15 results on '"Wedebye, Eva B."'
Search Results
2. Identification of substances with a carcinogenic potential in spray-formulated engine/brake cleaners and lubricating products, available in the European Union (EU) – based on IARC and EU-harmonised classifications and QSAR predictions
- Author
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Sørli, Jorid B., Frederiksen, Marie, Nikolov, Nikolai G., Wedebye, Eva B., and Hadrup, Niels
- Published
- 2022
- Full Text
- View/download PDF
3. QSAR screening of 70,983 REACH substances for genotoxic carcinogenicity, mutagenicity and developmental toxicity in the ChemScreen project
- Author
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Wedebye, Eva B., Dybdahl, Marianne, Nikolov, Nikolai G., Jónsdóttir, Svava Ó., and Niemelä, Jay R.
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- 2015
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4. hERG blocking potential of acids and zwitterions characterized by three thresholds for acidity, size and reactivity
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Nikolov, Nikolai G., Dybdahl, Marianne, Jónsdóttir, Svava Ó., and Wedebye, Eva B.
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- 2014
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5. Potentiale for astma-induktion for 28 stoffer i rengøringsprodukter på sprayform:review og evaluering ved quantitative structure activity relationship (QSAR)
- Author
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Hadrup, Niels, Frederiksen, Marie, Wedebye, Eva B., Nikolov, Nikolai G., Carøe, Tanja K., Sørli, Jorid B., Frydendall, Karin B., Liguori, Biase, Sajbaek, Camilla S., Wolkoff, Peder, Flachs, Esben M., Schlünssen, Vivi, Meyer, Harald W., Clausen, Per A., and S. Hougaard, Karin
- Abstract
Eksponering for rengøringsprodukter på spray-form udgør en potentiel risiko for udvikling af astma. Derfor har vi reviewet, om stoffer i sådanne produkter er potentielt astmaudløsende. Vi identificerede 101 sprayformulerede rengøringsprodukter. Otteogtyve indholdsstoffer blev udvalgt på baggrund af a) positiv prædiktion for sensibilisering af luftvejene hos mennesker, baseret på quantitative structure activity relationship (QSAR) i Danish (Q)SAR Database; b) positiv QSAR prædiktion for alvorlig hudirritation i kaniner (også baseret på QSAR); og c) viden om stoffernes fysisk-kemiske egenskaber og toksicitet. Ved at kombinere viden fra den toksikologiske litteratur med QSAR prædiktioner kunne vi gruppere stofferne i fire klasser: 1) nogen indikation for induktion af astma i mennesker: chloramin, benzalkoniumchlorid, 2) nogen indikation for induktion af astma i dyr: ethylendiamintetraeddikesyre (EDTA), citron-syre, 3) tvetydige data: hypochlorit, 4) få eller manglende data: nitrilotrieddikesyre, monoethanolamin; 2-(2-aminoethoxy)ethanol, 2-diethylaminoethanol, alkyldimethylaminoxid, 1-aminopropan-2-ol, methylisothiazolinon, benzisothiazolinon, og chlormethylisothiazo-linon; tre specifikke sulfonater og sulfamin-syre, salicylsyre og dets analoge salt natrium-benzoat, propan-1,2-diol, glycerol, propylidyn-trimethanol, mælkesyre, dinatriummalat, morfolin, bronopol, og benzylalkohol.Vi identificerede betydelige huller i viden om potentialet for induktion af astma for langt de fleste af de udvalgte stoffer. Vi konkluderer derfor, at vi må have mere viden for at kunne bidrage til en safe-by-design strategi, hvor stoffer der øger risikoen for astma-induktion undgås i fremtidige sprayformulerede ren-gøringsprodukter. Vi foreslår desuden, at QSAR prædiktioner kan bruges til at prioritere stoffer til fremtidig toksikologisk testning.
- Published
- 2021
6. Asthma‐inducing potential of 28 substances in spray cleaning products—Assessed by quantitative structure activity relationship (QSAR) testing and literature review
- Author
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Hadrup, Niels, primary, Frederiksen, Marie, additional, Wedebye, Eva B., additional, Nikolov, Nikolai G., additional, Carøe, Tanja K., additional, Sørli, Jorid B., additional, Frydendall, Karen B., additional, Liguori, Biase, additional, Sejbaek, Camilla S., additional, Wolkoff, Peder, additional, Flachs, Esben M., additional, Schlünssen, Vivi, additional, Meyer, Harald W., additional, Clausen, Per A., additional, and Hougaard, Karin S., additional
- Published
- 2021
- Full Text
- View/download PDF
7. TIMES-SS—A promising tool for the assessment of skin sensitization hazard. A characterization with respect to the OECD validation principles for (Q)SARs and an external evaluation for predictivity
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Patlewicz, Grace, Dimitrov, Sabcho D., Low, Lawrence K., Kern, Petra S., Dimitrova, Gergana D., Comber, Mike I.H., Aptula, Aynur O., Phillips, Richard D., Niemelä, Jay, Madsen, Charlotte, Wedebye, Eva B., Roberts, David W., Bailey, Paul T., and Mekenyan, Ovanes G.
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- 2007
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8. CoMPARA : Collaborative Modeling Project for Androgen Receptor Activity
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Mansouri, Kamel, Kleinstreuer, Nicole, Abdelaziz, Ahmed M., Alberga, Domenico, Alves, Vinicius M., Andersson, Patrik L., Andrade, Carolina H., Bai, Fang, Balabin, Ilya, Ballabio, Davide, Benfenati, Emilio, Bhhatarai, Barun, Boyer, Scott, Chen, Jingwen, Consonni, Viviana, Farag, Sherif, Fourches, Denis, García-Sosa, Alfonso T., Gramatica, Paola, Grisoni, Francesca, Grulke, Chris M., Hong, Huixiao, Horvath, Dragos, Hu, Xin, Huang, Ruili, Jeliazkova, Nina, Li, Jiazhong, Li, Xuehua, Liu, Huanxiang, Manganelli, Serena, Mangiatordi, Giuseppe F., Maran, Uko, Marcou, Gilles, Martin, Todd, Muratov, Eugene, Nguyen, Dac-Trung, Nicolotti, Orazio, Nikolov, Nikolai G., Norinder, Ulf, Papa, Ester, Petitjean, Michel, Pür, Geven, Pogodin, Pavel, Poroikov, Vladimir, Qiao, Xianliang, Richard, Ann M., Roncaglioni, Alessandra, Ruiz, Patricia, Rupakheti, Chetan, Sakkiah, Sugunadevi, Sangion, Alessandro, Schramm, Karl-Werner, Selvaraj, Chandrabose, Shah, Imran, Sild, Sulev, Sun, Lixia, Taboureau, Olivier, Tang, Yun, Tetko, Igor V., Todeschini, Roberto, Tong, Weida, Trisciuzzi, Daniela, Tropsha, Alexander, Van Den Driessche, George, Varnek, Alexandre, Wang, Zhongyu, Wedebye, Eva B., Williams, Antony J., Xie, Hongbin, Zakharov, Alexey V., Zheng, Ziye, Judson, Richard S., Mansouri, Kamel, Kleinstreuer, Nicole, Abdelaziz, Ahmed M., Alberga, Domenico, Alves, Vinicius M., Andersson, Patrik L., Andrade, Carolina H., Bai, Fang, Balabin, Ilya, Ballabio, Davide, Benfenati, Emilio, Bhhatarai, Barun, Boyer, Scott, Chen, Jingwen, Consonni, Viviana, Farag, Sherif, Fourches, Denis, García-Sosa, Alfonso T., Gramatica, Paola, Grisoni, Francesca, Grulke, Chris M., Hong, Huixiao, Horvath, Dragos, Hu, Xin, Huang, Ruili, Jeliazkova, Nina, Li, Jiazhong, Li, Xuehua, Liu, Huanxiang, Manganelli, Serena, Mangiatordi, Giuseppe F., Maran, Uko, Marcou, Gilles, Martin, Todd, Muratov, Eugene, Nguyen, Dac-Trung, Nicolotti, Orazio, Nikolov, Nikolai G., Norinder, Ulf, Papa, Ester, Petitjean, Michel, Pür, Geven, Pogodin, Pavel, Poroikov, Vladimir, Qiao, Xianliang, Richard, Ann M., Roncaglioni, Alessandra, Ruiz, Patricia, Rupakheti, Chetan, Sakkiah, Sugunadevi, Sangion, Alessandro, Schramm, Karl-Werner, Selvaraj, Chandrabose, Shah, Imran, Sild, Sulev, Sun, Lixia, Taboureau, Olivier, Tang, Yun, Tetko, Igor V., Todeschini, Roberto, Tong, Weida, Trisciuzzi, Daniela, Tropsha, Alexander, Van Den Driessche, George, Varnek, Alexandre, Wang, Zhongyu, Wedebye, Eva B., Williams, Antony J., Xie, Hongbin, Zakharov, Alexey V., Zheng, Ziye, and Judson, Richard S.
- Abstract
BACKGROUND: Endocrine disrupting chemicals (EDCs) are xenobiotics that mimic the interaction of natural hormones and alter synthesis, transport, or metabolic pathways. The prospect of EDCs causing adverse health effects in humans and wildlife has led to the development of scientific and regulatory approaches for evaluating bioactivity. This need is being addressed using high-throughput screening (HTS) in vitro approaches and computational modeling. OBJECTIVES: In support of the Endocrine Disruptor Screening Program, the U.S. Environmental Protection Agency (EPA) led two worldwide consortiums to virtually screen chemicals for their potential estrogenic and androgenic activities. Here, we describe the Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA) efforts, which follows the steps of the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP). METHODS: The CoMPARA list of screened chemicals built on CERAPP's list of 32,464 chemicals to include additional chemicals of interest, as well as simulated ToxCast (TM) metabolites, totaling 55,450 chemical structures. Computational toxicology scientists from 25 international groups contributed 91 predictive models for binding, agonist, and antagonist activity predictions. Models were underpinned by a common training set of 1,746 chemicals compiled from a combined data set of 11 ToxCast (TM)/Tox21 HTS in vitro assays. RESULTS: The resulting models were evaluated using curated literature data extracted from different sources. To overcome the limitations of single-model approaches, CoMPARA predictions were combined into consensus models that provided averaged predictive accuracy of approximately 80% for the evaluation set. DISCUSSION: The strengths and limitations of the consensus predictions were discussed with example chemicals; then, the models were implemented into the free and open-source OPERA application to enable screening of new chemicals with a defined applicability domain and accura
- Published
- 2020
- Full Text
- View/download PDF
9. CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity
- Author
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Mansouri, K, Kleinstreuer, N, Abdelaziz, A, Alberga, D, Alves, V, Andersson, P, Andrade, C, Bai, F, Balabin, I, Ballabio, D, Benfenati, E, Bhhatarai, B, Boyer, S, Chen, J, Consonni, V, Farag, S, Fourches, D, García-Sosa, A, Gramatica, P, Grisoni, F, Grulke, C, Hong, H, Horvath, D, Hu, X, Huang, R, Jeliazkova, N, Li, J, Li, X, Liu, H, Manganelli, S, Mangiatordi, G, Maran, U, Marcou, G, Martin, T, Muratov, E, Nguyen, D, Nicolotti, O, Nikolov, N, Norinder, U, Papa, E, Petitjean, M, Piir, G, Pogodin, P, Poroikov, V, Qiao, X, Richard, A, Roncaglioni, A, Ruiz, P, Rupakheti, C, Sakkiah, S, Sangion, A, Schramm, K, Selvaraj, C, Shah, I, Sild, S, Sun, L, Taboureau, O, Tang, Y, Tetko, I, Todeschini, R, Tong, W, Trisciuzzi, D, Tropsha, A, Van Den Driessche, G, Varnek, A, Wang, Z, Wedebye, E, Williams, A, Xie, H, Zakharov, A, Zheng, Z, Judson, R, Mansouri, Kamel, Kleinstreuer, Nicole, Abdelaziz, Ahmed M., Alberga, Domenico, Alves, Vinicius M., Andersson, Patrik L., Andrade, Carolina H., Bai, Fang, Balabin, Ilya, Ballabio, Davide, Benfenati, Emilio, Bhhatarai, Barun, Boyer, Scott, Chen, Jingwen, Consonni, Viviana, Farag, Sherif, Fourches, Denis, García-Sosa, Alfonso T., Gramatica, Paola, Grisoni, Francesca, Grulke, Chris M., Hong, Huixiao, Horvath, Dragos, Hu, Xin, Huang, Ruili, Jeliazkova, Nina, Li, Jiazhong, Li, Xuehua, Liu, Huanxiang, Manganelli, Serena, Mangiatordi, Giuseppe F., Maran, Uko, Marcou, Gilles, Martin, Todd, Muratov, Eugene, Nguyen, Dac-Trung, Nicolotti, Orazio, Nikolov, Nikolai G., Norinder, Ulf, Papa, Ester, Petitjean, Michel, Piir, Geven, Pogodin, Pavel, Poroikov, Vladimir, Qiao, Xianliang, Richard, Ann M., Roncaglioni, Alessandra, Ruiz, Patricia, Rupakheti, Chetan, Sakkiah, Sugunadevi, Sangion, Alessandro, Schramm, Karl-Werner, Selvaraj, Chandrabose, Shah, Imran, Sild, Sulev, Sun, Lixia, Taboureau, Olivier, Tang, Yun, Tetko, Igor V., Todeschini, Roberto, Tong, Weida, Trisciuzzi, Daniela, Tropsha, Alexander, Van Den Driessche, George, Varnek, Alexandre, Wang, Zhongyu, Wedebye, Eva B., Williams, Antony J., Xie, Hongbin, Zakharov, Alexey V., Zheng, Ziye, Judson, Richard S., Mansouri, K, Kleinstreuer, N, Abdelaziz, A, Alberga, D, Alves, V, Andersson, P, Andrade, C, Bai, F, Balabin, I, Ballabio, D, Benfenati, E, Bhhatarai, B, Boyer, S, Chen, J, Consonni, V, Farag, S, Fourches, D, García-Sosa, A, Gramatica, P, Grisoni, F, Grulke, C, Hong, H, Horvath, D, Hu, X, Huang, R, Jeliazkova, N, Li, J, Li, X, Liu, H, Manganelli, S, Mangiatordi, G, Maran, U, Marcou, G, Martin, T, Muratov, E, Nguyen, D, Nicolotti, O, Nikolov, N, Norinder, U, Papa, E, Petitjean, M, Piir, G, Pogodin, P, Poroikov, V, Qiao, X, Richard, A, Roncaglioni, A, Ruiz, P, Rupakheti, C, Sakkiah, S, Sangion, A, Schramm, K, Selvaraj, C, Shah, I, Sild, S, Sun, L, Taboureau, O, Tang, Y, Tetko, I, Todeschini, R, Tong, W, Trisciuzzi, D, Tropsha, A, Van Den Driessche, G, Varnek, A, Wang, Z, Wedebye, E, Williams, A, Xie, H, Zakharov, A, Zheng, Z, Judson, R, Mansouri, Kamel, Kleinstreuer, Nicole, Abdelaziz, Ahmed M., Alberga, Domenico, Alves, Vinicius M., Andersson, Patrik L., Andrade, Carolina H., Bai, Fang, Balabin, Ilya, Ballabio, Davide, Benfenati, Emilio, Bhhatarai, Barun, Boyer, Scott, Chen, Jingwen, Consonni, Viviana, Farag, Sherif, Fourches, Denis, García-Sosa, Alfonso T., Gramatica, Paola, Grisoni, Francesca, Grulke, Chris M., Hong, Huixiao, Horvath, Dragos, Hu, Xin, Huang, Ruili, Jeliazkova, Nina, Li, Jiazhong, Li, Xuehua, Liu, Huanxiang, Manganelli, Serena, Mangiatordi, Giuseppe F., Maran, Uko, Marcou, Gilles, Martin, Todd, Muratov, Eugene, Nguyen, Dac-Trung, Nicolotti, Orazio, Nikolov, Nikolai G., Norinder, Ulf, Papa, Ester, Petitjean, Michel, Piir, Geven, Pogodin, Pavel, Poroikov, Vladimir, Qiao, Xianliang, Richard, Ann M., Roncaglioni, Alessandra, Ruiz, Patricia, Rupakheti, Chetan, Sakkiah, Sugunadevi, Sangion, Alessandro, Schramm, Karl-Werner, Selvaraj, Chandrabose, Shah, Imran, Sild, Sulev, Sun, Lixia, Taboureau, Olivier, Tang, Yun, Tetko, Igor V., Todeschini, Roberto, Tong, Weida, Trisciuzzi, Daniela, Tropsha, Alexander, Van Den Driessche, George, Varnek, Alexandre, Wang, Zhongyu, Wedebye, Eva B., Williams, Antony J., Xie, Hongbin, Zakharov, Alexey V., Zheng, Ziye, and Judson, Richard S.
- Abstract
BACKGROUND: Endocrine disrupting chemicals (EDCs) are xenobiotics that mimic the interaction of natural hormones and alter synthesis, transport, or metabolic pathways. The prospect of EDCs causing adverse health effects in humans and wildlife has led to the development of scientific and regulatory approaches for evaluating bioactivity. This need is being addressed using high-throughput screening (HTS) in vitro approaches and computational modeling. OBJECTIVES: In support of the Endocrine Disruptor Screening Program, the U.S. Environmental Protection Agency (EPA) led two worldwide consortiums to virtually screen chemicals for their potential estrogenic and androgenic activities. Here, we describe the Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA) efforts, which follows the steps of the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP). METHODS: The CoMPARA list of screened chemicals built on CERAPP’s list of 32,464 chemicals to include additional chemicals of interest, as well as simulated ToxCastTM metabolites, totaling 55,450 chemical structures. Computational toxicology scientists from 25 international groups contributed 91 predictive models for binding, agonist, and antagonist activity predictions. Models were underpinned by a common training set of 1,746 chemicals compiled from a combined data set of 11 ToxCastTM/Tox21 HTS in vitro assays. RESULTS: The resulting models were evaluated using curated literature data extracted from different sources. To overcome the limitations of single-model approaches, CoMPARA predictions were combined into consensus models that provided averaged predictive accuracy of approximately 80% for the evaluation set. DISCUSSION: The strengths and limitations of the consensus predictions were discussed with example chemicals; then, the models were implemented into the free and open-source OPERA application to enable screening of new chemicals with a defined applicability domain and accuracy ass
- Published
- 2020
10. Asthma-inducing potential of 28 substances in spray cleaning products—Assessed by quantitative structure activity relationship (QSAR) testing and literature review.
- Author
-
Hadrup, Niels, Frederiksen, Marie, Wedebye, Eva B., Nikolov, Nikolai G., Carøe, Tanja K., Sørli, Jorid B., Frydendall, Karen B., Liguori, Biase, Sejbaek, Camilla S., Wolkoff, Peder, Flachs, Esben M., Schlünssen, Vivi, Meyer, Harald W., Clausen, Per A., and Hougaard, Karin S.
- Subjects
STRUCTURE-activity relationships ,CLEANING compounds ,ETHYLENEDIAMINETETRAACETIC acid ,SULFAMIC acid ,LITERATURE reviews ,BENZYL alcohol ,CITRIC acid - Abstract
Exposure to spray cleaning products constitutes a potential risk for asthma induction. We set out to review whether substances in such products are potential inducers of asthma. We identified 101 spray cleaning products for professional use. Twenty-eight of their chemical substances were selected. We based the selection on (a) positive prediction for respiratory sensitisation in humans based on quantitative structure activity relationship (QSAR) in the Danish (Q)SAR Database, (b) positive QSAR prediction for severe skin irritation in rabbits and (c) knowledge on the substances' physico-chemical characteristics and toxicity. Combining the findings in the literature and QSAR predictions, we could group substances into four classes: (1) some indication in humans for asthma induction: chloramine, benzalkonium chloride; (2) some indication in animals for asthma induction: ethylenediaminetetraacetic acid (EDTA), citric acid; (3) equivocal data: hypochlorite; (4) few or lacking data: nitriloacetic acid, monoethanolamine, 2-(2-aminoethoxy) ethanol, 2-diethylaminoethanol, alkyldimethylamin oxide, 1-aminopropan-2-ol, methylisothiazolinone, benzisothiazolinone and chlormethylisothiazolinone; three specific sulphonates and sulfamic acid, salicylic acid and its analogue sodium benzoate, propane-1,2-diol, glycerol, propylidynetrimethanol, lactic acid, disodium malate, morpholine, bronopol and benzyl alcohol. In conclusion, we identified an asthma induction potential for some of the substances. In addition, we identified major knowledge gaps for most substances. Thus, more data are needed to feed into a strategy of safe-by-design, where substances with potential for induction of asthma are avoided in future (spray) cleaning products. Moreover, we suggest that QSAR predictions can serve to prioritise substances that need further testing in various areas of toxicology. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
11. CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity
- Author
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Mansouri, Kamel, primary, Kleinstreuer, Nicole, additional, Abdelaziz, Ahmed M., additional, Alberga, Domenico, additional, Alves, Vinicius M., additional, Andersson, Patrik L., additional, Andrade, Carolina H., additional, Bai, Fang, additional, Balabin, Ilya, additional, Ballabio, Davide, additional, Benfenati, Emilio, additional, Bhhatarai, Barun, additional, Boyer, Scott, additional, Chen, Jingwen, additional, Consonni, Viviana, additional, Farag, Sherif, additional, Fourches, Denis, additional, García-Sosa, Alfonso T., additional, Gramatica, Paola, additional, Grisoni, Francesca, additional, Grulke, Chris M., additional, Hong, Huixiao, additional, Horvath, Dragos, additional, Hu, Xin, additional, Huang, Ruili, additional, Jeliazkova, Nina, additional, Li, Jiazhong, additional, Li, Xuehua, additional, Liu, Huanxiang, additional, Manganelli, Serena, additional, Mangiatordi, Giuseppe F., additional, Maran, Uko, additional, Marcou, Gilles, additional, Martin, Todd, additional, Muratov, Eugene, additional, Nguyen, Dac-Trung, additional, Nicolotti, Orazio, additional, Nikolov, Nikolai G., additional, Norinder, Ulf, additional, Papa, Ester, additional, Petitjean, Michel, additional, Piir, Geven, additional, Pogodin, Pavel, additional, Poroikov, Vladimir, additional, Qiao, Xianliang, additional, Richard, Ann M., additional, Roncaglioni, Alessandra, additional, Ruiz, Patricia, additional, Rupakheti, Chetan, additional, Sakkiah, Sugunadevi, additional, Sangion, Alessandro, additional, Schramm, Karl-Werner, additional, Selvaraj, Chandrabose, additional, Shah, Imran, additional, Sild, Sulev, additional, Sun, Lixia, additional, Taboureau, Olivier, additional, Tang, Yun, additional, Tetko, Igor V., additional, Todeschini, Roberto, additional, Tong, Weida, additional, Trisciuzzi, Daniela, additional, Tropsha, Alexander, additional, Van Den Driessche, George, additional, Varnek, Alexandre, additional, Wang, Zhongyu, additional, Wedebye, Eva B., additional, Williams, Antony J., additional, Xie, Hongbin, additional, Zakharov, Alexey V., additional, Zheng, Ziye, additional, and Judson, Richard S., additional
- Published
- 2020
- Full Text
- View/download PDF
12. QSAR modelling of a large imbalanced aryl hydrocarbon activation dataset by rational and random sampling and screening of 80,086 REACH pre-registered and/or registered substances
- Author
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Klimenko, Kyrylo, primary, Rosenberg, Sine A., additional, Dybdahl, Marianne, additional, Wedebye, Eva B., additional, and Nikolov, Nikolai G., additional
- Published
- 2019
- Full Text
- View/download PDF
13. The Danish (Q)SAR Database Update Project
- Author
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Nikolov, Nikolai G., primary, Dybdahl, Marianne, additional, Rosenberg, Sine A., additional, and Wedebye, Eva B., additional
- Published
- 2013
- Full Text
- View/download PDF
14. Identification of potential CMR substances under REACH by (Q)SAR
- Author
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Wedebye, Eva B., primary, Nikolov, Nikolai G., additional, Dybdahl, Marianne, additional, Rosenberg, Sine A., additional, and Niemelä, Jay R., additional
- Published
- 2013
- Full Text
- View/download PDF
15. TIMES-SS—A Mechanistic Evaluation of an External Validation Study Using Reaction Chemistry Principles
- Author
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Roberts, David W., primary, Patlewicz, Grace, additional, Dimitrov, Sabcho D., additional, Low, Lawrence K., additional, Aptula, Aynur O., additional, Kern, Petra S., additional, Dimitrova, Gergana D., additional, Comber, Mike I. H., additional, Phillips, Richard D., additional, Niemelä, Jay, additional, Madsen, Charlotte, additional, Wedebye, Eva B., additional, Bailey, Paul T., additional, and Mekenyan, Ovanes G., additional
- Published
- 2007
- Full Text
- View/download PDF
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