Mechanism of Action of TiO2: Recommendations to Reduce Uncertainties Related to Carcinogenic Potential.

The Risk Assessment Committee of the European Chemicals Agency issued an opinion on classifying titanium dioxide (TiO2) as a suspected human carcinogen upon inhalation. Recent animal studies indicate that TiO2 may be carcinogenic through the oral route. There is considerable uncertainty on the carcinogenicity of TiO2, which may be decreased if its mechanism of action becomes clearer. Here we consider adverse outcome pathways and present the available information on each of the key events (KEs). Inhalation exposure to TiO2 can induce lung tumors in rats via a mechanism that is also applicable to other poorly soluble, low-toxicity particles. To reduce uncertainties regarding human relevance, we recommend gathering information on earlier KEs such as oxidative stress in humans. For oral exposure, insufficient information is available to conclude whether TiO2 can induce intestinal tumors. An oral carcinogenicity study with well-characterized (food-grade) TiO2 is needed, including an assessment of toxicokinetics and early KEs.

Predicting the Metal Mixture Toxicity with a Toxicokinetic-Toxicodynamic Model Considering the Time-Dependent Adverse Outcome Pathways.

Chemicals mainly exist in ecosystems as mixtures, and understanding and predicting their effects are major challenges in ecotoxicology. While the adverse outcome pathway (AOP) and toxicokinetic-toxicodynamic (TK-TD) models show promise as mechanistic approaches in chemical risk assessment, there is still a lack of methodology to incorporate the AOP into a TK-TD model. Here, we describe a novel approach that integrates the AOP and TK-TD models to predict mixture toxicity using metal mixtures (specifically Cd-Cu) as a case study. We preliminarily constructed an AOP of the metal mixture through temporal transcriptome analysis together with confirmatory bioassays. The AOP revealed that prolonged exposure time activated more key events and adverse outcomes, indicating different modes of action over time. We selected a potential key event as a proxy for damage and used it as a measurable parameter to replace the theoretical parameter (scaled damage) in the TK-TD model. This refined model, which connects molecular responses to organism outcomes, effectively predicts Cd-Cu mixture toxicity over time and can be extended to other metal mixtures and even multicomponent mixtures. Overall, our results contribute to a better understanding of metal mixture toxicity and provide insights for integrating the AOP and TK-TD models to improve risk assessment for chemical mixtures.

Assessing the Toxicity of Benzotriazole Ultraviolet Stabilizers to Fishes: Insights into Aryl Hydrocarbon Receptor-Mediated Effects.

Benzotriazole ultraviolet stabilizers (BUVSs) are chemicals used to mitigate UV-induced damage to manufactured goods. Their presence in aquatic environments and biota raises concerns, as certain BUVSs activate the aryl hydrocarbon receptor (AhR), which is linked to adverse effects in fish. However, potencies of BUVSs as AhR agonists and species sensitivities to AhR activation are poorly understood. This study evaluated the toxicity of three BUVSs using embryotoxicity assays. Zebrafish (Danio rerio) embryos exposed to BUVSs by microinjection suffered dose-dependent increases in mortality, with LD50 values of 4772, 11 608, and 56 292 ng/g-egg for UV-P, UV-9, and UV-090, respectively. The potencies and species sensitivities to AhR2 activation by BUVSs were assessed using a luciferase reporter gene assay with COS-7 cells transfected with the AhR2 of zebrafish and eight other fishes. The rank order of potency for activation of the AhR2 from all nine species was UV-P > UV-9 > UV-090. However, AhR2s among species differed in sensitivities to activation by up to 100-fold. An approximate reversed rank order of species sensitivity was observed compared to the rank order of sensitivity to 2,3,7,8-tetrachlorodibenzo[p]dioxin, the prototypical AhR agonist. Despite this, a pre-existing quantitative adverse outcome pathway linking AhR activation to embryo lethality could predict embryotoxicities of BUVSs in zebrafish.

Nontargeted Identification of Organic Components in Fine Particulate Matter Related to Lung Tumor Metastasis Based on an Adverse Outcome Pathway Strategy.

Emerging studies implicate fine particulate matter (PM2.5) and its organic components (OCs) as urgent hazard factors for lung cancer progression in nonsmokers. Establishing the adverse outcome pathway (AOP)-directed nontargeted identification method, this study aimed to explore whether PM2.5 exposure in coal-burning areas promoted lung tumor metastasis and how we identify its effective OCs to support traceability and control of regional PM2.5 pollution. First, we used a nude mouse model of lung cancer for PM2.5 exposure and found that the exposure significantly promoted the hematogenous metastases of A549-Luc cells in lung tissues and the adverse outcomes (AOs), with key events (KEs) including the changed expression of epithelial-mesenchymal transition (EMT) markers, such as suppression of E-cad and increased expression of Fib. Subsequently, using AOs and KEs as adverse outcome directors, we identified a total of 35 candidate chemicals based on the in vitro model and nontargeted analysis. Among them, tributyl phosphate (C12H27O4P), 2-bromotetradecane (C14H29Br), and methyl decanoate (C11H22O2) made greater contributions to the AOs. Finally, we clarified the interactions between these OCs and EMT-activating transcription factors (EMT-ATFs) as the molecular initiation event (MIE) to support the feasibility of the above identification strategy. The present study updates a new framework for identifying tumor metastasis-promoting OCs in PM2.5 and provides solid data for screening out chemicals that need priority control in polluted areas posing higher lung cancer risk.

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.

Development of an adverse outcome pathway network for nephrotoxicity.

Adverse outcome pathways (AOPs) were introduced in modern toxicology to provide evidence-based representations of the events and processes involved in the progression of toxicological effects across varying levels of the biological organisation to better facilitate the safety assessment of chemicals. AOPs offer an opportunity to address knowledge gaps and help to identify novel therapeutic targets. They also aid in the selection and development of existing and new in vitro and in silico test methods for hazard identification and risk assessment of chemical compounds. However, many toxicological processes are too intricate to be captured in a single, linear AOP. As a result, AOP networks have been developed to aid in the comprehension and placement of associated events underlying the emergence of related forms of toxicity-where complex exposure scenarios and interactions may influence the ultimate adverse outcome. This study utilised established criteria to develop an AOP network that connects thirteen individual AOPs associated with nephrotoxicity (as sourced from the AOP-Wiki) to identify several key events (KEs) linked to various adverse outcomes, including kidney failure and chronic kidney disease. Analysis of the modelled AOP network and its topological features determined mitochondrial dysfunction, oxidative stress, and tubular necrosis to be the most connected and central KEs. These KEs can provide a logical foundation for guiding the selection and creation of in vitro assays and in silico tools to substitute for animal-based in vivo experiments in the prediction and assessment of chemical-induced nephrotoxicity in human health.

Leveraging integrative toxicogenomic approach towards development of stressor-centric adverse outcome pathway networks for plastic additives.

Plastics are widespread pollutants found in atmospheric, terrestrial and aquatic ecosystems due to their extensive usage and environmental persistence. Plastic additives, that are intentionally added to achieve specific functionality in plastics, leach into the environment upon plastic degradation and pose considerable risk to ecological and human health. Limited knowledge concerning the presence of plastic additives throughout plastic life cycle has hindered their effective regulation, thereby posing risks to product safety. In this study, we leveraged the adverse outcome pathway (AOP) framework to understand the mechanisms underlying plastic additives-induced toxicities. We first identified an exhaustive list of 6470 plastic additives from chemicals documented in plastics. Next, we leveraged heterogenous toxicogenomics and biological endpoints data from five exposome-relevant resources, and identified associations between 1287 plastic additives and 322 complete and high quality AOPs within AOP-Wiki. Based on these plastic additive-AOP associations, we constructed a stressor-centric AOP network, wherein the stressors are categorized into ten priority use sectors and AOPs are linked to 27 disease categories. We visualized the plastic additives-AOP network for each of the 1287 plastic additives and made them available in a dedicated website: https://cb.imsc.res.in/saopadditives/ . Finally, we showed the utility of the constructed plastic additives-AOP network by identifying highly relevant AOPs associated with benzo[a]pyrene (B[a]P), bisphenol A (BPA), and bis(2-ethylhexyl) phthalate (DEHP) and thereafter, explored the associated toxicity pathways in humans and aquatic species. Overall, the constructed plastic additives-AOP network will assist regulatory risk assessment of plastic additives, thereby contributing towards a toxic-free circular economy for plastics.

From hazard to risk prioritization: a case study to predict drug-induced cholestasis using physiologically based kinetic modeling.

Cholestasis is characterized by hepatic accumulation of bile acids. Clinical manifestation of cholestasis only occurs in a small proportion of exposed individuals. The present study aims to develop a new approach methodology (NAM) to predict drug-induced cholestasis as a result of drug-induced hepatic bile acid efflux inhibition and the resulting bile acid accumulation. To this end, hepatic concentrations of a panel of drugs were predicted by a generic physiologically based kinetic (PBK) drug model. Their effects on hepatic bile acid efflux were incorporated in a PBK model for bile acids. The predicted bile acid accumulation was used as a measure for a drug's cholestatic potency. The selected drugs were known to inhibit hepatic bile acid efflux in an assay with primary suspension-cultured hepatocytes and classified as common, rare, or no for cholestasis incidence. Common cholestasis drugs included were atorvastatin, chlorpromazine, cyclosporine, glimepiride, ketoconazole, and ritonavir. The cholestasis incidence of the drugs appeared not to be adequately predicted by their Ki for inhibition of hepatic bile acid efflux, but rather by the AUC of the PBK model predicted internal hepatic drug concentration at therapeutic dose level above this Ki. People with slower drug clearance, a larger bile acid pool, reduced bile salt export pump (BSEP) abundance, or given higher than therapeutic dose levels were predicted to be at higher risk to develop drug-induced cholestasis. The results provide a proof-of-principle of using a PBK-based NAM for cholestasis risk prioritization as a result of transporter inhibition and identification of individual risk factors.

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.

Construction of an adverse outcome pathway framework for arsenic-induced lung cancer using a network-based approach.

Environmental pollutants are considered as a cause of tumorigenesis, but approaches to assess their risk of causing tumors remain insufficient. As an alternative approach, the adverse outcome pathway (AOP) framework is used to assess the risk of tumors caused by environmental pollutants. Arsenic is a pollutant associated with lung cancer, but early assessment of lung cancer risk is lacking. Therefore, we applied the AOP framework to arsenic-induced lung cancer. A systematic review revealed increased risks of lung cancer following exposure to a range of arsenic concentrations in drinking water (OR = 1.83, 95 % CI = 1.46-2.30). We obtained, from public databases, genes related to risk of arsenic-induced lung cancer. Then, Cox and LASSO regressions were used to screen target genes from the risk genes. Subsequently, target genes, phenotypes, and pathways were used to construct the computational AOP network, which was determined by Cytoscape to have 156 edges and 45 nodes. Further, target genes, phenotypes, and pathways were used as molecular initiating events and key events to construct the AOP framework depending on upstream and downstream relationships. In the AOP framework, by Weight of Evidence, arsenic exposure increased levels of EGFR, activated the PI3K/AKT pathway, regulated cell proliferation by promoting the G1/S phase transition, and caused generation of lung cancers. External validation was achieved through arsenite-induced, malignant transformed human bronchial epithelial (HBE) cells. Overall, these results, by integration into existing data to construct an AOP framework, provide insights into the assessment of lung cancer risk for arsenic exposure. Special attention needs to be focused on populations with low-dose arsenic exposure.

Three perspectives on the prediction of chemical effects in ecosystems.

The increasing production, use and emission of synthetic chemicals into the environment represents a major driver of global change. The large number of synthetic chemicals, limited knowledge on exposure patterns and effects in organisms and their interaction with other global change drivers hamper the prediction of effects in ecosystems. However, recent advances in biomolecular and computational methods are promising to improve our capacity for prediction. We delineate three idealised perspectives for the prediction of chemical effects: the suborganismal, organismal and ecological perspective, which are currently largely separated. Each of the outlined perspectives includes essential and complementary theories and tools for prediction but captures only part of the phenomenon of chemical effects. Links between the perspectives may foster predictive modelling of chemical effects in ecosystems and extrapolation between species. A major challenge for the linkage is the lack of data sets simultaneously covering different levels of biological organisation (here referred to as biological levels) as well as varying temporal and spatial scales. Synthesising the three perspectives, some central aspects and associated types of data seem particularly necessary to improve prediction. First, suborganism- and organism-level responses to chemicals need to be recorded and tested for relationships with chemical groups and organism traits. Second, metrics that are measurable at many biological levels, such as energy, need to be scrutinised for their potential to integrate across levels. Third, experimental data on the simultaneous response over multiple biological levels and spatiotemporal scales are required. These could be collected in nested and interconnected micro- and mesocosm experiments. Lastly, prioritisation of processes involved in the prediction framework needs to find a balance between simplification and capturing the essential complexity of a system. For example, in some cases, eco-evolutionary dynamics and interactions may need stronger consideration. Prediction needs to move from a static to a real-world eco-evolutionary view.

Towards a science-based testing strategy to identify maternal thyroid hormone imbalance and neurodevelopmental effects in the progeny - Part IV: the ECETOC and CLE Proposal for a Thyroid Function-Related Neurodevelopmental Toxicity Testing and Assessment Scheme (Thyroid-NDT-TAS).

Following the European Commission Endocrine Disruptor Criteria, substances shall be considered as having endocrine disrupting properties if they (a) elicit adverse effects, (b) have endocrine activity, and (c) the two are linked by an endocrine mode-of-action (MoA) unless the MoA is not relevant for humans. A comprehensive, structured approach to assess whether substances meet the Endocrine Disruptor Criteria for the thyroid modality (EDC-T) is currently unavailable. Here, the European Centre for Ecotoxicology and Toxicology of Chemicals Thyroxine Task Force and CropLife Europe propose a Thyroid Function-Related Neurodevelopmental Toxicity Testing and Assessment Scheme (Thyroid-NDT-TAS). In Tier 0, before entering the Thyroid-NDT-TAS, all available in vivo, in vitro and in silico data are submitted to weight-of-evidence (WoE) evaluations to determine whether the substance of interest poses a concern for thyroid disruption. If so, Tier 1 of the Thyroid-NDT-TAS includes an initial MoA and human relevance assessment (structured by the key events of possibly relevant adverse outcome pathways) and the generation of supportive in vitro/in silico data, if relevant. Only if Tier 1 is inconclusive, Tier 2 involves higher-tier testing to generate further thyroid- and/or neurodevelopment-related data. Tier 3 includes the final MoA and human relevance assessment and an overarching WoE evaluation to draw a conclusion on whether, or not, the substance meets the EDC-T. The Thyroid-NDT-TAS is based on the state-of-the-science, and it has been developed to minimise animal testing. To make human safety assessments more accurate, it is recommended to apply the Thyroid-NDT-TAS during future regulatory assessments.

Verification of In Vivo Estrogenic Activity for Four Per- and Polyfluoroalkyl Substances (PFAS) Identified as Estrogen Receptor Agonists via New Approach Methodologies.

Given concerns about potential toxicological hazards of the thousands of data-poor per- and polyfluorinated alkyl substances (PFAS) currently in commerce and detected in the environment, tiered testing strategies that employ high-throughput in vitro screening as an initial testing tier have been implemented. The present study evaluated the effectiveness of previous in vitro screening for identifying PFAS capable, or incapable, of inducing estrogenic responses in fish exposed in vivo. Fathead minnows (Pimephales promelas) were exposed for 96 h to five PFAS (perfluorooctanoic acid [PFOA]; 1H,1H,8H,8H-perfluorooctane-1,8-diol [FC8-diol]; 1H,1H,10H,10H-perfluorodecane-1,10-diol [FC10-diol]; 1H,1H,8H,8H-perfluoro-3,6-dioxaoctane-1,8-diol [FC8-DOD]; and perfluoro-2-methyl-3-oxahexanoic acid [HFPO-DA]) that showed varying levels of in vitro estrogenic potency. In agreement with in vitro screening results, exposure to FC8-diol, FC10-diol, and FC8-DOD caused concentration-dependent increases in the expression of transcript coding for vitellogenin and estrogen receptor alpha and reduced expression of insulin-like growth factor and apolipoprotein eb. Once differences in bioconcentration were accounted for, the rank order of potency in vivo matched that determined in vitro. These results provide a screening level benchmark for worst-case estimates of potential estrogenic hazards of PFAS and a basis for identifying structurally similar PFAS to scrutinize for putative estrogenic activity.

Integrating Concentration-Dependent Toxicity Data and Toxicokinetics To Inform Hepatotoxicity Response Pathways.

Failure of animal models to predict hepatotoxicity in humans has created a push to develop biological pathway-based alternatives, such as those that use in vitro assays. Public screening programs (e.g., ToxCast/Tox21 programs) have tested thousands of chemicals using in vitro high-throughput screening (HTS) assays. Developing pathway-based models for simple biological pathways, such as endocrine disruption, has proven successful, but development remains a challenge for complex toxicities like hepatotoxicity, due to the many biological events involved. To this goal, we aimed to develop a computational strategy for developing pathway-based models for complex toxicities. Using a database of 2171 chemicals with human hepatotoxicity classifications, we identified 157 out of 1600+ ToxCast/Tox21 HTS assays to be associated with human hepatotoxicity. Then, a computational framework was used to group these assays by biological target or mechanisms into 52 key event (KE) models of hepatotoxicity. KE model output is a KE score summarizing chemical potency against a hepatotoxicity-relevant biological target or mechanism. Grouping hepatotoxic chemicals based on the chemical structure revealed chemical classes with high KE scores plausibly informing their hepatotoxicity mechanisms. Using KE scores and supervised learning to predict in vivo hepatotoxicity, including toxicokinetic information, improved the predictive performance. This new approach can be a universal computational toxicology strategy for various chemical toxicity evaluations.

Advancing Computational Toxicology by Interpretable Machine Learning.

Chemical toxicity evaluations for drugs, consumer products, and environmental chemicals have a critical impact on human health. Traditional animal models to evaluate chemical toxicity are expensive, time-consuming, and often fail to detect toxicants in humans. Computational toxicology is a promising alternative approach that utilizes machine learning (ML) and deep learning (DL) techniques to predict the toxicity potentials of chemicals. Although the applications of ML- and DL-based computational models in chemical toxicity predictions are attractive, many toxicity models are "black boxes" in nature and difficult to interpret by toxicologists, which hampers the chemical risk assessments using these models. The recent progress of interpretable ML (IML) in the computer science field meets this urgent need to unveil the underlying toxicity mechanisms and elucidate the domain knowledge of toxicity models. In this review, we focused on the applications of IML in computational toxicology, including toxicity feature data, model interpretation methods, use of knowledge base frameworks in IML development, and recent applications. The challenges and future directions of IML modeling in toxicology are also discussed. We hope this review can encourage efforts in developing interpretable models with new IML algorithms that can assist new chemical assessments by illustrating toxicity mechanisms in humans.

Multiomics Point of Departure (moPOD) Modeling Supports an Adverse Outcome Pathway Network for Ionizing Radiation.

While adverse biological effects of acute high-dose ionizing radiation have been extensively investigated, knowledge on chronic low-dose effects is scarce. The aims of the present study were to identify hazards of low-dose ionizing radiation to Daphnia magna using multiomics dose-response modeling and to demonstrate the use of omics data to support an adverse outcome pathway (AOP) network development for ionizing radiation. Neonatal D. magna were exposed to γ radiation for 8 days. Transcriptomic analysis was performed after 4 and 8 days of exposure, whereas metabolomics and confirmative bioassays to support the omics analyses were conducted after 8 days of exposure. Benchmark doses (BMDs, 10% benchmark response) as points of departure (PODs) were estimated for both dose-responsive genes/metabolites and the enriched KEGG pathways. Relevant pathways derived using the BMD modeling and additional functional end points measured by the bioassays were overlaid with a previously published AOP network. The results showed that several molecular pathways were highly relevant to the known modes of action of γ radiation, including oxidative stress, DNA damage, mitochondrial dysfunction, protein degradation, and apoptosis. The functional assays showed increased oxidative stress and decreased mitochondrial membrane potential and ATP pool. Ranking of PODs at the pathway and functional levels showed that oxidative damage related functions had relatively low PODs, followed by DNA damage, energy metabolism, and apoptosis. These were supportive of causal events in the proposed AOP network. This approach yielded promising results and can potentially provide additional empirical evidence to support further AOP development for ionizing radiation.

Are New Phthalate Ester Substitutes Safer than Traditional DBP and DiBP? Comparative Endocrine-Disrupting Analyses on Zebrafish Using In Vivo, Transcriptome, and In Silico Approaches.

Although previous studies have confirmed the association between phthalate esters (PAEs) exposure and endocrine disorders in humans, few studies to date have systematically assessed the threats of new PAE alternatives to endocrine disruptions. Herein, zebrafish embryos were continuously exposed to two PAEs [di-n-butyl phthalate (DBP) and diisobutyl phthalate (DiBP)], two structurally related alternatives [diiononyl phthalate (DINP) and diisononyl hexahydrophthalate (DINCH)], and two non-PAE substitutes [dipropylene glycol dibenzoate (DGD) and glyceryl triacetate (GTA)], and the endocrine-disrupting effects were investigated during the early stages (8-48 hpf). For five endogenous hormones, including progesterone, testosterone, 17ß-estradiol, triiodothyronine (T3), and cortisol, the tested chemicals disturbed the contents of at least one hormone at environmentally relevant concentrations (≤3.9 µM), except DINCH and GTA. Then, the concentration-dependent reduced zebrafish transcriptome analysis was performed. Thyroid hormone (TH)- and androgen/estrogen-regulated adverse outcome pathways (AOPs) were the two types of biological pathways most sensitive to PAE exposure. Notably, six compounds disrupted four TH-mediated AOPs, from the inhibition of deiodinases (molecular initiating event, MIE), a decrease in T3 levels (key event, KE), to mortality (adverse outcome, AO) with the quantitatively linear relationships between MIE-KE (|r| = 0.96, p = 0.002), KE-AO (|r| = 0.88, p = 0.02), and MIE-AO (|r| = 0.89, p = 0.02). Multiple structural analyses showed that benzoic acid is the critical toxicogenic fragment. Our data will facilitate the screening and development of green alternatives.

Optimization of an adverse outcome pathway network on chemical-induced cholestasis using an artificial intelligence-assisted data collection and confidence level quantification approach.

BACKGROUND: Adverse outcome pathway (AOP) networks are versatile tools in toxicology and risk assessment that capture and visualize mechanisms driving toxicity originating from various data sources. They share a common structure consisting of a set of molecular initiating events and key events, connected by key event relationships, leading to the actual adverse outcome. AOP networks are to be considered living documents that should be frequently updated by feeding in new data. Such iterative optimization exercises are typically done manually, which not only is a time-consuming effort, but also bears the risk of overlooking critical data. The present study introduces a novel approach for AOP network optimization of a previously published AOP network on chemical-induced cholestasis using artificial intelligence to facilitate automated data collection followed by subsequent quantitative confidence assessment of molecular initiating events, key events, and key event relationships. METHODS: Artificial intelligence-assisted data collection was performed by means of the free web platform Sysrev. Confidence levels of the tailored Bradford-Hill criteria were quantified for the purpose of weight-of-evidence assessment of the optimized AOP network. Scores were calculated for biological plausibility, empirical evidence, and essentiality, and were integrated into a total key event relationship confidence value. The optimized AOP network was visualized using Cytoscape with the node size representing the incidence of the key event and the edge size indicating the total confidence in the key event relationship. RESULTS: This resulted in the identification of 38 and 135 unique key events and key event relationships, respectively. Transporter changes was the key event with the highest incidence, and formed the most confident key event relationship with the adverse outcome, cholestasis. Other important key events present in the AOP network include: nuclear receptor changes, intracellular bile acid accumulation, bile acid synthesis changes, oxidative stress, inflammation and apoptosis. CONCLUSIONS: This process led to the creation of an extensively informative AOP network focused on chemical-induced cholestasis. This optimized AOP network may serve as a mechanistic compass for the development of a battery of in vitro assays to reliably predict chemical-induced cholestatic injury.

Applying the adverse outcome pathway concept for assessing non-monotonic dose responses: biphasic effect of bis(2-ethylhexyl) phthalate (DEHP) on testosterone levels.

Male reproduction is one of the primary health endpoints identified in rodent studies for some phthalates, such as DEHP (Bis(2-ethylhexyl) phthalate), DBP (Dibutyl phthalate), and BBP (Benzyl butyl phthalate). The reduction in testosterone level was used as an intermediate key event for grouping some phthalates and to establish a reference point for risk assessment. Phthalates, and specifically DEHP, are one of the chemicals for which the greatest number of non-monotonic dose responses (NMDRs) are observed. These NMDRs cover different endpoints and situations, often including testosterone levels. The presence of NMDR has been the subject of some debate within the area of chemical risk assessment, which is traditionally anchored around driving health-based guidance values for apical endpoints that typically follow a clear monotonic dose-response. The consequence of NMDR for chemical risk assessment has recently received considerable attention amongst regulatory agencies, which confirmed its relevance particularly for receptor-mediated effects. The present review explores the relationship between DEHP exposure and testosterone levels, investigating the biological plausibility of the observed NMDRs. The Adverse Outcome Pathway (AOP) concept is applied to integrate NMDRs into Key Event Relationships (KERs) for exploring a mechanistic understanding of initial key events and possibly associated reproductive and non-reproductive adverse outcomes.

Effects of low doses of methylmercury (MeHg) exposure on definitive endoderm cell differentiation in human embryonic stem cells.

Fetal development is one of the most sensitive windows to methylmercury (MeHg) toxicity. Laboratory and epidemiological studies have shown a dose-response relationship between fetal MeHg exposure and neuro performance in different life stages from infants to adults. In addition, MeHg exposure has been reported to be associated with disorders in endoderm-derived organs, such as morphological changes in liver cells and pancreatic cell dysfunctions. However, the mechanisms of the effects of MeHg on non-neuronal organs or systems, especially during the early development of endoderm-derived organs, remain unclear. Here we determined the effects of low concentrations of MeHg exposure during the differentiation of definitive endoderm (DE) cells from human embryonic stem cells (hESCs). hESCs were exposed to MeHg (0, 10, 100, and 200 nM) that covers the range of Hg concentrations typically found in human maternal blood during DE cell induction. Transcriptomic analysis showed that sub-lethal doses of MeHg exposure could alter global gene expression patterns during hESC to DE cell differentiation, leading to increased expression of endodermal genes/proteins and the over-promotion of endodermal fate, mainly through disrupting calcium homeostasis and generating ROS. Bioinformatic analysis results suggested that MeHg exerts its developmental toxicity mainly by disrupting ribosome biogenesis during early cell lineage differentiation. This disruption could lead to aberrant growth or dysfunctions of the developing endoderm-derived organs, and it may be the underlying mechanism for the observed congenital diseases later in life. Based on the results, we proposed an adverse outcome pathway for the effects of MeHg exposure during human embryonic stem cells to definitive endoderm differentiation.