Untersuchungen zur Struktur/Aktivitätsbeziehung des humanen CC-Chemokins HCC-2

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OpenTox predictive toxicology framework: toxicological ontology and semantic media wiki-based OpenToxipedia

<p>Abstract</p> <p>Background</p> <p>The OpenTox Framework, developed by the partners in the OpenTox project (<url>http://www.opentox.org</url>), aims at providing a unified access to toxicity data, predictive models and validation procedures. Interoperability of resources is achieved using a common information model, based on the OpenTox ontologies, describing predictive algorithms, models and toxicity data. As toxicological data may come from different, heterogeneous sources, a deployed ontology, unifying the terminology and the resources, is critical for the rational and reliable organization of the data, and its automatic processing.</p> <p>Results</p> <p>The following related ontologies have been developed for OpenTox: a) Toxicological ontology – listing the toxicological endpoints; b) Organs system and Effects ontology – addressing organs, targets/examinations and effects observed in <it>in vivo</it> studies; c) ToxML ontology – representing semi-automatic conversion of the ToxML schema; d) OpenTox ontology– representation of OpenTox framework components: chemical compounds, datasets, types of algorithms, models and validation web services; e) ToxLink–ToxCast assays ontology and f) OpenToxipedia community knowledge resource on toxicology terminology.</p> <p>OpenTox components are made available through standardized REST web services, where every compound, data set, and predictive method has a unique resolvable address (URI), used to retrieve its Resource Description Framework (RDF) representation, or to initiate the associated calculations and generate new RDF-based resources.</p> <p>The services support the integration of toxicity and chemical data from various sources, the generation and validation of computer models for toxic effects, seamless integration of new algorithms and scientifically sound validation routines and provide a flexible framework, which allows building arbitrary number of applications, tailored to solving different problems by end users (e.g. toxicologists).</p> <p>Availability</p> <p>The OpenTox toxicological ontology projects may be accessed via the OpenTox ontology development page <url>http://www.opentox.org/dev/ontology</url>; the OpenTox ontology is available as OWL at <url>http://opentox.org/api/1 1/opentox.owl</url>, the ToxML - OWL conversion utility is an open source resource available at <url>http://ambit.svn.sourceforge.net/viewvc/ambit/branches/toxml-utils/</url></p

A k-NN Algorithm for Predicting Oral Sub-Chronic Toxicity in the Rat

Summary Repeated dose toxicity is of the utmost importance to characterize the toxicological profile o

Threshold of Toxicological Concern - an update for non-genotoxic carcinogens

The Threshold of Toxicological Concern (TTC) concept can be applied to organic compounds with known chemical structure to derive a threshold for exposure below which a toxic effect on human health by the compound is not expected. The TTC concept distinguishes between carcinogens that may act as genotoxic and non-genotoxic compounds. A positive prediction of a genotoxic mode of action, either by structural alerts or experimental data, leads to the application of the threshold value for genotoxic compounds. Non-genotoxic substances are assigned to the TTC value of their respective Cramer class even though it is recognized that they could test positive in a rodent cancer bioassay. This study investigated the applicability of the Cramer classes specifically to provide adequate protection for non-genotoxic carcinogens. For this purpose, benchmark dose levels based on tumour incidence were compared with no observed effect levels (NOEL) derived from non-, pre- or neoplastic lesions. One key aspect was the categorization of compounds as non-genotoxic carcinogens. The recently finished CEFIC LRI project B18 classified the carcinogens of the CPDB as either non- or genotoxic compounds based on experimental or in silico data. A detailed consistency check resulted in a data set of 137 non-genotoxic organic compounds. For these 137 compounds, NOEL values were derived from high quality animal studies with oral exposure and chronic duration using well known repositories including RepDose, ToxRef and COSMOS DB. Further, an effective tumour dose (ETD10) was calculated and compared to the lower confidence limit on benchmark dose levels (BMDL10) derived by model averaging. Comparative analysis of NOEL/EDT10/BMDL10 values showed that potentially bioaccumulative compounds in humans, as well as steroids, which both belong to the exclusion categories, occur predominantly in region of the 5th percentiles of the distributions. Excluding these 25 compounds resulted in significantly higher, but comparable 5th percentile chronic NOEL and BMDL10 values, while the 5th percentile EDT10 value was slightly higher, but not statistically significant. The comparison of the obtained distributions of NOELs with the existing Cramer classes and their derived TTC values supports the application of Cramer class thresholds to all non genotoxic compounds, including non_genotoxic carcinogens

Substantiate a read-across hypothesis by using transcriptome data—A case study on volatile diketones

This case study explores the applicability of transcriptome data to characterize a common mechanism of action within groups of short-chain aliphatic α-, β-, and γ-diketones. Human reference in vivo data indicate that the α-diketone diacetyl induces bronchiolitis obliterans in workers involved in the preparation of microwave popcorn. The other three α-diketones induced inflammatory responses in preclinical in vivo animal studies, whereas beta and gamma diketones in addition caused neuronal effects. We investigated early transcriptional responses in primary human bronchiolar (PBEC) cell cultures after 24 h and 72 h of air-liquid exposure. Differentially expressed genes (DEGs) were assessed based on transcriptome data generated with the EUToxRisk gene panel of Temp-O-Seq®. For each individual substance, genes were identified displaying a consistent differential expression across dose and exposure duration. The log fold change values of the DEG profiles indicate that α- and β-diketones are more active compared to γ-diketones. α-diketones in particular showed a highly concordant expression pattern, which may serve as a first indication of the shared mode of action. In order to gain a better mechanistic understanding, the resultant DEGs were submitted to a pathway analysis using ConsensusPathDB. The four α-diketones showed very similar results with regard to the number of activated and shared pathways. Overall, the number of signaling pathways decreased from α-to β-to γ-diketones. Additionally, we reconstructed networks of genes that interact with one another and are associated with different adverse outcomes such as fibrosis, inflammation or apoptosis using the TRANSPATH-database. Transcription factor enrichment and upstream analyses with the geneXplain platform revealed highly interacting gene products (called master regulators, MRs) per case study compound. The mapping of the resultant MRs on the reconstructed networks, visualized similar gene regulation with regard to fibrosis, inflammation and apoptosis. This analysis showed that transcriptome data can strengthen the similarity assessment of compounds, which is of particular importance, e.g., in read-across approaches. It is one important step towards grouping of compounds based on biological profiles

Integration of NAMs in risk assessment: The read-across approach of the EU-ToxRisk project

The integration of NAMs, new approach methodologies, into a comprehensive risk assessment framework is challenging, in particular for complex endpoints such as repeated dose or reproductive toxicity. In EUToxRisk, scientists with different types of expertise develop integrated approaches to testing and assessment (IATAs) for these two endpoints. We believe that the in vitro and ex vivo models selected from the EUToxRisk toolbox will provide a better understanding of adverse outcome pathways moving human toxicology towards mechanistic risk assessment. In this talk, we present the concept and outcomes of the read-across case studies from the EUToxRisk programme. We report on the recent results and limitations of integrating cellular and molecular physiology, system biology and high-content technologies (omics, HCI) using computational modelling to uncover the causal relationships with apical findings arising from traditional in vivo. We will set a focus on the analysis and comparison of transcriptomic data within and between the case study compounds. This data integration will be illustrated by one case study based on structural analogues and one case study in which structurally diverse compounds share a common mode of action. In both case studies the IATA will predict the hazard of the target compound(s) after repeated low dose exposure. Besides qualitative hazard assessment EUToxRisk aims to derive threshold values, below which compounds are considered to be safe. Therefore, we will show the application of the newly developed “in vitro distribution model” that estimates the intracellular concentration of compounds in the tested cell or tissue model. The intracellular concentration is the starting point to model the oral equivalent dose with IVIVE-PBPK models, which are also under development in EUToxRisk

EUToxRisk case studies: new approach methodologies in read-across

The integration of new approach methodologies (NAM) into a comprehensive risk assessment framework is challenging, in particular for complex endpoints such as repeated dose or reproductive toxicity. EUToxRisk is a consortium of academic and industry scientists funded by the EU Horizon 2020 research program that aims to develop scientific underpinning for replacement of in vivo testing as far as possible by in vitro, ex vivo, and in silico models. This presentation will report on several recent case studies results, and limitations of integrating cellular and molecular physiology, systems biology, high-content technologies, and computational modeling to uncover the causal relationships with apical toxicity findings observed in traditional in vivo (mainly rodent) studies. Specifically, this talk will focus on two case studies: 1) the VPA case study in which we aim to predict microvesicular liver steatosis and reproductive effects; and 2) the mitochondrial toxicity case study, which is based on a concept of biological read-across. Each case study has specific scientific questions as well as technical and modeling challenges. Differences in toxicokinetics from in vivo and in vitro investigations are one key aspect of the integrated approach for testing and assessment of EUToxRisk. PBPK models informed by in vitro methods and reverse dosimetry are also used to determine in vivo-relevant test conditions. Overall, the case-study approach is aimed at developing demonstration blueprints on how NAM data may be used in regulatory submissions

Linking organic anion transporting polypeptide 1B1 and 1B3 (OATP1B1 and OATP1B3) interaction profiles to hepatotoxicity - The hyperbilirubinemia use case

Hyperbilirubinemia is a pathological condition of excessive accumulation of conjugated or unconjugated bilirubin in blood. It has been associated with neurotoxicity and non-neural organ dysfunctions, while it can also be a warning of liver side effects. Hyperbilirubinemia can either be a result of overproduction of bilirubin due to hemolysis or dyserythropoiesis, or the outcome of impaired bilirubin elimination due to liver transporter malfunction or inhibition. There are several reports in literature that inhibition of organic anion transporting polypeptides 1B1 and 1B3 (OATP1B1 and OATP1B3) might lead to hyperbilirubinemia. In this study we created a set of classification models for hyperbilirubinemia, which, besides physicochemical descriptors, also include the output of classification models of human OATP1B1 and 1B3 inhibition. Models were based on either human data derived from public toxicity reports or animal data extracted from the eTOX database VITIC. The gener ated models showed satisfactory accuracy (68%) and area under the curve (AUC) for human data and 71% accuracy and 70% AUC for animal data. However, our results did not indicate strong association between OATP inhibition and hyperbilirubinemia, neither for humans nor for animals

Curated human hyperbilirubinemia data and the respective OATP1B1 and 1B3 inhibition predictions

Hyperbilirubinemia is a pathological condition, very often indicative of underlying liver condition that is characterized by excessive accumulation of conjugated or unconjugated bilirubin in sinusoidal blood. In literature there are several indications associating the inhibition of the basolateral hepatic transporters Organic anion transporting polypeptide 1B1 and 1B3 (OATP1B1 and 1B3) with hyperbilirubinemia. In this article, we present a curated human hyperbilirubinemia dataset and the respective OATP1B1 and 1B3 inhibition predictions obtained from an effort to generate a classification model for hyperbilirubinemia. These data originate from the research article "Linking organic anion transporting polypeptide 1b1 and 1b3 (oatp1b1 and oatp1b3) interaction profiles to hepatotoxicity- the hyperbilirubinemia use case" (E. Kotsampasakou, S.E. Escher, G.F. Ecker, 2017) [1]. We further provide the full list of descriptors used for generating the hyperbilirubinemia classification models as well as the calculated descriptors for each compound of the dataset that was used to build the classification model