Evaluation of database-derived pathway development for enabling biomarker discovery for hepatotoxicity

Hepatotoxic mechanism of cantharidin: insights and strategies for therapeutic intervention

Cantharidin (CTD), a natural compound derived from Mylabris, is widely used in traditional Oriental medicine for its potent anticancer properties. However, its clinical application is restricted due to its high toxicity, particularly towards the liver. This review provides a concise understanding of the hepatotoxic mechanisms of CTD and highlights novel therapeutic strategies to mitigate its toxicity while enhancing its anticancer efficacy. We systematically explore the molecular mechanisms underlying CTD-induced hepatotoxicity, focusing on the involvement of apoptotic and autophagic processes in hepatocyte injury. We further discuss the endogenous and exogenous pathways implicated in CTD-induced liver damage and potential therapeutic targets. This review also summarizes the structural modifications of CTD derivatives and their impact on anticancer activity. Additionally, we delve into the advancements in nanoparticle-based drug delivery systems that hold promise in overcoming the limitations of CTD derivatives. By offering valuable insights into the hepatotoxic mechanisms of CTD and outlining potential avenues for future research, this review contributes to the ongoing efforts to develop safer and more effective CTD-based therapies.

The progression of chronicity and autoimmune hepatitis in recurrent drug-induced liver injury

BACKGROUND AND AIMS

Recurrent drug-induced liver injury (DILI) is not well documented. We retrospectively analysed the characteristics of patients who had a history of two separate DILI episodes due to different drugs.

METHODS

We collected data from 57 patients with recurrent DILI from 9582 confirmed DILI cases. Demographic, laboratory, and clinical data from both episodes were collected and analysed to determine the relationship between recurrent DILI, chronic DILI, and autoimmune hepatitis (AIH).

RESULTS

The incidence rate of recurrent DILI in our cohort was 0.59%. Most of the 57 patients with recurrent DILI were female (73.68%). The latency period of the initial episode was 30 days, whereas that of the second episode was 13 days (P = 0.003). The pattern of liver injury was not significantly different between episodes (P = 0.52). Laboratory indicators, such as alanine aminotransferase, aspartate transaminase, alkaline phosphatase, and total bilirubin, were significantly lower in the second episode than in the initial episode (P < 0.05). The incidence of chronic DILI was 43.86% during the initial episode and increased to 63.16% during the second episode. After the initial episode, 15 patients developed AIH during the second episode.

CONCLUSIONS

The latency period of recurrent DILI was gradually shortened. The clinical indices of liver injury tended to be less elevated during the second episode. Female post-menopausal patients with abnormal serum immunoglobulin G levels may be predisposed to AIH. The second DILI episode was more likely to have features of AIH.

Integration of the Connectivity Map and Pathway Analysis to Predict Plant Extract’s Medicinal Properties—The Study Case of Sarcopoterium spinosum L

Medicinal properties of plants are usually identified based on knowledge of traditional medicine or using low-throughput screens for specific pharmacological activities. The former is very biased since it requires prior knowledge of plants’ properties, while the latter depends on a specific screening system and will miss medicinal activities not covered by the screen. We sought to enrich our understanding of the biological activities of Sarcopoterium spinosum L. root extract based on transcriptome changes to uncover a plurality of possible pharmacological effects without the need for prior knowledge or functional screening. We integrated Gene Set Enrichment Analysis of the RNAseq data to identify pathways affected by the treatment of cells with the extract and perturbational signatures in the CMAP database to enhance the validity of the results. Activities of signaling pathways were measured using immunoblotting with phospho-specific antibodies. Mitochondrial membrane potential was assessed using JC-1 staining. SARS-CoV-2-induced cell killing was assessed in Vero E6 and A549 cells using an MTT assay. Here, we identified transcriptome changes following exposure of cultured cells to the medicinal plant Sarcopoterium spinosum L. root extract. By integrating algorithms of GSEA and CMAP, we confirmed known anti-cancer activities of the extract and predicted novel biological effects on oxidative phosphorylation and interferon pathways. Experimental validation of these pathways uncovered strong activation of autophagy, including mitophagy, and excellent protection from SARS-CoV-2 infection. Our study shows that gene expression analysis alone is insufficient for predicting biological effects since some of the changes reflect compensatory effects, and additional biochemical tests provide necessary corrections. This study defines the advantages and limitations of transcriptome analysis in predicting the biological and medicinal effects of the Sarcopoterium spinosum L. extract. Such analysis could be used as a general approach for predicting the medicinal properties of plants.

Hepatic Organoid-Based High-Content Imaging Boosts Evaluation of Stereoisomerism-Dependent Hepatotoxicity of Stilbenes in Herbal Medicines

The complexity of chemical components of herbal medicines often causes great barriers to toxicity research. In our previous study, we have found the critical divergent hepatotoxic potential of a pair of stilbene isomers in a famous traditional Chinese herb, Polygonum multiflorum (Heshouwu in Chinese). However, the high-throughput in vitro evaluation for such stereoisomerism-dependent hepatotoxicity is a critical challenge. In this study, we used a hepatic organoids–based in vitro hepatotoxic evaluation system in conjunction with using high content imaging to differentiate in vivo organ hepatotoxicity of the 2,3,5,4′-tetrahydroxy-trans-stilbene-2-O-β-glucoside (trans-SG) and its cis-isomer (cis-SG). By using such an organoid platform, we successfully differentiated the two stereoisomers’ hepatotoxic potentials, which were in accordance with their differences in rodents and humans. The lesion mechanism of the toxic isomer (cis-SG) was further found as the mitochondrial injury by high-content imaging, and its hepatotoxicity could be dose-dependently inhibited by the mitochondrial protective agent. These results demonstrated the utility of the organoids-based high-content imaging approach in evaluating and predicting organ toxicity of natural products in a low-cost and high-throughput way. It also suggested the rationale to use long-term cultured organoids as an alternative toxicology platform to identify early and cautiously the hepatotoxic new drug candidates in the preclinical phase.

A Computational Toxicology Approach to Screen the Hepatotoxic Ingredients in Traditional Chinese Medicines: Polygonum multiflorum Thunb as a Case Study

In recent years, liver injury induced by Traditional Chinese Medicines (TCMs) has gained increasing attention worldwide. Assessing the hepatotoxicity of compounds in TCMs is essential and inevitable for both doctors and regulatory agencies. However, there has been no effective method to screen the hepatotoxic ingredients in TCMs available until now. In the present study, we initially built a large scale dataset of drug-induced liver injuries (DILIs). Then, 13 types of molecular fingerprints/descriptors and eight machine learning algorithms were utilized to develop single classifiers for DILI, which resulted in 5416 single classifiers. Next, the NaiveBayes algorithm was adopted to integrate the best single classifier of each machine learning algorithm, by which we attempted to build a combined classifier. The accuracy, sensitivity, specificity, and area under the curve of the combined classifier were 72.798, 0.732, 0.724, and 0.793, respectively. Compared to several prior studies, the combined classifier provided better performance both in cross validation and external validation. In our prior study, we developed a herb-hepatotoxic ingredient network and a herb-induced liver injury (HILI) dataset based on pre-clinical evidence published in the scientific literature. Herein, by combining that and the combined classifier developed in this work, we proposed the first instance of a computational toxicology to screen the hepatotoxic ingredients in TCMs. Then Polygonum multiflorum Thunb (PmT) was used as a case to investigate the reliability of the approach proposed. Consequently, a total of 25 ingredients in PmT were identified as hepatotoxicants. The results were highly consistent with records in the literature, indicating that our computational toxicology approach is reliable and effective for the screening of hepatotoxic ingredients in Pmt. The combined classifier developed in this work can be used to assess the hepatotoxic risk of both natural compounds and synthetic drugs. The computational toxicology approach presented in this work will assist with screening the hepatotoxic ingredients in TCMs, which will further lay the foundation for exploring the hepatotoxic mechanisms of TCMs. In addition, the method proposed in this work can be applied to research focused on other adverse effects of TCMs/synthetic drugs.

Chinese Herbal Medicine Hepatotoxicity: The Evaluation and Recognization Based on Large-scale Evidence Database

Background:

Due to the special nature of Chinese Herbal medicine and the complexity of its clinical use, it is difficult to identify and evaluate its toxicity and resulting herb induced liver injury (HILI).

Methods:

First, the database would provide full profile of HILI from the basic ingredients to clinical out-comes by the most advanced algorithms of artificial intelligence, and it is also possible that we can predict possibilities of HILI after patients taking Chinese herbs by individual patient evaluation and prediction. Second, the database would solve the chaos and lack of the relevant data faced by the current basic re-search and clinical practice of Chinese Herbal Medicine. Third, we can also screen the susceptible patients from the database and thus prevent the accidents of HILI from the very beginning.

Results:

The Roussel Uclaf Causality Assessment Method (RUCAM) is the most accepted method to evalu-ate DILI, but at present before using the RUCAM evaluation method, data resource collection and analysis are yet to be perfected. Based on existing research on drug-metabolizing enzymes mediating reactive me-tabolites (RMs), the aim of this study is to explore the possibilities and methods of building multidimen-sional hierarchical database composing of RMs evidence library, Chinese herbal evidence library, and indi-vidualized reports evidence library of herb induced liver injury HILI.

Conclusion:

The potential benefits lie in its ability to organize, use vast amounts of evidence and use big data mining techniques at the center for Chinese herbal medicine liver toxicity research, which is the most difficult key point of scientific research to be investigated in the next few years.

Herb-Induced Liver Injury: Phylogenetic Relationship, Structure-Toxicity Relationship, and Herb-Ingredient Network Analysis

Currently, hundreds of herbal products with potential hepatotoxicity were available in the literature. A comprehensive summary and analysis focused on these potential hepatotoxic herbal products may assist in understanding herb-induced liver injury (HILI). In this work, we collected 335 hepatotoxic medicinal plants, 296 hepatotoxic ingredients, and 584 hepatoprotective ingredients through a systematic literature retrieval. Then we analyzed these data from the perspectives of phylogenetic relationship and structure-toxicity relationship. Phylogenetic analysis indicated that hepatotoxic medicinal plants tended to have a closer taxonomic relationship. By investigating the structures of the hepatotoxic ingredients, we found that alkaloids and terpenoids were the two major groups of hepatotoxicity. We also identified eight major skeletons of hepatotoxicity and reviewed their hepatotoxic mechanisms. Additionally, 15 structural alerts (SAs) for hepatotoxicity were identified based on SARpy software. These SAs will help to estimate the hepatotoxic risk of ingredients from herbs. Finally, a herb-ingredient network was constructed by integrating multiple datasets, which will assist to identify the hepatotoxic ingredients of herb/herb-formula quickly. In summary, a systemic analysis focused on HILI was conducted which will not only assist to identify the toxic molecular basis of hepatotoxic herbs but also contribute to decipher the mechanisms of HILI.

Analysis of Time-Series Gene Expression Data to Explore Mechanisms of Chemical-Induced Hepatic Steatosis Toxicity

Non-alcoholic fatty liver disease (NAFLD) represents a wide spectrum of disease, ranging from simple fatty liver through steatosis with inflammation and necrosis to cirrhosis. One of the most challenging problems in biomedical research and within the chemical industry is to understand the underlying mechanisms of complex disease, and complex adverse outcome pathways (AOPs). Based on a set of 28 steatotic chemicals with gene expression data measured on primary hepatocytes at three times (2, 8, and 24 h) and three doses (low, medium, and high), we identified genes and pathways, defined as molecular initiating events (MIEs) and key events (KEs) of steatosis using a combination of a time series and pathway analyses. Among the genes deregulated by these compounds, the study highlighted OSBPL9, ALDH7A1, MYADM, SLC51B, PRDX6, GPAT3, TMEM135, DLGDA5, BCO2, APO10LA, TSPAN6, NEURL1B, and DUSP1. Furthermore, pathway analysis indicated deregulation of pathways related to lipid accumulation, such as fat digestion and absorption, linoleic and linolenic acid metabolism, calcium signaling pathway, fatty acid metabolism, peroxisome, retinol metabolism, and steroid metabolic pathways in a time dependent manner. Such transcription profile analysis can help in the understanding of the steatosis evolution over time generated by chemical exposure.

In Vitro and In Vivo Experimental Hepatotoxic Models in Liver Research: Applications to the Assessment of Potential Hepatoprotective Drugs

This mini-review highlights our and others’ experience about in vitro and in vivo models that are being used to follow up events of liver injuries under various hepatotoxic agents and potential hepatoprotective drugs. Due to limitations of the outcomes in each model, we focus primarily on two models. First, a developed perfusion method for isolated immobilized hepatocytes that improves the process of oxygenation and helps in end-product removal is of considerable value in improving cell maintenance. This cellular model is presented as a short-term research-scale laboratory bioreactor with various physiological, biochemical, molecular, toxicological and pharmacological applications. Second, the in vivo model of D-galactosamine and lipopolysaccharide (D-GalN/LPS) combination-induced liver damage is described with some details. Recently, we have revealed that resveratrol and other natural polyphenols attenuate D-GalN/LPS-induced hepatitis. Moreover, we reported that D-GalN/LPS down-regulates sirtuin 1 in rat liver. Therefore, we discuss here the role of sirtuin 1 modulation in hepatoprotection. A successful development of pharmacotherapy for liver diseases depends on the suitability of in vitro and in vivo hepatic injury systems. Several models are available to screen the hepatotoxic or hepatoprotective activity of any substance. It is important to combine different methods for confirmation of the findings.

Characterization of Conserved Toxicogenomic Responses in Chemically Exposed Hepatocytes across Species and Platforms

BACKGROUND

Genome-wide expression profiling is increasingly being used to identify transcriptional changes induced by drugs and environmental stressors. In this context, the Toxicogenomics Project-Genomics Assisted Toxicity Evaluation system (TG-GATEs) project generated transcriptional profiles from rat liver samples and human/rat cultured primary hepatocytes exposed to more than 100 different chemicals.

OBJECTIVES

To assess the capacity of the cell culture models to recapitulate pathways induced by chemicals in vivo, we leveraged the TG-GATEs data set to compare the early transcriptional responses observed in the liver of rats treated with a large set of chemicals with those of cultured rat and human primary hepatocytes challenged with the same compounds in vitro.

METHODS

We developed a new pathway-based computational pipeline that efficiently combines gene set enrichment analysis (GSEA) using pathways from the Reactome database with biclustering to identify common modules of pathways that are modulated by several chemicals in vivo and in vitro across species.

RESULTS

We found that some chemicals induced conserved patterns of early transcriptional responses in in vitro and in vivo settings, and across human and rat genomes. These responses involved pathways of cell survival, inflammation, xenobiotic metabolism, oxidative stress, and apoptosis. Moreover, our results support the transforming growth factor beta receptor (TGF-βR) signaling pathway as a candidate biomarker associated with exposure to environmental toxicants in primary human hepatocytes.

CONCLUSIONS

Our integrative analysis of toxicogenomics data provides a comprehensive overview of biochemical perturbations affected by a large panel of chemicals. Furthermore, we show that the early toxicological response occurring in animals is recapitulated in human and rat primary hepatocyte cultures at the molecular level, indicating that these models reproduce key pathways in response to chemical stress. These findings expand our understanding and interpretation of toxicogenomics data from human hepatocytes exposed to environmental toxicants.

Development of novel tools for the in vitro investigation of drug-induced liver injury

INTRODUCTION

Due to its complex mechanisms and unpredictable occurrence, drug-induced liver injury (DILI) complicates drug identification and classification. Since species-specific differences in metabolism and pharmacokinetics exist, data obtained from animal studies may not be sufficient to predict DILI in humans.

AREAS COVERED

Over the last few decades, numerous in vitro models have been developed to replace animal testing. The advantages and disadvantages of commonly used liver-derived in vitro models (e.g., cell lines, hepatocyte models, liver slices, three-dimensional (3D) hepatospheres, etc.) are discussed. Toxicogenomics-based methodologies (genomics, epigenomics, transcriptomics, proteomics and metabolomics) and next-generation sequencing have also been used to enhance the reliability of DILI prediction. This review presents an overview of the currently used alternative toxicological models and of the most advanced approaches in the field of DILI research.

EXPERT OPINION

It seems unlikely that a single in vitro system will be able to mimic the complex interactions in the human liver. Three-dimensional multicellular systems may bridge the gap between conventional 2D models and in vivo clinical studies in humans and provide a reliable basis for hepatic toxicity assay development. Next-generation sequencing technologies, in comparison to microarray-based technologies, may overcome the current limitations and are promising for the development of predictive models in the near future.

diXa: a data infrastructure for chemical safety assessment

MOTIVATION

The field of toxicogenomics (the application of '-omics' technologies to risk assessment of compound toxicities) has expanded in the last decade, partly driven by new legislation, aimed at reducing animal testing in chemical risk assessment but mainly as a result of a paradigm change in toxicology towards the use and integration of genome wide data. Many research groups worldwide have generated large amounts of such toxicogenomics data. However, there is no centralized repository for archiving and making these data and associated tools for their analysis easily available.

RESULTS

The Data Infrastructure for Chemical Safety Assessment (diXa) is a robust and sustainable infrastructure storing toxicogenomics data. A central data warehouse is connected to a portal with links to chemical information and molecular and phenotype data. diXa is publicly available through a user-friendly web interface. New data can be readily deposited into diXa using guidelines and templates available online. Analysis descriptions and tools for interrogating the data are available via the diXa portal.

AVAILABILITY AND IMPLEMENTATION

http://www.dixa-fp7.eu

CONTACT

d.hendrickx@maastrichtuniversity.nl; info@dixa-fp7.eu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.