Search

This read-across case study characterises thirteen, structurally similar carboxylic acids demonstrating the application of in vitro and in silico human-based new approach methods, to determine biological similarity. Based on data from in vivo animal studies, the read-across hypothesis is that all analogues are steatotic and so should be considered hazardous. Transcriptomic analysis to determine differentially expressed genes (DEGs) in hepatocytes served as first tier testing to confirm a common mode-of-action and identify differences in the potency of the analogues. An adverse outcome pathway (AOP) network for hepatic steatosis, informed the design of an in vitro testing battery, targeting AOP relevant MIEs and KEs, and Dempster-Shafer decision theory was used to systematically quantify uncertainty and to define the minimal testing scope. The case study shows that the read-across hypothesis is the critical core to designing a robust, NAM-based testing strategy. By summarising the current mechanistic understanding, an AOP enables the selection of NAMs covering MIEs, early KEs, and late KEs. Experimental coverage of the AOP in this way is vital since MIEs and early KEs alone are not confirmatory of progression to the AO. This strategy exemplifies the workflow previously published by the EUTOXRISK project driving a paradigm shift towards NAM-based NGRA.

Read-across approaches often remain inconclusive as they do not provide sufficient evidence on a common mode of action across the category members. This read-across case study on thirteen, structurally similar, branched aliphatic carboxylic acids investigates the concept of using human-based new approach methods, such as in vitro and in silico models, to demonstrate biological similarity. Five out of the thirteen analogues have preclinical in vivo studies. Three out of them induced lipid accumulation or hypertrophy in preclinical studies with repeated exposure, which leads to the read-across hypothesis that the analogues can potentially induce hepatic steatosis. To confirm the selection of analogues, the expression patterns of the induced differentially expressed genes (DEGs) were analysed in a human liver model. With increasing dose, the expression pattern within the tested analogues got more similar, which serves as a first indication of a common mode of action and suggests differences in the potency of the analogues. Hepatic steatosis is a well-known adverse outcome, for which over 55 adverse outcome pathways have been identified. The resulting adverse outcome pathway (AOP) network, comprised a total 43 MIEs/KEs and enabled the design of an in vitro testing battery. From the AOP network, ten MIEs, early and late KEs were tested to systematically investigate a common mode of action among the grouped compounds. The targeted testing of AOP specific MIE/KEs shows that biological activity in the category decreases with side chain length. A similar trend was evident in measuring liver alterations in zebra fish embryos. However, activation of single MIEs or early KEs at in vivo relevant doses did not necessarily progress to the late KE “lipid accumulation”. KEs not related to the read-across hypothesis, testing for example general mitochondrial stress responses in liver cells, showed no trend or biological similarity. Testing scope is a key issue in the design of in vitro test batteries. The Dempster-Shafer decision theory predicted those analogues with in vivo reference data correctly using one human liver model or the CALUX reporter assays. The case study shows that the read-across hypothesis is the key element to designing the testing strategy. In the case of a good mechanistic understanding, an AOP facilitates the selection of reliable human in vitro models to demonstrate a common mode of action. Testing DEGs, MIEs and early KEs served to show biological similarity, whereas the late KEs become important for confirmation, as progression from MIEs to AO is not always guaranteed.

Hazard assessment, based on new approach methods (NAM), requires the use of batteries of assays, where individual tests may be contributed by different laboratories. A unified strategy for such collaborative testing is presented. It details all procedures required to allow test information to be usable for integrated hazard assessment, strategic project decisions and/or for regulatory purposes. The EU-ToxRisk project developed a strategy to provide regulatorily valid data, and exemplified this using a panel of > 20 assays (with > 50 individual endpoints), each exposed to 19 well-known test compounds (e.g. rotenone, colchicine, mercury, paracetamol, rifampicine, paraquat, taxol). Examples of strategy implementation are provided for all aspects required to ensure data validity: (i) documentation of test methods in a publicly accessible database; (ii) deposition of standard operating procedures (SOP) at the European Union DB-ALM repository; (iii) test readiness scoring accoding to defined criteria; (iv) disclosure of the pipeline for data processing; (v) link of uncertainty measures and metadata to the data; (vi) definition of test chemicals, their handling and their behavior in test media; (vii) specification of the test purpose and overall evaluation plans. Moreover, data generation was exemplified by providing results from 25 reporter assays. A complete evaluation of the entire test battery will be described elsewhere. A major learning from the retrospective analysis of this large testing project was the need for thorough definitions of the above strategy aspects, ideally in form of a study pre-registration, to allow adequate interpretation of the data and to ensure overall scientific/toxicological validity. Electronic supplementary material The online version of this article (10.1007/s00204-020-02802-6) contains supplementary material, which is available to authorized users.

Adverse outcome pathways (AOPs) are a recent toxicological construct that connects, in a formalized, transparent and quality-controlled way, mechanistic information to apical endpoints for regulatory purposes. AOP links a molecular initiating event (MIE) to the adverse outcome (AO) via key events (KE), in a way specified by key event relationships (KER). Although this approach to formalize mechanistic toxicological information only started in 2010, over 200 AOPs have already been established. At this stage, new requirements arise, such as the need for harmonization and re-assessment, for continuous updating, as well as for alerting about pitfalls, misuses and limits of applicability. In this review, the history of the AOP concept and its most prominent strengths are discussed, including the advantages of a formalized approach, the systematic collection of weight of evidence, the linkage of mechanisms to apical end points, the examination of the plausibility of epidemiological data, the identification of critical knowledge gaps and the design of mechanistic test methods. To prepare the ground for a broadened and appropriate use of AOPs, some widespread misconceptions are explained. Moreover, potential weaknesses and shortcomings of the current AOP rule set are addressed (1) to facilitate the discussion on its further evolution and (2) to better define appropriate vs. less suitable application areas. Exemplary toxicological studies are presented to discuss the linearity assumptions of AOP, the management of event modifiers and compensatory mechanisms, and whether a separation of toxicodynamics from toxicokinetics including metabolism is possible in the framework of pathway plasticity. Suggestions on how to compromise between different needs of AOP stakeholders have been added. A clear definition of open questions and limitations is provided to encourage further progress in the field. © 2017, Springer-Verlag GmbH Germany.

In the original article, we neglected to mention that this work was partly supported by the EU-ToxRisk project (An Integrated European "Flagship" Program Driving Mechanism-Based Toxicity Testing and Risk Assessment for the 21st Century) funded by the European Commission under the Horizon 2020 programme (Grant Agreement No. 681002). A correction has therefore been made to the Acknowledgments: "The research leading to these results has received support from the Innovative Medicines Initiative Joint Undertaking (IMIJU) under grant agreement number 115439, resources of which are composed of financial contribution from the European Union's Seventh Framework Programme (FP7/2007-2013) and EFPIA companies' in kind contribution. This work was supported by the 2015 CEFIC-LRI award (AL) and partly supported by the EU-ToxRisk project (An Integrated European "Flagship" Program Driving Mechanism-Based Toxicity Testing and Risk Assessment for the 21st Century) funded by the European Commission under the Horizon 2020 programme (Grant Agreement No. 681002). This publication reflects only the author's views and neither the IMI JU nor EFPIA nor the European Commission are liable for any use that may be made of the information contained therein." The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.

While hazard assessment of chemicals can make direct use of descriptive adverse outcome pathways (AOPs), risk assessment requires quantitative relationships from exposure to effect timing and magnitude. To seamlessly integrate the data generated by alternative methods or in vivo testing, quantitative AOPs (qAOPs) providing dose-time-response predictions are more valuable than qualitative AOPs. Here, we compare three approaches to qAOP building: empirical dose-response modeling, Bayesian network (BN) calibration, and systems biology (SB) modeling. These methods were applied to the quantification of a simplified oxidative stress induced chronic kidney disease AOP, on the basis of in vitro data obtained on RPTEC/TERT1 cells exposed to potassium bromate. Effectopedia was used to store the experimental data and the developed models in a unified representation so they can be compared and further analyzed. We argue that despite the fact that dose-response models give adequate fits to the data they should be accompanied by mechanistic SB modeling to gain a proper perspective on the quantification. BNs can be both more precise than dose-response models and simpler than SB models, but more experience with their usage is needed.

The assessment of the carcinogenic potential of chemicals with alternative, human-based in vitro systems has become a major goal of toxicogenomics. The central read-out of these assays is the transcriptome, and while many studies exist that explored the gene expression responses of such systems, reports on robustness and reproducibility, when testing them independently in different laboratories, are still uncommon. Furthermore, there is limited knowledge about variability induced by the data analysis protocols. We have conducted an inter-laboratory study for testing chemical carcinogenicity evaluating two human in vitro assays: hepatoma-derived cells and hTERT-immortalized renal proximal tubule epithelial cells, representing liver and kidney as major target organs. Cellular systems were initially challenged with thirty compounds, genome-wide gene expression was measured with microarrays, and hazard classifiers were built from this training set. Subsequently, each system was independently established in three different laboratories, and gene expression measurements were conducted using anonymized compounds. Data analysis was performed independently by two separate groups applying different protocols for the assessment of inter-laboratory reproducibility and for the prediction of carcinogenic hazard. As a result, both workflows came to very similar conclusions with respect to (1) identification of experimental outliers, (2) overall assessment of robustness and inter-laboratory reproducibility and (3) re-classification of the unknown compounds to the respective toxicity classes. In summary, the developed bioinformatics workflows deliver accurate measures for inter-laboratory comparison studies, and the study can be used as guidance for validation of future carcinogenicity assays in order to implement testing of human in vitro alternatives to animal testing.

Untargeted metabolomics aims at obtaining quantitative information on the highest possible number of low-molecular biomolecules present in a biological sample. Rather small changes in mass spectrometric spectrum acquisition parameters may have a significant influence on the detectabilities of metabolites in untargeted global-scale studies by means of high-performance liquid chromatography-mass spectrometry (HPLC-MS). Employing whole cell lysates of human renal proximal tubule cells, we present a systematic global-scale study of the influence of mass spectrometric scan parameters and post-acquisition data treatment on the number and intensity of metabolites detectable in whole cell lysates. Ion transmission and ion collection efficiencies in an Orbitrap-based mass spectrometer basically depend on the m/z range scanned, which, ideally, requires different instrument settings for the respective mass ranges investigated. Therefore, we split a full scan range of m/z 50-1000 relevant for metabolites into two separate segments (m/z 50-200 and m/z 200-1,000), allowing an independent tuning of the ion transmission parameters for both mass ranges. Three different implementations, involving either scanning from m/z 50-1000 in a single scan, or scanning from m/z 50-200 and from m/z 200-1000 in two alternating scans, or performing two separate HPLC-MS runs with m/z 50-200 and m/z 200-1000 scan ranges were critically assessed. The detected features were subjected to rigorous background filtering and quality control in order to obtain reliable metabolite features for subsequent differential quantification. The most efficient approach in terms of feature number, which forms the basis for statistical analysis, identification, and for generating biological hypotheses, was the separate analysis of two different mass ranges. This lead to an increase in the number of detectable metabolite features, especially in the higher mass range (m/z greater than 400), by 2.5 (negative mode) to 6-fold (positive mode) as compared to analysis involving a single scan range. The total number of features confidently detectable was 560 in positive ion mode, and 436 in negative ion mode.

High content omic methods provide a deep insight into cellular events occurring upon chemical exposure of a cell population or tissue. However, this improvement in analytic precision is not yet matched by a thorough understanding of molecular mechanisms that would allow an optimal interpretation of these biological changes. For transcriptomics (TCX), one type of molecular effects that can be assessed already is the modulation of the transcriptional activity of a transcription factor (TF). As more ChIP-seq datasets reporting genes specifically bound by a TF become publicly available for mining, the generation of target gene lists of TFs of toxicological relevance becomes possible, based on actual protein-DNA interaction and modulation of gene expression. In this study, we generated target gene signatures for Nrf2, ATF4, XBP1, p53, HIF1a, AhR and PPAR gamma and tracked TF modulation in a large collection of in vitro TCX datasets from renal and hepatic cell models exposed to clinical nephro- and hepato-toxins. The result is a global monitoring of TF modulation with great promise as a mechanistically based tool for chemical hazard identification.

There is a growing impetus to develop more accurate, predictive and relevant in vitro models of renal xenobiotic exposure. As part of the EU-FP7, Predict-IV project, a major aim was to develop models that recapitulate not only normal tissue physiology but also aspects of disease conditions that exist as predisposing risk factors for xenobiotic toxicity. Hypoxia, as a common micro-environmental alteration associated with pathophysiology in renal disease, was investigated for its effect on the toxicity profile of a panel of 14 nephrotoxins, using the human proximal tubular epithelial RPTECT/TERT1 cell line. Changes in ATP, glutathione and resazurin reduction, after 14 days of daily repeat exposure, revealed a number of compounds, including adefovir dipivoxil with enhanced toxicity in hypoxia. We observed intracellular accumulation of adefovir in hypoxia and suggest decreases in the efflux transport proteins MRP4, MRP5, NHERF1 and NHERF3 as a possible explanation. MRP5 and NHERF3 were also down-regulated upon treatment with the HIF-1 activator, dimethyloxalylglycine. Interestingly, adefovir dependent gene expression shifted from alterations in cell cycle gene expression to an inflammatory response in hypoxia. The ability to investigate aspects of disease states and their influence on renal toxin handling is a key advantage of in vitro systems developed here. They also allow for detailed investigations into mechanisms of compound toxicity of potential importance for compromised tissue exposure.

Toxicological responses to chemical insult are largely regulated by transcriptionally activated pathways that may be independent, correlated and partially or fully overlapping. Investigating the dynamics of the interactions between stress responsive transcription factors from toxicogenomic data and defining the signature of each of them is an additional step toward a system level understanding of perturbation driven mechanisms. To this end, we investigated the segregation of the genes belonging to the three following transcriptionally regulated pathways: the AhR pathway, the Nrf2 pathway and the ATF4 pathway. Toxicogenomic datasets from three projects (carcinoGENOMICS, Predict-IV and TG-GATEs) obtained in various experimental conditions (in human and rat in vitro liver and kidney models and rat in vivo, with bolus administration and with repeated doses) were combined and consolidated where overlaps between datasets existed. A bioinformatic analysis was performed to refine pathways' signatures and to create chemical activation capacity scores to classify chemicals by their potency and selectivity of activation of each pathway. With some refinement such an approach may improve chemical safety classification and allow biological read across on a pathway level.Corrigendum: Investigation of Nrf2, AhR and ATF4 Activation in Toxicogenomic Databases10.3389/fgene.2019.00517

To date, toxicologists use in vivo animal studies to estimate a threshold below which the exposure of in vivo humans to a compounds is safe. EU-ToxRisk aims to support the use of new approach methods (NAMs), such as in vitro and in silico models, in risk assessment. Here we describe a proof of concept case study that investigates the toxicity of a group of (un)branched aliphatic carboxylic acids. Five carboxylic acids induce microvesicular liver steatosis in repeated dose toxicity studies (termed in vivo positive), whereas six structurally highly similar compounds do not (in vivo negative). Here we demonstrate the use of NAMs for a category approach comprising ethylbutyric acid and valproic acid, 4-ene valproic acid, 2-ethylhexanoic acid and pivalic acid. All compounds were tested in HepG2 cells. Exposure of HepG2 cells to test compounds resulted in lipid accumulation at subcytotoxic concentrations after 1 and 3 days of treatment. The onset of lipid accumulation was seen at lower concentration with longer exposure periods. A panel of 27 CALUX reporter gene assays and BAC GFP reporter cell lines showed activation of cellular stress pathways in acute exposures (up to 72h). All assays distinguish clearly between in vivo positive and negative compounds. The nominal EC20 values correlate to NOELs of in vivo studies. These data are compared to recent results obtained from repeated exposures of 3D HepaRG cells, PHHs and zebra fish embryos. We also used an in silico model to predict the corresponding intracellular concentration in the in vitro models. These data are included as additional information into the in vitro to in vivo correlations. This exemplary category approach demonstrates that NAMs from the EU-ToxRisk predict the toxicity of compounds after repeated exposure in a qualitative and quantitative manner. The continued development of integrated testing strategies represents the beginning of a paradigm shift, reducing, and ultimately replacing, in vivo studies in human risk assessment.

Untargeted metabolomics has the potential to improve the predictivity of in vitro toxicity models and therefore may aid the replacement of expensive and laborious animal models. Here we describe a long term repeat dose nephrotoxicity study conducted on the human renal proximal tubular epithelial cell line, RPTEC/TERT1, treated with 10 and 35 μmol·liter(-1) of chloroacetaldehyde, a metabolite of the anti-cancer drug ifosfamide. Our study outlines the establishment of an automated and easy to use untargeted metabolomics workflow for HPLC-high resolution mass spectrometry data. Automated data analysis workflows based on open source software (OpenMS, KNIME) enabled a comprehensive and reproducible analysis of the complex and voluminous metabolomics data produced by the profiling approach. Time- and concentration-dependent responses were clearly evident in the metabolomic profiles. To obtain a more comprehensive picture of the mode of action, transcriptomics and proteomics data were also integrated. For toxicity profiling of chloroacetaldehyde, 428 and 317 metabolite features were detectable in positive and negative modes, respectively, after stringent removal of chemical noise and unstable signals. Changes upon treatment were explored using principal component analysis, and statistically significant differences were identified using linear models for microarray assays. The analysis revealed toxic effects only for the treatment with 35 μmol·liter(-1) for 3 and 14 days. The most regulated metabolites were glutathione and metabolites related to the oxidative stress response of the cells. These findings are corroborated by proteomics and transcriptomics data, which show, among other things, an activation of the Nrf2 and ATF4 pathways.

The formation, maintenance, and repair of epithelial barriers are of critical importance for whole-body homeostasis. However, the molecular events involved in epithelial tissue maturation are not fully established. To this end, we investigated the molecular processes involved in renal epithelial proximal-tubule monolayer maturation utilizing transcriptomic, metabolomic, and functional parameters. We uncovered profound dynamic alterations in transcriptional regulation, energy metabolism, and nutrient utilization over the maturation process. Proliferating cells exhibited high glycolytic rates and high transcript levels for fatty acid synthesis genes (FASN), whereas matured cells had low glycolytic rates, increased oxidative capacity, and preferentially expressed genes for beta oxidation. There were dynamic alterations in the expression and localization of several adherens (CDH1, -4, and -16) and tight junction (TJP3 and CLDN2 and -10) proteins. Genes involved in differentiated proximal-tubule function, cilium biogenesis (BBS1), and transport (ATP1A1 and ATP1B1) exhibited increased expression during epithelial maturation. Using TransAM transcription factor activity assays, we could demonstrate that p53 and FOXO1 were highly active in matured cells, whereas HIF1A and c-MYC were highly active in proliferating cells. The data presented here will be invaluable in the further delineation of the complex dynamic cellular processes involved in epithelial cell regulation.