Mechanism-based risk assessment strategy for drug-induced cholestasis using the transcriptional benchmark dose derived by toxicogenomics

Kawamoto, Taisuke; Itô, Yûichi; Morita, Osamu; Honda, Hiroshi

doi:10.2131/jts.42.427

Cited by 18 publications

(12 citation statements)

References 39 publications

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“…In contrast, CSA did not show a good correlation with the MoA of APAP datasets, suggesting that oxidative stress is a secondary/late effect to high dose CSA exposure. Although several publications indicate that oxidative stress is involved during CSA induced cholestasis in liver (Kawamoto et al 2017 ; Koido et al 2020 ; Wolters et al 2016 ), our analysis of CSA exposed hepatocytes using the TXG-MAPr tool showed that the ER stress should not be neglected in the mechanism of CSA induced liver injury as was also suggested by others (Koido et al 2020 ; Van den Hof et al 2015b ). To validate the applicability of the PHH TXG-MAPr for other transcriptomic platforms, we showed that Agilent microarray data of APAP exposure in PHH showed strong correlation with Affymetrix based data in TG-GATEs.…”

Section: Discussionsupporting

confidence: 61%

The human hepatocyte TXG-MAPr: gene co-expression network modules to support mechanism-based risk assessment

et al. 2021

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Mechanism-based risk assessment is urged to advance and fully permeate into current safety assessment practices, possibly at early phases of drug safety testing. Toxicogenomics is a promising source of mechanisms-revealing data, but interpretative analysis tools specific for the testing systems (e.g. hepatocytes) are lacking. In this study, we present the TXG-MAPr webtool (available at https://txg-mapr.eu/WGCNA_PHH/TGGATEs_PHH/), an R-Shiny-based implementation of weighted gene co-expression network analysis (WGCNA) obtained from the Primary Human Hepatocytes (PHH) TG-GATEs dataset. The 398 gene co-expression networks (modules) were annotated with functional information (pathway enrichment, transcription factor) to reveal their mechanistic interpretation. Several well-known stress response pathways were captured in the modules, were perturbed by specific stressors and showed preservation in rat systems (rat primary hepatocytes and rat in vivo liver), with the exception of DNA damage and oxidative stress responses. A subset of 87 well-annotated and preserved modules was used to evaluate mechanisms of toxicity of endoplasmic reticulum (ER) stress and oxidative stress inducers, including cyclosporine A, tunicamycin and acetaminophen. In addition, module responses can be calculated from external datasets obtained with different hepatocyte cells and platforms, including targeted RNA-seq data, therefore, imputing biological responses from a limited gene set. As another application, donors’ sensitivity towards tunicamycin was investigated with the TXG-MAPr, identifying higher basal level of intrinsic immune response in donors with pre-existing liver pathology. In conclusion, we demonstrated that gene co-expression analysis coupled to an interactive visualization environment, the TXG-MAPr, is a promising approach to achieve mechanistic relevant, cross-species and cross-platform evaluation of toxicogenomic data.

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Section: Discussionsupporting

confidence: 61%

The human hepatocyte TXG-MAPr: gene co-expression network modules to support mechanism-based risk assessment

et al. 2021

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“…In contrast, CSA did not show a good correlation with the MoA of APAP datasets, suggesting that oxidative stress is a secondary/late effect to high dose CSA exposure. Although several publications indicate that oxidative stress is involved during CSA induced cholestasis in liver (Kawamoto et al, 2017; Koido et al, 2020; Wolters et al, 2016), our analysis of CSA exposed hepatocytes using the TXG-MAPr tool showed that the ER stress should not be neglected in the mechanism of CSA induced liver injury as was also suggested by other literature (Koido et al, 2020; Van den Hof et al, 2015b). To validate the applicability of the PHH TXG-MAPr for other transcriptomic platforms, we showed that Agilent microarray data of APAP exposure in PHH showed strong correlation with Affymetrix based data in TG-GATEs.…”

Section: Discussionsupporting

confidence: 58%

The human hepatocyte TXG-MAPr: WGCNA transcriptomic modules to support mechanism-based risk assessment

Callegaro

Kunnen

Trairatphisan

et al. 2021

Preprint

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Mechanism-based risk assessment is urged to advance and fully permeate into current safety assessment practices, possibly at early phases of drug safety testing. Toxicogenomics is a promising source of comprehensive and mechanisms-revealing data, but analysis tools to interpret mechanisms of toxicity and specific for the testing systems (e.g. hepatocytes) are lacking. In this study we present the TXG-MAPr webtool (available at https://txg-mapr.eu/WGCNA_PHH/TGGATEs_PHH/), an R-Shiny-based implementation of weighted gene co-expression networks (WGCNA) obtained from the Primary Human Hepatocytes (PHH) TG-GATEs dataset. Gene co-expression networks (modules) were annotated with functional information (pathway enrichment, transcription factor) to reveal their mechanistic interpretation. Several well-known stress response pathways were captured in the modules, are perturbed by specific stressors and show preserved in rat systems (rat primary hepatocytes and rat in vivo liver), highlighting stress responses that translate across species/testing systems. The TXG-MAPr tool was successfully applied to investigate the mechanism of toxicity of TG-GATEs compounds and using external datasets obtained from different hepatocyte cells and microarray platforms. Additionally, we suggest that module responses can be calculated from targeted RNA-seq data therefore imputing biological responses from a limited gene. By analyzing 50 different PHH donors' responses to a common stressor, tunicamycin, we were able to suggest modules associated with donor's traits, e.g. pre-existing disease state, therefore connected to donors' variability. In conclusion, we demonstrated that gene co-expression analysis coupled to an interactive visualization environment, the TXG-MAPr, is a promising approach to achieve mechanistic relevant, cross-species and cross-platform evaluation of toxicogenomic data.

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“…In both studies, transcriptional BMDs were comparable to traditional BMDs. Other groups also use this approach to derive PODs for various chemical compounds, such as naphthalene, nickel subsulfide or cholestatic drugs (Clewell et al 2014;Efremenko et al 2014;Kawamoto et al 2017). An important and critical point in the application of toxicogenomics in this field is the determination of the best way to select predictive groups of genes.…”

Section: Risk Assessmentmentioning

confidence: 99%

Mode of action-based risk assessment of genotoxic carcinogens

et al. 2020

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The risk assessment of chemical carcinogens is one major task in toxicology. Even though exposure has been mitigated effectively during the last decades, low levels of carcinogenic substances in food and at the workplace are still present and often not completely avoidable. The distinction between genotoxic and non-genotoxic carcinogens has traditionally been regarded as particularly relevant for risk assessment, with the assumption of the existence of no-effect concentrations (threshold levels) in case of the latter group. In contrast, genotoxic carcinogens, their metabolic precursors and DNA reactive metabolites are considered to represent risk factors at all concentrations since even one or a few DNA lesions may in principle result in mutations and, thus, increase tumour risk. Within the current document, an updated risk evaluation for genotoxic carcinogens is proposed, based on mechanistic knowledge regarding the substance (group) under investigation, and taking into account recent improvements in analytical techniques used to quantify DNA lesions and mutations as well as “omics” approaches. Furthermore, wherever possible and appropriate, special attention is given to the integration of background levels of the same or comparable DNA lesions. Within part A, fundamental considerations highlight the terms hazard and risk with respect to DNA reactivity of genotoxic agents, as compared to non-genotoxic agents. Also, current methodologies used in genetic toxicology as well as in dosimetry of exposure are described. Special focus is given on the elucidation of modes of action (MOA) and on the relation between DNA damage and cancer risk. Part B addresses specific examples of genotoxic carcinogens, including those humans are exposed to exogenously and endogenously, such as formaldehyde, acetaldehyde and the corresponding alcohols as well as some alkylating agents, ethylene oxide, and acrylamide, but also examples resulting from exogenous sources like aflatoxin B1, allylalkoxybenzenes, 2-amino-3,8-dimethylimidazo[4,5-f] quinoxaline (MeIQx), benzo[a]pyrene and pyrrolizidine alkaloids. Additionally, special attention is given to some carcinogenic metal compounds, which are considered indirect genotoxins, by accelerating mutagenicity via interactions with the cellular response to DNA damage even at low exposure conditions. Part C finally encompasses conclusions and perspectives, suggesting a refined strategy for the assessment of the carcinogenic risk associated with an exposure to genotoxic compounds and addressing research needs.

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Mechanism-based risk assessment strategy for drug-induced cholestasis using the transcriptional benchmark dose derived by toxicogenomics

Cited by 18 publications

References 39 publications

The human hepatocyte TXG-MAPr: gene co-expression network modules to support mechanism-based risk assessment

The human hepatocyte TXG-MAPr: gene co-expression network modules to support mechanism-based risk assessment

The human hepatocyte TXG-MAPr: WGCNA transcriptomic modules to support mechanism-based risk assessment

Mode of action-based risk assessment of genotoxic carcinogens

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