory decision-making. Traditionally, AOPs have been constructed using a narrative review approach where the collection of evidence that supports each pathway is based on prior knowledge of influential studies that can also be supplemented by individually selecting and reviewing relevant references.

OBJECTIVES

We aimed to create a protocol for AOP weight of evidence gathering that harnesses elements of both scoping review methods and artificial intelligence (AI) tools to increase transparency while reducing bias and workload of human screeners.

METHODS

To develop this protocol, an existing space-health AOP in the workplan of the Organisation for Economic Co-operation and Development (OECD) AOP Programme was used as a case example. To balance the benefits of both scoping review tools and narrative approaches, a study protocol outlining a screening and search strategy was developed, and three reference collection workflows were tested to identify the most efficient method to inform weight of evidence. The workflows differed in their literature search strategies, and combinations of software tools used.

RESULTS

Across the three tested workflows, over 59 literature searches were completed, retrieving over 34,000 references of which over 3300 were human reviewed. The most effective of the three methods used a search strategy with searches across each component of the AOP network, SWIFT Review as a pre-filtering software, and DistillerSR to create structured screening and data extraction forms. This methodology effectively retrieved relevant studies while balancing efficiency in data retrieval without compromising transparency, leading to a well-synthesized evidence base to support the AOP.

CONCLUSIONS

The workflow is still exploratory in the context of AOP development, and we anticipate adaptations to the protocol with further experience. To further the systematicity, future iterations of the workflow could include structured quality assessment and risk of bias analysis. Overall, the workflow provides a transparent and documented approach to support AOP development, which in turn will support the need for rigorous methods to identify relevant scientific evidence while being practical to allow uptake by the broader community.

Orthogonal assay for validation of Tox21 PPARγ data and applicability to in silico prediction model

High-throughput screening data from the Tox21 database is used for prioritizing hazardous chemicals and building in silico-based toxicity prediction models. One of the Tox21 dataset, peroxisome proliferator-activated receptor-gamma (PPARγ), a nuclear receptor superfamily, identified various endpoints in HEK293 cells. PPARγ mediates various toxic effects when its receptors are activated or inhibited by ligands such as thiazolidinedione and GW9662. In this study, an orthogonal assay was constructed to verify the effectiveness of the Tox21 PPARγ data, and the effect of highly reliable data on in silico model construction was investigated. The orthogonal assay was a reporter gene assay based on the PPARγ ligand binding domain in CV-1 cells. Only 39% of agonists and 55% of antagonists had similar responses in CV-1 and HEK293 cells. Thus, the effectiveness of Tox21 data on PPARγ may vary depending on the cell line. However, in silico PLS-DA analysis with only high-reliability data (i.e., the same response in both cell lines), yielded more accurate prediction of the activity of potential chemical ligands, despite the small number of samples. Thus, obtaining reliable chemical screening data for PPARγ through orthogonal analysis, even for only limited chemicals, supports the construction of highly predictive in silico models with improved screening efficiency.

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.

Deriving time-concordant event cascades from gene expression data: A case study for Drug-Induced Liver Injury (DILI)

Adverse event pathogenesis is often a complex process which compromises multiple events ranging from the molecular to the phenotypic level. In toxicology, Adverse Outcome Pathways (AOPs) aim to formalize this as temporal sequences of events, in which event relationships should be supported by causal evidence according to the tailored Bradford-Hill criteria. One of the criteria is whether events are consistently observed in a certain temporal order and, in this work, we study this time concordance using the concept of “first activation” as data-driven means to generate hypotheses on potentially causal mechanisms. As a case study, we analysed liver data from repeat-dose studies in rats from the TG-GATEs database which comprises measurements across eight timepoints, ranging from 3 hours to 4 weeks post-treatment. We identified time-concordant gene expression-derived events preceding adverse histopathology, which serves as surrogate readout for Drug-Induced Liver Injury (DILI). We find known mechanisms in DILI to be time-concordant, and show further that significance, frequency and log fold change (logFC) of differential expression are metrics which can additionally prioritize events although not necessary to be mechanistically relevant. Moreover, we used the temporal order of transcription factor (TF) expression and regulon activity to identify transcriptionally regulated TFs and subsequently combined this with prior knowledge on functional interactions to derive detailed gene-regulatory mechanisms, such as reduced Hnf4a activity leading to decreased expression and activity of Cebpa. At the same time, also potentially novel events are identified such as Sox13 which is highly significantly time-concordant and shows sustained activation over time. Overall, we demonstrate how time-resolved transcriptomics can derive and support mechanistic hypotheses by quantifying time concordance and how this can be combined with prior causal knowledge, with the aim of both understanding mechanisms of toxicity, as well as potential applications to the AOP framework. We make our results available in the form of a Shiny app (https://anikaliu.shinyapps.io/dili_cascades), which allows users to query events of interest in more detail.

Understanding mechanisms from systems-scale biological data is of great relevance in toxicology as well as drug discovery; however how to generate causal hypotheses instead of correlations is by no means clear. In this work, we study the conserved temporal order of events and present an automatable framework to quantify and characterize time concordance across a large set of time-series. We apply this concept to events derived from time-resolved gene expression and histopathology from the TG-GATEs in vivo liver data as a case study. We were able to recover known events involved in the pathogenesis of Drug-Induced Liver Injury (DILI), and identify potentially novel pathway and transcription factors (TFs) which precede adverse histopathology. As complementary sources of evidence for causality, we additionally show how time concordance and prior knowledge on plausible interactions between TFs can be combined to derive causal hypotheses on the TFs’ mode of regulation and interaction partners. Overall, the results derived in our case study can serve as valuable hypothesis-free starting points for the development of Adverse Outcome Pathways for DILI, and demonstrate that our approach provides a novel angle to prioritize mechanistically relevant events.

From Causal Networks to Adverse Outcome Pathways: A Developmental Neurotoxicity Case Study

The last decade has seen the adverse outcome pathways (AOP) framework become one of the most powerful tools in chemical risk assessment, but the development of new AOPs remains a slow and manually intensive process. Here, we present a faster approach for AOP generation, based on manually curated causal toxicological networks. As a case study, we took a recently published zebrafish developmental neurotoxicity network, which contains causally connected molecular events leading to neuropathologies, and developed two new adverse outcome pathways: Inhibition of Fyna (Src family tyrosine kinase A) leading to increased mortality via decreased eye size (AOP 399 on AOP-Wiki) and GSK3beta (Glycogen synthase kinase 3 beta) inactivation leading to increased mortality via defects in developing inner ear (AOP 410). The approach consists of an automatic separation of the toxicological network into candidate AOPs, filtering the AOPs according to available evidence and length as well as manual development of new AOPs and weight-of-evidence evaluation. The semiautomatic approach described here provides a new opportunity for fast and straightforward AOP development based on large network resources.

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-β (TGF-β) 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-β (KE1) inhibitors were used to examine the effects of downstream events following inhibition of their upstream events. PPARγ inhibition (MIE) led to TGF-β activation (KE1), upregulation in vimentin expression (KE3), and an increase in the fibronectin level (KE4). Similarly, activated TGF-β (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.

Multi-step structure-activity relationship screening efficiently predicts diverse PPARγ antagonists

In discovering the potential antagonist of peroxisome proliferator-activated receptor gamma (PPARγ), the structure-activity relationship (SAR) is a useful in silico method. However, it is difficult for conventional SAR approaches to predict the activities of antagonists owing to the large structural diversity of antagonistic compounds. This study provides evidence that multi-step SAR screening is applicable for predicting PPARγ antagonists by combining different complementary methodologies. We constructed three models: read-across-like SAR, docking-simulation-interpreting SAR, and deep-learning-based SAR. To provide user-customized prediction results, our multi-step SAR screening model combined the three SAR models in a stepwise manner, which subdivided them according to potential levels of the PPARγ antagonist. The read-across-like SAR, which considered specific antagonist scaffolds, revealed the highest positive predictive value (PPV). The docking-simulation-interpreting SAR, which considered the molecular surface features, revealed high statistics for the PPV and the true-positive rate (TPR). The deep-learning-based SAR showed the highest TPR at the last classification step. This multi-step SAR screening covered the antagonists of high reliability provided by a read-across-like SAR, as well as the antagonists of diverse scaffolds provided by docking-simulation-interpreting SAR and deep-learning-based SAR. Therefore, to predict PPARγ antagonists, multi-step SAR screening could be as a useful tool.

In silico approaches in organ toxicity hazard assessment: Current status and future needs for predicting heart, kidney and lung toxicities

The kidneys, heart and lungs are vital organ systems evaluated as part of acute or chronic toxicity assessments. New methodologies are being developed to predict these adverse effects based on in vitro and in silico approaches. This paper reviews the current state of the art in predicting these organ toxicities. It outlines the biological basis, processes and endpoints for kidney toxicity, pulmonary toxicity, respiratory irritation and sensitization as well as functional and structural cardiac toxicities. The review also covers current experimental approaches, including off-target panels from secondary pharmacology batteries. Current in silico approaches for prediction of these effects and mechanisms are described as well as obstacles to the use of in silico methods. Ultimately, a commonly accepted protocol for performing such assessment would be a valuable resource to expand the use of such approaches across different regulatory and industrial applications. However, a number of factors impede their widespread deployment including a lack of a comprehensive mechanistic understanding, limited in vitro testing approaches and limited in vivo databases suitable for modeling, a limited understanding of how to incorporate absorption, distribution, metabolism, and excretion (ADME) considerations into the overall process, a lack of in silico models designed to predict a safe dose and an accepted framework for organizing the key characteristics of these organ toxicants.

Transcriptomic Changes and the Roles of Cannabinoid Receptors and PPARγ in Developmental Toxicities Following Exposure to Δ9-Tetrahydrocannabinol and Cannabidiol

Human consumption of cannabinoid-containing products during early life or pregnancy is rising. However, information about the molecular mechanisms involved in early life stage Δ9-tetrahydrocannabinol (THC) and cannabidiol (CBD) toxicities is critically lacking. Here, larval zebrafish (Danio rerio) were used to measure THC- and CBD-mediated changes on transcriptome and the roles of cannabinoid receptors (Cnr) 1 and 2 and peroxisome proliferator activator receptor γ (PPARγ) in developmental toxicities. Transcriptomic profiling of 96-h postfertilization (hpf) cnr+/+ embryos exposed (6 - 96 hpf) to 4 μM THC or 0.5 μM CBD showed differential expression of 904 and 1095 genes for THC and CBD, respectively, with 360 in common. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enriched in the THC and CBD datasets included those related to drug, retinol, and steroid metabolism and PPAR signaling. The THC exposure caused increased mortality and deformities (pericardial and yolk sac edemas, reduction in length) in cnr1-/- and cnr2-/- fish compared with cnr+/+ suggesting Cnr receptors are involved in protective pathways. Conversely, the cnr1-/- larvae were more resistant to CBD-induced malformations, mortality, and behavioral alteration implicating Cnr1 in CBD-mediated toxicity. Behavior (decreased distance travelled) was the most sensitive endpoint to THC and CBD exposure. Coexposure to the PPARγ inhibitor GW9662 and CBD in cnr+/+ and cnr2-/- strains caused more adverse outcomes compared with CBD alone, but not in the cnr1-/- fish, suggesting that PPARγ plays a role in CBD metabolism downstream of Cnr1. Collectively, PPARγ, Cnr1, and Cnr2 play important roles in the developmental toxicity of cannabinoids with Cnr1 being the most critical.

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.

Novel QSAR Models for Molecular Initiating Event Modeling in Two Intersecting Adverse Outcome Pathways Based Pulmonary Fibrosis Prediction for Biocidal Mixtures

The adverse outcome pathway (AOP) was introduced as an alternative method to avoid unnecessary animal tests. Under the AOP framework, an in silico methods, molecular initiating event (MIE) modeling is used based on the ligand-receptor interaction. Recently, the intersecting AOPs (AOP 347), including two MIEs, namely peroxisome proliferator-activated receptor-gamma (PPAR-γ) and toll-like receptor 4 (TLR4), associated with pulmonary fibrosis was proposed. Based on the AOP 347, this study developed two novel quantitative structure-activity relationship (QSAR) models for the two MIEs. The prediction performances of different MIE modeling methods (e.g., molecular dynamics, pharmacophore model, and QSAR) were compared and validated with in vitro test data. Results showed that the QSAR method had high accuracy compared with other modeling methods, and the QSAR method is suitable for the MIE modeling in the AOP 347. Therefore, the two QSAR models based on the AOP 347 can be powerful models to screen biocidal mixture related to pulmonary fibrosis.

Bringing together scientific disciplines for collaborative undertakings: a vision for advancing the adverse outcome pathway framework

BACKGROUND

Decades of research to understand the impacts of various types of environmental occupational and medical stressors on human health have produced a vast amount of data across many scientific disciplines. Organizing these data in a meaningful way to support risk assessment has been a significant challenge. To address this and other challenges in modernizing chemical health risk assessment, the Organisation for Economic Cooperation and Development (OECD) formalized the adverse outcome pathway (AOP) framework, an approach to consolidate knowledge into measurable key events (KEs) at various levels of biological organisation causally linked to disease based on the weight of scientific evidence (http://oe.cd/aops). Currently, AOPs have been considered predominantly in chemical safety but are relevant to radiation. In this context, the Nuclear Energy Agency's (NEA's) High-Level Group on Low Dose Research (HLG-LDR) is working to improve research co-ordination, including radiological research with chemical research, identify synergies between the fields and to avoid duplication of efforts and resource investments. To this end, a virtual workshop was held on 7 and 8 October 2020 with experts from the OECD AOP Programme together with the radiation and chemical research/regulation communities. The workshop was a coordinated effort of Health Canada, the Electric Power Research Institute (EPRI), and the Nuclear Energy Agency (NEA). The AOP approach was discussed including key issues to fully embrace its value and catalyze implementation in areas of radiation risk assessment.

CONCLUSIONS

A joint chemical and radiological expert group was proposed as a means to encourage cooperation between risk assessors and an initial vision was discussed on a path forward. A global survey was suggested as a way to identify priority health outcomes of regulatory interest for AOP development. Multidisciplinary teams are needed to address the challenge of producing the appropriate data for risk assessments. Data management and machine learning tools were highlighted as a way to progress from weight of evidence to computational causal inference.

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.

Collaborative efforts are needed among the scientific community to advance the adverse outcome pathway concept in areas of radiation risk assessment

Disease prevention and prediction have led to the generation of phenotypically based methods for deriving the limits of safety across toxicological disciplines. In the ionizing radiation field, human data has formed the basis of the linear-no-threshold (LNT) model for risk estimates. However, uncertainties around its accuracy at low doses and low dose-rates have led to passionate debates on its effectiveness to derive radiation risk estimates under these conditions. Concerns arise from the linear extrapolation of data from high doses to low doses, below 0.1 Gy where there is considerable variability in the scientific literature. Efforts to address these controversies have led to a mountain of mechanistic data to improve the understanding of molecular and cellular effects related to phenotypic changes. These data provide fragments of information that have yet to be combined and used effectively to improve modeling, reduce uncertainties, and update radiation protection approaches. This paper suggests a better consolidation of mechanistic research may serve to guide priority research and facilitate translation to risk assessment. An effective approach that may be implemented is the organization of data using the adverse outcome pathway (AOP) framework, a programme that has been launched by the Organization for Economic Cooperation and Development in the chemical toxicology field. The AOP concept has proved beneficial to human health and ecological toxicological fields, demonstrating possibilities for better linkages of mechanistic data to phenotypic effects. A similar approach may be beneficial to the field of radiation research. However, for this to work effectively, collaborative efforts are needed among the scientific communities in the area of AOP development and documentation. Studies will need to be evaluated, re-organized and integrated into AOPs. Here, details of the AOP approach and areas it could support in the radiation field are discussed. In addition, challenges are highlighted and steps to integration are outlined. Organizing studies in this manner will facilitate a better understanding of our current knowledge in the radiation field and help identify areas where more focused work can be undertaken. This will, in turn, allow for improved linkage of mechanistic data to human relevance and better support radiation risk assessments.

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-β 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.

Quantitative adverse outcome pathway (qAOP) models for toxicity prediction

The quantitative adverse outcome pathway (qAOP) concept is gaining interest due to its potential regulatory applications in chemical risk assessment. Even though an increasing number of qAOP models are being proposed as computational predictive tools, there is no framework to guide their development and assessment. As such, the objectives of this review were to: (i) analyse the definitions of qAOPs published in the scientific literature, (ii) define a set of common features of existing qAOP models derived from the published definitions, and (iii) identify and assess the existing published qAOP models and associated software tools. As a result, five probabilistic qAOPs and ten mechanistic qAOPs were evaluated against the common features. The review offers an overview of how the qAOP concept has advanced and how it can aid toxicity assessment in the future. Further efforts are required to achieve validation, harmonisation and regulatory acceptance of qAOP models.

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-β) 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.

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.