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Endpoint:
bioaccumulation in aquatic species, other
Type of information:
(Q)SAR
Adequacy of study:
key study
Study period:
2011
Reliability:
2 (reliable with restrictions)
Rationale for reliability incl. deficiencies:
other: Calculation with a validated QSAR (BCFBAF): l-limonene falls within the applicability domain of the (Q)SAR model, the prediction fits for the regulatory purpose, and the information is enough documented.
Justification for type of information:
1. Relevance of the model

EPISUITE 4.0 (Syracuse Research Corporation, SRC) is reported as a usable valid model by ECHA (R6: QSARs and grouping of chemicals, May 2008).

2. Validation of the model

- Endpoint (OECD Principle 1)
Bioconcentration Factor (BCF)

- Algorithm (OECD Principle 2)
This method is based on the multiple-linear regression-derived equation which is used by the BCFBAF program to estimate the kM Biotransformation Half-Life with the following equation:

Log kM/Half-Life (in days) = 0.30734215*LogKow - 0.0025643319*MolWt - 1.53706847 + Σ(Fi*ni)

where LogKow is the log octanol-water partition coefficient, MolWt is the Molecular Weight, and Σ(Fi*ni) is the summation of the individual Fragment coefficient values (Fi) (from Appendix F, BCFBAF documentation) times the number of times the individual fragment occurs in the structure (ni). The -1.53706847 is the equation constant.

- Applicability domain (OECD Principle 3)

Training Dataset (421 Compounds)
- Molecular Weight:
o Minimum MW: 68.08 (Furan)
o Maximum MW: 959.17 (Decabromodiphenyl ether)
o Average MW: 259.75

- Log Kow:
o Minimum Log Kow: 0.31 (Benzenesulfonamide)
o Maximum Log Kow: 8.70 (Decabromodiphenyl ether)

The MW for the chemicals range from 136.24 to 208.35 g/mol. Thus, all chemicals fall within the applicability domain of the model. Chemicals of interest are simple molecules and all structural fragments appear to be present in the chemical domain of the QSAR model.


- Uncertainty of the prediction (OECD Principle 4)

The following gives statistical information for the BCF model training and validation datasets (includes ionic and non-ionic compounds).
For the BCF training dataset;
Number = 527
R² = 0.833
std deviation = 0.502
avg deviation = 0.382

Arnot-Gobas BCF and BAF statistics for the kM biotransformation half-life of the training dataset are;
Number = 421
R² = 0.821
std deviation = 0.494
avg deviation = 0.383

Currently, BCFBAF has been tested on two external validation datasets (not including training datasets). The BCF model has been tested on 158 chemicals and the kM model for the Arnot-Gobas BAF tested using 211 chemicals. These validation datasets include a diverse selection of chemical structures that test the predictive accuracy of any model. They contain many chemicals that are similar in structure to chemicals in the training set, but also chemicals that are different from and structurally more complex than chemicals in the training set.
For the BCF validation dataset;

Number = 158
R² = 0.82
std deviation = 0.59
avg deviation = 0.46

Arnot-Gobas BCF and BAF statistics for the kM biotransformation half-life of the validation dataset;
Number = 211
R² = 0.734
std deviation = 0.602
avg deviation = 0.446


Uncertainty in the model predictions must be considered for model applications. The median confidence (uncertainty) factor for the database used to develop the model is about 5.5. A confidence factor of 5.5 suggests that 95% of the expected values for a biotransformation rate constant fall between 5.5 x kM and kM / 5.5 assuming a log normal distribution. This degree of uncertainty corresponds to approximately 1.5 orders of magnitude of variance in the distribution. The log MAE from the test set corresponds to a confidence factor of about 7 (~1.7 orders of magnitude variance in the distribution) and could also provide screening level guidance for the expected range of values for application of HLN, kM, and N estimates. This level of uncertainty (1.5 – 1.7 orders of magnitude) is also generally consistent with present estimates for intra- and inter-species and route of exposure variability (Arnot et al., 2008a).

The model contains a large set of unique structural fragments so that it can be broadly applicable to diverse chemical structures; however, these fragments do not reflect the entire domain of possible structural fragments for organic chemicals. The model is not expected to provide accurate results for all chemicals in all fish species and it is difficult to define precisely the domain of applicability. The model may not successfully predict biotransformation rates for substances that have molecular components that significantly affect biotransformation processes and were not included in model development. The database used to develop the model did not include many substances that appreciably ionize at physiological pH or larger molecules (molar mass >600); therefore, the model may not accurately predict values for such substances. The data set used to develop the model did not include metals or organometals, pigments or dyes, or perfluorinated substances and the model should not be used for these substances.

3. Adequacy of result for classification & labelling and/or risk assessment
The described equations aim at calculating BCF values (partition coefficient between soil and water) that are adequate for risk assessment and classification&labelling purposes.
Qualifier:
according to
Guideline:
other: ECHA guidance on QSAR (R6)
Principles of method if other than guideline:
The Arnot-Gobas model estimates steady-state bioconcentration factor (BCF; L/kg) and bioaccumulation factor (BAF; L/kg) values for non-ionic organic chemicals in three general trophic levels of fish (i.e., lower, middle, and upper). The model is described in Arnot et al. (2008a,b and 2009). Additional documentation can be found in the help files of EPI Suite v4. Appendix K of BCFBAF model documentation contains detailed equations and model descriptions for BAF calculations
GLP compliance:
no
Remarks:
Calculations
Radiolabelling:
no
Details on sampling:
None
Vehicle:
not specified
Details on preparation of test solutions, spiked fish food or sediment:
None
Test organisms (species):
no data
Details on test organisms:
No data
Route of exposure:
aqueous
Lipid content:
ca. 5 %
Remarks on result:
other: Lower trophic is for fish with about 5% lipid content
Type:
BCF
Value:
864.8 L/kg
Basis:
whole body w.w.
Details on kinetic parameters:
Bio Half-Life Normalized to 10 g fish at 15 deg C : 3.531 days
Details on results:
1. Relevance of the model

EPISUITE 4.0 (Syracuse Research Corporation, SRC) is reported as a usable valid model by ECHA (R6: QSARs and grouping of chemicals, May 2008).

2. Validation of the model

- Endpoint (OECD Principle 1)
Bioconcentration Factor (BCF)

- Algorithm (OECD Principle 2)
This method is based on the multiple-linear regression-derived equation which is used by the BCFBAF program to estimate the kM Biotransformation Half-Life with the following equation:

Log kM/Half-Life (in days) = 0.30734215*LogKow - 0.0025643319*MolWt - 1.53706847 + Σ(Fi*ni)

where LogKow is the log octanol-water partition coefficient, MolWt is the Molecular Weight, and Σ(Fi*ni) is the summation of the individual Fragment coefficient values (Fi) (from Appendix F, BCFBAF documentation) times the number of times the individual fragment occurs in the structure (ni). The -1.53706847 is the equation constant.

- Applicability domain (OECD Principle 3)

Training Dataset (421 Compounds)
- Molecular Weight:
o Minimum MW: 68.08 (Furan)
o Maximum MW: 959.17 (Decabromodiphenyl ether)
o Average MW: 259.75

- Log Kow:
o Minimum Log Kow: 0.31 (Benzenesulfonamide)
o Maximum Log Kow: 8.70 (Decabromodiphenyl ether)

The MW for the chemicals range from 136.24 to 208.35 g/mol. Thus, all chemicals fall within the applicability domain of the model. Chemicals of interest are simple molecules and all structural fragments appear to be present in the chemical domain of the QSAR model.


- Uncertainty of the prediction (OECD Principle 4)

The following gives statistical information for the BCF model training and validation datasets (includes ionic and non-ionic compounds).
For the BCF training dataset;
Number = 527
R² = 0.833
std deviation = 0.502
avg deviation = 0.382

Arnot-Gobas BCF and BAF statistics for the kM biotransformation half-life of the training dataset are;
Number = 421
R² = 0.821
std deviation = 0.494
avg deviation = 0.383

Currently, BCFBAF has been tested on two external validation datasets (not including training datasets). The BCF model has been tested on 158 chemicals and the kM model for the Arnot-Gobas BAF tested using 211 chemicals. These validation datasets include a diverse selection of chemical structures that test the predictive accuracy of any model. They contain many chemicals that are similar in structure to chemicals in the training set, but also chemicals that are different from and structurally more complex than chemicals in the training set.
For the BCF validation dataset;

Number = 158
R² = 0.82
std deviation = 0.59
avg deviation = 0.46

Arnot-Gobas BCF and BAF statistics for the kM biotransformation half-life of the validation dataset;
Number = 211
R² = 0.734
std deviation = 0.602
avg deviation = 0.446


Uncertainty in the model predictions must be considered for model applications. The median confidence (uncertainty) factor for the database used to develop the model is about 5.5. A confidence factor of 5.5 suggests that 95% of the expected values for a biotransformation rate constant fall between 5.5 x kM and kM / 5.5 assuming a log normal distribution. This degree of uncertainty corresponds to approximately 1.5 orders of magnitude of variance in the distribution. The log MAE from the test set corresponds to a confidence factor of about 7 (~1.7 orders of magnitude variance in the distribution) and could also provide screening level guidance for the expected range of values for application of HLN, kM, and N estimates. This level of uncertainty (1.5 – 1.7 orders of magnitude) is also generally consistent with present estimates for intra- and inter-species and route of exposure variability (Arnot et al., 2008a).

The model contains a large set of unique structural fragments so that it can be broadly applicable to diverse chemical structures; however, these fragments do not reflect the entire domain of possible structural fragments for organic chemicals. The model is not expected to provide accurate results for all chemicals in all fish species and it is difficult to define precisely the domain of applicability. The model may not successfully predict biotransformation rates for substances that have molecular components that significantly affect biotransformation processes and were not included in model development. The database used to develop the model did not include many substances that appreciably ionize at physiological pH or larger molecules (molar mass >600); therefore, the model may not accurately predict values for such substances. The data set used to develop the model did not include metals or organometals, pigments or dyes, or perfluorinated substances and the model should not be used for these substances.

3. Adequacy of result for classification & labelling and/or risk assessment
The described equations aim at calculating BCF values (partition coefficient between soil and water) that are adequate for risk assessment and classification&labelling purposes.

Results from EPIWIN

===========================================================

Whole Body Primary Biotransformation Rate Estimate for Fish:

===========================================================

------+-----+--------------------------------------------+---------+---------

 TYPE | NUM | LOG BIOTRANSFORMATION FRAGMENT DESCRIPTION | COEFF | VALUE 

------+-----+--------------------------------------------+---------+---------

 Frag | 2 | Methyl [-CH3]                           | 0.2451 | 0.4902

 Frag | 3 | -CH2- [cyclic]                          | 0.0963 | 0.2888

 Frag | 1 | -CH - [cyclic]                          | 0.0126 | 0.0126

 Frag | 3 | -C=CH [alkenyl hydrogen]                | 0.0988 | 0.2965

 Frag | 3 | -C=CH [alkenyl hydrogen]                | 0.0000 | 0.0000

 L Kow| * | Log Kow =  4.38 (user-entered  )       | 0.3073 | 1.3462

 MolWt| * | Molecular Weight Parameter               |        | -0.3494

 Const| * | Equation Constant                        |        | -1.5058

============+============================================+=========+=========

  RESULT  |       LOG Bio Half-Life (days)           |        | 0.5478

  RESULT  |           Bio Half-Life (days)           |        |  3.531

  NOTE    | Bio Half-Life Normalized to 10 g fish at 15 deg C  |

============+============================================+=========+=========

 

Biotransformation Rate Constant:

 kM (Rate Constant): 0.1963 /day (10 gram fish)

 kM (Rate Constant): 0.1104 /day (100 gram fish)

 kM (Rate Constant): 0.06209 /day (1 kg fish)

 kM (Rate Constant): 0.03491 /day (10 kg fish)

 

Arnot-Gobas BCF & BAF Methods (including biotransformation rate estimates):

  Estimated Log BCF (upper trophic) = 2.963 (BCF = 917.8 L/kg wet-wt)

  Estimated Log BAF (upper trophic) = 2.969 (BAF = 931.1 L/kg wet-wt)

  Estimated Log BCF (mid trophic)  = 2.953 (BCF = 896.6 L/kg wet-wt)

  Estimated Log BAF (mid trophic)  = 2.976 (BAF = 945.2 L/kg wet-wt)

  Estimated Log BCF (lower trophic) = 2.937 (BCF = 864.8 L/kg wet-wt)

  Estimated Log BAF (lower trophic) = 2.988 (BAF = 973.7 L/kg wet-wt)

Validity criteria fulfilled:
not applicable
Remarks:
Calculations
Conclusions:
The calculated BCF is 864.8 L/kg wet/wet
Executive summary:

The bioconcentration factor (BCF) of l-limonene in fish was estimated using the model of Arnot and Gobas (2003) which is reported as a usable valid model by ECHA (R6: QSAR and grouping of chemicals, May 2008). l-limonene feld in the applicability domain of this QSAR model and the other OECD principles are fulfilled. The calculation was ran using an experimental log Kow value of 4.38.

The calculated BCFof l-limonene was 864.8 L/kg wet/wet and this prediction is adequate for the purpose of risk assessment and classification and labelling.

Endpoint:
bioaccumulation in aquatic species, other
Type of information:
(Q)SAR
Adequacy of study:
key study
Study period:
2011
Reliability:
2 (reliable with restrictions)
Rationale for reliability incl. deficiencies:
other: Calculation with a validated QSAR (BCFWIN): l-limonene falls within the applicability domain of the (Q)SAR model, the prediction fits for the regulatory purpose, and the information is enough documented.
Justification for type of information:
1. Relevance of the model
EPISUITE 4.0 (Syracuse Research Corporation, SRC) is reported as a usable valid model by ECHA (R6: QSARs and grouping of chemicals, May 2008).

2. Validation of the model

- Endpoint (OECD Principle 1)
Bioconcentration Factor (BCF) and Bioaccumulation Factor (BAF)

- Algorithm (OECD Principle 2)
This model classifies a compound as either ionic or non-ionic. 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.

Training Dataset Included:
466 Non-Ionic Compounds
61 Ionic Compounds (carboxylic acids, sulfonic acids, quats)

Methodology for Non-Ionic was to separate compounds into three divisions by Log Kow value as follows:
Log Kow < 1.0
Log Kow 1.0 to 7.0
Log Kow > 7.0

Non-ionic compounds are predicted by the following relationships:
For Log Kow 1.0 to 7.0 the derived QSAR estimation equation is:
Log BCF = 0.6598 Log Kow - 0.333 + Σ correction factors
(n = 396, r2 = 0.792, Q2 = 0.78, std dev = 0.511, avg dev = 0.395)

For Log Kow > 7.0 the derived QSAR estimation equation is:
Log BCF = -0.49 Log Kow + 7.554 + Σ correction factors
(n = 35, r2 = 0.634, Q2 = 0.57, std dev = 0.538, avg dev = 0.396)

Certain super-hydrophobic chemicals (Log Kow >7.0) selected from the empirical database had reported BCF values with measured water concentrations that exceed water solubility limits. These BCF values were corrected based on estimates of water solubility limits (Arnot and Gobas, 2006).

For Log Kow < 1.0 the derived QSAR estimation equation is: All compounds with a log Kow of less than 1.0 are assigned an estimated log BCF of 0.50 (same as in BCFWIN).

Ionic compounds are predicted as follows:
log BCF = 0.50 (log Kow < 5.0)
log BCF = 0.75 (log Kow 5.0 to 6.0)
log BCF = 1.75 (log Kow 6.0 to 7.0)
log BCF = 1.00 (log Kow 7.0 to 9.0)
log BCF = 0.50 (log Kow > 9.0)
Metals (tin and mercury), long chain alkyls and aromatic azo compounds require special treatment.


- Applicability domain (OECD Principle 3)
The estimation domain for BCF model is based on the number of instances given for each correction factor in any of the 527 training set compounds (the minimum number of instances is of course zero, since not all compounds had every correction factor). The minimum and maximum values for molecular weight and log Kow are listed below.

Training Dataset Included:
466 Non-Ionic Compounds
61 Ionic Compounds (carboxylic acids, sulfonic acids, quats)

Training DataSet (527 Compounds)
- Molecular Weight
o Minimum MW: 68.08 (Furan)
o Maximum MW: 991.80 Ionic: (2,7-Naphthalenedisulfonic acid, 4-amino-5-hydroxy-3,6-bis[[4-[[2-(sulfooxy)ethyl]sulfonyl]phenyl]azo]-, tetrasodium salt)
o Maximum MW: 959.17 Non-Ionic: (Benzene, 1,1 -oxybis[2,3,4,5,6-pentabromo-)
o Average MW: 244.00

- Log Kow
o Minimum Log Kow: -6.50 Ionic: (2,7-Naphthalenedisulfonic acid, 4-amino-5-hydroxy-3,6- bis[[4-[[2-(sulfooxy)ethylsulfonyl]phenyl]azo]-, tetrasodium salt)
o Minimum Log Kow: -1.37 Non-Ionic: (1,3,5-Triazine-2,4,6-triamine)
o Maximum Log Kow: 11.26 (Benzenamine, ar-octyl-N-(octylphenyl)-)

- Uncertainty of the prediction (OECD Principle 4)

The following gives statistical information for the BCF model training and validation datasets (includes ionic and non-ionic compounds).
For the BCF training dataset;
Number = 527
R² = 0.833
std deviation = 0.502
avg deviation = 0.382

For Non-ionic compounds with Log Kow in the range 1.0 to 7.0
n= 396,
r² = 0.792,
Std. Dev.= 0.511,
Ave. Dev. = 0.395

3. Adequacy of result for classification & labelling and/or risk assessment
The BCFBAF program estimation of bioaccumulation coefficient in aquatic organisms of organic chemical is adequate for risk assessment and classification & labelling.
Qualifier:
according to
Guideline:
other: R6: ECHA guidance on QSARs
Deviations:
no
Principles of method if other than guideline:
The BCFBAF Program updates the BCF estimation methodology of the BCFWIN program by using an updated and better evaluated BCF database for selecting training and validation datasets (Arnot et al. 2003, 2006, 2008a, 2008b, 2009). The same regression methodology used to derive the original BCFWIN method was used to derive the BCFBAF model for estimating BCF. The BCFBAF program estimates BCF of organic chemicals using the chemical’s Log Kow. .
GLP compliance:
no
Remarks:
Calculations
Radiolabelling:
no
Details on sampling:
None
Vehicle:
not specified
Details on preparation of test solutions, spiked fish food or sediment:
None
Test organisms (species):
no data
Details on test organisms:
None
Route of exposure:
aqueous
Test type:
not specified
Water / sediment media type:
not specified
Hardness:
no data
Test temperature:
no data
pH:
no data
Dissolved oxygen:
no data
TOC:
no data
Salinity:
no data
Details on test conditions:
no data
Lipid content:
ca. 5 %
Remarks on result:
other: Dataset median about 5%
Key result
Type:
BCF
Value:
360.5 L/kg
Basis:
whole body w.w.
Details on kinetic parameters:
No kinetic parameter
Metabolites:
no data
Results with reference substance (positive control):
not applicable
Details on results:
1. Relevance of the model
EPISUITE 4.0 (Syracuse Research Corporation, SRC) is reported as a usable valid model by ECHA (R6: QSARs and grouping of chemicals, May 2008).

2. Validation of the model

- Endpoint (OECD Principle 1)
Bioconcentration Factor (BCF) and Bioaccumulation Factor (BAF)

- Algorithm (OECD Principle 2)
This model classifies a compound as either ionic or non-ionic. 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.

Training Dataset Included:
466 Non-Ionic Compounds
61 Ionic Compounds (carboxylic acids, sulfonic acids, quats)

Methodology for Non-Ionic was to separate compounds into three divisions by Log Kow value as follows:
Log Kow < 1.0
Log Kow 1.0 to 7.0
Log Kow > 7.0

Non-ionic compounds are predicted by the following relationships:
For Log Kow 1.0 to 7.0 the derived QSAR estimation equation is:
Log BCF = 0.6598 Log Kow - 0.333 + Σ correction factors
(n = 396, r2 = 0.792, Q2 = 0.78, std dev = 0.511, avg dev = 0.395)

For Log Kow > 7.0 the derived QSAR estimation equation is:
Log BCF = -0.49 Log Kow + 7.554 + Σ correction factors
(n = 35, r2 = 0.634, Q2 = 0.57, std dev = 0.538, avg dev = 0.396)

Certain super-hydrophobic chemicals (Log Kow >7.0) selected from the empirical database had reported BCF values with measured water concentrations that exceed water solubility limits. These BCF values were corrected based on estimates of water solubility limits (Arnot and Gobas, 2006).

For Log Kow < 1.0 the derived QSAR estimation equation is: All compounds with a log Kow of less than 1.0 are assigned an estimated log BCF of 0.50 (same as in BCFWIN).

Ionic compounds are predicted as follows:
log BCF = 0.50 (log Kow < 5.0)
log BCF = 0.75 (log Kow 5.0 to 6.0)
log BCF = 1.75 (log Kow 6.0 to 7.0)
log BCF = 1.00 (log Kow 7.0 to 9.0)
log BCF = 0.50 (log Kow > 9.0)
Metals (tin and mercury), long chain alkyls and aromatic azo compounds require special treatment.


- Applicability domain (OECD Principle 3)
The estimation domain for BCF model is based on the number of instances given for each correction factor in any of the 527 training set compounds (the minimum number of instances is of course zero, since not all compounds had every correction factor). The minimum and maximum values for molecular weight and log Kow are listed below.

Training Dataset Included:
466 Non-Ionic Compounds
61 Ionic Compounds (carboxylic acids, sulfonic acids, quats)

Training DataSet (527 Compounds)
- Molecular Weight
o Minimum MW: 68.08 (Furan)
o Maximum MW: 991.80 Ionic: (2,7-Naphthalenedisulfonic acid, 4-amino-5-hydroxy-3,6-bis[[4-[[2-(sulfooxy)ethyl]sulfonyl]phenyl]azo]-, tetrasodium salt)
o Maximum MW: 959.17 Non-Ionic: (Benzene, 1,1 -oxybis[2,3,4,5,6-pentabromo-)
o Average MW: 244.00

- Log Kow
o Minimum Log Kow: -6.50 Ionic: (2,7-Naphthalenedisulfonic acid, 4-amino-5-hydroxy-3,6- bis[[4-[[2-(sulfooxy)ethylsulfonyl]phenyl]azo]-, tetrasodium salt)
o Minimum Log Kow: -1.37 Non-Ionic: (1,3,5-Triazine-2,4,6-triamine)
o Maximum Log Kow: 11.26 (Benzenamine, ar-octyl-N-(octylphenyl)-)

- Uncertainty of the prediction (OECD Principle 4)

The following gives statistical information for the BCF model training and validation datasets (includes ionic and non-ionic compounds).
For the BCF training dataset;
Number = 527
R² = 0.833
std deviation = 0.502
avg deviation = 0.382

For Non-ionic compounds with Log Kow in the range 1.0 to 7.0
n= 396,
r² = 0.792,
Std. Dev.= 0.511,
Ave. Dev. = 0.395

3. Adequacy of result for classification & labelling and/or risk assessment
The BCFBAF program estimation of bioaccumulation coefficient in aquatic organisms of organic chemical is adequate for risk assessment and classification & labelling.

Results from EPIWIN

=============================

BCF (Bioconcentration Factor):

=============================

 

Log Kow (experimental): 4.38

Log Kow used by BCF estimates: 4.38 (user entered)

 

Equation Used to Make BCF estimate:

  Log BCF = 0.6598 log Kow - 0.333 + Correction

 

     Correction(s):                   Value

      No Applicable Correction Factors

 

  Estimated Log BCF = 2.557 (BCF = 360.5 L/kg wet-wt)

Validity criteria fulfilled:
not applicable
Remarks:
Calculations
Conclusions:
The calculated BCF is 360.5 L/kg wet/wet
Executive summary:

The bioconcentration factor (BCF) of l-limonene in fish was estimated using the model of Meylan & al. (1999) which is reported as a usable valid model by ECHA (R6: QSAR and grouping of chemicals, May 2008). l-limonene feld in the applicability domain of this QSAR model and the other OECD principles are fulfilled. The calculation was ran using an experimental log Kow value of 4.38.

The calculated BCFof l-limonene was 350.5 L/kg wet/wet and this prediction is adequate for the purpose of risk assessment and classification and labelling.

Endpoint:
bioaccumulation in aquatic species, other
Type of information:
(Q)SAR
Adequacy of study:
key study
Study period:
2011
Reliability:
2 (reliable with restrictions)
Rationale for reliability incl. deficiencies:
other: Calculation with a validated QSAR (OASIS): l-limonene falls within the applicability domain of the (Q)SAR model, the prediction fits for the regulatory purpose, and the information is enough documented.
Justification for type of information:
1. Relevance of the model

EPISUITE 4.0 (Syracuse Research Corporation, SRC) is reported as a usable valid model by ECHA (R6: QSARs and grouping of chemicals, May 2008).

2. Validation of the model

- Endpoint (OECD Principle 1)
Bioconcentration Factor (BCF)

- Algorithm (OECD Principle 2)

Fi stands for the set of mitigating factors: metabolism, molecular size, ionization, Fw is the organism water content, FWS is water solubility factor, and a and n are model parameters.
a = 2.24E-07 +/- 1.428
n = 0.746 +/- 0.04997
Fw = 9.15 +/- 5.843

- Applicability domain (OECD Principle 3)
The following classes of chemicals are included in the training set: alkanes, alkenes, mono and diaromatic hydrocarbons, polycyclic aromatic hydrocarbons (PAH), polychlorinated dibenzofuranes (PCDF), polychlorinated dibenzodioxines (PCDDO), polychlorinated biphenyles (PCB), cycloalkanes and cycloalkenes, chloraromatic chemicals, perfluorinated acids (PFA) with 6 to 13 difluoromethylene functions in the chain, chlorinated biphenyl esters, aliphatic esters, chlororganic chemicals, aliphatic and aromatic N-containing compounds, polycyclic aromatic N-containing compounds, organotin compounds, sulfur-containing heterocyclic compounds. Functional groupings in these classes of chemicals “cover” the chemicals of interest.

The model applicability domain consists of three layers: general parametric requirements, structural domain characterized via atom-centered fragments (ACFs) confined within their first neighbors, mechanistic component of the domain. The domain of the general parametric requirements included the range of variation of hydrophobicity (log KOW) and molecular weight (MW) of chemicals in the training set. Respectively, chemicals with MW from 16 to 1131.3 and log KOW in the range of -3.89 and 16.07 are assigned to belong to the domain of the general requirements. The MW for the chemicals range from 136.24 to 208.35 g/mol.


- Uncertainty of the prediction (OECD Principle 4)
n=706 chemicals,
r2= 0.85,
S2 = 0.27,
RSS=175.5

3. Adequacy of result for classification & labelling and/or risk assessment
The described equations aim at calculating BCF values (bioaccumulation coefficient in aquatic organisms), they are therefore adequate for derivation of BCF (endpoint value) and completion of endpoint 5.4.1 of IUCLID dossier.
Qualifier:
according to
Guideline:
other: ECHA guidance on QSAR (R6)
Principles of method if other than guideline:
The Laboratory of Mathematical Chemistry of the Bourgas “Prof. As. Zlatarov” University team developed several models that are recommended for use by ECHA. One of these models is OASIS. The POP (persistent organic pollutant) profiler of OASIS is used to classify chemicals according to their persistence (P), bioaccumulation (B), and acute/chronic toxicity (T). The OASIS_Forecast module is used for predicting the toxicity endpoints (e.g., fish, daphnia, algae). This is the only available expert system that predicts the PBT properties of chemicals accounting for their stable degradants. The POPs framework advances hazard identification by integrating a metabolic simulator that generates metabolic maps for each parent chemical. Both the parent chemicals and plausible metabolites are systematically evaluated for their bioaccumulation and toxicity profile. The base-line BCF model provides predictions of the maximum bioaccumulation potential based on passive diffusion while considering a series of mitigating factors such as molecular size, ionization and fish liver metabolism (Dimitrov et al. 2005; 2010). The performance of this system for assessing PBT properties of chemicals has been used for categorization of chemicals on Canada’s Domestic Substances List, US EPA, Environment Canada, NITE, Japan, Danish EPA and major industries. Environment Canada customized the interface of OASIS for the Existing Chemicals Program. This shell program is called OASIS Canadian POPs. Version 1.1.11 of this program was used for predicting the BCF (“BCF_all mitigating factors” taking into consideration metabolism and maximum cross-sectional diameter (Dmax) which is a measure of bioavailability– discussed below)
GLP compliance:
no
Remarks:
Calculations
Specific details on test material used for the study:
Details on properties of test surrogate or analogue material (migrated information):
None
Radiolabelling:
no
Details on sampling:
None
Vehicle:
not specified
Details on preparation of test solutions, spiked fish food or sediment:
None
Test organisms (species):
no data
Route of exposure:
aqueous
Key result
Type:
BCF
Value:
1 022 L/kg
Basis:
whole body w.w.
Details on results:
1. Relevance of the model

EPISUITE 4.0 (Syracuse Research Corporation, SRC) is reported as a usable valid model by ECHA (R6: QSARs and grouping of chemicals, May 2008).

2. Validation of the model

- Endpoint (OECD Principle 1)
Bioconcentration Factor (BCF)

- Algorithm (OECD Principle 2)

Fi stands for the set of mitigating factors: metabolism, molecular size, ionization, Fw is the organism water content, FWS is water solubility factor, and a and n are model parameters.
a = 2.24E-07 +/- 1.428
n = 0.746 +/- 0.04997
Fw = 9.15 +/- 5.843

- Applicability domain (OECD Principle 3)
The following classes of chemicals are included in the training set: alkanes, alkenes, mono and diaromatic hydrocarbons, polycyclic aromatic hydrocarbons (PAH), polychlorinated dibenzofuranes (PCDF), polychlorinated dibenzodioxines (PCDDO), polychlorinated biphenyles (PCB), cycloalkanes and cycloalkenes, chloraromatic chemicals, perfluorinated acids (PFA) with 6 to 13 difluoromethylene functions in the chain, chlorinated biphenyl esters, aliphatic esters, chlororganic chemicals, aliphatic and aromatic N-containing compounds, polycyclic aromatic N-containing compounds, organotin compounds, sulfur-containing heterocyclic compounds. Functional groupings in these classes of chemicals “cover” the chemicals of interest.

The model applicability domain consists of three layers: general parametric requirements, structural domain characterized via atom-centered fragments (ACFs) confined within their first neighbors, mechanistic component of the domain. The domain of the general parametric requirements included the range of variation of hydrophobicity (log KOW) and molecular weight (MW) of chemicals in the training set. Respectively, chemicals with MW from 16 to 1131.3 and log KOW in the range of -3.89 and 16.07 are assigned to belong to the domain of the general requirements. The MW for the chemicals range from 136.24 to 208.35 g/mol.


- Uncertainty of the prediction (OECD Principle 4)
n=706 chemicals,
r2= 0.85,
S2 = 0.27,
RSS=175.5

3. Adequacy of result for classification & labelling and/or risk assessment
The described equations aim at calculating BCF values (bioaccumulation coefficient in aquatic organisms), they are therefore adequate for derivation of BCF (endpoint value) and completion of endpoint 5.4.1 of IUCLID dossier.
Validity criteria fulfilled:
not applicable
Remarks:
Calculations
Conclusions:
The bioconcentration factor was estimated by calculation at 1022 L/kg wet/wet.
Executive summary:

The bioconcentration factor (BCF) of l-limonene in fish was estimated using the EPISUITE 4.0 (Syracuse Research Corporation, SRC) which is reported as a usable valid model by ECHA (R6: QSAR and grouping of chemicals, May 2008). l-limonene feld in the applicability domain of this QSAR model and the other OECD principles are fulfilled. The calculation was ran using an experimental log Kow value of 4.38.

The calculated BCF of l-limonene was1022 L/kg wet/wet and this prediction is adequate for the purpose of risk assessment and classification and labelling.

Description of key information

Key studies: Calculated values of BCF have been performed using three validated models and the geometric mean value of 683.1 L/kg w/w is used for risk assessment and classififcation&labelling purposes.

Key value for chemical safety assessment

BCF (aquatic species):
683 L/kg ww

Additional information

Bioaccumulation factor has been calculated according to 3 commonly used QSAR models.

Chemical Name

CAS RN

SMILES

Log Kow


BCFWIN

Meylan et al. (1999)

(Regression- Based Method)

BCF


BCFBAF

Arnot-Gobas model (2003) with Biotransformation

(upper trophic range)

BCF

 

 OASIS Canadian POPs

BCF

L- LIMONENE

5989-54-8

C(=CCC(C(=C)C)C1)(C1)C

4.38

361 L/kg ww

918 L/kg ww

1022 L/kg ww

 

An estimated BCF of 683 L/kg ww was calculated for l-limonene, using a log Kow of 4.53 and the geometric mean value of three validated QSARs results as recommended in ECHA guidance R7c.

This value is indicative of the potential to bioaccumulate for classification purposes. Indeed, according to a classification scheme, a BCF suggests a potential for bioconcentration in aquatic organisms if:

- BCF > 500 under the GHS CLP (Regulation EU No 286/2011)