Tine Ringsted

Umeå University, Umeå, Västerbotten, Sweden

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Publications (7)16.99 Total impact

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    ABSTRACT: For a better understanding of species-specific relative effect potencies (REPs), responses of dioxin-like compounds (DLCs) were assessed. REPs were calculated using chemical-activated luciferase gene expression assays (CALUX) derived from guinea pig, rat and mouse cell lines. Almost all 20 congeners tested in the rodent cell lines were partial agonists and less efficacious than 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). For this reason, REPs were calculated for each congener using concentrations at which 20% of the maximal TCDD response was reached (REP20TCDD). REP20TCDD values obtained for PCDD/Fs were comparable with their toxic equivalency factors assigned by the World Health Organization (WHO-TEF) while those for PCBs were in general lower than the WHO-TEF values. Moreover, the guinea pig cell line was the most sensitive as indicated by the 20% effect concentrations of TCDD of 1.5, 5.6 and 11.0 pM for guinea pig, rat and mouse cells, respectively. A similar response pattern was observed using multivariate statistical analysis between the three CALUX assays and the WHO-TEFs. The mouse assay showed minor deviation due to higher relative induction potential for 2,3,7,8-tetrachlorodibenzofuran and 2,3,4,6,7,8-hexachlorodibenzofuran and lower for 1,2,3,4,6,7,8-heptachlorodibenzofuran and 3,3',4,4',5-pentachlorobiphenyl (PCB126). 2,3,7,8-tetrachlorodibenzofuran was more than two times more potent in the mouse assay as compared with rat and guinea pig cells while measured REP20TCDD for PCB126 was lower in mouse cells (0.05) as compared with guinea pig (0.2) and rat (0.07). In order to provide REP20TCDD values for all WHO-TEF assigned compounds, quantitative structure-activity relationship (QSAR) models were developed. The QSAR models showed that specific electronic properties and molecular surface characteristics play important roles in the AhR-mediated response. In silico derived REP20TCDD values were generally consistent with the WHO-TEFs with a few exceptions. The QSAR models indicated that e.g. 1,2,3,7,8-pentachlorodibenzofuran and 1,2,3,7,8,9-hexachlorodibenzofuran were more potent than given by their assigned WHO-TEF values and the non-ortho PCB 81 was predicted, based on the guinea-pig model, to be one order of magnitude above its WHO-TEF value. By combining in vitro and in silico approaches, REPs were established for all WHO-TEF assigned compounds (except OCDD), which will provide future guidance in testing AhR mediated responses of DLCs and to increase our understanding of species variation in AhR-mediated effects.
    Chemical Research in Toxicology 06/2014; 27(7). DOI:10.1021/tx5001255 · 3.53 Impact Factor
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    ABSTRACT: The European REACH regulation requires information on ready biodegradation, which is a screening test to assess the biodegradability of chemicals. At the same time REACH encourages the use of alternatives to animal testing which includes predictions from QSAR models. The aim of this study was to build QSAR models to predict ready biodegradation of chemicals by using different modelling methods and types of molecular descriptors. Particular attention was given to data screening and validation procedures in order to build predictive models. Experimental values of 1055 chemicals were collected from the webpage of the National Institute of Technology and Evaluation of Japan (NITE): 837 and 218 molecules were used for calibration and testing purposes, respectively. In addition, models were further evaluated using an external validation set consisting of 670 molecules. Classification models were produced in order to discriminate biodegradable and non-biodegradable chemicals by means of different mathematical methods: k Nearest Neighbours, Partial Least Squares Discriminant Analysis and Support Vector Machines, as well as their consensus models. The proposed models and the derived consensus analysis demonstrated good classification performances with respect to already published QSAR models on biodegradation. Relationships between the molecular descriptors selected in each QSAR model and biodegradability were evaluated.
    Journal of Chemical Information and Modeling 03/2013; 53:867-878. DOI:10.1021/ci4000213 · 3.74 Impact Factor
  • Journal of Chemical Information and Modeling 01/2013; 53:867-878. · 3.74 Impact Factor
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    ABSTRACT: This paper presents four new QSAR models for CYP2C9 and CYP2D6 substrate recognition and inhibitor identification based on human clinical data. The models were used to screen a large data set of environmental chemicals for CYP activity, and to analyze the frequency of CYP activity among these compounds. A large fraction of these chemicals were found to be CYP active, and thus potentially capable of affecting human physiology. 20% of the compounds within applicability domain of the models were predicted to be CYP2C9 substrates, and 17% to be inhibitors. The corresponding numbers for CYP2D6 were 9% and 21%. Where the majority of CYP2C9 active compounds were predicted to be both a substrate and an inhibitor at the same time, the CYP2D6 active compounds were primarily predicted to be only inhibitors. It was demonstrated that the models could identify compound classes with a high occurrence of specific CYP activity. An overrepresentation was seen for poly-aromatic hydrocarbons (group of procarcinogens) among CYP2C9 active and mutagenic compounds compared to CYP2C9 inactive and mutagenic compounds. The mutagenicity was predicted with a QSAR model based on Ames in vitro test data.
    Bioorganic & medicinal chemistry 03/2012; 20(6):2042-53. DOI:10.1016/j.bmc.2012.01.049 · 2.79 Impact Factor
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    ABSTRACT: Three modelling systems (MultiCase®, LeadScope® and MDL® QSAR) were used for construction of androgenic receptor antagonist models. There were 923-942 chemicals in the training sets. The models were cross-validated (leave-groups-out) with concordances of 77-81%, specificity of 78-91% and sensitivity of 51-76%. The specificity was highest in the MultiCase® model and the sensitivity was highest in the MDL® QSAR model. A complementary use of the models may be a valuable tool when optimizing the prediction of chemicals for androgenic receptor antagonism. When evaluating the fitness of the model for a particular application, balance of training sets, domain definition, and cut-offs for prediction interpretation should also be taken into account. Different descriptors in the modelling systems are illustrated with hydroxyflutamide and dexamethasone as examples (a non-steroid and a steroid anti-androgen, respectively). More research concerning the mechanism of anti-androgens would increase the possibility for further optimization of the QSAR models. Further expansion of the basis for the models is in progress, including the addition of more drugs.
    SAR and QSAR in environmental research 03/2011; 22(1-2):35-49. DOI:10.1080/1062936X.2010.528981 · 1.60 Impact Factor
  • T Ringsted · N Nikolov · G E Jensen · E B Wedebye · J Niemelä
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    ABSTRACT: Human Cytochrome P450 (CYP) is a large group of enzymes that possess an essential function in metabolising different exogenous and endogenous compounds. Humans have more than 50 different genes encoding CYP enzymes, among these a gene encoding for the CYP isoenzyme 2D6, a CYP able to metabolise drugs and other chemicals. A training set of 747 chemicals primarily based on in vivo human data for the CYP isoenzyme 2D6 was collected from the literature. QSAR models focusing on substrate/non-substrate activity were constructed by the use of MultiCASE, Leadscope and MDL quantitative structure-activity relationship (QSAR) modelling systems. They cross validated (leave-groups-out) with concordances of 71%, 81% and 82%, respectively. Discrete organic European Inventory of Existing Commercial Chemical Substances (EINECS) chemicals were screened to predict an approximate percentage of CYP 2D6 substrates. These chemicals are potentially present in the environment. The biological importance of the CYP 2D6 and the use of the software mentioned above were discussed.
    SAR and QSAR in environmental research 02/2009; 20(3-4):309-25. DOI:10.1080/10629360902949195 · 1.60 Impact Factor
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Publication Stats

36 Citations
16.99 Total Impact Points


  • 2014
    • Umeå University
      • Department of Chemistry
      Umeå, Västerbotten, Sweden
  • 2013
    • Università degli Studi di Milano-Bicocca
      • Department of Earth and Environmental Sciences
      Milano, Lombardy, Italy
  • 2011–2012
    • Technical University of Denmark
      • Division of Toxicology and Risk Assessment
      Lyngby, Capital Region, Denmark