EU-SICHERHEITSDATENBLATT Dieselkraftstoff ... - Schmierstoffe

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method, the LLNA value was accepted as representative for the structure; where the LLNA test was not available, the GPMT value was taken (Patlewicz et al., 2007, Roberts et al., 2007). 1.2.2.3. AMES Mutagenicity model The mutagenicity model (Serafimova et al., 2006) was derived on the basis of 345 chemicals that were found experimentally to be positive without S-9 metabolic system and a further 2357 chemicals – including 2012 negatives, and 397 positives with S-9 system. The simulator used for predicting metabolic activation of chemicals under S-9 is in fact predicting a mammalian liver metabolism where the settings of the simulator were adjusted in a way to avoid missing mutagenic metabolites. The simulator used in the model mimics formation of enzyme complexes and channeling effects. According to the adopted modeling scheme, the target chemicals are submitted to a metabolic simulator and generated metabolites are subsequently screened by the 3D QSAR model to identify mutagenic metabolites. 1.2.2.4. Model for Chromosomal aberrations The model for chromosomal aberrations (CA) accounts for two principal types of interaction mechanisms – interactions with DNA and interactions with proteins or nuclear enzymes (Mekenyan et al., 2007). The model is used to predict metabolism in rat liver. The alerting groups associated with these mechanisms are defined by specific structural boundaries as well as by 2D and 3D parameter ranges describing effects of bioavailability and reactivity alerts that are conditioned by the rest of the molecule. The model was derived on the basis of 497 (166 positive and 331 negative) chemicals that have experimental data without S-9 metabolic system and other 162 chemicals – including 81 positives, and 81 negatives with S-9 system. The performance of the model without metabolic activation was characterized by sensitivity and specificity values of 77% and 82%, respectively. For the model coupled with the metabolic simulator (trained to reproduce documented maps for mammalian (mainly rat) liver metabolism of 332 chemicals), the performance is 75% and 60% for sensitivity and specificity respectively. 1.2.3 OASIS LMC Models: Toxicological models - Receptor mediated effects 1.2.3.1. Androgen Binding Affinity QSAR model Some of the environmental and industrial chemicals can interact with the androgen receptor (AR) by mimicking the functions of natural hormones. The multiparameter formulation of COmmon REactivity PAttern (COREPA) (Mekenyan et al., 2004) approach was used to describe the structural requirements for eliciting androgen potency. A structurally diverse training data set containing 202 chemicals was obtained from the National Center for Toxicology Research (NCTR, US). The chemical affinities for the rat AR were related to distances between nucleophilic sites and structural features describing electronic interactions between the receptor and ligands. 1.2.3.2. Estrogen binding affinity QSAR model The multiparameter formulation of COmmon REactivity PAttern (COREPA) (Mekenyan et al., 2004) approach was used to describe the structural requirements for eliciting estrogen binding potency. A training set of 645 chemicals which includes 497 steroid and 114
environmental chemicals (database of Chemical Evaluation and Research Institute, Japan - CERI) and 148 chemicals to further explore hER-structure interactions (selected J. Katzenellenbogen references) were used. Analysis of the reactivity patterns resulted in identification of distinct interaction types: a steroid-like A–B type described by frontier orbital energies and distance between nucleophilic sites with specific charge requirements; an A–C type where local hydrophobic effects are combined with electronic interactions to modulate binding; and mixed A–B–C (AD) type. Chemicals were grouped by type, then COREPA models were developed for within specific relative binding affinity ranges greater then 10%, 0.1
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Gas Oils (Vacuum) 4 DNEL (inhalatio
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Industrial - Tabletting, CS100 Indo
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Combined Professional - Manual spra
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Industrial - Bulk transfers CS14 Da
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Professional - Maintenance and CS77
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Note: differentiated Industrial -SU
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Professional - Treatment and dispos
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Industrial - operation of open CS16
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Gas Oils (Vacuum) 4 DNEL (inhalatio
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Industrial - Tabletting, CS100 Indo
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Combined Professional - Manual spra
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Industrial - Bulk transfers CS14 Da
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Professional - Maintenance and CS77
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Note: differentiated Industrial -SU
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Professional - Treatment and dispos
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Industrial - operation of open CS16
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This tool is provided for informati
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An Evaluation of the Persistence, B
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Table of Contents Executive Summary
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2.0 Outline of PBT/vPvB Assessment
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3.0 Persistence Assessment of Petro
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100.0 Half-life predicted (days) 10
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2,3-dimethylheptane 9 7.7 6.2 7.4 2
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ioHCwin half-life (days) 10000.0 10
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criterion. It can be concluded that
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Hydrocarbon C nr BioHCwin Predicted
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ioHCwin half-life (days) 1000000.0
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Hydrocarbon C nr BioHCwin Predicted
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enzo[a]pyrene 20 421.6 16.5 Table 1
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(see Appendix 2). Since BCF predict
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10000 BCF (Arnot) 1000 100 10 1 n-P
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exhibit BMFs near unity (Figure 28)
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2,2,4,4,6,8,8- heptamethyl nonane 1
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Hydrocarbon C BMF BCF (Arnot) Dieta
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BCF (regression) 100000 10000 1000
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4.5 Polynaphthenic hydrocarbons Reg
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Experimental BMF 10 1 Polynaphth. D
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enzene n-octylbenzene 14 0.034 403
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10000 1000 BCF (Arnot) 100 10 1 NMA
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and appears to be an outlier. This
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4-ethylbiphenyl 14 863 1039 C Yakat
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100000 BCF (regression) 10000 1000
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10 Experimental BMF 1 0.1 NDiAr Die
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Hydrocarbon C BMF Arnot BCF Dietary
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Hydrocarbon C BMF Arnot BCF Dietary
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Acenaphthene Acenaphthylene Benz(a)
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Page 237 and 238: EMBSI (2007c). Fish, dietary bioacc
Page 239 and 240: Prince RC, Walters CC. (2007). Biod
Page 241 and 242: Appendix 1. Hydrocarbon structures
Page 243 and 244: iP 14 2,4,10-Trimethylundecane CC(C
Page 245 and 246: MN 7 1,2-Dimethylcyclopentane CC1C(
Page 247 and 248: MN 14 n-Nonylcyclopentane CCCCCCCCC
Page 249 and 250: MN 29 MN 29 MN 30 n-Tetracosylcyclo
Page 251 and 252: hexahydroindane DN 20 2,4-dimethylo
Page 253 and 254: PN 27 Phenanthrene 2,6-dimethylhept
Page 255 and 256: MAr 13 1-Methyl-3-hexylbenzene Cc1c
Page 257 and 258: NMAr 12 n-Propylindan c1ccc2CC(CCC)
Page 259 and 260: NMAr 24 c1cc(CC(C)CCCC(C)CCCCC)c2CC
Page 261 and 262: NMAr 29 NMAr 29 NMAr 29 NMAr 29 NMA
Page 263 and 264: DAr 16 2-Hexylnaphthalene CCCCCCc1c
Page 265 and 266: NDAr 16 n-Butylacenaphthene c12cccc
Page 267 and 268: NDAr 26 Dimethyloctyl-1,2,3,6,7,8-
Page 269 and 270: PAr 19 Ethylbenzo(a)fluorene C3c1cc
Page 271 and 272: PAr 22 n-Pentylbenzo(a)fluorene C3c
Page 273 and 274: PAr 28 Isohexyl-octadecahydro-picen
Page 275 and 276: Appendix 3. CONCAWE Position Paper
Page 277 and 278: probabilities are adjusted to best
Page 279: 1.2.2 OASIS LMC Models: Toxicologic
Page 283 and 284: dihydrodiol. These dihydroxylated i
Page 285 and 286: Table 1 : PBT assessment of C15 str
Page 287 and 288: 1: The following structures were pr
Page 289 and 290: Dimitrov SD, Dimitrova N, Mekenyan
Page 291 and 292: hydrocarbons and azaarenes original
Page 293 and 294: Verhaar HJM, van Leeuwen CJ, Hermen
Page 295 and 296: CONCAWE AQUATIC TOXICITY PREDICTION
Page 297 and 298: CONTENTS (Continued) Section Page 1
Page 299 and 300: 1-2 The model predictions include m
Page 301 and 302: 2-1 SECTION 2 2 SUMMARY OF COMPOSIT
Page 309 and 310: 10-1 SECTION 10 10 SUMMARY OF COMPO
Page 319: 20-1 SECTION 20 20 SUMMARY OF COMPO
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