2-propylheptan-1-ol

Bioaccumulation: aquatic / sediment

Link to relevant study record(s)

Reference

Reference 1

Endpoint:

bioaccumulation in aquatic species: fish

Type of information:

(Q)SAR

Adequacy of study:

weight of evidence

Study period:

2019

Reliability:

1 (reliable without restriction)

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
OASIS Catalogic v5.13.1

2. MODEL (incl. version number)
BCF base-line model v02.09 - July 2016

3. SMILES OR OTHER IDENTIFIERS USED AS INPUT FOR THE MODEL
See section 'Test Material'.

4. SCIENTIFIC VALIDITY OF THE (Q)SAR MODEL
See attached QMRF.

5. APPLICABILITY DOMAIN
See attached QPRF.

6. ADEQUACY OF THE RESULT
- The model is scientifically valid (see attached QMRF).
- The model estimates the Bioconcentration factor (BCF) as required information point according to Regulation (EC) No 1907/2006 [REACH], Annex IX, 9.3.2 Bioaccumulation in aquatic species (preferably fish);
further related predictions: Apparent effect of mitigating factors / Maximum bioconcentration factor (BCFmax) / Maximum diameter of energetically stable conformers / Whole body primary biotransformation half-life / Metabolic biotransformation rate constant Km / Metabolites and their quantitative distribution
- See attached QPRF for reliability assessment.

Principles of method if other than guideline:

Calculation using Catalogic v.5.13.1, BCF base-line model v.03.10

GLP compliance:

no

Details on estimation of bioconcentration:

BASIS FOR CALCULATION OF BCF
- Estimation software: OASIS Catalogic v5.13.1 [BCF base line model - v.03.10]

Type:

BCF

Value:

32 L/kg

Remarks on result:

other: considering all mitigating factors; the substance is not within the applicability domain of the model.

Type:

BCF

Value:

598 L/kg

Remarks on result:

other: without considering any mitigating factors; the substance is not within the applicability domain of the model.

Reference 2

Endpoint:

bioaccumulation: aquatic / sediment

Type of information:

(Q)SAR

Adequacy of study:

weight of evidence

Reliability:

1 (reliable without restriction)

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

Principles of method if other than guideline:

Estimation of BCF, BAF and biotransformation rate using BCFBAF v3.01

GLP compliance:

no

Radiolabelling:

no

Test organisms (species):

other: fish

Details on estimation of bioconcentration:

BASIS INFORMATION
- Measured logPow: 4.1


BASIS FOR CALCULATION OF BCF
- Estimation software: EpiSuite v.4.11
- Result based on measured log Pow of: 236 / 247

Type:

BCF

Value:

236 L/kg

Remarks on result:

other:

Remarks:

The substance is within the applicability domain of the BCFBAF submodel: Bioconcentration factor (BCF; Meylan et al., 1997/1999).

Type:

BCF

Value:

247 L/kg

Remarks on result:

other:

Remarks:

Upper trophic, incl. biotransformation estimates; The substance is within the applicability domain of the BCFBAF submodel: Arnot & Gobas BAF and steady-state BCF Arnot & Gobas, 2003).

Type:

BCF

Value:

1 290 L/kg

Remarks on result:

other:

Remarks:

Upper trophic, incl. biotransformation rate of zero; The substance is within the applicability domain of the BCFBAF submodel: Arnot & Gobas BAF and steady-state BCF Arnot & Gobas, 2003).

Details on kinetic parameters:

Biotransformation half-life (days): 0.573 (normalised to 10 g fish)
Biotransformation rate (kM, normalised to 10 g fish at 15 °C): 1.21 /day

Summary Results:

Log BCF (regression-based estimate): 2.37 (BCF = 236 L/kg wet-wt)

Biotransformation Half-Life (days) : 0.573 (normalized to 10 g fish)

Log BAF (Arnot-Gobas upper trophic): 2.31 (BAF = 205 L/kg wet-wt)

 

Log Kow (experimental): not available from database

Log Kow used by BCF estimates: 4.10 (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 = 2.372 (BCF = 235.6 L/kg wet-wt)

 

Whole Body Primary Biotransformation Rate Estimate for Fish:

Whole Body Primary Biotransformation Rate Estimate for Fish:

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

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

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

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

Frag | 1 | Linear C4 terminal chain [CCC-CH3]                           | 0.0341 |        0.0341

Frag | 1 | Aliphatic alcohol [-OH]                                            | -0.0616 |    -0.0616

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

Frag | 7 | -CH2- [linear]                                                              | 0.0242 | 0.1693

Frag | 1 | -CH-  [linear]                                                            | -0.1912 | -0.1912

L Kow| * | Log Kow =  4.10 (user-entered  )                                | 0.3073 | 1.2601

MolWt| * | Molecular Weight Parameter                                            |        | -0.4059

Const| * | Equation Constant                                                              |        | -1.5371

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

RESULT  |       LOG Bio Half-Life (days)           |        | -0.2420

RESULT  |           Bio Half-Life (days)           |        | 0.5728

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

 

Biotransformation Rate Constant:

kM (Rate Constant): 1.21 /day (10 gram fish)

kM (Rate Constant): 0.6805 /day (100 gram fish)

kM (Rate Constant): 0.3827 /day (1 kg fish)

kM (Rate Constant): 0.2152 /day (10 kg fish)

 

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

Estimated Log BCF (upper trophic) = 2.312 (BCF = 205.1 L/kg wet-wt)

Estimated Log BAF (upper trophic) = 2.312 (BAF = 205.1 L/kg wet-wt)

Estimated Log BCF (mid trophic)  = 2.380 (BCF = 240.1 L/kg wet-wt)

Estimated Log BAF (mid trophic)  = 2.382 (BAF = 241.2 L/kg wet-wt)

Estimated Log BCF (lower trophic) = 2.392 (BCF = 246.7 L/kg wet-wt)

Estimated Log BAF (lower trophic) = 2.404 (BAF = 253.6 L/kg wet-wt)

 

Arnot-Gobas BCF & BAF Methods (assuming a biotransformation rate of zero):

Estimated Log BCF (upper trophic) = 3.111 (BCF = 1290 L/kg wet-wt)

Estimated Log BAF (upper trophic) = 3.527 (BAF = 3369 L/kg wet-wt)

Reference 3

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
T.E.S.T. (version 4.2.1) (Toxicity Estimation Software Tool). US EPA, 2012.

2. MODEL (incl. version number)
T.E.S.T. (version 4.2.1)

3. SMILES OR OTHER IDENTIFIERS USED AS INPUT FOR THE MODEL
c32c(cccc3)nn(c1c(O)ccc(C(C)(C)CC(C)(C)C)c1)n2

4. SCIENTIFIC VALIDITY OF THE (Q)SAR MODEL
See QMRF in section "Overall remarks, attachments".

5. APPLICABILITY DOMAIN
See QPRF in section "Executive summary".

6. ADEQUACY OF THE RESULT
- The model is scientifically valid (see QMRF).
- The model estimates the bioconcentration factor (BCF) as required information point under Regulation (EC) No 1907/2006 [REACH], Annex IX, 9.3.2 Bioaccumulation in aquatic species, preferably fish (see also QPRF).
- See QPRF for reliability assessment.

Principles of method if other than guideline:

T.E.S.T. is a toxicity estimation software tool. The program requires only the molecular structure of the test item, all other molecular descriptors which are required to estimate the toxicity are calculated within the tool itself. The molecular descriptors describe physical characteristics of the molecule (e.g. E-state values and E-state counts, constitutional descriptors, topological descriptors, walk and path counts, connectivity, information content, 2d autocorrelation, Burden eigenvalue, molecular property (such as the octanol-water partition coefficient), Kappa, hydrogen bond acceptor/donor counts, molecular distance edge, and molecular fragment counts). Each of the available methods uses a different set of these descriptors to estimate the toxicity. The bioaccumulation factor (BCF) was estimated using several available methods: hierarchical clustering method; FDA method, single model method; group contribution method; nearest neighbor method; consensus method. The methods were validated using statistical external validation using separate training and test data sets. The experimental data set was obtained from several different databases (Dimitrov et al., 2005; Arnot and Gobas, 2006; EURAS; Zhao, 2008). From the available data set salts, mixtures and ambiguous compounds were removed. The final data set contained 676 chemicals.

References:
- Dimitrov, S., N. Dimitrova, T. Parkerton, M. Combers, M. Bonnell, and O. Mekenyan. 2005. Base-line model for identifying the bioaccumulation potential of chemicals. SAR and QSAR in Environmental Research 16:531-554.
- Arnot, J.A., and F.A.P.C. Gobas. 2006. A review of bioconcentration factor (BCF) and bioaccumulation factor (BAF) assessments for organic chemicals in aquatic organisms. Environ. Rev. 14:257-297.
- EURAS. Establishing a bioconcentration factor (BCF) Gold Standard Database. EURAS [cited 5/20/09]. Available from http://www.euras.be/eng/project.asp?ProjectId=92.
- Zhao, C.; Boriani, E.; Chana, A.; Roncaglioni, A.; Benfenati, E. 2008. A new hybrid system of QSAR models for predicting bioconcentration factors (BCF). Chemosphere 73:1701-1707.

GLP compliance:

no

Test organisms (species):

other: fish

Details on estimation of bioconcentration:

BASIS FOR CALCULATION OF BCF
- Estimation software: US EPA T.E.S.T. v4.2.1

Applied estimation methods:
- Hierarchical method : The toxicity for a given query compound is estimated using the weighted average of the predictions from several different cluster models.
- FDA method : The prediction for each test chemical is made using a new model that is fit to the chemicals that are most similar to the test compound. Each model is generated at runtime.
- Single model method : Predictions are made using a multilinear regression model that is fit to the training set (using molecular descriptors as independent variables).
- Group contribution method : Predictions are made using a multilinear regression model that is fit to the training set (using molecular fragment counts as independent variables).
- Nearest neighbor method : The predicted toxicity is estimated by taking an average of the 3 chemicals in the training set that are most similar to the test chemical.
- Consensus method : The predicted toxicity is estimated by taking an average of the predicted toxicities from the above QSAR methods (provided the predictions are within the respective applicability domains; recommended method by T.E.S.T. for providing the most accurate predictions).

Type:

BCF

Value:

58 L/kg

Remarks on result:

other: method: Consensus method. Based on the mean absolute error, the confidence in the predicted BCF values is low.

Type:

BCF

Value:

141 L/kg

Remarks on result:

other: method: Hierachical clustering method. Based on the mean absolute error, the confidence in the predicted BCF values is low.

Type:

BCF

Value:

48 L/kg

Remarks on result:

other: method: Single model method. Based on the mean absolute error, the confidence in the predicted BCF values is low.

Type:

BCF

Value:

27 L/kg

Remarks on result:

other: method: Group contribution method. Based on the mean absolute error, the confidence in the predicted BCF values is low.

Type:

BCF

Value:

14 L/kg

Remarks on result:

other: method:FDA method. Based on the mean absolute error, the confidence in the predicted BCF values is low.

Type:

BCF

Value:

240 L/kg

Remarks on result:

other: method:Nearest neighbor method. Based on the mean absolute error, the confidence in the predicted BCF values is low.

Method	Predicted value		Model statistics			MAE (in log10)
						External test set		Training set
	log BCF	BCF	r²	q²	No. of chemicals	Entire set	SC >= 0.5	Entire set	SC >= 0.5
Consensus method	1.76	57.51	-	-	-	0.51	0.60	0.42	0.53
Hierarchical clustering	2.15	141.50 (94.93-210.90)	0.764 - 0.981	0.733 - 0.970	12 - 540 (cluster models: 10)	0.54	0.88	0.23	0.18
Single model	1.68	47.73 (3.62-628.88)	0.764	0.733	540	0.54	0.62	0.53	0.69
Group contribution	1.43	26.78 (1.31-546.80)	0.719	0.527	499	0.62	0.86	0.60	0.75
FDA	1.16	14.37 (3.41-60.46)	0.948	0.904	30	0.57	0.81	0.53	0.75
Nearest neighbor	2.38	242.15	-	-	3	0.60	0.86	0.55	0.43

Reference 4

Endpoint:

bioaccumulation in aquatic species: fish

Type of information:

(Q)SAR

Adequacy of study:

weight of evidence

Study period:

2019

Reliability:

2 (reliable with restrictions)

Justification for type of information:

The BCF models incorporated into the VEGA platform v.1.2.3 have been used to estimate the bioaccumulative potential of the compound in a weight-of-evidence approach.
- CAESAR v2.1.14
- Meylan v1.0.3
- KNN/Read-Across v1.1.0

Principles of method if other than guideline:

CAESAR
The BCF is estimated based on several molecular descriptors.
The applicability domain of predictions is assessed using an Applicability Domain Index (ADI) calculated by grouping several other indices, e.g. by a similarity index that consider molecule's fingerprint and structural aspects (count of atoms, rings and relevant fragments).

Meylan
The BCF is estimated based on log Kow. The applicability domain of predictions is assessed using an Applicability Domain Index (ADI) calculated by grouping several other indices, e.g. by a similarity index that consider molecule's fingerprint and structural aspects (count of atoms, rings and relevant fragments).

KNN/Read-Across
The model performs a read-across and provides a quantitative prediction of bioconcentration factor (BCF) in fish, given in log(L/kg). The read-across is based on the similarity index developed inside the VEGA platform; the index takes into account several structural aspects of the compounds, such as their fingerprint, the number of atoms, of cycles, of heteroatoms, of halogen atoms, and of particular fragments (such as nitro groups). On the basis of this structural similarity index, the three compounds from the dataset resulting most similar to the chemical to be predicted are taken into account: the estimated BCF value is calculated as the weighted average value of the experimental values of the three selected compounds, using their similarity values as weight.

Type:

BCF

Value:

19.49 L/kg

Remarks on result:

other: CAESAR model

Remarks:

Outside of the applicability domain but aregarded as adequate in a weight-of-evidence approach.

Type:

BCF

Value:

131.83 L/kg

Remarks on result:

other: Meylan model

Remarks:

Outside of the applicability domain but aregarded as adequate in a weight-of-evidence approach.

Type:

BCF

Value:

95 L/kg

Remarks on result:

other: KNN/Read-Across model

Remarks:

Outside of the applicability domain but aregarded as adequate in a weight-of-evidence approach.

CAESAR

Experimental value [log(L/kg)]: -

Predicted BCF [log(L/kg)]: 1.29

Predicted BCF [L/kg]: 20

Predicted BCF from sub-model 1 (HM) [log(L/kg)]: 1.26

Predicted BCF from sub-model 2 (GA) [log(L/kg)]: 1.83

Predicted LogP (MLogP): 2.89

Structural alerts: OH group (PG 06)

Reliability: the predicted compound is outside the Applicability Domain of the model

  

Meylan

Experimental value [log(L/kg)]: -

Predicted BCF [log(L/kg)]:2.12

Predicted BCF [L/kg]:131

Predicted LogP (Meylan/Kowwin): 3.71

Predicted LogP reliability: Moderate

MW:157.22

Ionic compound: no

Reliability: the predicted compound could be out of the Applicability Domain of the model

 

KNN/Read-Across

Experimental value [log(L/kg)]: -

Predicted BCF [log(L/kg)]: 1.98

Molecules used for prediction: 4

Reliability:

the predicted compound is outside the Applicability Domain of the model

Description of key information

  Based on the log Kow and the results of several generally accepted QSAR models, a very high probability is given that the substance will not bioaccumulate in organisms.    

Additional information

Weight of evidence approach for estimation of bioaccumulative potential of CAS 10042-59-8

The bioaccumulative potential of CAS 10042-59-8 was estimated in a weight of evidence approach using different QSAR models, biodegradation potential and its experimental determined logKow.

Molecular parameters

The log Kow of CAS 10042-59-8 was determined in current study (BASF SE, 2019) based on the individual n-Octanol and water solubilities. As the substance is surface active, the determination of the log Kow according to the OECD Guideline for the Testing of Chemicals 107 (shake flask method) as well as OECD Guideline for the Testing of Chemicals 117 (HPLC method) was not applicable. Therefore, the logKow could only be estimated from the single solubilities of the substance in water and in n-Octanol. Using this method the log Kow was determined as 4.1. In ECHA’s Guidance on Information Requirements and Chemical Safety Assessment, Chapter R.11: PBT/vPvB assessment draft version 3.0 (March 2017) it is stated, that for organic substances with a log Kow value below 4.5 it is assumed that the affinity for the lipids of an organism is insufficient to exceed the B criterion, i.e. a BCF value of 2000 (based on wet weight of the organism, which refers to fish in most cases). Therefore, the determined log Kow of 4.1 indicates the low bioaccumulative potential of the substance.

 

QSAR calculations

The bioaccumulative potential of CAS 10042-59-8 was estimated in a weight of evidence approach using different QSAR models (Table 1) and molecular parameters (i.e. logKow, molecular weight and molecular dimensions). The identifiers of CAS 10042-59-8 can be found in Table 1 below.

Table 1: Evaluated Substance

CAS	Molecular formula	Smilescode	Chemical Name
10042-59-8	C10H22O	CCCCCC(CO)CCC	2-propylheptan-1-ol

 

As supporting information several QSAR models were used to calculate the BCF (Table 2). The results and further information on the reliability (e.g. applicability domain) can be found in Table 3.

 

Table 2: QSAR models used in the weight of evidence approach.

Software	Version	Model	Version	Sub-model
EPISuite	v4.11	BCFBAF model	v3.01	Regression-based estimate
				Arnot-Gobas BCF & BAF methods
VEGA	v1.1.3	CAESAR	v2.1.14
		Meylan	v1.0.3
		KNN/Read-Across	v1.1.0
T.E.S.T	v4.2.1	Hierarchical clustering
		FDA method
		Single model
		Group contribution
		Nearest neighbor
		Consensus
OASIS Catalogic	v5.13.1	BCF base-line model	v03.10

 

Table 3: Results and further details of the supporting QSAR estimations.

CAS		10042-59-8
SMILES code		CCCCCC(CO)CCC
Chemical Name		2-propylheptan-1-ol
Software	Model	Results [L/kg]	Remarks
EPISuite	BCFBAF model Regression-based estimate	236	within the applicability domain
	BCFBAF model Arnot-Gobas BCF & BAF methods incl. biotransformation rate estimates	247	within the applicability domain
	BCFBAF model Arnot-Gobas BCF & BAF methods incl. biotransformation rate of zero	1290	within the applicability domain
VEGA	CAESAR	19	outside the applicability domain
	Meylan	95	outside the applicability domain
	KNN/Read-Across	14	outside the applicability domain
T.E.S.T	Hierarchical clustering	141	- within applicability domain - Confidence in the estimated BCF: low
	Single model	48	- within applicability domain - Confidence in the estimated BCF: low
	Group contribution	27	- within applicability domain - Confidence in the estimated BCF: low
	FDA method	14	- within applicability domain - Confidence in the estimated BCF: low
	Nearest neighbor	240	- within applicability domain - Confidence in the estimated BCF: low
	Consensus	58	- within applicability domain - Confidence in the estimated BCF: low
OASIS Catalogic	BCF base-line model	32 [BCFmax = 598]	- all mitigating factors applied - within parameter and mechanistic domain - within structural fragment domain

 

EPISuite

EPISuite includes the BCFBAF model which encompasses two BCF estimation methodologies. The regression-based model is based on the works from Meylan et al. (1997 and 1999). The second model is based on the works from Arnot and Gobas (2003) and includes estimates on the biotransformation rate in fish. The regression-based model derived a BCF value of 236 L/kg based on an experimental determined logKow of 4.1. The model did not apply any correction factors and derived the BCF by applying common statistical regression methodology (n = 396; r2= 0.792). As the substance is within the applicability domain of the model (molecular weight and logKow ranges) the result is regarded as reliable information in a weight of evidence approach. The result of the BCFBAF model is regarded as a reliable information for an estimation of bioaccumulative properties of the substance.

The model based on the works of Arnot and Gobas (2003) takes the biotransformation rate of the compound into account. The model derived a BCF value of 247 L/kg. As the substance is within the applicability domain of the model (molecular weight and logKow range) the result is regarded as reliable information in a weight of evidence approach. Therefore, the result of the model based on the works of Arnot and Gobas is regarded as a reliable information for an estimation of bioaccumulative properties of the substance.

Both models showed similar results for the BCF determination. Therefore, the result of the BCFBAF model is regarded as a reliable information for an estimation of bioaccumulative properties of the substance.

 

Additionally, biotransformation in fish was estimated using the OECD QSAR-Toolsbox v.4.3.1, based on the BCFBAF model included in EPISuite as an additional information regarding behavior of the substance in the fish as a model organism.A description of the model for biotransformation in fish included in the BCFBAF model isavailable in the publication Arnot JA et al. (2009). Detailed information on this model is also available from the model in EPISuite. This profiler groups the chemicals into categories of biotransformation: very slow, slow, moderate, fast, very fast. This will allow the user to identify chemicals that have a similar biotransformation rate. Cut-off values for the 5 categories could be the following, as proposed by Arnot JA  and et al. (2009):

Half-life HLNrange (d)	Qualitative category
HLN> 100	Very slow
10 < HLN≤ 100	Slow
1 < HLN≤ 10	Moderate
0.1 < HLN≤ 1	Fast
HLN≤ 0.1	Very Fast

 

The biotransformation rate for CAS 10042-59-8 was classified in qualitative category “Fast” according to the QSAR Toolbox. Although this statement should not be used as a stand-alone value for prediction purposes, it indicates the quick biotransformation in fishes mitigating the bioaccumulative potential.

 

VEGA

The current VEGA package comprises three different models. (1) CAESAR, (2) Meylan and (3) KNN/Read-Across. The applicability domain of the predictions is assessed using an Applicability Domain Index (ADI) that has values from 0 (worst case) to 1 (best case). The ADI is calculated by grouping several other indices, each one taking into account a particular issue of the applicability domain. Most of the indices are based on the calculation of the most similar compounds found in the training and test set of the model, calculated by a similarity index that consider molecule's fingerprint and structural aspects (count of atoms, rings and relevant fragments).

In regards to the CAESAR model the following indices are checked: (1) Similar molecules with known experimental value, (2) accuracy (average error) of prediction for similar molecules, (3) concordance with similar molecules (average difference between target compound prediction and experimental values of similar molecules), (4) maximum error of prediction among similar molecules, (5) atom centered fragments similarity check, (6) model descriptors range check and (7) global AD index which takes into account all the previous indices in order to give a general global assessment on the applicability domain. The model predicted a BCF value of 19 L/kg but the substance was out of the applicability domain. Nevertheless, the result clearly showed that the estimated BCFs are way below the regulatory threshold. Therefore, the result is regarded as a reliable indication of the bioaccumulative potential of the substance.

The Meylan model is a reconstruction of the regression-based model integrated in EPISuite (Meylan et al. 1997, 1999). The original dataset from EPISuite has been processed and cleared from duplicates and compounds provided with structures that had problems. The final dataset has 662 compounds. Similar to the CAESAR model, the applicability domain is assessed with several indices. (1) Similar molecules with known experimental value, (2) accuracy (average error) of prediction for similar molecules, (3) concordance with similar molecules (average difference between target compound prediction and experimental values of similar molecules), (4) maximum error of prediction among similar molecules, (5) logP reliability, (6) model descriptors range check and (7) global AD index. The model predicted a BCF value of 132 L/kg. The reliability of the prediction was moderate as the substance was partly out of the applicability domain. Therefore, the result is regarded as a reliable indication of the bioaccumulative potential of the substance.

The KNN/Read-Across model performs a read-across on a dataset of 860 chemicals. The applicability domain takes the following indices into account: (1) Similar molecules with known experimental value, (2) accuracy (average error) of prediction for similar molecules, (3) concordance with similar molecules (average difference between target compound prediction and experimental values of similar molecules), (4) maximum error of prediction among similar molecules, (5) atom centered fragments similarity check, (6) global AD index. The model predicted a BCF values of 95 L/kg but the substance was out of the applicability domain. Nevertheless, the result clearly showed that the estimated BCFs are way below the regulatory threshold. Therefore, the result is regarded as a reliable indication of the bioaccumulative potential of the substance.

 

T.E.S.T.

The T.E.S.T. package encompasses five separate methods to estimate the BCF. The sixth method is the consensus method which simply averages the results of the prediction from the other QSAR methodologies (taking into account the applicability domain of each method). This method typically provides the highest prediction accuracy since errant predictions are dampened by the predictions from the other methods. In addition, this method provides the highest prediction coverage because several methods with slightly different applicability domains are used to make a prediction. For the five separate methodologies, the results range from 14 to 240. The averaged consensus result is 58. The models only make predictions if the substance is within the respective applicability domains. For the present case, the substance is within the applicability domains of all of the five models. However, the mean absolute errors regarding the similarity coefficients for both the external test sets and the training sets are above the respective thresholds. Therefore, the confidence in the estimated BCF values is low. Nevertheless, the result of the consensus method clearly showed that the estimated BCFs are way below the regulatory threshold. Therefore, the model is regarded as reliable for the estimation of the bioaccumulative potential of the substance.

 

OASIS Catalogic

The BCF base-line model integrated in OASIS Catalogic reflects the current understanding of the process by which lipophilic organic chemicals are bioaccumulated in fish through the respiratory organs only. Chemicals bioaccumulating by other mechanisms (e.g., binding to proteins) are considered out of the mechanistic domain of the model. The model consists of two major components: a model for predicting the maximum potential for bioaccumulation based solely on chemicals’ lipophilicity (i.e., BCFmax model), and a set of mitigating factors that account for the reduction of the bioaccumulation potential of chemicals based on chemical (e.g., molecular size and ionization) and organism-dependent factors (e.g., metabolism). BCFmax model is a theoretical model based on the assumption that the only driving force of bioconcentration is lipophilicity and the effect of any other factors are insignificant. It mathematical formalism is derived considering multi-compartment diffusion. The bioconcentration predicted by BCFmax model could be limited by variety of mitigating factors that account for the reduction of the bioaccumulation potential of chemicals based on chemical and organism-dependent factors. The effect of mitigating factors mathematically is quantified by probabilities: to penetrate through the cell membrane, to be ionized, to be metabolised, etc. In the BCF base-line model the tissue metabolism simulator is used to account for the effect of metabolism. It consists of a consequence of spontaneous abiotic and enzyme controlled steps. Probabilities of these molecular transformations are assessed by fitting the training set data. The CATALOGIC platform utilizes a multi-stage applicability domain that has been described by Dimitrov et al. (2005). The applicability domain of the BCF base-line model contains three layers: (1) General properties requirements. These requirements specify in the domain only those chemicals that fall in the range of variation of physicochemical properties that may affect significantly the quality of the measured endpoint. For the BCF base-line model attention is focused on lipophilicity (log KOW), molecular weight (MW) and water solubility (WS). Only correctly predicted chemicals from the training set are used to determine the range of variation of these properties. (2) The structural domain. It determines the maximum structural similarity between the target chemical and chemicals from the training set. The structural neighborhood of atom-centered fragments (ACF) accounting for 1st neighbors, atom type, hybridization and attached hydrogen atoms are used to determine this similarity. The target chemical could contain the following types of ACF:

 

-      Fragments present in correctly predicted training chemicals only (i.e. correct fragments)

-      Fragments found both in correctly and non-correctly predicted training chemicals (i.e. fuzzy fragments). These fragments are treated as correct fragments

-      Fragments present in non-correctly predicted training chemicals only (i.e. incorrect fragments),

-      Fragments not present in the training chemicals (i.e. unknown fragments).

 

(3) The mechanistic domain. It discriminates between modes of bioaccumulation - passive (partitioning in lipid phase) or active (based on protein binding). Only chemicals with expected passive diffusion driven bioaccumulation are considered to be in the mechanistic domain of the model.

 

In the present case, the substance fulfills the general properties requirements, i.e. its logKow, molecular weight and water solubility is within the ranges of the model. Furthermore, the substance is within the mechanistic domain, i.e. it is expected to be taken up by passive diffusion only. Additionally, it is within in the structural domain. For the substance 100% of its fragments could be found in correctly predicted training set chemicals. If a chemical is within all of the specified layers mentioned above, it will be classified as within of the applicability domain. This classification means that the prediction is reliable. The estimated BCF (all mitigating factors applied) was determined to be 32 L/kg. For the substance a mitigating effect of Metabolism was taken into account, resulting in diverging values for the calculated BCF (32 L/kg) and the calculated BCF max (598 L/kg). As it is assumable, that the molecule will be metabolized, the calculated BCF max value is considered to be too high to truly reflect the bioaccumulative potential. Nevertheless, it clearly shows that even without considering any mitigating factors at all, the substance has a low bioaccumulative potential.

 

Conclusion for CAS 10042-59-8

The results of the calculations using EPISuite, T.E.S.T. package, VEGA and OASIS Catalogic were regarded as reliable information and used in the bioaccumulative assessment. According to the results of the QSAR calculations, the substance is regarded to have a negligible bioaccumulative potential.

 

Overall Conclusion

The target of this analysis was the mono-constituent CAS 10042-59-8 which was considered in an analysis regarding the bioaccumulative properties. Regarding the outcome of the 11 used models for the substance, a bioaccumulative potential regarding the BCF value was not shown. None of the models calculated a BCF value higher the bioaccumulation trigger of 500 according to Regulation 1272/2008/EC resp. the PBT trigger of 2000 according to Regulation 1907/2006/EC Annex XIII (Calculated values incl. biotransformation rate estimates lay between 14 - 247.)

Based on the results of models, there is a high probability that the substance is not bioaccumulative.

Biodegradation

Based on several screening tests for biodegradation in water, the substance was classified as readily biodegradable according to the OECD criteria (> 60 % degradation based on CO₂evolution after 28 days, including the 10-day window). Therefore, a quick mineralization of the substance in the environment is expected. A long-term exposure necessary for significant bioaccumulation can be excluded under natural conditions.

 

Final Conclusion

All gathered data from QSAR models and experimental results were used to evaluate the bioaccumulation potential of CAS 10042-59-8. Amongst others its transformation and resulting possible elimination in organism was estimated. The experimental results of the biodegradation study resulted in a quick mineralization of the substance by microorganisms. The calculated outcome ofthe OECD QSAR-Toolsbox, based on the BCFBAF model also resulted in fast transformation in fishes. Both results give a clear conclusion, that CAS 10042-59-8 will be quickly biotransformed as indicated by the model organisms bacteria und fish.

Based on the experimentally determined log Kow, the results of several generally accepted QSAR models, resulting in a low BCF and a quick biotransformation and the readily biodegradation of the substance, a very high probability is given that the substance will not bioaccumulate in organisms.