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EC number: 500-101-4 | CAS number: 38294-64-3
- Life Cycle description
- Uses advised against
- Endpoint summary
- Appearance / physical state / colour
- Melting point / freezing point
- Boiling point
- Density
- Particle size distribution (Granulometry)
- Vapour pressure
- Partition coefficient
- Water solubility
- Solubility in organic solvents / fat solubility
- Surface tension
- Flash point
- Auto flammability
- Flammability
- Explosiveness
- Oxidising properties
- Oxidation reduction potential
- Stability in organic solvents and identity of relevant degradation products
- Storage stability and reactivity towards container material
- Stability: thermal, sunlight, metals
- pH
- Dissociation constant
- Viscosity
- Additional physico-chemical information
- Additional physico-chemical properties of nanomaterials
- Nanomaterial agglomeration / aggregation
- Nanomaterial crystalline phase
- Nanomaterial crystallite and grain size
- Nanomaterial aspect ratio / shape
- Nanomaterial specific surface area
- Nanomaterial Zeta potential
- Nanomaterial surface chemistry
- Nanomaterial dustiness
- Nanomaterial porosity
- Nanomaterial pour density
- Nanomaterial photocatalytic activity
- Nanomaterial radical formation potential
- Nanomaterial catalytic activity
- Endpoint summary
- Stability
- Biodegradation
- Bioaccumulation
- Transport and distribution
- Environmental data
- Additional information on environmental fate and behaviour
- Ecotoxicological Summary
- Aquatic toxicity
- Endpoint summary
- Short-term toxicity to fish
- Long-term toxicity to fish
- Short-term toxicity to aquatic invertebrates
- Long-term toxicity to aquatic invertebrates
- Toxicity to aquatic algae and cyanobacteria
- Toxicity to aquatic plants other than algae
- Toxicity to microorganisms
- Endocrine disrupter testing in aquatic vertebrates – in vivo
- Toxicity to other aquatic organisms
- Sediment toxicity
- Terrestrial toxicity
- Biological effects monitoring
- Biotransformation and kinetics
- Additional ecotoxological information
- Toxicological Summary
- Toxicokinetics, metabolism and distribution
- Acute Toxicity
- Irritation / corrosion
- Sensitisation
- Repeated dose toxicity
- Genetic toxicity
- Carcinogenicity
- Toxicity to reproduction
- Specific investigations
- Exposure related observations in humans
- Toxic effects on livestock and pets
- Additional toxicological data
Bioaccumulation: aquatic / sediment
Administrative data
Link to relevant study record(s)
- Endpoint:
- bioaccumulation in aquatic species: fish
- Type of information:
- (Q)SAR
- Adequacy of study:
- key study
- Reliability:
- 2 (reliable with restrictions)
- Rationale for reliability incl. deficiencies:
- other: Well documented prediction using analogues for validation. Prediction within parametric domain; however, only within 50% of structural domain of QSAR prediction model.
- Justification for type of information:
- 1. SOFTWARE
Model: OASIS CATALOGIC v. 5.11.3.
Submodel: BCF baseline model v. 01.02.
2. SMILES OR OTHER IDENTIFIERS USED AS INPUT FOR THE MODEL
c1(C(C)(C)c2ccc(OCC(O)CNCc3cc(CN)ccc3)cc2)ccc(OCC(O)CNCc2cc(CN)ccc2)cc1
3. SCIENTIFIC VALIDITY OF THE (Q)SAR MODEL
- Defined endpoint: The model estimates whole fish steady state bioconcentration factor (BCF). The training set consists of 396 authentic BCF test data with Cyprinos caprio (MITI database,) and 118 BCF data estimated from dietary bioaccumulation test results with salmonids (Exxon Mobile).
Estimation BCF is based on a maximum BCF and mitigating factors that reduce the BCF. The maximum bioconcentration potential is calculated for a multi-compartment partitioning model and passive diffusion. The most relevant mitigating factor is metabolism, which is accounted for by means of a fish liver model, which has been trained to reproduce fish metabolism based on related mammalian metabolic pathways. Other mitigating factors like molecular size and ionization are also taken into account in the model (Dimitrov et al. 2005).
- Unambiguous algorithm:
- Defined domain of applicability:
- Appropriate measures of goodness-of-fit and robustness and predictivity:
- Mechanistic interpretation:
4. APPLICABILITY DOMAIN
i. Descriptor domain
1. Predicted Log Kow: -4.052. Molecular weight (MW):16.04 < MW < 1131.21: in domain
3. Predicted Water solubility (Csat): 0ii. Structural fragment domain: Structural domain is represented by the list of atom-centred fragments (accounting for the first neighbours) extracted fromtraining set chemicals which are correctly predicted. A correct prediction was assumed for those chemicals for which the residuals between predicted and observed values were less than or equal to 0.75 log units.
iii. Mechanistic domain: The model prediction is based on the assumption of a maximal uptake through passive diffusion and mitigating factors in the uptake (size, dissociation) or the elimination (metabolism).
In order to verify the relevance of the model assumption for the unknown, structural analogues were selected from the training set and processed in parallel
iv. Metabolic domain, if relevant:Covered by the structural domain.
5. ADEQUACY OF THE RESULT
The model prediction is based on the assumption of a maximal uptake through passive diffusion and mitigating factors in the uptake (size, dissociation) or the elimination(metabolism). Based on comparison of predicted BCF and experimental BCF as reported in the training set for a series of analogues, it is anticipated that the underlying assumptions of the model can be applied to the unknowns. The result is considered to adequate for risk assessment. - Principles of method if other than guideline:
- Estimation of the bioconcentration factor (BCF) of BADGE-IPD #33 using CATABOL BCF baseline model v. 1.02
- GLP compliance:
- no
- Remarks:
- Not applicable for QSAR
- Type:
- BCF
- Value:
- 5.13 dimensionless
- Basis:
- whole body w.w.
- Calculation basis:
- other: QSAR
- Remarks on result:
- other: Conc.in environment / dose:Not applicable
- Executive summary:
The bioconcentration potential of the reaction mass of BADGE IPD adduct has been assessed using a QSAR algorithm as described by Dimitrov et al (2005) and implemented in OASIS CATALOGIC. The model estimates steady state whole fish bioconcentration factor (BCF) based on a maximum BCF and mitigating factors that reduce the BCF.
Structural analogues identified from the training set have been processed parallel to the unknown. The unknowns are within the parametric domain of the model but within less than 50% of the structural domain as defined by first neighbour atom centred fragments.
To assess the relevance of the metabolism as mitigating factor for the unknowns, a comparison of the metabolic reactions proposed for the analogues and the unknowns is made.
Assuming that the prediction falls within the range of accurate predictions as defined by the developer the following BCF are calculated for the constituents of BADGE IPD adduct:
Molecule
SMILES used to describe the Material
log BCFcalc
BCF
[(mg/kg w.w.) /(mg/L)]BAGE IPD adduct
c1(C(C)(C)c2ccc(OCC(O)CNCc3cc(CN)ccc3)cc2)ccc(OCC(O)CNCc2cc(CN)ccc2)cc1
0.710± 0.28
5.13 (2.7–9.77)
Based on a decision rule derived by the developer of the QSAR model, BADGE IPD adduct is not bioaccumulative with high confidence.
Reference
a. Predicted value (model result):
Table 1a: Log BCF and BCF prediction for unknowns.
Molecule |
log BCFcalc |
BCF |
Constituent 1) |
0.710± 0.28 |
5.13 (2.7–9.77) |
Table 1b: Intermediate Results from LogBCF prediction.
|
Log KOW |
Log BCFmax |
log BCFcalc |
mitigating effect of : |
||||
Acids |
Metabolism |
Phenols |
Size3 |
Water solubility |
||||
1) |
6.22 |
4.43 |
0.710 |
0 |
1.80 |
0 |
1.8017 |
0.3275 |
The developers of the QSAR present a decision rule for the interpretation of the predicted Log BCF based on the assessment of their training and validation data (See Table 1c). Based on this decision rule the BADGE IPD adduct is not bioaccumulative with high confidence.
Table 1c: Decision rule for the interpretation of predicted Log BCF values.
Calculated BCF |
Conclusion |
Log BCFclalc≥ 3.699+0.75 |
Bioaccumulative – high confidence |
3.699 < Log BCFclalc< 3.699+0.75 |
Bioaccumulative – low confidence |
3.699 - 0.75< Log BCFclalc< 3.699 |
Not bioaccumulative – low confidence |
Log BCFclalc< 3.699 - 0.75 |
Not Bioaccumulative – high confidence |
b. Predicted value (comments):Metabolism and water solubility are the most relevant mitigating factor for BADGE-IPD adduct.
c. Input for prediction: SMILES as specified under 1.5a
d. Descriptor values: Prediction is based on Kow prediction based on Epiwin(US Environmental Protection Agency and Syracuse Research Corporation (SRC) 2008). Table 1b gives intermediate results used for in the calculation.
Description of key information
Well documented prediction using analogues for validation. Prediction within parametric domain; however, only within 50% of structural domain of QSAR prediction model.
Key value for chemical safety assessment
- BCF (aquatic species):
- 5.13
Additional information
The bioconcentration potential of the reaction mass constituents in BADGE IPD adduct have been assessed using a QSAR algorithm as described by Dimitrov et al (2005) and implemented in OASIS CATALOGIC. The model estimates steady state whole fish bioconcentration factor (BCF) based on a maximum BCF and mitigating factors that reduce the BCF.
Structural analogues identified from the training set have been processed parallel to the unknown. The unknowns are within the parametric domain of the model but within less than 50% of the structural domain as defined by first neighbour atom centred fragments.
Assuming that the prediction falls within the range of accurate predictions as defined by the developer the following BCF are calculated for the reaction mass BAGE IPD adduct constituents:
Molecule |
SMILES used to describe the Material |
log BCFcalc |
BCF |
BAGE IPD adduct |
c1(C(C)(C)c2ccc(OCC(O)CNCc3cc(CN)ccc3)cc2)ccc(OCC(O)CNCc2cc(CN)ccc2)cc1 |
0.710± 0.28 |
5.13 (2.7–9.77) |
Based on a decision rule derived by the developer of the QSAR model, the reaction mass of BADGE IPD are not bioaccumulative with high confidence.
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