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The Danish (Q)SAR Database Update Project
Abstracts / Toxicology Letters 221S (2013) S59–S256
the first study, virtual screening identified benzophenones used as UV-filters (e.g. in cosmetics) as 17-hydroxysteroid dehydrogenase type 3 inhibitors. This enzyme is essential for gonadal testosterone synthesis. The most active benzophenone inhibited the enzyme with an IC50 of 1 M. In the second case study, a silane compound used in rubber production was discovered as both 11-hydroxysteroid dehydrogenase type 2 inhibitor and mineralocorticoid receptor agonist. The two targets are involved in the regulation of blood pressure,and their impaired functions may lead to cardiovascular events. These two studies emphasize that pharmacophore-based virtual screening is not only a tool for drug discovery, but a way to set priorities when testing small molecules for toxic effects. http://dx.doi.org/10.1016/j.toxlet.2013.05.096
P05-21 Skin sensitization study by a new qualitative structure–toxicity relationships (QSTR) approach: K-step Yard Sampling (KY) methods Kazuhiro Sato 1,∗ , Yukinori Kusaka 1 , Kohtaro Yuta 2 1 Department of Environmental Health, School of Medicine, University of Fukui, Fukui, Japan, 2 In Silico Data Ltd., Narashino, Japan
In silico assessment of skin sensitization is increasingly needed owing to the problems concerning animal welfare, as well as excessive time consumed and cost involved in the development and testing of new chemicals. We could perfectly classify skin sensitizers (positive/negative) using a newly developed K-step Yard sampling (KY) methods (U.S. Patent No. 7725413, 2010). A total of 593 compounds (419 positive and 174 negative sensitizers) were used in this study. Parameters were generated from 2-D and 3-D structures of compounds. All of the 1015 parameters generated were reduced by various feature selection methods. KY methods were performed using ADMEWORKS/ModelBuilder software. All compounds were perfectly classified by 3 steps. Discriminant function of each step was a linear dicriminant function (TILSQ). KY methods were referred to as a meta-algorithm approach because it requires ordinary data analysis methods to generate discriminant functions. KY methods were the repetition of removal of gray zone of samples and reclassification of them to attain no gray zone (100% classification) at final step. This methods always attain perfect classification at final step, even though samples are large number, large of structural diversity or highly overlapped on the sample space. http://dx.doi.org/10.1016/j.toxlet.2013.05.097
P05-22 The Danish (Q)SAR Database Update Project Nikolai G. Nikolov, Marianne Dybdahl, Sine A. Rosenberg, Eva B. Wedebye Technical University of Denmark, National Food Institute, Moerkhoej Bygade 19, 2860 Soeborg, Denmark The Danish (Q)SAR Database is a collection of predictions from quantitative structure–activity relationship ((Q)SAR) models for over 70 environmental and human health-related endpoints (covering biodegradation, metabolism, allergy, irritation, endocrine disruption, teratogenicity, mutagenicity, carcinogenicity and others), each of them available for 185,000 organic
S85
substances. The database has been available online since 2005 (http://qsar.food.dtu.dk). A major update project for the Danish (Q)SAR database is under way, with a new online release planned in the beginning of 2015. The updated version will contain more than 600,000 discrete organic structures and new, more precise predictions for all endpoints, derived by consensus algorithms from a number of state-of-the-art individual predictions. http://dx.doi.org/10.1016/j.toxlet.2013.05.098
P05-23 Toward better understanding of liver steatosis MoA: Molecular modelling study of PPAR gamma receptor Merilin Al Sharif 1,∗ , Petko Alov 1 , Mark Cronin 2 , Elena Fioravanzo 3 , Ivanka Tsakovska 1 , Vessela Vitcheva 4 , Andrew Worth 5 , Chihae Yang 6 , Ilza Pajeva 7 1
Institute of Biophysics and Biomedical Engineering, Sofia, Bulgarien, Liverpool John Moores University, Liverpool, UK, 3 S-IN Soluzioni Informatiche SRL, Vicenza, Italiy, 4 CFSAN, U.S. FDA, College Park, USA, 5 European Commission, Joint Research Centre, Institute for Health and Consumer Protection, Ispra, Italien, 6 Altamira LLC, Columbus, USA, 7 Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences Room 206, 2th fl., Bl. 105 Acad. George Bontchev Str. 1113 Sofia, Bulgaria 2
Within the mode of action/adverse outcome pathway (MoA/AOP) framework the description and characterisation of the toxicological MoAs leading to liver toxicity are of specific interest. There is a particular emphasis in the development of this framework by in silico modelling of ligand-receptor interactions identified as key molecular initiating events (MIE) in MoAs leading to liver fibrosis and steatosis. The peroxisome proliferator-activated receptor gamma (PPAR gamma) has been recently proposed as one of the receptors involved in the MIE for liver steatosis. Emerging evidences have pointed to an important function of hepatic PPAR gamma in fatty acid transport, triglyceride synthesis pathway and eventually pathogenesis of fatty liver disease. In this study a systematic literature search has been performed to identify key studies and review papers with significant data regarding the dysregulation of lipid metabolism mediated by PPAR gamma resulting in liver steatosis and consequently in the development of non-alcoholic fatty liver disease and nonalcoholic steatohepatitis. Further, the results of a molecular modelling study based on the analysis of more than 100 of this receptor’s 3D structural complexes published in Protein Data Bank (http://www.rcsb.org) are presented. The analysis included characterisation of the ligand-binding pocket and structural characterisation of the PPAR gamma binding ligands. These results have provided essential information about the receptor structure–function relationship and will have aided in a better understanding and computational modelling of the MoA. http://dx.doi.org/10.1016/j.toxlet.2013.05.099
the first study, virtual screening identified benzophenones used as UV-filters (e.g. in cosmetics) as 17-hydroxysteroid dehydrogenase type 3 inhibitors. This enzyme is essential for gonadal testosterone synthesis. The most active benzophenone inhibited the enzyme with an IC50 of 1 M. In the second case study, a silane compound used in rubber production was discovered as both 11-hydroxysteroid dehydrogenase type 2 inhibitor and mineralocorticoid receptor agonist. The two targets are involved in the regulation of blood pressure,and their impaired functions may lead to cardiovascular events. These two studies emphasize that pharmacophore-based virtual screening is not only a tool for drug discovery, but a way to set priorities when testing small molecules for toxic effects. http://dx.doi.org/10.1016/j.toxlet.2013.05.096
P05-21 Skin sensitization study by a new qualitative structure–toxicity relationships (QSTR) approach: K-step Yard Sampling (KY) methods Kazuhiro Sato 1,∗ , Yukinori Kusaka 1 , Kohtaro Yuta 2 1 Department of Environmental Health, School of Medicine, University of Fukui, Fukui, Japan, 2 In Silico Data Ltd., Narashino, Japan
In silico assessment of skin sensitization is increasingly needed owing to the problems concerning animal welfare, as well as excessive time consumed and cost involved in the development and testing of new chemicals. We could perfectly classify skin sensitizers (positive/negative) using a newly developed K-step Yard sampling (KY) methods (U.S. Patent No. 7725413, 2010). A total of 593 compounds (419 positive and 174 negative sensitizers) were used in this study. Parameters were generated from 2-D and 3-D structures of compounds. All of the 1015 parameters generated were reduced by various feature selection methods. KY methods were performed using ADMEWORKS/ModelBuilder software. All compounds were perfectly classified by 3 steps. Discriminant function of each step was a linear dicriminant function (TILSQ). KY methods were referred to as a meta-algorithm approach because it requires ordinary data analysis methods to generate discriminant functions. KY methods were the repetition of removal of gray zone of samples and reclassification of them to attain no gray zone (100% classification) at final step. This methods always attain perfect classification at final step, even though samples are large number, large of structural diversity or highly overlapped on the sample space. http://dx.doi.org/10.1016/j.toxlet.2013.05.097
P05-22 The Danish (Q)SAR Database Update Project Nikolai G. Nikolov, Marianne Dybdahl, Sine A. Rosenberg, Eva B. Wedebye Technical University of Denmark, National Food Institute, Moerkhoej Bygade 19, 2860 Soeborg, Denmark The Danish (Q)SAR Database is a collection of predictions from quantitative structure–activity relationship ((Q)SAR) models for over 70 environmental and human health-related endpoints (covering biodegradation, metabolism, allergy, irritation, endocrine disruption, teratogenicity, mutagenicity, carcinogenicity and others), each of them available for 185,000 organic
S85
substances. The database has been available online since 2005 (http://qsar.food.dtu.dk). A major update project for the Danish (Q)SAR database is under way, with a new online release planned in the beginning of 2015. The updated version will contain more than 600,000 discrete organic structures and new, more precise predictions for all endpoints, derived by consensus algorithms from a number of state-of-the-art individual predictions. http://dx.doi.org/10.1016/j.toxlet.2013.05.098
P05-23 Toward better understanding of liver steatosis MoA: Molecular modelling study of PPAR gamma receptor Merilin Al Sharif 1,∗ , Petko Alov 1 , Mark Cronin 2 , Elena Fioravanzo 3 , Ivanka Tsakovska 1 , Vessela Vitcheva 4 , Andrew Worth 5 , Chihae Yang 6 , Ilza Pajeva 7 1
Institute of Biophysics and Biomedical Engineering, Sofia, Bulgarien, Liverpool John Moores University, Liverpool, UK, 3 S-IN Soluzioni Informatiche SRL, Vicenza, Italiy, 4 CFSAN, U.S. FDA, College Park, USA, 5 European Commission, Joint Research Centre, Institute for Health and Consumer Protection, Ispra, Italien, 6 Altamira LLC, Columbus, USA, 7 Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences Room 206, 2th fl., Bl. 105 Acad. George Bontchev Str. 1113 Sofia, Bulgaria 2
Within the mode of action/adverse outcome pathway (MoA/AOP) framework the description and characterisation of the toxicological MoAs leading to liver toxicity are of specific interest. There is a particular emphasis in the development of this framework by in silico modelling of ligand-receptor interactions identified as key molecular initiating events (MIE) in MoAs leading to liver fibrosis and steatosis. The peroxisome proliferator-activated receptor gamma (PPAR gamma) has been recently proposed as one of the receptors involved in the MIE for liver steatosis. Emerging evidences have pointed to an important function of hepatic PPAR gamma in fatty acid transport, triglyceride synthesis pathway and eventually pathogenesis of fatty liver disease. In this study a systematic literature search has been performed to identify key studies and review papers with significant data regarding the dysregulation of lipid metabolism mediated by PPAR gamma resulting in liver steatosis and consequently in the development of non-alcoholic fatty liver disease and nonalcoholic steatohepatitis. Further, the results of a molecular modelling study based on the analysis of more than 100 of this receptor’s 3D structural complexes published in Protein Data Bank (http://www.rcsb.org) are presented. The analysis included characterisation of the ligand-binding pocket and structural characterisation of the PPAR gamma binding ligands. These results have provided essential information about the receptor structure–function relationship and will have aided in a better understanding and computational modelling of the MoA. http://dx.doi.org/10.1016/j.toxlet.2013.05.099