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Diss Factsheets
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EC number: 262-992-3 | CAS number: 61788-64-5
- 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)
Description of key information
Weight of evidence comparison of predicted BCFs and BAFs to B Criterion: BCF>2000 L/kg and vB Criterion: BCF>5000 L/kg indicates that the fish oil derivative is "not B" and "not vB" according to REACH and applicable guidance.
Key value for chemical safety assessment
- BCF (aquatic species):
- 70.79 L/kg ww
Additional information
The aquatic bioaccumulation can be estimated with reliable methods.
The QSAR predictions and available empirical data were evaluated with respect to the following considerations:
1. Applicability of the data to the specific information requirements for the substances in terms of relevance and adequacy.
2. Reliability of the information according to the guidance provided in Chapter R.4 of the REACH guidance (ECHA 2008a), Annex XI of REACH and/or alternative considerations, where appropriate.
3. Implications of the endpoint results on potential PBT conclusions based on the Integrated Testing Strategies (ITS) outlined in Chapter R.11 of the REACH guidance (ECHA 2008b).
The following QSAR models were included for bioaccumulation analysis:
· EPIWEB-BCFWin – BCFWin is an empirical QSAR which uses a bi-linear regression through a training data set along with correction factors for certain chemical functional groups in order to predict the BCF as a function of log Kow (Meylan et al., 1999). The predicted log BCF generally increases up to log Kow of 7 and decreases thereafter. For chemicals with special behaviour (i.e., low log Kow or ionization potential), a set of rules are applied, rather than the bi-linear regression. The key input parameters for BCFWin include log Kow and chemical structure.
· EPIWEB-Biotransformation – The Biotransformation model estimates whole body biotransformation half-lives and rate constants for organic chemicals in fish. It was developed from an evaluated data based on biotransformation estimates (Arnot et al. 2008a+b) and biotransformation is estimated based on multiple linear regressions of molecular substructures, log Kow and molecular weight. The Biotransformation model does not predict the BCF or BAF, rather the half-lives and rate constants are used as model inputs for the Arnot and Gobas BCF-BAF model.
· EPIWEB-Arnot-Gobas BAF-BCF – The Arnot-Gobas BCF-BAF model is a mechanistic bioaccumulation model which predicts the BAF or the BCF (Arnot and Gobas, 2003). Predictions are made based on chemical log Kow, a standard set of aquatic food web and organism parameters and, if known, the chemical metabolic transformation rate (kM). The generic food web consists of 3 representative fish trophic levels with the lowest trophic level consuming prey which are assumed to be in equilibrium with the water column. The Arnot-Gobas model assumes default lipid contents of 10.7%, 6.85% and 5.98% for the upper, middle and lower trophic levels, respectively. For the assessment of the derivative oil molecules, the upper trophic level BAF and the lower trophic level BCF were considered. Note that the Arnot-Gobas model does not account for the mitigating influence of ionization on chemical absorption and may overestimate the bioaccumulation potential of ionizing substances.
The bioaccumulation predictions from EPIWEB were considered applicable for the bioaccumulation assessment because the models make predictions of either the bioconcentration factor (BCF) or the bioaccumulation factor (BAF) and they consider mitigating factors for bioaccumulation potential including ionization and metabolic transformation. The BCF estimation equation of Veith et al. (1979) which is recommended in the European Technical Guidance Document on Risk Assessment (ECB 2003) was also considered, but excluded from the assessment because it does not account for these mitigating factors and would, therefore, likely overestimate the bioaccumulation potential of the representative molecules.
The reliability of the bioaccumulation predictions was assessed according to the following requirements from Annex XI of the REACH Regulation:
· Results are derived from a (Q)SAR model whose scientific validity has been established– Each of the bioaccumulation QSAR models have been published in peer-reviewed journal articles and the predictive capacity of each model has been evaluated using various measures of fit and in general, the models result in accurate predictions of the BCF or BAF at least 80% of the time. In addition, the models have been applied for regulatory purposes in jurisdictions outside of Europe (e.g., for Categorization of Canada’s Domestic Substances List).
· The substance falls within the applicability domain of the (Q)SAR model– castor oil derivative falls within the molecular fragment and rule domains of the QSAR models. In addition. Instances where the substance properties were not within the model domains, and the associated implications, include:
o The castor oil derivative is slightly higher than the MW domains for each of the models. However, this is not considered a major concern because each of the QSAR models is intended for organic chemicals and include very similar, albeit, smaller alkanoic acids in their training sets.
o The KOWWIN-predicted log Kow values (19.2 and 21.3) fall outside of the log Kow model domains for all of the QSAR models. While this may introduce uncertainty in the predictions, the QSAR models make predictions as expected for very hydrophobic and lipophilic substances. In general, the uptake of extremely hydrophobic chemicals is expected to be very low, limiting their bionconcentration and bioaccumulation potential (for a discussion see Gobas 1993 and Gobas and Mackay, 1987). For the case of the regression-based estimate for BCF and BAF, a rule-based approach (for ionic substances with 11 or more -CH2- groups) is applied that is independent of log Kow, meaning this model domain issue does not affect the prediction. Thus, the low estimates of BAF and BCF for the KOWWIN estimates are considered appropriate for decision-making purposes, when considered within the weight ofevidence. While the predicted values may be mildly uncertain, they do demonstrate that the BCF and BAF would be far below the B threshold criteria.
Adequate and reliable documentation of the applied method is provided– the methods for the EPIWEB models are documented within EPIWEB, as well as multiple peer-reviewed journal articles and the OASIS log BCF model is also documented in a peer-reviewed journal article (see the model descriptions, above).
The results can be summarized as follows:
Method |
Results |
Aqueous
BCFWin log BCF (regression-based)
QSAR for BCF estimation (based on testing similar to OECD305) (BCFBAF v3.00) |
BCF: 70.79L/kg (whole bodyw.w. (arithmetic (anti-loggedvalue))) (using KOWWIN logKow value16.73)
Range: 3.16 - 70.8 (See below)
|
Aqueous
Biotransformation rate constant
QSAR for biotransformation estimation (BCFBAF v3.00) |
Biotransformation rate constant (Half life): 1.6 days (whole body w.w.) (using KOWWIN log Kow value)
Range: 1.2 - 101 (See below)
|
Aqueous
Upper trophic level log BAF (Arnot-Gobas BAF-BCF)
QSAR for BAF estimation (BCFBAF v3.00) |
BCF: 0.893 L/kg (whole body w.w. (arithmetic (anti-logged))) (using KOWWIN log Kow value)
|
Evaluation of log Kow
Each of the QSAR-predicted log Kowvalues for the molecule exceeded the screening criterion, indicating a potential for bioaccumulation. Based on this comparison, it was not possible to classify the molecule as “not B” according to the log Kowcriterion alone. As a screening criterion, the log Kowthreshold represents bioaccumulation potential based on partitioning and uptake behaviour but does not account for mitigating factors such as molecular size and biotransformation and, therefore, the BCF and BAF predictions provide a better representation of overall bioaccumulation potential. Thus, the bioaccumulation assessment relied primarily on comparison of the QSAR estimates of BCF and BAF to the definitive criteria from Annex XIII.
EvaluationofBCFandBAF
Using the KOWWIN estimates for log Kow, the ranges of BAF and BCF predictions are as follows:
· Oils, fish Derivative: 0.3 to 70.8L/kg;and
All of these values were far below the BCF and BAF criterion of 2000 providing support for a “not B” conclusion for the castor oil derivative.
The
ranges have been calculated for all potential constituents of the
substance
According to the ECHA considerations long term testing on fish and aquatic invertebrates is proposed. Once results of the long-term toxicity test are available, the Registrant shall revise the chemical safety assessment and update the dossier as necessary according to Annex I of the REACH Regulation, i.e. addressing the repercussions for secondary poisoning and re-considering the need for bioaccummulation testing
Information on Registered Substances comes from registration dossiers which have been assigned a registration number. The assignment of a registration number does however not guarantee that the information in the dossier is correct or that the dossier is compliant with Regulation (EC) No 1907/2006 (the REACH Regulation). This information has not been reviewed or verified by the Agency or any other authority. The content is subject to change without prior notice.
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