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.

OpenTox provides an interoperable, standards-based Framework for the support of predictive toxicology data management, algorithms, modelling, validation and reporting. It is relevant to satisfying the chemical safety assessment requirements of the REACH legislation as it supports access to experimental data, (Quantitative) Structure-Activity Relationship models, and toxicological information through an integrating platform that adheres to regulatory requirements and OECD validation principles. Initial research defined the essential components of the Framework including the approach to data access, schema and management, use of controlled vocabularies and ontologies, architecture, web service and communications protocols, and selection and integration of algorithms for predictive modelling. OpenTox provides end-user oriented tools to non-computational specialists, risk assessors, and toxicological experts in addition to Application Programming Interfaces (APIs) for developers of new applications. OpenTox actively supports public standards for data representation, interfaces, vocabularies and ontologies, Open Source approaches to core platform components, and community-based collaboration approaches, so as to progress system interoperability goals.The OpenTox Framework includes APIs and services for compounds, datasets, features, algorithms, models, ontologies, tasks, validation, and reporting which may be combined into multiple applications satisfying a variety of different user needs. OpenTox applications are based on a set of distributed, interoperable OpenTox API-compliant REST web services. The OpenTox approach to ontology allows for efficient mapping of complementary data coming from different datasets into a unifying structure having a shared terminology and representation.Two initial OpenTox applications are presented as an illustration of the potential impact of OpenTox for high-quality and consistent structure-activity relationship modelling of REACH-relevant endpoints: ToxPredict which predicts and reports on toxicities for endpoints for an input chemical structure, and ToxCreate which builds and validates a predictive toxicity model based on an input toxicology dataset. Because of the extensible nature of the standardised Framework design, barriers of interoperability between applications and content are removed, as the user may combine data, models and validation from multiple sources in a dependable and time-effective way.

A long-term goal of numerous research projects is to identify biomarkers for in vitro systems predicting toxicity in vivo. Often, transcriptomics data are used to identify candidates for further evaluation. However, a systematic directory summarizing key features of chemically influenced genes in human hepatocytes is not yet available. To bridge this gap, we used the Open TG-GATES database with Affymetrix files of cultivated human hepatocytes incubated with chemicals, further sets of gene array data with hepatocytes from human donors generated in this study, and publicly available genome-wide datasets of human liver tissue from patients with non-alcoholic steatohepatitis (NASH), cirrhosis, and hepatocellular cancer (HCC). After a curation procedure, expression data of 143 chemicals were included into a comprehensive biostatistical analysis. The results are summarized in the publicly available toxicotranscriptomics directory (http://wiki.toxbank.net/toxicogenomics-map/) which provides information for all genes whether they are up- or downregulated by chemicals and, if yes, by which compounds. The directory also informs about the following key features of chemically influenced genes: (1) Stereotypical stress response. When chemicals induce strong expression alterations, this usually includes a complex but highly reproducible pattern named 'stereotypical response.' On the other hand, more specific expression responses exist that are induced only by individual compounds or small numbers of compounds. The directory differentiates if the gene is part of the stereotypical stress response or if it represents a more specific reaction. (2) Liver disease-associated genes. Approximately 20 % of the genes influenced by chemicals are up- or downregulated, also in liver disease. Liver disease genes deregulated in cirrhosis, HCC, and NASH that overlap with genes of the aforementioned stereotypical chemical stress response include CYP3A7, normally expressed in fetal liver; the phase II metabolizing enzyme SULT1C2; ALDH8A1, known to generate the ligand of RXR, one of the master regulators of gene expression in the liver; and several genes involved in normal liver functions: CPS1, PCK1, SLC2A2, CYP8B1, CYP4A11, ABCA8, and ADH4. (3) Unstable baseline genes. The process of isolating and the cultivation of hepatocytes was sufficient to induce some stress leading to alterations in the expression of genes, the so-called unstable baseline genes. (4) Biological function. Although more than 2,000 genes are transcriptionally influenced by chemicals, they can be assigned to a relatively small group of biological functions, including energy and lipid metabolism, inflammation and immune response, protein modification, endogenous and xenobiotic metabolism, cytoskeletal organization, stress response, and DNA repair. In conclusion, the introduced toxicotranscriptomics directory offers a basis for a rationale choice of candidate genes for biomarker evaluation studies and represents an easy to use source of background information on chemically infl...

Chemokines mediate the recruitment of leukocytes to the sites of inflammation. N-terminal truncation of chemokines by the protease dipeptidyl peptidase IV (DPPIV) potentially restricts their activity during inflammatory processes such as allergic reactions, but direct evidence in vivo is very rare. After demonstrating that N-terminal truncation of the chemokine CCL11/eotaxin by DPPIV results in a loss of CCR3-mediated intracellular calcium mobilization and CCR3 internalization in human eosinophils, we focused on the in vivo role of CCL11 and provide direct evidence for specific kinetic and rate-determining effects by DPPIV-like enzymatic activity on CCL11-mediated responses of eosinophils. Namely, it is demonstrated that i.v. administration of CCL11 in wild-type F344 rats leads to mobilization of eosinophils into the blood, peaking at 30 min. This mobilization is significantly increased in DPPIV-deficient F344 rats. Intradermal administration of CCL11 is followed by a dose-dependent recruitment of eosinophils into the skin and is significantly more effective in DPPIV-deficient F344 mutants as well as after pharmacological inhibition of DPPIV. Interestingly, CCL11 application leads to an up-regulation of DPPIV, which is not associated with negative feedback inhibition via DPPIV-cleaved CCL11(3–74). These findings demonstrate regulatory effects of DPPIV for the recruitment of eosinophils. Furthermore, they illustrate that inhibitors of DPPIV have the potential to interfere with chemokine-mediated effects in vivo including but not limited to allergy.

The objective of this study was to develop and test a procedure for the identification of chemicals registered under the REACH Regulation that are of potential health concern and are likely to occur in the food chain. For this purpose, 100 data-rich substances registered under REACH together with four positive controls were evaluated. The evaluation of the 104 substances took into account parameters related to exposure (tonnage, release, biodegradation and potential bioaccumulation) and toxicity (repeated dose toxicity, genotoxicity and reproductive toxicity) organised in six blocks. All substances were scored for each block. ACC-HUMANsteady software was used to evaluate the potential for bioaccumulation in eleven different food items using input data derived from QSAR predictions. The extraction of the relevant experimental data generated under REACH was successful, but encountered several problems in relation to the data extraction process and subsequent evaluation steps. Several weighting scenarios were tested to aggregate scores for the six blocks into a total score to enable a ranking of the 104 substances. Scenarios that assigned high total scores to chemicals that combined high scores in the exposure blocks with high scores in the toxicity blocks identified a set of substances of potential concern (including the positive controls). In addition, a Pivot table selection was implemented that can be used without weighting. Further analyses compared the scores derived from experimental data with those derived from predicted data. These analyses found a good agreement of scores for biodegradability, but considerable disagreement of scores for toxicity endpoints. In conclusion, a scoring and ranking procedure was developed for the identification of chemicals of potential concern in the food chain (potential emerging risks) that showed a good level of differentiation. The focus on (semi-)automated processes ensures that this procedure can be applied to all chemicals registered under the REACH Regulation. © European Food Safety Authority

The aim of this review is to give an overview of the role of chemokines, particularly ligands of the CC chemokine receptor CCR3, in allergic diseases and to show the new concept in the treatment of allergies using chemokine receptor antagonists. Allergic diseases such as allergic asthma, allergic rhinitis and atopic dermatitis are characterized by a complex interaction of different cell types and mediators. Among this, Th2 cells, mast cells, basophils and eosinophils are found in the inflamed tissue due to the attraction of chemokines. Of all the known chemokine receptors, the chemokine receptor CCR3 seems to play the major role in allergic diseases which is supported by the detection of this receptor on the cell types mentioned above. Therefore, academic and industrial research focus on compounds to block this receptor. To date, certain chemokine receptor antagonists derived from peptides and small molecules exist to block the chemokine receptor CCR3. However, the in vivo data about these compounds and the mechanisms of receptor interaction are poorly understood, as yet. For the development of additional chemokine receptor antagonists, more details about the interaction between the ligands and their receptors are required. Therefore, additional studies will lead to the identification of novel CCR3 chemokine receptor antagonists, which can be therapeutically used in allergic asthma, allergic rhinitis, and atopic dermatitis.

Read-across is one of the most frequently used alternative tools for hazard assessment, in particular for complex endpoints such as repeated dose or developmental and reproductive toxicity. Read-across extrapolates the outcome of a specific toxicological in vivo endpoint from tested (source) compounds to "similar" (target) compound(s). If appropriately applied, a read-across approach can be used instead of de novo animal testing. The read-across approach starts with structural/ physicochemical similarity between target and source compounds, assuming that similar structural characteristics lead to similar human hazards. In addition, similarity also has to be shown for the toxicokinetic and toxicodynamic properties of the grouped compounds. To date, many read-across cases fail to demonstrate toxicokinetic and toxicodynamic similarities. New concepts, in vitro and in silico tools are needed to better characterise these properties, collectively called new approach methodologies (NAMs). This white paper outlines a general read-across assessment concept using NAMs to support hazard characterization of the grouped compounds by generating data on their dynamic and kinetic properties. Based on the overarching read-across hypothesis, the read-across workflow suggests targeted or untargeted NAM testing also outlining how mechanistic knowledge such as adverse outcome pathways (AOPs) can be utilized. Toxicokinetic models (biokinetic and PBPK), enriched by in vitro parameters such as plasma protein binding and hepatocellular clearance, are proposed to show (dis)similarity of target and source compound toxicokinetics. Furthermore, in vitro to in vivo extrapolation is proposed to predict a human equivalent dose, as potential point of departure for risk assessment. Finally, the generated NAM data are anchored to the existing in vivo data of source compounds to predict the hazard of the target compound in a qualitative and/ or quantitative manner. To build this EU-ToxRisk read-across concept, case studies have been conducted and discussed with the regulatory community. These case studies are briefly outlined.

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