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Bioaccumulation: aquatic / sediment

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Endpoint:
bioaccumulation: aquatic / sediment
Type of information:
(Q)SAR
Adequacy of study:
weight of evidence
Reliability:
2 (reliable with restrictions)
Rationale for reliability incl. deficiencies:
results derived from a valid (Q)SAR model and falling into its applicability domain, with adequate and reliable documentation / justification
Justification for type of information:
1. SOFTWARE
Individual model BCFBAF included in the Estimation Programs Interface (EPI) Suite.

2. MODEL (incl. version number)
BCFBAF v3.01 included in EPISuite v 4.11, 2000 - 2012

3. SMILES OR OTHER IDENTIFIERS USED AS INPUT FOR THE MODEL
The CAS NUMBER was entered in the initial data entry screen. An experimental determined log Kow value was provided prior to estimation.
A searchable database of CAS RNs and corresponding SMILES structures are provided within the KOWWIN program. CAS RNs are available for approximately 112,000 organic chemicals. If a CAS RN is not available, a SMILES notation can be directly entered by the user. Alternatively, logKow values can be manually entered if available. The correction factors are linked to the presence of certain structural fragments or functional groups.

4. SCIENTIFIC VALIDITY OF THE (Q)SAR MODEL
a. Defined endpoint: Bioconcentration factor (BCF). As a coefficient the logBCF is given without unit. The BCF can be given with L/kg wet wt.
b. Explicit algorithm (OECD Principle 2): The BCF is usually estimated from regression equations of the general form logBCF= a logKow + b, whereas a and b are empirically determined constants and Kow is the n octanol/water partition coefficient. Therefore, logBCF values for the non-ionic and ionics were plotted separately against the respective logKow values yielding a linear relationship for certain logKow ranges. Furthermore, compounds sharing certain structural features were identified resulting in residuals that were relatively consistent in sing and magnitude. On this basis several compound classes were identified that seemed amenable to derivation of correction factors.

Non-ionic compounds:
(i) For LogKow 1.0 to 7.0 the derived QSAR estimation equation is:
Log BCF= 0.6598 LogKow - 0.333 + Σ correction factors
(ii) For LogKow > 7.0 the derived QSAR estimation equation is:
Log BCF= -0.49 LogKow + 7.554 + Σ correction factors
(iii) For LogKow < 1.0 the derived QSAR estimation equation is:
All compounds with a logKow of less than 1.0 are assigned an estimated log BCF of 0.50.

Ionic compounds:
logKow < 5.0: log BCF= 0.50
logKow 5.0 to 6.0: log BCF= 0.75
logKow 6.0 to 7.0: log BCF= 1.75
logKow 7.0 to 9.0: log BCF= 1.00
logKow > 9.0: log BCF= 0.50

Ionic compounds include carboxylic acids, sulfonic acids and salts of sulfonic acids, and charged nitrogen compounds (nitrogen with a +5 valence such as quaternary ammonium compounds). All other compounds are classified as non-ionic.
Metals (tin and mercury), long chain alkyls and aromatic azo compounds require special treatment (see Meylan et al., 1999).
For ionic substances with long alkyl chains (≥ 11 carbons) a general log BCF of 1.85 was assigned by the program.

c. Applicability domain: The minimum and maximum values for molecular weight are the following:
Training Set Molecular Weight:
Minimum MW (Non-Ionic): 68.08
Maximum MW (Non-Ionic): 959.17
Average MW (Non-Ionic): 513.63

Minimum MW (Ionic): 102.13
Maximum MW (Ionic): 991.80
Average MW (Ionic): 546.97

The minimum and maximum values for logKow are the following:
Training Set logKow:
Minimum LogKow (Non-Ionic): -1.37
Maximum LogKow (Non-Ionic): 11.26

Minimum LogKow (Ionic): -6.50
Maximum LogKow (Ionic): 7.86

d. Statistics for goodness-of-fit:
number in dataset = 527
correlation coef (r^2) = 0.833
standard deviation = 0.502
average deviation = 0.382

e. Predictivity – Statistics obtained by external validation:
number in dataset = 158
correlation coef (r2) = 0.82
standard deviation = 0.59
average deviation = 0.46

f. Mechanistic interpretation: The BCF is an inherent property used to describe the accumulation of a substance dissolved in water by an aquatic organism. The BCFBAF program estimates BCF of an organic compound using the compound's log octanol-water partition coefficient (Kow).
Measured BCFs and other experimental details were collected and analysed to derive subsets of data on non-ionic, ionic, aromatic and azo compounds, tin and mercury compounds. Because of the deviation from rectilinearity, different models were developed for different log Kow ranges, and a set of 12 correction factors and rules were introduced to improve the accuracy of the BCF predictions.

g. The uncertainty of the prediction (OECD principle 4): The rules applied for estimating the BCF of solvent red 179 appear appropriate. An individual uncertainty for the investigated substance is not available.

h. The chemical and biological mechanisms according to the model underpinning the predicted result (OECD principle 5): Not applicable.

i. Limits of applicability: Model predictions may be highly uncertain for chemicals that have estimated log KOW values > 9. The model is not recommended at this time for chemicals that appreciably ionize, for pigments and dyes, or for perfluorinated substances.

5. APPLICABILITY DOMAIN
a. Descriptor Domains:
i. log Kow: With a log Kow value of 5.5, the substance is within the range of the training set (Non-Ionics: -1.37 – 11.26).
ii. Molecular weight: With a molecular weight of 320.35 g/mole the substance is within the range of the training set (Non-Ionics 68.08 – 959.17).
iii. Structural fragment domain: Not applicable as the BCF is not estimated on the basis of fragments.
iv. Mechanism domain: NO INFORMATION AVAILABLE
v. Metabolic domain: NOT RELEVANT
b. Structural analogues: Not relevant as the BCF is not estimated based on structural fragments.

6. ADEQUACY OF THE RESULT
a. Regulatory purpose: The data may be used for regulatory purpose.
b. Approach for regulatory interpretation of the model result: If no experimental data are available, the estimated value may be used to fill data gaps needed for hazard and risk assessment.
c. Outcome: The estimation of the bioconcentration factor (BCF) yields a useful result for further evaluation.
d. Conclusion: The result is considered as useful for regulatory purposes.
Guideline:
other: REACH guidance on QSARs R.6, May 2008
Principles of method if other than guideline:
Calculated with BCF Program BCFBAF v.3.01 included in the Estimation Programs Interface (EPI)-Suite. The estimation methodology is based on the chemical structure of an organic compound and its log octanol-water partition coefficient (log Kow). Depending on chemical structure, structural correction factors are applied.
GLP compliance:
no
Specific details on test material used for the study:
- Related to pure substance
- Smiles Code: O=C(N(c(c(c(ccc1)cc2)c1N=3)c2)C3c(c4c(ccc5)cc6)c6)c45
- Molecular mass: 320.35 g/mole
Radiolabelling:
no
Test organisms (species):
other: none, estimated by calculation
Type:
BCF
Value:
1 977 L/kg
Basis:
other: calculation

Any decomposition of the substance in water is not considered by the program.

Validity of model:

- Defined endpoint: bioconcentration of a substance in biota

- Unambiguous algorithm: linear regression QSAR. Because of the deviation from rectilinearity, different models were developed for different log Kow ranges. Metals (tin and mercury), long chain alkyls and aromatic azo compounds are specially treated.

- Applicability domain: the model is applicable to ionic as well as non-ionic compounds. It is applicable to substances with a logKow in the following range: -6.50 to 7.86 (ionic compounds) and -1.37 to 11.26 (non-ionic compounds). Applicable to substances with a molecular weight in the following range: 102.13 to 991.80 g/mole (ionic substances) and 68.08 and 959.17 g/mole (non-ionic compounds). ). Model predictions may be highly uncertain for chemicals that have estimated logKow values > 9. The model is not recommended at this time for chemicals that appreciably ionize, for pigments and dyes, or for perfluorinated substances.

- Statistical characteristics:

number in dataset = 527

correlation coef (r2) = 0.833

standard deviation = 0.502

- Mechanistic interpretation: The BCF is an inherent property used to describe the accumulation of a substance dissolved in water by an aquatic organism based on the lipophilicity of the compound.

Adequacy of prediction: Solvent red 179 falls within the applicability domain described above and, therefore, the predicted value can be considered reliable taking into account that the standard deviation error of prediction of the external test set is 0.59 (logBCF). Considering that error, the predicted value is not above or close to the criterion to consider a substance as potential bioaccumulative.

Conclusions:
The bioaccumulation factor (BCF) of Solvent red 179 was estimated to be 1977 L/kg wet-wt sing the BCFBAF model included in the EPI-Suite Programm concluding that the substance has a moderate potential to bioaccumulate in biota.
Executive summary:

The bioaccumulation factor (BCF) of Solvent red 179 was estimated to be 1977 L/kg wet-wt sing the BCFBAF model included in the EPI-Suite Programm concluding that the substance has a moderate potential to bioaccumulate in biota.

Within the scope of the Persistency-Bioaccumulation-Toxicity (PBT)-Assessment, the substance does not fullfil the B-criterion.

Solvent red 179 falls within the applicability domain described above and, therefore, the predicted value can be considered reliable.

Endpoint:
bioaccumulation: aquatic / sediment
Type of information:
(Q)SAR
Adequacy of study:
weight of evidence
Reliability:
2 (reliable with restrictions)
Rationale for reliability incl. deficiencies:
results derived from a valid (Q)SAR model and falling into its applicability domain, with adequate and reliable documentation / justification
Justification for type of information:
BCF base-line model developed by Laboratory of Mathematical Chemistry, OASIS-LMC Ltd, Burgas, Bulgaria
Guideline:
other: REACH guidance on QSARs R.6, May 2008
Principles of method if other than guideline:
Calculated withBCF base-line model developed by Laboratory of Mathematical Chemistry
GLP compliance:
no
Specific details on test material used for the study:
- Related to pure substance
- Smiles Code: O=C(N(c(c(c(ccc1)cc2)c1N=3)c2)C3c(c4c(ccc5)cc6)c6)c45
- Molecular mass: 320.35 g/mole
Radiolabelling:
no
Test organisms (species):
other: none, estimated by calculation
Type:
BCF
Value:
1 738 L/kg
Basis:
other: calculation

For the prediction of the BCF, an experimental value log Kow = 5.5 and an experimental value for water solubility 0.011mg/l are used.

Applicability domain of prediction

The target substance belongs 100% to the parametric and mechanistic sub-domains of the BCF base-line model and only 14.29% to the structural domain of the model. The new sub-domain named metabolic domain was added to the BCF model. This domain describes how well the metabolism of the target is simulated based on the available observed metabolism in database of the model. The target belongs 14.29% to the metabolic sub-domain. The chemical is out of the structural and metabolic sub-domains because more than 85% of its ACFs do not present in the training chemicals with observed BCFs values and more than 85% of its ACFs do not present in the chemicals with documented metabolism data in BCF base-line model database (i.e. these are unknown fragments for the model).

The prediction of bioconcentration of Macrolex Rot E2G is based on its lipophilicity (defining ability of chemical to bioaccumulate by passive diffusion and defining log BCFmax), the effect of metabolism and the molecular size (acting as mitigating factors). The substance is not ionic (i.e. no effect of ionisation is expected) and the effect of water solubility is negligible.

The simulated metabolic transformations reducing significantly the maximum bioconcentration potential (i.e. log BCFmax) are reactions of arene oxidation with high probability and N-oxidation with very low probability. The simulated multi-level metabolism by BCF base-line model of Macrolex Rot E2G is illustrated,

Because Macrolex Rot E2G is out of the structural and metabolic sub-domains of the BCF base-line model, a literature search was undertaken for documented BCFs and metabolism data of Macrolex Rot E2G and its structurally related compounds to support the BCF model prediction. No BCF and metabolism data were found for target compound.

The following 4 analogues of Macrolex Rot E2G were found:

  • CAS No. 6925-69-5: 12H-Isoindolo [2,1-a] perimidin-12-one; 12H-phthaloperin-12-one; Solvaperm Orange 3G; Solvent orange 60
  • CAS No. 13220-57-0: Indolo[2,1-b]quinazoline-6,12-dione; Tryptanthrine
  • CAS No. 84-26-4: 8,13-Dihydroindolo[2',3':3,4]pyrido[2,1-b]quinazolin-5(7H)-one; Rutecarpine
  • CAS No. 195-84-6: 5,10,14c,15-Tetraazanaphtho[1,2,3-gh]tetraphene; Tricycloquinazoline

The literature search shows that structurally related compounds (analogues) are metabolized in vitro/in vivo via hydroxylation of aromatic rings (Phase I) to the corresponding hydroxy-derivatives [8-11].

The hydroxy-metabolites can be further metabolized via glucuronidation and sulfation (Phase II) to easily excreted conjugates [9, 11].

Conclusion for metabolism of structural analogues of Macrolex Rot E2G. Based on the metabolism data of structurally related compounds it was suggested that the target chemical Macrolex Rot E2G is getting metabolized in vivo via hydroxylation of aromatic rings to corresponding hydroxy-derivatives. In the next transformation steps the Phase I metabolites are excreted as conjugates due to reactions of glucuronidation and sulfation. Thus, the simulated metabolism of Macrolex Rot E2G by BCF base-line model was found to be consistent with observed metabolism of the analogue chemicals.

Conclusions:
The bioaccumulation factor (BCF) of Solvent red 179 was estimated to be 1728 L/kg wet-wt sing the BCF Baeline Model leading to the conclusion that the substance has a moderate potential to bioaccumulate in biota.
Executive summary:

Applicability domain.

Macrolex Rot E2G) belongs to the parametric and mechanistic domain of the model. The target is out structural and metabolic sub domains of the model. More than 85% of its structural features are unknown structural domain and more than 85% are unknown for its metabolism domain.

Role of metabolism.

Detailed analysis of documented metabolism of structurally similar substances revealed that the simulated metabolism of Macrolex Rot E2G is in agreement with documented metabolism in fish (rat).

Model predictions. The predicted bioconcentration factors for Macrolex Rot E2G by BCF base line model are as follow:

• log BCF = 3.24 in l/kg wet, if experimental value log Kow = 5.5 and experimental value for water solubility 0.011mg/l are used for prediction

• log BCF = 2.46 in l/kg wet, if calculated value log Kow = 4.39 and calculated value for water solubility 0.0099 mg/l are used for prediction.

The model predictions support the significant role of metabolism for considering the target chemical as having non bioaccumulative nature as the BCF factor (according to both predictions) is less than the bio-concentration threshold of 2000.

Description of key information

The bioaccumulation factor (BCF) of Solvent red 179 was estimated in a weight of evidence approach using to different QSARs. Episuite calculated a BCF of 1977 L/kg wet-wt using the BCFBAF model and the BCF baseline model a BCF of 1738. The results indicate that the substance has a moderate potential to bioaccumulate in biota.

Key value for chemical safety assessment

BCF (aquatic species):
1 977 L/kg ww

Additional information