Dipentaerythritol hexaesters of 3,5,5-trimethylhexanoic and n-heptanoic acids

Bioaccumulation: aquatic / sediment

Link to relevant study record(s)

Reference

Reference 1

Endpoint:

bioaccumulation in aquatic species, other

Type of information:

(Q)SAR

Adequacy of study:

weight of evidence

Study period:

December 2018

Reliability:

2 (reliable with restrictions)

Rationale for reliability incl. deficiencies:

results derived from a valid (Q)SAR model, but not (completely) falling into its applicability domain, with adequate and reliable documentation / justification

Justification for type of information:

1. SOFTWARE
Program BCFBAF included in EPISUITE (Estimation Programs Interface Suite™ for Microsoft® Windows, v 4.11)

2. MODEL (incl. version number)
Arnot-Gobas BCF and BAF model
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) in temperate environments. The model calculations represent general trophic levels (i.e., not for a particular fish species) and are derived for “representative” environmental conditions (e.g., dissolved and particulate organic carbon content in the water column, water temperature). Thus, it provides general estimates for these conditions in absence of site-specific measurements or estimates. The default temperature for the BCF and BAF calculations is 10 °C (temperate regions); therefore, the model predictions are not recommended for arctic, sub-tropical or tropical regions or for comparisons with other vastly different conditions (e.g., laboratory tests at ~25 °C). Site-specific food web models, bioaccumulation models and bioconcentration models are available for specific modeling requirements (e.g., http://www.rem.sfu.ca/toxicology/models/models.htm , http://www.trentu.ca/cemc).
 
The model requires the octanol-water partition coefficient (KOW) of the chemical and the normalized whole-body metabolic biotransformation rate constant (kM, N; /day) as input parameters to predict BCF and BAF values. The required kM, N value must be normalized to a fish of 10 g and usually this is an outcome of the whole body primary biotransformation rate constant model for fish (see below). 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. 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.
 
The whole body primary biotransformation rate constant (kM) model for fish
The model estimates screening level whole body primary biotransformation half-lives (HL; day) and rate constants (kM;/day) for discrete organic chemicals in fish. An evaluated database of kM estimates in fish (Arnot et al., 2008) was used to develop and evaluate the model. The model predicts half-life (HLN) and rate constant (kM,N) values “normalized” for a 10 g fish at 15ºC. The model calculates kM as a whole body value, namely the fraction of the mass in the whole body biotransformed per unit of time. The model does not provide predictions for the formation of specific biotransformation products (some of which may be more toxic than the parent compound), nor does it identify specific pathways for the biotransformation process (Phase I oxidations or reductions or Phase II conjugations). The model assumes first-order processes and cannot estimate biotransformation rates that may occur under non-first order conditions (e.g., enzyme saturation).
 
3. SMILES OR OTHER IDENTIFIERS USED AS INPUT FOR THE MODEL
DPE777777 (constituent #1): O=C(CCCCCC)OCC(COC(=O)CCCCCC)(COC(=O)CCCCCC)COCC(COC(=O)CCCCCC)(COC(=O)CCCCCC)COC(=O)CCCCCC

DPE777779 (constituent #2):
O=C(CCCCCC)OCC(COC(=O)CCCCCC)(COC(=O)CC(C)CC(C)(C)C)COCC(COC(=O)CCCCCC)(COC(=O)CCCCCC)COC(=O)CCCCCC

DPE777799 (constituent #3):
O=C(CCCCCC)OCC(COC(=O)CCCCCC)(COC(=O)CC(C)CC(C)(C)C)COCC(COC(=O)CCCCCC)(COC(=O)CC(C)CC(C)(C)C)COC(=O)CCCCCC

DPE777999 (constituen #4):
O=C(CCCCCC)OCC(COC(=O)CC(C)CC(C)(C)C)(COC(=O)CC(C)CC(C)(C)C)COCC(COC(=O)CCCCCC)(COC(=O)CC(C)CC(C)(C)C)COC(=O)CCCCCC

DPE779999 (constituent #5):
O=C(CCCCCC)OCC(COC(=O)CC(C)CC(C)(C)C)(COC(=O)CC(C)CC(C)(C)C)COCC(COC(=O)CC(C)CC(C)(C)C)(COC(=O)CC(C)CC(C)(C)C)COC(=O)CCCCCC

DPE799999 (constituent #6):
O=C(CCCCCC)OCC(COC(=O)CC(C)CC(C)(C)C)(COC(=O)CC(C)CC(C)(C)C)COCC(COC(=O)CC(C)CC(C)(C)C)(COC(=O)CC(C)CC(C)(C)C)COC(=O)CC(C)CC(C)(C)C

DPE999999 (constituent #7):
O=C(CC(C)CC(C)(C)C)OCC(COC(=O)CC(C)CC(C)(C)C)(COC(=O)CC(C)CC(C)(C)C)COCC(COC(=O)CC(C)CC(C)(C)C)(COC(=O)CC(C)CC(C)(C)C)COC(=O)CC(C)CC(C)(C)(C)

4. SCIENTIFIC VALIDITY OF THE (Q)SAR MODEL
Validity of the model according to the OECD principles 

1. Defined endpoint: log BAF (bioconcentration factor)

2. Unambiguous algorithm
The final multiple-linear regression-derived equation (which is used by the BCFBAF program to estimate the kM Biotransformation Half-Life) is:
Log kM/Half-Life (in days) =  0.30734215*Log Kow  -  0.0025643319*Mol Wt  - 1.53706847  +Σ(Fi*ni)
Σ(Fi*ni) is the summation of the individual Fragment coefficient values (Fi) times the number of times the individual fragment occurs in the structure (ni). The -1.53706847 is the equation constant.

3. Applicability domain: currently, there is no universally accepted definition of the model domain. The training set of the model contains diverse molecules, so that the fragment libraries are abundant. It is however possible that a compound has functional groups or other structural features that are not represented in the training set and for which no fragment coefficients were developed. Additionally, there can be more instances of a given fragment than the maximum for all training set compounds. These points should be taken into consideration while interpreting test results.
Molecular weight limits of the training set: 68-959 g/mol
Log Kow limits: 0.31-8.70

4. Appropriate measures of goodness of fit, robustness and predictivity.
The basis for log BAF calculation is determination of biotransformation kM. For the training set, experimental vs estimated log kM half lifes are characterized with the following statistics: N = 421 compounds, correlation coefficient R2= 0.821, Q2= 0.752, standard deviation sd = 0.494, average deviation ad = 0.383. The corresponding values for the validation set are as follows: R2= 0.734, standard deviation sd = 0.602, average deviation ad = 0.446. For the training set, 69.4% predictions lie within 0.5 log unit and 95.2% within 1.0 log unit.

5. Mechanistic interpretation if possible: The model includes mechanistic processes for bioconcentration and bioaccumulation such as chemical uptake from the water at the gill surface and the diet, and chemical elimination at the gill surface, fecal egestion, growth dilution and metabolic biotransformation (Arnot and Gobas 2003). Other processes included in the calculations are bioavailability in the water column (only the freely dissolved fraction can bioconcentrate) and absorption efficiencies at the gill and in the gastrointestinal tract.


5. APPLICABILITY DOMAIN
[Explain how the substance falls within the applicability domain of the model]
Currently there is no universally accepted definition of model domain. However, users may wish to consider the possibility that biotransformation estimates are less accurate for compounds outside the MW and logKow ranges of the training set compounds, and/or that have more instances of a given fragment than the maximum for all training set compounds. It is also possible that a compound may have a functional group(s) or other structural features not represented in the training set, and for which no fragment coefficient was developed; and that a compound has none of the fragments in the model’s fragment library. These points should be taken into consideration when interpreting model results.
The fragements present in the constituents of the substance are included in the model's dataset and fragment coefficients are applied for them. The log Kow is above the maximum log Kow within the training set, as well as the molecular weight, which means that some fragments, for some constituents, are present in higher amount than in the training set, while others are within the training set maximum number of that fragment in any individual compound.

6. ADEQUACY OF THE RESULT
Although the definite calculated BCF and BAFvalues may not be fully reliable, they iindicate bioaccumulation is not expected.



References:
Arnot JA, Gobas FAPC. 2003. A generic QSAR for assessing the bioaccumulation potential of organic chemicals in aquatic food webs. QSAR and Combinatorial Science 22: 337-345.
Arnot JA, Mackay D, Parkerton TF, Bonnell M. 2008. A database of fish biotransformation rates for organic chemicals. Environmental Toxicology and Chemistry 27(11): 2263-2270.

Reason / purpose for cross-reference:

(Q)SAR model reporting (QMRF)

Reason / purpose for cross-reference:

other: QPRF reports

Principles of method if other than guideline:

Arnot JA, Gobas FAPC. 2003. A generic QSAR for assessing the bioaccumulation potential of organic chemicals in aquatic food webs. QSAR and Combinatorial Science 22: 337-345.

Type:

BCF

Value:

0.89 L/kg

Basis:

whole body w.w.

Remarks on result:

other: Arnot Gobas (including biotransformatio rate estimates, upper trophic)

Type:

BAF

Value:

0.89 L/kg

Basis:

whole body w.w.

Remarks on result:

other: Arnot Gobas (includng biotransformation rate estimates, upper trophic)

Conclusions:

The log Kow and the molecular weight of some constituents is higher than the highest molecular weight in the training set of the (Q)SAR model, but the functional groups, the fragments present in the constituents are within the model domain. The calculated BCFvalues may not be precise but they indicate bioaccumulation is not expected, and this prediction is considered reliable.

Reference 2

Endpoint:

bioaccumulation in aquatic species, other

Type of information:

(Q)SAR

Adequacy of study:

weight of evidence

Study period:

December 2018

Reliability:

2 (reliable with restrictions)

Rationale for reliability incl. deficiencies:

results derived from a valid (Q)SAR model, but not (completely) falling into its applicability domain, with adequate and reliable documentation / justification

Justification for type of information:

1. SOFTWARE
Program BCFBAF included in EPISUITE (Estimation Programs Interface Suite™ for Microsoft® Windows, v 4.11)

2. MODEL (incl. version number)
BCFBAF, BCF Regression
 The BCFBAF Program is an update and expansion of the previous BCFWIN Program that was part of the EPI Suite version 3.20. The update pertains to estimation of Bioconcentration Factor (BCF). The BCFBAF program estimates BCF of an organic compound using the compound's log octanol-water partition coefficient (Kow). For the update, a more recent and better evaluated database of BCF values was used for both training and validation. The BCF data were re-regressed using the same methodology as in the original BCFWIN program.
 
The original estimation methodology used by the original BCFWIN program is described in a document prepared for the Environmental Protection Agency (Meylan et al., 1997). The estimation methodology was then published in journal article (Meylan et al, 1999). Meylan, WM, Howard, PH, Boethling, RS et al. 1999. Improved Method for Estimating Bioconcentration / Bioaccumulation Factor from Octanol/Water Partition Coefficient. Environ. Toxicol. Chem. 18(4): 664-672 (1999).
 
The BCFBAF method 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.

3. SMILES OR OTHER IDENTIFIERS USED AS INPUT FOR THE MODEL
DPE777777 (constituent #1): O=C(CCCCCC)OCC(COC(=O)CCCCCC)(COC(=O)CCCCCC)COCC(COC(=O)CCCCCC)(COC(=O)CCCCCC)COC(=O)CCCCCC

DPE777779 (constituent #2):
O=C(CCCCCC)OCC(COC(=O)CCCCCC)(COC(=O)CC(C)CC(C)(C)C)COCC(COC(=O)CCCCCC)(COC(=O)CCCCCC)COC(=O)CCCCCC

DPE777799 (constituent #3):
O=C(CCCCCC)OCC(COC(=O)CCCCCC)(COC(=O)CC(C)CC(C)(C)C)COCC(COC(=O)CCCCCC)(COC(=O)CC(C)CC(C)(C)C)COC(=O)CCCCCC

DPE777999 (constituen #4):
O=C(CCCCCC)OCC(COC(=O)CC(C)CC(C)(C)C)(COC(=O)CC(C)CC(C)(C)C)COCC(COC(=O)CCCCCC)(COC(=O)CC(C)CC(C)(C)C)COC(=O)CCCCCC

DPE779999 (constituent #5):
O=C(CCCCCC)OCC(COC(=O)CC(C)CC(C)(C)C)(COC(=O)CC(C)CC(C)(C)C)COCC(COC(=O)CC(C)CC(C)(C)C)(COC(=O)CC(C)CC(C)(C)C)COC(=O)CCCCCC

DPE799999 (constituent #6):
O=C(CCCCCC)OCC(COC(=O)CC(C)CC(C)(C)C)(COC(=O)CC(C)CC(C)(C)C)COCC(COC(=O)CC(C)CC(C)(C)C)(COC(=O)CC(C)CC(C)(C)C)COC(=O)CC(C)CC(C)(C)C

DPE999999 (constituent #7):
O=C(CC(C)CC(C)(C)C)OCC(COC(=O)CC(C)CC(C)(C)C)(COC(=O)CC(C)CC(C)(C)C)COCC(COC(=O)CC(C)CC(C)(C)C)(COC(=O)CC(C)CC(C)(C)C)COC(=O)CC(C)CC(C)(C)(C)

4. SCIENTIFIC VALIDITY OF THE (Q)SAR MODEL
Validity of the model according to the OECD principles 
1. Defined endpoint: log BCF (bioconcentration factor)
2. Unambiguos algorithm:
Non-Ionic compounds:
Log Kow  <  1.0: All compounds with a log Kow of less than 1.0 are assigned an estimated log BCF of 0.50 (same as in BCFWIN).
Log Kow  1.0  to 7.0: Log BCF  =  0.6598 Log Kow  -  0.333  +Σcorrection factors
Log Kow  > 7.0: Log BCF  =  -0.49 Log Kow  +  7.554  +Σcorrection factors
Ionic compounds:
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)
3. Applicability domain: Currently, there is no universally accepted definition of model domains. The training sets of the models contain diverse molecules, so that the fragment libraries are abundant. It is however possible that a compound has functional groups or other structural features that are not represented in the training set and for which no fragment coefficients were developed. Additionally, there can be more instances of a given fragment than the maximum for all training set compounds. These points should be taken into consideration while interpreting test results.
Molecular weight limits of the training set: 68-959 g/mol
Log Kow limits: 1 (validity range of the QSAR equation) - 11.26 (maximum value in the training set).
4. Appropriate measures of goodness of fit, robustness and predictivity.
a) 1.0 < log Kow < 7.0: N = 396 compounds, correlation coefficient R2= 0.792, Q2= 0.78, standard deviation sd = 0.511, average deviation ad = 0.395.
b) log Kow > 7.0: N = 35 compounds, correlation coefficient R2= 0.634, Q2= 0.57, standard deviation sd = 0.538, average deviation ad = 0.396.
Overall statistics including all Non-Ionic and Ionic compounds: training set N = 537, R2= 0.833, sd = 0.502, ad = 0.382.
Validation set: N = 158, R2= 0.82, sd = 0.59, ad = 0.46.
For the training set, 72.7% predictions are within 0.5 log unit and 93.5% within 1.0 log unit.
5. Mechanistic interpretation if possible: Bioaccumulation of stable substances is determined by partitioning between aqueous and lipid phases. Estimating bioconcentration factors from octanol-water partition coefficients is well established and essentially valid for neutral organics of intermediate liphophilicity (0 < log Kow < 6). For higher log Kow values, a maximum range in log BCF of approx. 6-7 (log Kow between ca. 6-8) is observed, followed by a plateau or a gradual decrease. (Source: M. Müller and M. Nendza, Literature Study: Effects of Molecular Size and Lipid Solubility on Bioaccumulation Potential).


5. APPLICABILITY DOMAIN
[Explain how the substance falls within the applicability domain of the model]
Currently there is no universally accepted definition of model domain. However, users may wish to consider the possibility that biotransformation estimates are less accurate for compounds outside the MW of the training set compounds, and/or that have more instances of a given fragment than the maximum for all training set compounds. It is also possible that a compound may have a functional group(s) or other structural features not represented in the training set, and for which no fragment coefficient was developed; and that a compound has none of the fragments in the model’s fragment library. These points should be taken into consideration when interpreting model results.
The fragements present in the constituents of the substance are included in the model's dataset and fragment coefficients are applied for them. The log Kow is included in the logKow range of the training set, but the molecular weight is above for some constituents.

6. ADEQUACY OF THE RESULT
The molecular weight of some constituents is higher than the highest molecular weight in the training set, but the funtcional gourps, the fragments present in the constituents and the log Kow are within the model domain. The calculated BCFvalues may not be precise but they indicate bioaccumulation is not expected, and this prediction is considered reliable.

Reason / purpose for cross-reference:

other: QPRF reports

Principles of method if other than guideline:

Calculation based on BCFBAF v3.01, Estimation Programs Interface Suite™ for Microsoft® Windows v 4.10. US EPA, United States Environmental Protection Agency, Washington, DC, USA.

Type:

BCF

Value:

3.16 L/kg

Basis:

whole body w.w.

Remarks on result:

other: Regressin based estimate

Conclusions:

The molecular weight of some constituents is higher than the highest molecular weight in the training set of the (Q)SAR model, but the functional groups, the fragments present in the constituents and the log Kow are within the model domain. The calculated BCFvalues may not be precise but they indicate bioaccumulation is not expected, and this prediction is considered reliable.

Reference 3

Endpoint:

bioaccumulation in aquatic species, other

Type of information:

calculation (if not (Q)SAR)

Adequacy of study:

weight of evidence

Reliability:

2 (reliable with restrictions)

Rationale for reliability incl. deficiencies:

other: Acceptable, well documented publication which meets basic scientific principles.

Qualifier:

no guideline followed

Principles of method if other than guideline:

The main goal of this study is to describe the effect of molecular size on bioconcentration by accounting for conformational flexibility of molecules.

GLP compliance:

no

Radiolabelling:

not specified

Vehicle:

not specified

Test organisms (species):

other: fish

Route of exposure:

other: review article

Test type:

other: review article

Water / sediment media type:

not specified

Remarks on result:

other: Please see "Any other information on results incl. tables"

Results

The complexity of the effect of molecular geometry and flexibility on membrane permeation and subsequently on bioconcentration can be illustrated with n-pentadecane. The length of the n-pentadecane molecule, known also as the maximum diameter, is Dmax= 2.14 nm. The other two related dimensions, the effective and minimum cross-sections for this conformer, are Deff= 0.499 nm and Dmin= 0.492 nm, respectively. Due to the high hydrophobicity of n-pentadecane [log(Kow) = 7.71] and small effective cross-section of its lowest energy conformer, which is far below the critical 0.95 nm, one anticipates high bioconcentration for this chemical. The bioconcentration is in the range of 3.2 to 4.3 log units. Two other energetically reasonable conformers of n-pentadecane have got a heat formation of H°= –71.8 kcal/mol and H°= –68.5 kcal/mol, respectively. Based on the effective cross-section of the third conformer, Deff= 1.15 nm, a loss of membrane permeation may be expected, resulting in low bioconcentration. The experimentally measured BCF of n-pentadecane in carp, expressed as log (BCF), is in the range of 1.12 to 1.29. Two conclusions could be drawn from this simple example. First, molecular cross-sectional diameters appear to be strongly dependent on molecular flexibility Secondly, it is not clear which of the molecular dimensions controls the membrane permeation of chemicals.

The complex effect of molecular geometry and flexibility on the chemicals’ permeability is confirmed by superposition of the molecular effective cross-sectional diameters of the studied lipophilic chemicals with their experimentally measured BCFs.

The absence of any correlation indicates that this geometric characteristic is not related to the variation of BCF for highly hydrophobic chemicals. The hypothesis that the effective diameter controls permeability of chemicals assumes a strict spatial orientation of the molecules toward the cell membrane surface in a way that the molecular projection over the membrane does not exceed a certain threshold (anticipated to be around 0.95 nm). The appropriate orientation, however, is pre-vented by entropy (i.e., by chaotic movement of the molecules), which can explain the insufficiency of the effective cross-section of molecules to explain their permeability.

Two interesting features were displayed firstly, there is a well-outlined tendency of decreasing bioconcentration with the increase of the maximum cross-sectional areas of molecular conformers. The higher the molecular length (i.e., its Dmax value), the smaller are the chances of the molecule reaching the cell membrane at an appropriate angle. Secondly, most chemicals with Dmax under ~1.5 nm achieve high log (BCF)—in the range of 3 to 6, while the chemicals with Dmax greater than this threshold accumulate up to 3.3 units at most. The existence of such a transition point can be explained by a change in the mechanism of uptake of chemicals from passive diffusion through the phospholipid bilayer of the membrane to the more conservative passing of the membrane by the mechanism of exocytosis and endocytosis. Interestingly, the critical value of 1.5 nm for the threshold is comparable with the cell membrane architecture. The threshold of maximum diameter is comparable with the half thickness of leaflet constituting the lipid bilayer of the cell membrane.

From the present results, one could conclude that diffusion through the cell membrane is limited to molecules having a length not exceeding the threshold of about 1.5 nm. The latter could be assumed as the maximum tolerance of the cell membrane.

Conclusion

Analysis of the BCF data for narcotics with log (Kow) greater than 5.5 revealed that the maximum cross-sectional diameter can be used to explain the significant scatter around the maximum of the log (BCF)/log (Kow) curve. The chaotic collision of molecules with the cell membrane surface at different angles could explain the significance of this geometric characteristic, instead of generally accepted effective diameter. The drop in bioconcentration of chemicals at a maximum cross-sectional diameter of about 1.5 nm is an indication of a switch of the mechanism of uptake of chemicals into cells above this threshold. The value of this transition point can be used as an additional parameter of hydrophobicity for regression modeling of the BCF variation. Conformational flexibility tends to further increase the significance of entropy to cell permeability, which leads to additional decreases of BCF. The effect of this structural characteristic, however, needs to be further evaluated in order to be used for quantifying the bioconcentration of chemicals.

Reference 4

Endpoint:

bioaccumulation in aquatic species, other

Type of information:

calculation (if not (Q)SAR)

Adequacy of study:

weight of evidence

Reliability:

2 (reliable with restrictions)

Rationale for reliability incl. deficiencies:

other: Literature study

Qualifier:

no guideline followed

Principles of method if other than guideline:

Literature study addressing the effects of molecular size and lipid solubility on the bioaccumulation potential of environmental contaminants.

GLP compliance:

no

Test organisms (species):

other: Aquatic organisms

Route of exposure:

other: not applicable, review

Test type:

other: not applicable, review

Water / sediment media type:

not specified

Remarks on result:

other: Please see "Any other information on results incl. tables"

BCF measured values plotted against log Kow values

- Estimating bioconcentration factors (BCF) from octanol/water partition coefficients (log KOW) is well established and essentially valid for neutral organics of intermediate lipophilicity (0 < log Kow < 6).

- On the other hand, chemicals with log Kow > 6 often have measured BCFs lower than calculated from linear QSARs. Apparently, BCFs no longer increase in correspondence with log Kow. A maximum range in log BCF of approximately 6–7 for compounds with log Kow 6–8 is observed, followed by a plateau or a gradual decrease with further increase in log Kow. The maximum BCF

associated with a given lipophilicity can be described by a bilinear worst-case function:

log BCF = 0.99 log Kow - 1.47 log (4.97 x 10-8 KOW + 1) + 0.0135

The bilinear curve resumes a linearly increasing part between log Kow 0 and 6, where the empirically postulated coincidence of log Kow and log BCF is reflected by a near-unity slope (0.99) for the 1st-order log Kow term and the intercept of about 0. Maximum log BCF values of approximately 7 are obtained for compounds with log Kow between 7 and 8. Compounds that are more lipophilic are observed to be less accumulating, which corresponds to the negative slope derived for the second log Kow term of the bilinear function.

- The apparent loss in linear relationships for superlipophilic compounds has been attributed – in part – to experimental artefacts. Theoretical considerations substantiate curvilinear relationships for chemicals with log Kow > 6: - Aqueous phase diffusion control of water to lipid transfer - Differences in phase (solvent) properties of natural lipids and octanol - Influence of steric conformations - Differences in thermodynamic properties of partitioning, e.g. enthalpy changes To test established QSARs, BCF data were provided by Umweltbundesamt for 31 plant protection agents and for 18 new chemicals. These chemicals were selected by two criteria: molecular weight > 300 g/mol and bioaccumulation data available. Since this- 34 -data set includes no compounds with log BCF > 4 (range in BCF data: 1.5 to 14600), additional data for compounds with very high BCF were taken from the literature. Most BCF data from Umweltbundesamt were qualified as ‘valid data'. However, for two compounds Umweltbundesamt classified the measurements as ‘invalid’. Critical inspection of the available BCF data revealed major deficiencies in data quality of several superlipophilic compounds. For nine compounds, the accumulation experiments have been conducted at concentrations above the water solubility of the test compounds. Consequently, the resulting BCF values are too low artefacts due to invalid experiments. These data were excluded from further analyses. The remaining data set from Umweltbundesamt is considered not sufficiently representative, because it includes no compounds with log BCF > 4. It is too limited with regard to activity domain as to provide new insights to relationships between BCF and log Kow. New insights to relationships between BCF and log Kow could not be provided. The currently available database is insufficient to conclusively substantiate the effects of molecular size and lipid solubility on the bioaccumulation potential of environmental contaminants.

Reference 5

Endpoint:

bioaccumulation in aquatic species, other

Type of information:

calculation (if not (Q)SAR)

Adequacy of study:

weight of evidence

Reliability:

2 (reliable with restrictions)

Rationale for reliability incl. deficiencies:

other: Acceptable, well documented publication which meets basic scientific principles

Qualifier:

no guideline followed

Principles of method if other than guideline:

In this report, the application of selected physico-chemical and molecular attributes according to Lipinski's 'Rule of 5'* in the estimation of fish bioconcentration values is investigated.

*Lipinski et al. (1997). Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Deliv. Rev. 23, pp. 961-965).

GLP compliance:

no

Remarks:

not applicable, metananalysis of literature data

Test organisms (species):

other: Aquatic organisms

Route of exposure:

other: Not applicable, review

Test type:

other: Not applicable, review

Water / sediment media type:

not specified

Type:

BCF

Value:

< 2 000

Calculation basis:

other: Review literature dataset

Remarks on result:

other: Substances log Kow < 3 and log Kow > 10

Molecular weight or size-related cut-off criteria

- No robust evidence was found for cut-offs for bioconcentration related to molecular size. A criterion in molecular weight of 650 mg/mol seems to be more appropriate that the 500 g/mol stated by Lipinski et al. (1997) as it was only exceeded by a few non-B compounds (BCF < 2000) in the dataset. Cut-offs in molecular weight are pragmatic due to readily available metrics, but they lack a mechanistic rationale. As often, the problem is multivariate in nature and there exists a multitude of parameters than jointly and simultaneously influence each other. The correlation between molecular weight and size is rather weak (r square= 0.36 -0.45). Other properties related to molecular weight, such as solubilities in various media, absorption kinetics and bioavailability are also influential.

Lipophility related cut-off (log Kow)

The results from the analysis of log BCF values plotted against log Kow values confirm the implication of non-linear log Kow-based QSARs that chemicals with either low (log Kow < 3) of very high lipophilicity (logKow > 10) may have rather low BCF (non B compounds). The maximum BCF associated with a given lipophilicity has been described by a bilinear worst-case function. The curve was constructed (without regression and thus without calibration statistics) to represent the upper border of the data distribution of log BCF versus log Kow:

logBCFmax = 0.99 log Kow – 1.47 log (4.97x10-08 Kow + 1) + 0.0135    (1)

Chemicals with log Kow > 6 often have measured BCFs lower than calculated from QSARs. Apparently, these BCFs no longer increase in correspondence with log Kow, but plateau or gradually decrease with further increase in log Kow. Arnot and Gobas identified sorption as the main reason that accumulation decreases with increasing log Kow for highly lipophilic chemicals: if accumulation were quantified as the ratio of the concentrations in the organisms and the freely dissolved chemical concentration in water the BCF of high Kow chemicals would rise to 10⁷and the level off with a further increase Kow. The rationale is that for more hydrophobic chemicals (log Kow > 6) membrane permeation rates become increasingly controlled and ultimately dominated by aqueous boundary layer transport rather than phospholipid bilayer permeation.

Upward deviations relative to equation (1) only concern hydrophilic molecules (log Kow < 2), but their BCFs remain below 100. In the log Kow range between 3 and 10, many BCF values are lower than calculated by the QSAR, but the higher than the B (BCF > 2000) of vB (BCF > 5000) criteria. Above log Kow 10, none of the few available data in the test and validation datasets, exceeds the B criterion. Downward scatter relative to Equation (1) over several orders of magnitude increases for more lipophilic compounds, and many be attributed to mitigating factors such as ionization, degradation or metabolism of the test substances as well as to major errors particularly in very high log Kow values.

In the confirmation dataset, one bioaccumulative compound has a log Kow slightly above 10, octabromiddiphenyl ether, and two compounds bioaccumulating by different mechanisms have a log Kow < 3, methyl mercury and tetraethyl lead. The consequence of these observations is that in addition to polybrominated compounds, also alkylated heavy metals are excluded from the applicability domain of the classification scheme.

Description of key information

Bioaccumulation of Dipentaerythrol hexaesters of 3,5,5,-trimethylhexanoic and n-heptanoic acids is very unlikely due to the high molecular weight of its constituents. There are several publications that support it. It can be expected that the constituents of the substance are not bioavailable at all. According to the EU Technical Guidance Documents, substances with a molecular weight higher than 700 are unlikely to bioaccumulate significantly (regardless of the log Pow value).

Due to their hydrophobicity, it is very likely that components will adsorb to sediment and particulate matter.

If aquatic exposure occurs, Dipentaerythritol hexaesters of 3,5,5 -trimethylhexanoic and n-heptanoi acids (EC 945 -883 -1 CAS 1379424 -11 -9) will be mainly taken up by ingestion but its absorption is expected to be low based on the molecular weight, size and structural complexity of the substance. The substancel is not expected to bioaccumulate in aquatic or sediment organisms and secondary poisoning does not pose a risk.