Zooplankton-based adverse outcome pathways: A tool for assessing endocrine disrupting compounds in aquatic environments

Endocrine disrupting compounds (EDCs) pose a significant ecological risk, particularly in aquatic ecosystems. EDCs have become a focal point in ecotoxicology, and their identification and regulation have become a priority. Zooplankton have gained global recognition as bioindicators, benefiting from rigorous standardization and regulatory validation processes. This review aims to provide a comprehensive summary of zooplankton-based adverse outcome pathways (AOPs) with a focus on EDCs as toxicants and the utilisation of freshwater zooplankton as bioindicators in ecotoxicological assessments. This review presents case studies in which zooplankton have been used in the development of AOPs, emphasizing the identification of molecular initiating events (MIEs) and key events (KEs) specific to zooplankton exposed to EDCs. Zooplankton-based AOPs may become an important resource for understanding the intricate processes by which EDCs impair the endocrine system. Furthermore, the data sources, experimental approaches, advantages, and challenges associated with zooplankton-based AOPs are discussed. Zooplankton-based AOPs framework can provide vital tools for consolidating toxicological knowledge into a structured toxicity pathway of EDCs, offering a transformative platform for facilitating enhanced risk assessment and chemical regulation.

Fate of synthetic chemicals in the agronomic insect pest Spodoptera littoralis: experimental feeding-contact assay and toxicokinetic model

Insecticides prevent or reduce insect crop damage, maintaining crop quality and quantity. Physiological traits, such as an insect's feeding behavior, influence the way insecticides are absorbed and processed in the body (toxicokinetics), which can be exploited to improve species selectivity. To fully understand the uptake of insecticides, it is essential to study their total uptake and toxicokinetics independent of their toxic effects on insects. We studied the toxicokinetics (TK) of insecticidally inactive test compounds incorporating agro-like structural motifs in larvae of the Egyptian cotton leafworm (Spodoptera littoralis, Lepidoptera), and their distribution across all biological matrices, using laboratory experiments and modeling. We measured Spodoptera larval behavior and temporal changes of whole-body concentrations of test compounds during feeding on treated soybean leaf disks and throughout a subsequent depuration period. Differences in the distribution of the total quantities of compounds were found between the biological matrices leaf, larva, and feces. Rate constants for uptake and elimination of test compounds were derived by calibrating a toxicokinetic model to the whole-body concentrations. Uptake and elimination rate constants depended on the physicochemical properties of the test compounds. Increasing hydrophobicity increased the bioaccumulation potential of test compounds. Incomplete quantities in larval matrices indicated that some compounds may undergo biotransformation. As fecal excretion was a major elimination pathway, the variable time of release and number of feces pellets led to a high variability in the body burden. We provide quantitative models to predict the toxicokinetics and bioaccumulation potential of inactive insecticide analogs (parent compounds) in Spodoptera.

Hybrid non-animal modeling: A mechanistic approach to predict chemical hepatotoxicity

Developing mechanistic non-animal testing methods based on the adverse outcome pathway (AOP) framework must incorporate molecular and cellular key events associated with target toxicity. Using data from an in vitro assay and chemical structures, we aimed to create a hybrid model to predict hepatotoxicants. We first curated a reference dataset of 869 compounds for hepatotoxicity modeling. Then, we profiled them against PubChem for existing in vitro toxicity data. Of the 2560 resulting assays, we selected the mitochondrial membrane potential (MMP) assay, a high-throughput screening (HTS) tool that can test chemical disruptors for mitochondrial function. Machine learning was applied to develop quantitative structure-activity relationship (QSAR) models with 2536 compounds tested in the MMP assay for screening new compounds. The MMP assay results, including QSAR model outputs, yielded hepatotoxicity predictions for reference set compounds with a Correct Classification Ratio (CCR) of 0.59. The predictivity improved by including 37 structural alerts (CCR = 0.8). We validated our model by testing 37 reference set compounds in human HepG2 hepatoma cells, and reliably predicting them for hepatotoxicity (CCR = 0.79). This study introduces a novel AOP modeling strategy that combines public HTS data, computational modeling, and experimental testing to predict chemical hepatotoxicity.

Evaluation of the binding performance of flavonoids to estrogen receptor alpha by Autodock, Autodock Vina and Surflex-Dock

Molecular docking is a widely used method to predict the binding modes of small-molecule ligands to the target binding site. However, it remains a challenge to identify the correct binding conformation and the corresponding binding affinity for a series of structurally similar ligands, especially those with weak binding. An understanding of the various relative attributes of popular docking programs is required to ensure a successful docking outcome. In this study, we systematically compared the performance of three popular docking programs, Autodock, Autodock Vina, and Surflex-Dock for a series of structurally similar weekly binding flavonoids (22) binding to the estrogen receptor alpha (ERα). For these flavonoids-ERα interactions, Surflex-Dock showed higher accuracy than Autodock and Autodock Vina. The hydrogen bond overweighting by Autodock and Autodock Vina led to incorrect binding results, while Surflex-Dock effectively balanced both hydrogen bond and hydrophobic interactions. Moreover, the selection of initial receptor structure is critical as it influences the docking conformations of flavonoids-ERα complexes. The flexible docking method failed to further improve the docking accuracy of the semi-flexible docking method for such chemicals. In addition, binding interaction analysis revealed that 8 residues, including Ala350, Glu353, Leu387, Arg394, Phe404, Gly521, His524, and Leu525, are the key residues in ERα-flavonoids complexes. This work provides reference for assessing molecular interactions between ERα and flavonoid-like chemicals and provides instructive information for other environmental chemicals.

Quantitative Predictions for Molecular Initiating Events Using Three-Dimensional Quantitative Structure-Activity Relationships

The aim of human toxicity risk assessment is to determine a safe dose or exposure to a chemical for humans. This requires an understanding of the exposure of a person to a chemical and how much of the chemical is required to cause an adverse effect. To do this computationally, we need to understand how much of a chemical is required to perturb normal biological function in an adverse outcome pathway (AOP). The molecular initiating event (MIE) is the first step in an adverse outcome pathway and can be considered as a chemical interaction between a chemical toxicant and a biological molecule. Key chemical characteristics can be identified and used to model the chemistry of these MIEs. In this study, we do just this by using chemical substructures to categorize chemicals and 3D quantitative structure-activity relationships (QSARs) based on comparative molecular field analysis (CoMFA) to calculate molecular activity. Models have been constructed across a variety of human biological targets, the glucocorticoid receptor, mu opioid receptor, cyclooxygenase-2 enzyme, human ether-à-go-go related gene channel, and dopamine transporter. These models tend to provide molecular activity estimation well within one log unit and electronic and steric fields that can be visualized to better understand the MIE and biological target of interest. The outputs of these fields can be used to identify key aspects of a chemical's chemistry which can be changed to reduce its ability to activate a given MIE. With this methodology, the quantitative chemical activity can be predicted for a wide variety of MIEs, which can feed into AOP-based chemical risk assessments, and understanding of the chemistry behind the MIE can be gained.

A Mechanistic Framework for Integrating Chemical Structure and High-Throughput Screening Results to Improve Toxicity Predictions

Adverse Outcome Pathways (AOPs) establish a connection between a molecular initiating event (MIE) and an adverse outcome. Detailed understanding of the MIE provides the ideal data for determining chemical properties required to elicit the MIE. This study utilized high-throughput screening data from the ToxCast program, coupled with chemical structural information, to generate chemical clusters using three similarity methods pertaining to nine MIEs within an AOP network for hepatic steatosis. Three case studies demonstrate the utility of the mechanistic information held by the MIE for integrating biological and chemical data. Evaluation of the chemical clusters activating the glucocorticoid receptor identified activity differences in chemicals within a cluster. Comparison of the estrogen receptor results with previous work showed that bioactivity data and structural alerts can be combined to improve predictions in a customizable way where bioactivity data are limited. The aryl hydrocarbon receptor (AHR) highlighted that while structural data can be used to offset limited data for new screening efforts, not all ToxCast targets have sufficient data to define robust chemical clusters. In this context, an alternative to additional receptor assays is proposed where assays for proximal key events downstream of AHR activation could be used to enhance confidence in active calls. These case studies illustrate how the AOP framework can support an iterative process whereby in vitro toxicity testing and chemical structure can be combined to improve toxicity predictions. In vitro assays can inform the development of structural alerts linking chemical structure to toxicity. Consequently, structurally related chemical groups can facilitate identification of assays that would be informative for a specific MIE. Together, these activities form a virtuous cycle where the mechanistic basis for the in vitro results and the breadth of the structural alerts continually improve over time to better predict activity of chemicals for which limited toxicity data exist.

A critical review of adverse effects to the kidney: mechanisms, data sources, and in silico tools to assist prediction

INTRODUCTION

The kidney is a major target for toxicity elicited by pharmaceuticals and environmental pollutants. Standard testing which often does not investigate underlying mechanisms has proven not to be an adequate hazard assessment approach. As such, there is an opportunity for the application of computational approaches that utilize multiscale data based on the Adverse Outcome Pathway (AOP) paradigm, coupled with an understanding of the chemistry underpinning the molecular initiating event (MIE) to provide a deep understanding of how structural fragments of molecules relate to specific mechanisms of nephrotoxicity. Aims covered: The aim of this investigation was to review the current scientific landscape related to computational methods, including mechanistic data, AOPs, publicly available knowledge bases and current in silico models, for the assessment of pharmaceuticals and other chemicals with regard to their potential to elicit nephrotoxicity. A list of over 250 nephrotoxicants enriched with, where possible, mechanistic and AOP-derived understanding was compiled. Expert opinion: Whilst little mechanistic evidence has been translated into AOPs, this review identified a number of data sources of in vitro, in vivo, and human data that may assist in the development of in silico models which in turn may shed light on the interrelationships between nephrotoxicity mechanisms.

Assessing the impact of hazardous waste on children's health: The exposome paradigm

Assessment of the health impacts related to hazardous waste is a major scientific challenge with multiple societal implications. Most studies related to associations between hazardous waste and public health do not provide established of mechanistic links between environmental exposure and disease burden, resulting in ineffective waste management options. The exposome concept comes to overhaul the nature vs. nurture paradigm and embraces a world of dynamic interactions between environmental exposures, endogenous exposures and genetic expression in humans. In this context, the exposome paradigm provides a novel tool for holistic hazardous waste management. Waste streams and the related contamination of environmental media are not viewed in isolation, but rather as components of the expotype, the vector of exposures an individual is exposed to over time. Thus, a multi-route and multi-pathway exposure estimation can be performed setting a realistic basis for integrated health risk assessment. Waste management practices are thus assessed not only regarding their technological edge and efficacy but also their effects on human health at the individual and community level, considering intra-subject variability in the affected population. The effectiveness of the exposome approach is demonstrated in the case of Athens, the capital of Greece, where the health effects associated to long term and short term exposure to two major waste management facilities (landfill and plastic recycling) are presented. Using the exposome analysis tools, we confirmed that proximity to a landfill is critical for children neurodevelopment. However, this effect is significantly modified by parameters such as parental education level, socioeconomic status and nutrition. Proximity to a plastics recycling plant does not pose significant threats under normal operating conditions; yet, in the case of an accidental fire, release of persistent carcinogenic compounds (dioxins and furans) even for a short period results in increased lifelong risk, especially for breast feeding neonates.

A History of the Molecular Initiating Event

The adverse outcome pathway (AOP) framework provides an alternative to traditional in vivo experiments for the risk assessment of chemicals. AOPs consist of a number of key events (KEs) linked by key event relationships across a range of biological organization backed by scientific evidence. The first KE in the pathway is the molecular initiating event (MIE)-the initial chemical trigger that starts an AOP. Over the past 3 years the AOP conceptual framework has gained a large amount of momentum in toxicology as an alternative to animal methods, and so the MIE has come into the spotlight. What is an MIE? How can MIEs be measured or predicted? What research is currently contributing to our understanding of MIEs? In this Perspective we outline answers to these key questions.

Development of an in Silico Profiler for Mitochondrial Toxicity

This study outlines the analysis of mitochondrial toxicity for a variety of pharmaceutical drugs extracted from Zhang et al. ((2009) Toxicol. In Vitro, 23, 134-140). These chemicals were grouped into categories based upon structural similarity. Subsequently, mechanistic analysis was undertaken for each category to identify the molecular initiating event driving mitochondrial toxicity. The mechanistic information elucidated during the analysis enabled mechanism-based structural alerts to be developed and combined together to form an in silico profiler. This profiler is envisaged to be used to develop chemical categories based upon similar mechanisms as part of the adverse outcome pathway paradigm. Additionally, the profiler could be utilized in screening large data sets in order to identify chemicals with the potential to induce mitochondrial toxicity.

Glutathione metabolism in the HaCaT cell line as a model for the detoxification of the model sensitisers 2,4-dinitrohalobenzenes in human skin

Glutathione (GSH) is the most prominent antioxidant in cells and the co-factor of an important set of enzymes involved in the skin metabolic clearance system, glutathione S-transferases (GST). Here, we describe an LC-MS (liquid chromatography-mass spectroscopy) method to measure GSH and its disulfide form (GSSG) in HaCaT cells and a 3D Reconstructed Human Epidermis (RHE) model. In our assay, the basal level of GSH in both systems was in the low nmol/mg soluble protein range, while the level of GSSG was systematically below our limit of quantification (0.1 μM). We found that 2,4-dinitrohalobenzenes deplete the GSH present in HaCaT cells within the first hour of exposure, in a dose dependent manner. The level of GSH in HaCaT cells treated with a single non-toxic dose of 10 μM of dinitrohalobenzene was also shown to increase after two hours. While cells treated with 1-chloro-2,4-dinitrobenzene (DNCB) and 1-fluoro-2,4-dinitrobenzene (DNFB) repleted GSH to levels similar to untreated control cells within 24h, 1-bromo-2,4-dinitrobenzene (DNBB) seemed to prevent such a repletion and appeared to be the most toxic compound in all assays. A mathematical modelling of experimental results was performed to further rationalise the differences observed between test chemicals. For this purpose the biological phenomena observed were simplified into two sequential events: the initial depletion of the GSH stock after chemical treatment followed by the repletion of the GSH once the chemical was cleared. Activation of the nuclear factor E2-related factor 2 (Nrf2) pathway was observed with all compounds within two hours, and at concentrations less than 10 μM. These data show that GSH depletion and repletion occur rapidly in skin cells and emphasize the importance of conducting kinetic studies when performing in vitro experiments exploring skin sensitization.

Screening Chemicals for Receptor-Mediated Toxicological and Pharmacological Endpoints: Using Public Data to Build Screening Tools within a KNIME Workflow

Assessing compounds for their pharmacological and toxicological properties is of great importance for industry and regulatory agencies. In this study an approach using open source software and open access databases to build screening tools for receptor-mediated effects is presented. The retinoic acid receptor (RAR), as a pharmacologically and toxicologically relevant target, was chosen for this study. RAR agonists are used in the treatment of a number of dermal conditions and specific types of cancer, such as acute promyelocytic leukemia. However, when administered chronically, there is strong evidence that RAR agonists cause hepatosteatosis and liver injury. After compiling information on ligand-protein-interactions, common substructures and physico-chemical properties of ligands were identified manually and coded into SMARTS strings. Based on these SMARTS strings and calculated physico-chemical features, a rule-based screening workflow was built within the KNIME platform. The workflow was evaluated on two datasets: one with RAR agonists exclusively and another large, chemically diverse dataset containing only a few RAR agonists. Possible modifications and applications of screening workflows, dependent on their purpose, are presented.

Identifying potential protein targets for toluene using a molecular similarity search, in silico docking and in vitro validation

The toxicity of chemicals greatly depends on their interaction with macromolecular targets.

Correlation of transcriptomic responses and metal bioaccumulation in Mytilus edulis L. reveals early indicators of stress

Marine biomonitoring programs in the U.S. and Europe have historically relied on monitoring tissue concentrations of bivalves to monitor contaminant levels and ecosystem health. By integrating 'omic methods with these tissue residue approaches we can uncover mechanistic insight to link tissue concentrations to potential toxic effects. In an effort to identify novel biomarkers and better understand the molecular toxicology of metal bioaccumulation in bivalves, we exposed the blue mussel, Mytilus edulis L., to sub-lethal concentrations (0.54 μM) of cadmium, lead, and a Cd+Pb mixture. Metal concentrations were measured in gill tissues at 1, 2, and 4 weeks, and increased linearly over the 4 week duration. In addition, there was evidence that Pb interfered with Cd uptake in the mixture treatment. Using a 3025 sequence microarray for M. edulis, we performed transcriptomic analysis, identifying 57 differentially expressed sequences. Hierarchical clustering of these sequences successfully distinguished the different treatment groups demonstrating that the expression profiles were reproducible among the treatments. Enrichment analysis of gene ontology terms identified several biological processes that were perturbed by the treatments, including nucleoside phosphate biosynthetic processes, mRNA metabolic processes, and response to stress. To identify transcripts whose expression level correlated with metal bioaccumulation, we performed Pearson correlation analysis. Several transcripts correlated with gill metal concentrations including mt10, mt20, and contig 48, an unknown transcript containing a wsc domain. In addition, three transcripts directly involved in the unfolded protein response (UPR) were induced in the metal treatments at 2 weeks and were further up-regulated at 4 weeks. Overall, correlation of tissue concentrations and gene expression responses indicates that as mussels accumulate higher concentrations of metals, initial stress responses are mobilized to protect tissues. However, given the role of UPR in apoptosis, it serves as an early indicator of stress, which once overwhelmed will result in adverse physiological effects.

Proposal of an in silico profiler for categorisation of repeat dose toxicity data of hair dyes

This study outlines the analysis of 94 chemicals with repeat dose toxicity data taken from Scientific Committee on Consumer Safety opinions for commonly used hair dyes in the European Union. Structural similarity was applied to group these chemicals into categories. Subsequent mechanistic analysis suggested that toxicity to mitochondria is potentially a key driver of repeat dose toxicity for chemicals within each of the categories. The mechanistic hypothesis allowed for an in silico profiler consisting of four mechanism-based structural alerts to be proposed. These structural alerts related to a number of important chemical classes such as quinones, anthraquinones, substituted nitrobenzenes and aromatic azos. This in silico profiler is intended for grouping chemicals into mechanism-based categories within the adverse outcome pathway paradigm.

Adverse outcome pathway and risks of anticoagulant rodenticides to predatory wildlife

Despite a long history of successful use, routine application of some anticoagulant rodenticides (ARs) may be at a crossroad due to new regulatory guidelines intended to mitigate risk. An adverse outcome pathway for ARs was developed to identify information gaps and end points to assess the effectiveness of regulations. This framework describes chemical properties of ARs, established macromolecular interactions by inhibition of vitamin K epoxide reductase, cellular responses including altered clotting factor processing and coagulopathy, organ level effects such as hemorrhage, organism responses with linkages to reduced fitness and mortality, and potential consequences to predator populations. Risk assessments have led to restrictions affecting use of some second-generation ARs (SGARs) in North America. While the European regulatory community highlighted significant or unacceptable risk of ARs to nontarget wildlife, use of SGARs in most EU member states remains authorized due to public health concerns and the absence of safe alternatives. For purposes of conservation and restoration of island habitats, SGARs remain a mainstay for eradication of invasive species. There are significant data gaps related to exposure pathways, comparative species sensitivity, consequences of sublethal effects, potential hazards of greater AR residues in genetically resistant prey, effects of low-level exposure to multiple rodenticides, and quantitative data on the magnitude of nontarget wildlife mortality.