Person: Arning, Jürgen
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1978
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Arning
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Jürgen
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Veröffentlichung A tiered high-throughput screening approach for evaluation of estrogen and androgen receptor modulation by environmentally relevant bisphenol A substitutes(2020) Keminer, Oliver; Arning, Jürgen; Teigeler, Matthias; Kohler, ManfredBisphenol A (BPA) is a high production volume chemical with a broad application spectrum. As an endocrine disrupting chemical, mainly by modulation of nuclear receptors (NRs), BPA has an adverse impact on organisms and is identified as a substance of very high concern under the European REACH regulation. Various BPA substitution candidates have been developed in recent years, however, information concerning the endocrine disrupting potential of these substances is still incomplete or missing. In this study, we intended to investigate the endocrine potential of BPA substitution candidates used in environmentally relevant applications such as thermal paper or epoxy resins. Based on an extensive literature and patent search, 33 environmentally relevant BPA substitution candidates were identified. In order to evaluate the endocrine potential of the BPA replacements, a screening cascade consisting of biochemical and cell-based assays was employed to investigate substance binding to the NRs estrogen receptor ÎÌ and Î2, as well as androgen receptor, co-activator recruitment and NR-mediated reporter gene activation. In addition, a computational docking approach for retrospective prediction of receptor binding was carried out. Our results show that some BPA substitution candidates, for which so far no or only very few data were available, possess a substantial endocrine disrupting potential (TDP, BPZ), while several substances (BPS, D-8, DD70, DMP-OH, TBSA, D4, CBDO, ISO, VITC, DPA, and DOPO) did not reveal any NR binding. © 2019 The Author(s).Veröffentlichung Zebrafish AC50 modelling: (Q)SAR models to predict developmental toxicity in zebrafish embryo(2020) Lavado, Giovanna J.; Arning, Jürgen; Gadaleta, Domenico; Toma, CosimoDevelopmental toxicity refers to the occurrence of adverse effects on a developing organism as a consequence of exposure to hazardous chemicals. The assessment of developmental toxicity has become relevant to the safety assessment process of chemicals. The zebrafish embryo developmental toxicology assay is an emerging test used to screen the teratogenic potential of chemicals and it is proposed as a promising test to replace teratogenic assays with animals. Supported by the increased availability of data from this test, the developmental toxicity assay with zebrafish has become an interesting endpoint for the in silico modelling. The purpose of this study was to build up quantitative structure-activity relationship (QSAR) models. In this work, new in silico models for the evaluation of developmental toxicity were built using a well-defined set of data from the ToxCastTM Phase I chemical library on the zebrafish embryo. Categorical and continuous QSAR models were built by gradient boosting machine learning and the Monte Carlo technique respectively, in accordance with Organization for Economic Co-operation and Development principles and their statistical quality was satisfactory. The classification model reached balanced accuracy 0.89 and Matthews correlation coefficient 0.77 on the test set. The regression model reached correlation coefficient R2 0.70 in external validation and leave-one-out cross-validated Q2 0.73 in internal validation. © 2020 Elsevier Inc.Veröffentlichung New models to predict the acute and chronic toxicities of representative species of the main trophic levels of aquatic environments(2021) Toma, Cosimo; Arning, Jürgen; Cappelli, Claudia Ileana; Manganaro, AlbertoTo assess the impact of chemicals on an aquatic environment, toxicological data for three trophic levels are needed to address the chronic and acute toxicities. The use of non-testing methods, such as predictive computational models, was proposed to avoid or reduce the need for animal models and speed up the process when there are many substances to be tested. We developed predictive models for Raphidocelis subcapitata, Daphnia magna, and fish for acute and chronic toxicities. The random forest machine learning approach gave the best results. The models gave good statistical quality for all endpoints. These models are freely available for use as individual models in the VEGA platform and for prioritization in JANUS software. © 2021 by the authorsVeröffentlichung Development of new QSAR models for water, sediment, and soil half-life(2022) Lombardo, Anna; Arning, Jürgen; Manganaro, AlbertoChecking the persistence of a chemical in the environment is extremely important. Regulations like REACH, the European one on chemicals, require the measurements or estimates of the half-life of the chemical in water, sediment, and soil. The use of non-testing methods, like quantitative structure-activity relationship (QSAR) models, is encouraged because it reduces costs and time. To our knowledge, there are very few freely available models for these properties and some are for specific chemical classes. Here, we present three new semi-quantitative models, one for each of the required environmental compartments (water, sediment, and soil). Using literature and REACH registration data, we developed three new counter-propagation artificial neural network models using the CPANNatNIC tool. We calculated the VEGA descriptors, and selected the relevant ones using an internal method in R based on the forward selection technique. The best model for each compartment was implemented in two open-source stand-alone tools, the VEGA platform, and the JANUS tool (https://www.vegahub.eu/). These models were also used by ECHA to build their PBT profiler available in the OECD QSAR toolbox (https://qsartoolbox.org/). Screening and prioritization are also our main target. The models perform well, with R2 always above 0.8 in training and validation. The only exception is the validation set of the soil compartment, with R2 0.68, that is above 0.8 only for compounds inside the applicability domain (automatically calculated by the system). The root mean square error (RMSE) is good, 0.34 or less in log units (again, for soil validation it is higher but it reaches 0.21 when considering only the compounds in the applicability domain). Compared with one of the most widely used tools, BIOWIN3, the proposed models give better results in terms of R2 and RMSE. For the classification, the performance is better for water and soil, and comparable or lower for sediment. © 2022 ElsevierVeröffentlichung ERGO: Breaking down the wall between human health and environmental testing of endocrine disrupters(2020) Holbech, Henrik; Arning, Jürgen; Matthiessen, Peter; Hansen, MartinERGO (EndocRine Guideline Optimization) is the acronym of a European Union-funded research and innovation action, that aims to break down the wall between mammalian and non-mammalian vertebrate regulatory testing of endocrine disruptors (EDs), by identifying, developing and aligning thyroid-related biomarkers and endpoints (B/E) for the linkage of effects between vertebrate classes. To achieve this, an adverse outcome pathway (AOP) network covering various modes of thyroid hormone disruption (THD) in multiple vertebrate classes will be developed. The AOP development will be based on existing and new data from in vitro and in vivo experiments with fish, amphibians and mammals, using a battery of different THDs. This will provide the scientifically plausible and evidence-based foundation for the selection of B/E and assays in lower vertebrates, predictive of human health outcomes. These assays will be prioritized for validation at OECD (Organization for Economic Cooperation and Development) level. ERGO will re-think ED testing strategies from in silico methods to in vivo testing and develop, optimize and validate existing in vivo and early life-stage OECD guidelines, as well as new in vitro protocols for THD. This strategy will reduce requirements for animal testing by preventing duplication of testing in mammals and non-mammalian vertebrates and increase the screening capacity to enable more chemicals to be tested for ED properties. © 2020 by the authorsVeröffentlichung Assessment of Endocrine disruptor under European regulations(2020) Maack, Gerd; Arning, Jürgen; Frische, Tobias; Kehrer-Berger, Anja; Mordziol, Christoph