An Update on Adverse Outcome Pathways Leading to Liver Injury

IL-2-mediated hepatotoxicity: knowledge gap identification based on the irAOP concept

Drug-induced hepatotoxicity constitutes a major reason for non-approval and post-marketing withdrawal of pharmaceuticals. In many cases, preclinical models lack predictive capacity for hepatic damage in humans. A vital concern is the integration of immune system effects in preclinical safety assessment. The immune-related Adverse Outcome Pathway (irAOP) approach, which is applied within the Immune Safety Avatar (imSAVAR) consortium, presents a novel method to understand and predict immune-mediated adverse events elicited by pharmaceuticals and thus targets this issue. It aims to dissect the molecular mechanisms involved and identify key players in drug-induced side effects. As irAOPs are still in their infancy, there is a need for a model irAOP to validate the suitability of this tool. For this purpose, we developed a hepatotoxicity-based model irAOP for recombinant human IL-2 (aldesleukin). Besides producing durable therapeutic responses against renal cell carcinoma and metastatic melanoma, the boosted immune activation upon IL-2 treatment elicits liver damage. The availability of extensive data regarding IL-2 allows both the generation of a comprehensive putative irAOP and to validate the predictability of the irAOP with clinical data. Moreover, IL-2, as one of the first cancer immunotherapeutics on the market, is a blueprint for various biological and novel treatment regimens that are under investigation today. This review provides a guideline for further irAOP-directed research in immune-mediated hepatotoxicity.

The zebrafish embryo as a model for chemically-induced steatosis: A case study with three pesticides

There is an increasing incidence and prevalence of fatty liver disease in the western world, with steatosis as the most prevalent variant. Known causes of steatosis include exposure to food-borne chemicals, and overconsumption of alcohol, carbohydrates and fat, and it is a well-known side effect of certain pharmaceuticals such as tetracycline, amiodarone and tamoxifen (drug-induced hepatic steatosis). Mechanistic knowledge on chemical-induced steatosis has greatly evolved and has been organized into adverse outcome pathways (AOPs) describing the chain of events from first molecular interaction of a substance with a biological system to the adverse outcome, intrahepatic lipid accumulation. In this study, three known steatosis-inducing pesticides (imazalil, clothianidin, and thiacloprid) were tested for their ability to induce hepatic triglyceride accumulation in the zebrafish (Danio rerio) embryo (ZFE) at 5 days post fertilization, both as single compounds and equipotent binary mixtures. The results indicate that the ZFE is very well applicable as a higher tier testing model to confirm effects in downstream key events in AOPs, that is, chemically-induced triglyceride accumulation in the whole organism and production of visible steatosis. Moreover, dose addition could be concluded for binary mixtures of substances with similar and with dissimilar modes of action.

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.

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.

Combining a microphysiological system of three organ equivalents and transcriptomics to assess toxicological endpoints for cosmetic ingredients

Animal testing for cosmetic ingredients and final products has been banned in Europe and is gaining legal force worldwide. However, the need for reliable testing methodologies remains for safety assessment of cosmetic ingredients. While new approach methodologies exist for many toxicological endpoints, some complex ones lack appropriate testing methods. Microphysiological systems (MPSs) have emerged as a promising tool to address this gap in pre-clinical testing, offering higher predictivity compared to animal models due to the phylogenetic distance between humans and animals. Moreover, they provide a more physiological approach than traditional in vitro testing by mimicking interconnections between different culture compartments as seen in complex organisms. This study presents a three-organ microfluidic MPS comprising skin, liver, and intestine equivalents. Combining this model with gene expression analysis, we evaluated toxicological endpoints of chemicals, demonstrating its potential for diverse applications. Our findings highlight the MPS model as a reliable and ethical method to be applied in an integrated approach for safety assessment in the cosmetic industry. It offers a promising strategy to evaluate toxicological endpoints for cosmetic ingredients and other chemicals, supporting the elimination of animal testing while ensuring consumer safety.

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.

New Approach Methods for Hazard Identification: A Case Study with Azole Fungicides Affecting Molecular Targets Associated with the Adverse Outcome Pathway for Cholestasis

Triazole fungicides such as propiconazole (Pi) or tebuconazole (Te) show hepatotoxicity in vivo, e.g., hypertrophy and vacuolization of liver cells following interaction with nuclear receptors such as PXR (pregnane-X-receptor) and CAR (constitutive androstane receptor). Accordingly, azoles affect gene expression associated with these adverse outcomes in vivo but also in human liver cells in vitro. Additionally, genes indicative of liver cholestasis are affected in vivo and in vitro. We therefore analyzed the capability of Pi and Te to cause cholestasis in an adverse outcome pathway (AOP)-driven approach in hepatic cells of human origin in vitro, considering also previous in vivo studies. Bile salt export pump (BSEP) activity assays confirmed that both azoles are weak inhibitors of BSEP. They alternate the expression of various cholestasis-associated target genes and proteins as well as the mitochondrial membrane function. Published in vivo data, however, demonstrate that neither Pi nor Te cause cholestasis in rodent bioassays. This discrepancy can be explained by the in vivo concentrations of both azoles being well below their EC50 for BSEP inhibition. From a regulatory perspective, this illustrates that toxicogenomics and human in vitro models are valuable tools to detect the potential of a substance to cause a specific type of toxicity. To come to a sound regulatory conclusion on the in vivo relevance of such a finding, results will have to be considered in a broader context also including toxicokinetics in a weight-of-evidence approach.

Mechanism-driven modeling of chemical hepatotoxicity using structural alerts and an in vitro screening assay

Traditional experimental approaches to evaluate hepatotoxicity are expensive and time-consuming. As an advanced framework of risk assessment, adverse outcome pathways (AOPs) describe the sequence of molecular and cellular events underlying chemical toxicities. We aimed to develop an AOP that can be used to predict hepatotoxicity by leveraging computational modeling and in vitro assays. We curated 869 compounds with known hepatotoxicity classifications as a modeling set and extracted assay data from PubChem. The antioxidant response element (ARE) assay, which quantifies transcriptional responses to oxidative stress, showed a high correlation to hepatotoxicity (PPV=0.82). Next, we developed quantitative structure-activity relationship (QSAR) models to predict ARE activation for compounds lacking testing results. Potential toxicity alerts were identified and used to construct a mechanistic hepatotoxicity model. For experimental validation, 16 compounds in the modeling set and 12 new compounds were selected and tested using an in-house ARE-luciferase assay in HepG2-C8 cells. The mechanistic model showed good hepatotoxicity predictivity (accuracy = 0.82) for these compounds. Potential false positive hepatotoxicity predictions by only using ARE results can be corrected by incorporating structural alerts and vice versa. This mechanistic model illustrates a potential toxicity pathway for hepatotoxicity, and this strategy can be expanded to develop predictive models for other complex toxicities.

An adverse outcome pathway-based approach to assess aurantio-obtusin-induced hepatotoxicity

Cassiae semen (CS), a traditional Chinese medicine, has various bioactivities in preclinical and clinical practice. Aurantio-obtusin (AO) is a major anthraquinone (AQ) ingredient derived from CS, and has drawn public concerns over its potential hepatotoxicity. We previously found that AO induces hepatic necroinflammation by activating NOD-like receptor protein 3 inflammasome signaling. However, the mechanisms contributing to AO-motivated hepatotoxicity remain unclear. Herein, we evaluated hepatotoxic effects of AO on three liver cell lines by molecular and biochemical analyses. We found that AO caused cell viability inhibition and biochemistry dysfunction in the liver cells. Furthermore, AO elevated reactive oxygen species (ROS), followed by mitochondrial dysfunction (decreases in mitochondrial membrane potential and adenosine triphosphate) and apoptosis (increased Caspase-3, Cleaved caspase-3, Cytochrome c and Bax expression, and decreased Bcl-2 expression). We also found that AO increased the lipid peroxidation (LPO) and enhanced ferroptosis by activating cyclic adenosine monophosphate (cAMP)-protein kinase A (PKA)-cAMP response element-binding (CREB) pathway (increases in PKA, p-CREB, acyl-CoA synthetase long chain family member 4). Based on these results, we used an AOP framework to explore the mechanisms underlying AO's hepatotoxicity. It starts from molecular initiating event (ROS), and follows two critical toxicity pathways (i.e., mitochondrial dysfunction-mediated apoptosis and LPO-enhanced ferroptosis) over a series of key events (KEs) to the adverse outcome of hepatotoxicity. The results of an assessment confidence in the adverse outcome pathway (AOP) framework supported the evidence concordance in dose-response, temporal and incidence relationships between KEs in AO-induced hepatotoxicity. This study's findings offer a novel toxicity pathway network for AO-caused hepatotoxicity.

Identification of proteome markers for drug-induced liver injury in zebrafish embryos

The zebrafish embryo (ZFE) is a promising alternative non-rodent model in toxicology, and initial studies suggested its applicability in detecting hepatic responses related to drug-induced liver injury (DILI). Here, we hypothesize that detailed analysis of underlying mechanisms of hepatotoxicity in ZFE contributes to the improved identification of hepatotoxic properties of compounds and to the reduction of rodents used for hepatotoxicity assessment. ZFEs were exposed to nine reference hepatotoxicants, targeted at induction of steatosis, cholestasis, and necrosis, and effects compared with negative controls. Protein profiles of the individual compounds were generated using LC-MS/MS. We identified differentially expressed proteins and pathways, but as these showed considerable overlap, phenotype-specific responses could not be distinguished. This led us to identify a set of common hepatotoxicity marker proteins. At the pathway level, these were mainly associated with cellular adaptive stress-responses, whereas single proteins could be linked to common hepatotoxicity-associated processes. Applying several stringency criteria to our proteomics data as well as information from other data sources resulted in a set of potential robust protein markers, notably Igf2bp1, Cox5ba, Ahnak, Itih3b.2, Psma6b, Srsf3a, Ces2b, Ces2a, Tdo2b, and Anxa1c, for the detection of adverse responses.

Advances in Animal Models and Cutting-Edge Research in Alternatives: Proceedings of the Second International Conference on 3Rs Research and Progress, Hyderabad, 2021

The fact that animal models fail to replicate human disease faithfully is now being widely accepted by researchers across the globe. As a result, they are exploring the use of alternatives to animal models. The time has come to refine our experimental practices, reduce the numbers and eventually replace the animals used in research with human-derived and human-relevant 3-D disease models. Oncoseek Bio-Acasta Health, which is an innovative biotechnology start-up company based in Hyderabad and Vishakhapatnam, India, organises an annual International Conference on 3Rs Research and Progress. In 2021, this conference was on 'Advances in Research Animal Models and Cutting-Edge Research in Alternatives'. This annual conference is a platform that brings together eminent scientists and researchers from various parts of the world, to share recent advances from their research in the field of alternatives to animals including new approach methodologies, and to promote practices to help refine animal experiments where alternatives are not available. This report presents the proceedings of the conference, which was held in hybrid mode (i.e. virtual and in-person) in November 2021.

Adverse Outcome Pathways as Versatile Tools in Liver Toxicity Testing

Adverse outcome pathways (AOPs) are tools to capture and visualize mechanisms driving toxicological effects. They share a common structure consisting of a molecular initiating event, a series of key events connected by key event relationships and an adverse outcome. Development and evaluation of AOPs ideally comply with guidelines issued by the Organization for Economic Cooperation and Development. AOPs have been introduced for major types of hepatotoxicity, which is not a surprise, as the liver is a frequent target for systemic adversity. Various applications for AOPs have been proposed in the areas of toxicology and chemical risk assessment, in particular in relation to the establishment of quantitative structure-activity relationships, the elaboration of prioritization strategies, and the development of novel in vitro toxicity screening tests and testing strategies.

In silico approaches in organ toxicity hazard assessment: current status and future needs in predicting liver toxicity

Hepatotoxicity is one of the most frequently observed adverse effects resulting from exposure to a xenobiotic. For example, in pharmaceutical research and development it is one of the major reasons for drug withdrawals, clinical failures, and discontinuation of drug candidates. The development of faster and cheaper methods to assess hepatotoxicity that are both more sustainable and more informative is critically needed. The biological mechanisms and processes underpinning hepatotoxicity are summarized and experimental approaches to support the prediction of hepatotoxicity are described, including toxicokinetic considerations. The paper describes the increasingly important role of in silico approaches and highlights challenges to the adoption of these methods including the lack of a commonly agreed upon protocol for performing such an assessment and the need for in silico solutions that take dose into consideration. A proposed framework for the integration of in silico and experimental information is provided along with a case study describing how computational methods have been used to successfully respond to a regulatory question concerning non-genotoxic impurities in chemically synthesized pharmaceuticals.

Systematic Organization of COVID-19 Data Supported by the Adverse Outcome Pathway Framework

Adverse Outcome Pathways (AOP) provide structured frameworks for the systematic organization of research data and knowledge. The AOP framework follows a set of key principles that allow for broad application across diverse disciplines related to human health, including toxicology, pharmacology, virology and medical research. The COVID-19 pandemic engages a great number of scientists world-wide and data is increasing with exponential speed. Diligent data management strategies are employed but approaches for systematically organizing the data-derived information and knowledge are lacking. We believe AOPs can play an important role in improving interpretation and efficient application of scientific understanding of COVID-19. Here, we outline a newly initiated effort, the CIAO project (https://www.ciao-covid.net/), to streamline collaboration between scientists across the world toward development of AOPs for COVID-19, and describe the overarching aims of the effort, as well as the expected outcomes and research support that they will provide.

In Vitro Three-Dimensional Liver Models for Nanomaterial DNA Damage Assessment

Whilst the liver possesses the ability to repair and restore sections of damaged tissue following acute injury, prolonged exposure to engineered nanomaterials (ENM) may induce repetitive injury leading to chronic liver disease. Screening ENM cytotoxicity using 3D liver models has recently been performed, but a significant challenge has been the application of such in vitro models for evaluating ENM associated genotoxicity; a vital component of regulatory human health risk assessment. This review considers the benefits, limitations, and adaptations of specific in vitro approaches to assess DNA damage in the liver, whilst identifying critical advancements required to support a multitude of biochemical endpoints, focusing on nano(geno)toxicology (e.g., secondary genotoxicity, DNA damage, and repair following prolonged or repeated exposures).

A Critical Perspective on 3D Liver Models for Drug Metabolism and Toxicology Studies

The poor predictability of human liver toxicity is still causing high attrition rates of drug candidates in the pharmaceutical industry at the non-clinical, clinical, and post-marketing authorization stages. This is in part caused by animal models that fail to predict various human adverse drug reactions (ADRs), resulting in undetected hepatotoxicity at the non-clinical phase of drug development. In an effort to increase the prediction of human hepatotoxicity, different approaches to enhance the physiological relevance of hepatic in vitro systems are being pursued. Three-dimensional (3D) or microfluidic technologies allow to better recapitulate hepatocyte organization and cell-matrix contacts, to include additional cell types, to incorporate fluid flow and to create gradients of oxygen and nutrients, which have led to improved differentiated cell phenotype and functionality. This comprehensive review addresses the drug-induced hepatotoxicity mechanisms and the currently available 3D liver in vitro models, their characteristics, as well as their advantages and limitations for human hepatotoxicity assessment. In addition, since toxic responses are greatly dependent on the culture model, a comparative analysis of the toxicity studies performed using two-dimensional (2D) and 3D in vitro strategies with recognized hepatotoxic compounds, such as paracetamol, diclofenac, and troglitazone is performed, further highlighting the need for harmonization of the respective characterization methods. Finally, taking a step forward, we propose a roadmap for the assessment of drugs hepatotoxicity based on fully characterized fit-for-purpose in vitro models, taking advantage of the best of each model, which will ultimately contribute to more informed decision-making in the drug development and risk assessment fields.

Mechanisms of Drug-Induced Cholestasis

Cholestasis can be defined as any situation of impaired bile secretion with concomitant accumulation of bile acids in the liver or in the systemic circulation. A variety of factors may evoke cholestasis, including genetic disorders, metabolic pathologies, infectious diseases, immunogenic stimuli, and drugs. Drug-induced cholestasis is a mechanistically complex process. At least three triggering factors of drug-induced cholestasis have been described, including effects on drug transporters, various hepatocellular changes, and altered bile canaliculi dynamics. These stimuli induce two cellular responses, each typified by a number of key events, namely a deteriorative response activated by bile acid accumulation and an adaptive response aimed at decreasing the uptake and increasing the export of bile acids into and from the liver, respectively. The mechanistic scenario of drug-induced cholestasis is described in this chapter.