Investigation of parenteral nutrition-induced hepatotoxicity using human liver spheroid co-cultures.

Parenteral nutrition (PN) is typically administered to individuals with gastrointestinal dysfunction, a contraindication for enteral feeding, and a need for nutritional therapy. When PN is the only energy source in patients, it is defined as total parenteral nutrition (TPN). TPN is a life-saving approach for different patient populations, both in infants and adults. However, despite numerous benefits, TPN can cause adverse effects, including metabolic disorders and liver injury. TPN-associated liver injury, known as intestinal failure-associated liver disease (IFALD), represents a significant problem affecting up to 90% of individuals receiving TPN. IFALD pathogenesis is complex, depending on the TPN components as well as on the patient's medical conditions. Despite numerous animal studies and clinical observations, the molecular mechanisms driving IFALD remain largely unknown. The present study was set up to elucidate the mechanisms underlying IFALD. For this purpose, human liver spheroid co-cultures were treated with a TPN mixture, followed by RNA sequencing analysis. Subsequently, following exposure to TPN and its single nutritional components, several key events of liver injury, including mitochondrial dysfunction, endoplasmic reticulum stress, oxidative stress, apoptosis, and lipid accumulation (steatosis), were studied using various techniques. It was found that prolonged exposure to TPN substantially changes the transcriptome profile of liver spheroids and affects multiple metabolic and signaling pathways contributing to liver injury. Moreover, TPN and its main components, especially lipid emulsion, induce changes in all key events measured and trigger steatosis.

Accessible methods and tools to estimate chemical exposure in humans to support risk assessment: A systematic scoping review.

Exposure assessment is a crucial component of environmental health research, providing essential information on the potential risks associated with various chemicals. A systematic scoping review was conducted to acquire an overview of accessible human exposure assessment methods and computational tools to support and ultimately improve risk assessment. The systematic scoping review was performed in Sysrev, a web platform that introduces machine learning techniques into the review process aiming for increased accuracy and efficiency. Included publications were restricted to a publication date after the year 2000, where exposure methods were properly described. Exposure assessments methods were found to be used for a broad range of environmental chemicals including pesticides, metals, persistent chemicals, volatile organic compounds, and other chemical classes. Our results show that after the year 2000, for all the types of exposure routes, probabilistic analysis, and computational methods to calculate human exposure have increased. Sixty-three mathematical models and toolboxes were identified that have been developed in Europe, North America, and globally. However, only twelve occur frequently and their usefulness were associated with exposure route, chemical classes and input parameters used to estimate exposure. The outcome of the combined associations can function as a basis and/or guide for decision making for the selection of most appropriate method and tool to be used for environmental chemical human exposure assessments in Ontology-driven and artificial intelligence-based repeated dose toxicity testing of chemicals for next generation risk assessment (ONTOX) project and elsewhere. Finally, the choice of input parameters used in each mathematical model and toolbox shown by our analysis can contribute to the harmonization process of the exposure models and tools increasing the prospect for comparison between studies and consistency in the regulatory process in the future.

The application of natural language processing for the extraction of mechanistic information in toxicology.

To study the ways in which compounds can induce adverse effects, toxicologists have been constructing Adverse Outcome Pathways (AOPs). An AOP can be considered as a pragmatic tool to capture and visualize mechanisms underlying different types of toxicity inflicted by any kind of stressor, and describes the interactions between key entities that lead to the adverse outcome on multiple biological levels of organization. The construction or optimization of an AOP is a labor intensive process, which currently depends on the manual search, collection, reviewing and synthesis of available scientific literature. This process could however be largely facilitated using Natural Language Processing (NLP) to extract information contained in scientific literature in a systematic, objective, and rapid manner that would lead to greater accuracy and reproducibility. This would support researchers to invest their expertise in the substantive assessment of the AOPs by replacing the time spent on evidence gathering by a critical review of the data extracted by NLP. As case examples, we selected two frequent adversities observed in the liver: namely, cholestasis and steatosis denoting accumulation of bile and lipid, respectively. We used deep learning language models to recognize entities of interest in text and establish causal relationships between them. We demonstrate how an NLP pipeline combining Named Entity Recognition and a simple rules-based relationship extraction model helps screen compounds related to liver adversities in the literature, but also extract mechanistic information for how such adversities develop, from the molecular to the organismal level. Finally, we provide some perspectives opened by the recent progress in Large Language Models and how these could be used in the future. We propose this work brings two main contributions: 1) a proof-of-concept that NLP can support the extraction of information from text for modern toxicology and 2) a template open-source model for recognition of toxicological entities and extraction of their relationships. All resources are openly accessible via GitHub (https://github.com/ontox-project/en-tox).

Novel clinical phenotypes, drug categorization, and outcome prediction in drug-induced cholestasis: Analysis of a database of 432 patients developed by literature review and machine learning support.

BACKGROUND: Serum transaminases, alkaline phosphatase and bilirubin are common parameters used for DILI diagnosis, classification, and prognosis. However, the relevance of clinical examination, histopathology and drug chemical properties have not been fully investigated. As cholestasis is a frequent and complex DILI manifestation, our goal was to investigate the relevance of clinical features and drug properties to stratify drug-induced cholestasis (DIC) patients, and to develop a prognosis model to identify patients at risk and high-concern drugs. METHODS: DIC-related articles were searched by keywords and Boolean operators in seven databases. Relevant articles were uploaded onto Sysrev, a machine-learning based platform for article review and data extraction. Demographic, clinical, biochemical, and liver histopathological data were collected. Drug properties were obtained from databases or QSAR modelling. Statistical analyses and logistic regressions were performed. RESULTS: Data from 432 DIC patients associated with 52 drugs were collected. Fibrosis strongly associated with fatality, whereas canalicular paucity and ALP associated with chronicity. Drugs causing cholestasis clustered in three major groups. The pure cholestatic pattern divided into two subphenotypes with differences in prognosis, canalicular paucity, fibrosis, ALP and bilirubin. A predictive model of DIC outcome based on non-invasive parameters and drug properties was developed. Results demonstrate that physicochemical (pKa-a) and pharmacokinetic (bioavailability, CYP2C9) attributes impinged on the DIC phenotype and allowed the identification of high-concern drugs. CONCLUSIONS: We identified novel associations among DIC manifestations and disclosed novel DIC subphenotypes with specific clinical and chemical traits. The developed predictive DIC outcome model could facilitate DIC prognosis in clinical practice and drug categorization.

A quantitative weight-of-evidence method for confidence assessment of adverse outcome pathway networks: A case study on chemical-induced liver steatosis.

The field of chemical toxicity testing is undergoing a transition to overcome the limitations of in vivo experiments. This evolution involves implementing innovative non-animal approaches to improve predictability and provide a more precise understanding of toxicity mechanisms. Adverse outcome pathway (AOP) networks are pivotal in organizing existing mechanistic knowledge related to toxicological processes. However, these AOP networks are dynamic and require regular updates to incorporate the latest data. Regulatory challenges also persist due to concerns about the reliability of the information they offer. This study introduces a generic Weight-of-Evidence (WoE) scoring method, aligned with the tailored Bradford-Hill criteria, to quantitatively assess the confidence levels in key event relationships (KERs) within AOP networks. We use the previously published AOP network on chemical-induced liver steatosis, a prevalent form of human liver injury, as a case study. Initially, the existing AOP network is optimized with the latest scientific information extracted from PubMed using the free SysRev platform for artificial intelligence (AI)-based abstract inclusion and standardized data collection. The resulting optimized AOP network, constructed using Cytoscape, visually represents confidence levels through node size (key event, KE) and edge thickness (KERs). Additionally, a Shiny application is developed to facilitate user interaction with the dataset, promoting future updates. Our analysis of 173 research papers yielded 100 unique KEs and 221 KERs among which 72 KEs and 170 KERs, respectively, have not been previously documented in the prior AOP network or AOP-wiki. Notably, modifications in de novo lipogenesis, fatty acid uptake and mitochondrial beta-oxidation, leading to lipid accumulation and liver steatosis, garnered the highest KER confidence scores. In conclusion, our study delivers a generic methodology for developing and assessing AOP networks. The quantitative WoE scoring method facilitates in determining the level of support for KERs within the optimized AOP network, offering valuable insights into its utility in both scientific research and regulatory contexts. KERs supported by robust evidence represent promising candidates for inclusion in an in vitro test battery for reliably predicting chemical-induced liver steatosis within regulatory frameworks.

Assessment of Connexin43 Hemichannel Functionality Based on Cytosolic Uptake of Yo-Pro1.

Connexin proteins are the building blocks of gap junctions and connexin hemichannels. Both provide a pathway for cellular communication. Gap junctions support intercellular communication mechanisms and regulate homeostasis. In contrast, open connexin hemichannels connect the intracellular compartment and the extracellular environment, and their activation fuels inflammation and cell death. The development of clinically applicable connexin hemichannel blockers for therapeutic purposes is therefore gaining momentum. This chapter describes a well-established protocol optimized for assessing connexin hemichannel activity by using the reporter dye Yo-Pro1.

Chemical-induced liver cancer: an adverse outcome pathway perspective.

INTRODUCTION: The evaluation of the potential carcinogenicity is a key consideration in the risk assessment of chemicals. Predictive toxicology is currently switching toward non-animal approaches that rely on the mechanistic understanding of toxicity. AREAS COVERED: Adverse outcome pathways (AOPs) present toxicological processes, including chemical-induced carcinogenicity, in a visual and comprehensive manner, which serve as the conceptual backbone for the development of non-animal approaches eligible for hazard identification. The current review provides an overview of the available AOPs leading to liver cancer and discusses their use in advanced testing of liver carcinogenic chemicals. Moreover, the challenges related to their use in risk assessment are outlined, including the exploitation of available data, the need for semantic ontologies, and the development of quantitative AOPs. EXPERT OPINION: To exploit the potential of liver cancer AOPs in the field of risk assessment, 3 immediate prerequisites need to be fulfilled. These include developing human relevant AOPs for chemical-induced liver cancer, increasing the number of AOPs integrating quantitative toxicodynamic and toxicokinetic data, and developing a liver cancer AOP network. As AOPs and other areas in the field continue to evolve, liver cancer AOPs will progress into a reliable and robust tool serving future risk assessment and management.

Parenteral nutrition-associated liver injury: clinical relevance and mechanistic insights.

Intestinal failure-associated liver disease (IFALD) is a relatively common complication in individuals receiving parenteral nutrition (PN). IFALD can be manifested as different types of liver injury, including steatosis, cholestasis, and fibrosis, and could result in liver failure in some cases. The onset and progression of IFALD are highly dependent on various patient and PN-related risk factors. Despite still being under investigation, several mechanisms have been proposed. Liver injury can originate due to caloric overload, nutrient deficiency, and toxicity, as well as phytosterol content, and omega-6 to omega-3 fatty acids ratio contained in lipid emulsions. Additional mechanisms include immature or defective bile acid metabolism, acute heart failure, infections, and sepsis exerting negative effects via Toll-like receptor 4 and nuclear factor κB inflammatory signaling. Furthermore, lack of enteral feeding, gut dysbiosis, and altered enterohepatic circulation that affect the farnesoid x receptor-fibroblast growth factor 19 axis can also contribute to IFALD. Various best practices can be adopted to minimize the risk of developing IFALD, such as prevention and management of central line infections and sepsis, preservation of intestine's length, a switch to oral and enteral feeding, cyclic PN, avoidance of overfeeding and soybean oil-based lipid formulations, and avoiding hepatotoxic substances. The present review thus provides a comprehensive overview of all relevant aspects inherent to IFALD. Further research focused on clinical observations, translational models, and advanced toxicological knowledge frameworks is needed to gain more insight into the molecular pathogenesis of hepatotoxicity, reduce IFALD incidence, and encourage the safe use of PN.

Qualitative and quantitative concentration-response modelling of gene co-expression networks to unlock hepatotoxic mechanisms for next generation chemical safety assessment.

Next generation risk assessment of chemicals revolves around the use of mechanistic information without animal experimentation. In this regard, toxicogenomics has proven to be a useful tool to elucidate the underlying mechanisms of adverse effects of xenobiotics. In the present study, two widely used human in vitro hepatocyte culture systems, namely primary human hepatocytes (PHH) and human hepatoma HepaRG cells, were exposed to liver toxicants known to induce liver cholestasis, steatosis or necrosis. Benchmark concentration-response modelling was applied to transcriptomics gene co-expression networks (modules) to derive benchmark concentrations (BMCs) and to gain mechanistic insight into the hepatotoxic effects. BMCs derived by concentration-response modelling of gene co-expression modules recapitulated concentration-response modelling of individual genes. Although PHH and HepaRG cells showed overlap in deregulated genes and modules by the liver toxicants, PHH demonstrated a higher responsiveness, based on the lower BMCs of co-regulated gene modules. Such BMCs can be used as transcriptomics point of departure (tPOD) for assessing module-associated cellular (stress) pathways/processes. This approach identified clear tPODs of around maximum systemic concentration (Cmax) levels for the tested drugs, while for cosmetics ingredients the BMCs were 10-100-fold higher than the estimated plasma concentrations. This approach could serve next generation risk assessment practice to identify early responsive modules at low BMCs, that could be linked to key events in liver adverse outcome pathways. In turn, this can assist in delineating potential hazards of new test chemicals using in vitro systems and used in a risk assessment when BMCs are paired with chemical exposure assessment.

Risk assessment of chemicals has traditionally been focused on animal experiments. In contrast, next generation risk assessment uses biological information obtained from experiments in cell culture models without animals to identify potential hazards. Since the liver is the main target organ of toxicity, many liver cell (hepatocyte) models have been developed and applied for hazard assessment. In this study, two widely used human hepatocyte cell models, PHH and HepaRG, were exposed to liver toxic chemicals. Biological changes in gene expression were measured in a concentration range to identify the concentration at which a biological response was perturbed using concentration response modelling. Genes belonging to the same biological process were joined based on co-expression to derive an average concentration of this process. This animal-free approach could be applied for risk assessment when biological response concentrations were related to the expected human exposure to identify potential hazard of the test chemicals.

Unraveling the micro- and nanoplastic predicament: A human-centric insight.

Micro- and nanoplastics are vast anthropogenic pollutants in our direct surroundings with a robust environmental stability and a potential for a long-lasting and increasing global circulation. This has raised concerns among the public and policy makers for human health upon exposure to these particles. The micro- and nanoplastic burden on humans is currently under debate, along with criticism on the experimental approaches used in hazard assessment. The present review presents an overview of the human-relevant aspects associated with the current micro-and nanoplastic burden. We focus on environmental circulation and the estimation of exposure quantities to humans, along with a state-of-the-art overview of particle accumulation in over 15 human organs and other specimen. Additionally, data regarding particle characteristics used in toxicity testing was extracted from 91 studies and discussed considering their environmental and human relevance.

Report of the First ONTOX Stakeholder Network Meeting: Digging Under the Surface of ONTOX Together With the Stakeholders.

The first Stakeholder Network Meeting of the EU Horizon 2020-funded ONTOX project was held on 13-14 March 2023, in Brussels, Belgium. The discussion centred around identifying specific challenges, barriers and drivers in relation to the implementation of non-animal new approach methodologies (NAMs) and probabilistic risk assessment (PRA), in order to help address the issues and rank them according to their associated level of difficulty. ONTOX aims to advance the assessment of chemical risk to humans, without the use of animal testing, by developing non-animal NAMs and PRA in line with 21st century toxicity testing principles. Stakeholder groups (regulatory authorities, companies, academia, non-governmental organisations) were identified and invited to participate in a meeting and a survey, by which their current position in relation to the implementation of NAMs and PRA was ascertained, as well as specific challenges and drivers highlighted. The survey analysis revealed areas of agreement and disagreement among stakeholders on topics such as capacity building, sustainability, regulatory acceptance, validation of adverse outcome pathways, acceptance of artificial intelligence (AI) in risk assessment, and guaranteeing consumer safety. The stakeholder network meeting resulted in the identification of barriers, drivers and specific challenges that need to be addressed. Breakout groups discussed topics such as hazard versus risk assessment, future reliance on AI and machine learning, regulatory requirements for industry and sustainability of the ONTOX Hub platform. The outputs from these discussions provided insights for overcoming barriers and leveraging drivers for implementing NAMs and PRA. It was concluded that there is a continued need for stakeholder engagement, including the organisation of a 'hackathon' to tackle challenges, to ensure the successful implementation of NAMs and PRA in chemical risk assessment.

Osteopontin characterizes bile duct-associated macrophages and correlates with liver fibrosis severity in primary sclerosing cholangitis.

BACKGROUND AND AIMS: Primary sclerosing cholangitis (PSC) is an immune-mediated cholestatic liver disease for which pharmacological treatment options are currently unavailable. PSC is strongly associated with colitis and a disruption of the gut-liver axis, and macrophages are involved in the pathogenesis of PSC. However, how gut-liver interactions and specific macrophage populations contribute to PSC is incompletely understood. APPROACH AND RESULTS: We investigated the impact of cholestasis and colitis on the hepatic and colonic microenvironment, and performed an in-depth characterization of hepatic macrophage dynamics and function in models of concomitant cholangitis and colitis. Cholestasis-induced fibrosis was characterized by depletion of resident KCs, and enrichment of monocytes and monocyte-derived macrophages (MoMFs) in the liver. These MoMFs highly express triggering-receptor-expressed-on-myeloid-cells-2 ( Trem2 ) and osteopontin ( Spp1 ), markers assigned to hepatic bile duct-associated macrophages, and were enriched around the portal triad, which was confirmed in human PSC. Colitis induced monocyte/macrophage infiltration in the gut and liver, and enhanced cholestasis-induced MoMF- Trem2 and Spp1 upregulation, yet did not exacerbate liver fibrosis. Bone marrow chimeras showed that knockout of Spp1 in infiltrated MoMFs exacerbates inflammation in vivo and in vitro , while monoclonal antibody-mediated neutralization of SPP1 conferred protection in experimental PSC. In human PSC patients, serum osteopontin levels are elevated compared to control, and significantly increased in advanced stage PSC and might serve as a prognostic biomarker for liver transplant-free survival. CONCLUSIONS: Our data shed light on gut-liver axis perturbations and macrophage dynamics and function in PSC and highlight SPP1/OPN as a prognostic marker and future therapeutic target in PSC.

Effects of glyphosate exposure on western diet-induced non-alcoholic fatty liver disease in mice.

We evaluated whether glyphosate promotes western diet (WD)-induced non-alcoholic fatty liver disease (NAFLD). Male C57BL/6J mice were fed WD and received intragastrical glyphosate (0.05, 5 or 50 mg/kg) for 6 months. Glyphosate did not promote WD-induced obesity, hypercholesterolemia, glucose intolerance, hepatic steatosis, and fibrosis. Nonetheless, the higher dose (50 mg) enhanced hepatic CD68+ macrophage density, p65, TNF-α, and IL-6 protein levels. Furthermore, this dose decreased hepatic Nrf2 levels, while enhancing lipid peroxidation in the liver and adipose tissue. Hepatic transcriptome revealed that glyphosate at 50 mg upregulated 212 genes and downregulated 731 genes. Genes associated with oxidative stress and inflammation were upregulated, while key cell cycle-related genes were downregulated. Our results indicate that glyphosate exposure - in a dose within the toxicological limits - impairs hepatic inflammation/redox dynamics in a NAFLD microenvironment.

Nanobody-based pannexin1 channel inhibitors reduce inflammation in acute liver injury.

BACKGROUND: The opening of pannexin1 channels is considered as a key event in inflammation. Pannexin1 channel-mediated release of adenosine triphosphate triggers inflammasome signaling and activation of immune cells. By doing so, pannexin1 channels play an important role in several inflammatory diseases. Although pannexin1 channel inhibition could represent a novel clinical strategy for treatment of inflammatory disorders, therapeutic pannexin1 channel targeting is impeded by the lack of specific, potent and/or in vivo-applicable inhibitors. The goal of this study is to generate nanobody-based inhibitors of pannexin1 channels. RESULTS: Pannexin1-targeting nanobodies were developed as potential new pannexin1 channel inhibitors. We identified 3 cross-reactive nanobodies that showed affinity for both murine and human pannexin1 proteins. Flow cytometry experiments revealed binding capacities in the nanomolar range. Moreover, the pannexin1-targeting nanobodies were found to block pannexin1 channel-mediated release of adenosine triphosphate. The pannexin1-targeting nanobodies were also demonstrated to display anti-inflammatory effects in vitro through reduction of interleukin 1 beta amounts. This anti-inflammatory outcome was reproduced in vivo using a human-relevant mouse model of acute liver disease relying on acetaminophen overdosing. More specifically, the pannexin1-targeting nanobodies lowered serum levels of inflammatory cytokines and diminished liver damage. These effects were linked with alteration of the expression of several NLRP3 inflammasome components. CONCLUSIONS: This study introduced for the first time specific, potent and in vivo-applicable nanobody-based inhibitors of pannexin1 channels. As demonstrated for the case of liver disease, the pannexin1-targeting nanobodies hold great promise as anti-inflammatory agents, yet this should be further tested for extrahepatic inflammatory disorders. Moreover, the pannexin1-targeting nanobodies represent novel tools for fundamental research regarding the role of pannexin1 channels in pathological and physiological processes.

Structure-Based Design and Synthesis of Stapled 10Panx1 Analogues for Use in Cardiovascular Inflammatory Diseases.

Following a rational design, a series of macrocyclic ("stapled") peptidomimetics of 10Panx1, the most established peptide inhibitor of Pannexin1 (Panx1) channels, were developed and synthesized. Two macrocyclic analogues SBL-PX1-42 and SBL-PX1-44 outperformed the linear native peptide. During in vitro adenosine triphosphate (ATP) release and Yo-Pro-1 uptake assays in a Panx1-expressing tumor cell line, both compounds were revealed to be promising bidirectional inhibitors of Panx1 channel function, able to induce a two-fold inhibition, as compared to the native 10Panx1 sequence. The introduction of triazole-based cross-links within the peptide backbones increased helical content and enhanced in vitro proteolytic stability in human plasma (>30-fold longer half-lives, compared to 10Panx1). In adhesion assays, a "double-stapled" peptide, SBL-PX1-206 inhibited ATP release from endothelial cells, thereby efficiently reducing THP-1 monocyte adhesion to a TNF-α-activated endothelial monolayer and making it a promising candidate for future in vivo investigations in animal models of cardiovascular inflammatory disease.

Unraveling the mechanisms underlying drug-induced cholestatic liver injury: identifying key genes using machine learning techniques on human in vitro data sets.

Drug-induced intrahepatic cholestasis (DIC) is a main type of hepatic toxicity that is challenging to predict in early drug development stages. Preclinical animal studies often fail to detect DIC in humans. In vitro toxicogenomics assays using human liver cells have become a practical approach to predict human-relevant DIC. The present study was set up to identify transcriptomic signatures of DIC by applying machine learning algorithms to the Open TG-GATEs database. A total of nine DIC compounds and nine non-DIC compounds were selected, and supervised classification algorithms were applied to develop prediction models using differentially expressed features. Feature selection techniques identified 13 genes that achieved optimal prediction performance using logistic regression combined with a sequential backward selection method. The internal validation of the best-performing model showed accuracy of 0.958, sensitivity of 0.941, specificity of 0.978, and F1-score of 0.956. Applying the model to an external validation set resulted in an average prediction accuracy of 0.71. The identified genes were mechanistically linked to the adverse outcome pathway network of DIC, providing insights into cellular and molecular processes during response to chemical toxicity. Our findings provide valuable insights into toxicological responses and enhance the predictive accuracy of DIC prediction, thereby advancing the application of transcriptome profiling in designing new approach methodologies for hazard identification.

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.

New approach methodologies in human regulatory toxicology - Not if, but how and when!

The predominantly animal-centric approach of chemical safety assessment has increasingly come under pressure. Society is questioning overall performance, sustainability, continued relevance for human health risk assessment and ethics of this system, demanding a change of paradigm. At the same time, the scientific toolbox used for risk assessment is continuously enriched by the development of "New Approach Methodologies" (NAMs). While this term does not define the age or the state of readiness of the innovation, it covers a wide range of methods, including quantitative structure-activity relationship (QSAR) predictions, high-throughput screening (HTS) bioassays, omics applications, cell cultures, organoids, microphysiological systems (MPS), machine learning models and artificial intelligence (AI). In addition to promising faster and more efficient toxicity testing, NAMs have the potential to fundamentally transform today's regulatory work by allowing more human-relevant decision-making in terms of both hazard and exposure assessment. Yet, several obstacles hamper a broader application of NAMs in current regulatory risk assessment. Constraints in addressing repeated-dose toxicity, with particular reference to the chronic toxicity, and hesitance from relevant stakeholders, are major challenges for the implementation of NAMs in a broader context. Moreover, issues regarding predictivity, reproducibility and quantification need to be addressed and regulatory and legislative frameworks need to be adapted to NAMs. The conceptual perspective presented here has its focus on hazard assessment and is grounded on the main findings and conclusions from a symposium and workshop held in Berlin in November 2021. It intends to provide further insights into how NAMs can be gradually integrated into chemical risk assessment aimed at protection of human health, until eventually the current paradigm is replaced by an animal-free "Next Generation Risk Assessment" (NGRA).

Pannexin1 channels in the liver: an open enemy.

Pannexin1 proteins form communication channels at the cell plasma membrane surface, which allow the transfer of small molecules and ions between the intracellular compartment and extracellular environment. In this way, pannexin1 channels play an important role in various cellular processes and diseases. Indeed, a plethora of human pathologies is associated with the activation of pannexin1 channels. The present paper reviews and summarizes the structure, life cycle, regulation and (patho)physiological roles of pannexin1 channels, with a particular focus on the relevance of pannexin1 channels in liver diseases.