Registration Dossier

Administrative data

Endpoint:
adsorption / desorption
Remarks:
other: KOCWIN v. 2.0 prediction.
Type of information:
read-across based on grouping of substances (category approach)
Adequacy of study:
weight of evidence
Reliability:
2 (reliable with restrictions)
Rationale for reliability incl. deficiencies:
other: In domain and well documented prediction using analogues for validation.
Justification for type of information:
Read across is based on the category approach. Please refer to attached category document.

Data source

Reference
Reference Type:
study report
Title:
Unnamed
Year:
2014
Report Date:
2014

Materials and methods

Principles of method if other than guideline:
KOCWIN v. 2.0 prediction.
GLP compliance:
no
Remarks:
Not applicable to QSAR.
Type of method:
other: KOCWIN v. 2.0 prediction.
Media:
other: N.A. Majority of training set data is on water-soil partitioning.

Test material

Reference
Name:
Unnamed
Type:
Constituent
Type:
Constituent
Type:
Constituent
Details on test material:
The test material was assessed for the major constituent (approx 99%) described by the following SMILES: O(CCOCCOCCO)CCO

Results and discussion

Adsorption coefficientopen allclose all
Type:
Koc
Value:
0.05
Temp.:
20 °C
Remarks on result:
other: QSAR value KOCWIN Kow method.
Type:
log Koc
Value:
-1.29
Temp.:
20 °C
Remarks on result:
other: QSAR value KOCWIN Kow method

Any other information on results incl. tables

Result from the Domain assessment:

KOCWIN does not provide explicit information about applicability domain of the model. The applicability domain can be derived from the training data set and the fragments with correction. If a given unknown consist of fragments that are not aliphatic or aromatic and do not have a correction factor listed they have to be considered out of domain.       

i.     Parametric Domain: Not specified by developer.

Log Kow: minimum–maximum of in the training data:   
               -2.11 < Log Kow< –9.1           
Experimental log Kowof the unknown (column method): - 1.38

Calculated logKowof the unknown: -2.0

         

MW: minimum–maximum of molecular mass in the training data:         

               32 < MW < 665          [g/mol]
MW of unknown: 194.23 [g/mol]

=>In domain

ii.     Structural Domain: Not specified by developer. Assessed as all molecules with aliphatic ether or with a fragment with a correction factor defined for the model.  
Fragments of the unknown:

-         Aliphatic

-          Ether.

- Alcohol

A series of aliphatic ethers and alcohols are represented in the training set (see analogues)

Correction factors are explicityl defined for aliphatic ether (See App. D of the help file of Kocwin)

=>In domain

iii.     Mechanism domain: The model does not define a mechanism of partitioning. However, the dominant driving force for partitioning of the molecules in the training set is hydrophobicity. Likewise, partitioning of the unknown is driven by hydrophobicity.

=>In domain

iv.     Metabolic domain: Partitioning equilibrium can only be defined for molecules that are stable in the matrix. The unknown is expected to be stable in aqueous solutions.

Results from the structural analogue search b.    Structural analogues: The training data set and the validation data set have been searched for analogues using a sub-structural search for relevant fragments present in the unknown. Substructures that where searched are aliphatic ether and alcohols. No molecules with a combination of all three characteristics were identified in the training set. All partial analogues identified were from the training set and required correction factors. Partial analogues identified in this procedure are listed in Table1. Experimental results and predicted values are listed in Table2.

 

Table1: Structural analogues identified in the training dataset.

Chemical name

Smiles

Analogues

 

2,2' -dichloroethylether

C(Cl)COCCCl

dichloroisopropylether

ClC(C)OC(C)CCl

butanol

 C(O)CCC

propylene glycol

C(C)(O)CO

  

Unknown:

 

tetraethylene glycol

C(O)COCCOCCOCCO

 

 

Table2: Experimetal data and QSAR prediction for Analogues and unknown. 

Chemical name

Log Kow (a)

Log Kocexp. (a)

Log Kocest. (MCI)

Δ

Log Kocest. (Kow)

Δ

Analogues

 

2,2'-dichloroethylether

2.48

1.67

1.92

0.25

2.21

0.54

dichloroisopropylether

1.29

1.88

1.51

-0.37

1.55

-0.33

butanol

 0.88

 0.50

0.54

0.04 

1.0

0.5 

propylene glycole -0.92  0.36  0  -0.36  -0.41  -0.77
 Unknown 

tetraethylene glycol

- 2.0a    1.0    -1.28   

(a)Source: {Schüürmann, 2006}   

c.      Considerations on structural analogues: For both, chlorinated ethers and aliphatic alcohols, the prediction of both methods is reasoably close to the experimental value. A Delta of less than 0.7 of the calculated and the experimental value is considered as valid predictiong in line with the 95% confidence interval determined in the internal validation. Based on the analogy of the sorption process, preference is given to the LogKow method for estimating the unknown. 3.2          The uncertainty of the prediction (OECD principle 4): Based on the data presented by the developers, approx. 95% of all predictions of Log Koc for the trainingset fall within the range of ± 0.7 of the experimental value. This can be used to define the 95% confidence interval for theKoc of the unknown. The major contribution of the uncertainty is likely to come from the variability in the soil and sediment matrixes in the experimental data and does not reflect the uncertainty of the QSAR method.

3.3          The chemical and biological mechanisms according to the model underpinning the predicted result (OECD principle 5).  Not applicable. No mechanistic interpretation for the algorithm provided.

Applicant's summary and conclusion

Validity criteria fulfilled:
yes
Conclusions:
The  Log Koc is predicted as -1.29 +/- 0.7 using the Kow method in KOCWIN v. 2.0 and Kow=-2.0 as input.
Executive summary:

The KOCWIN model estimates Log Koc either from the octanol-water coefficient or from a first-order molecular connectivity index (MCI).Structural analogues as identified in the training set have been processed parallel to the unknown. The model does not define parametric nor structural domain. However, the analogues identified from within the training set suggest coverage of relevant substructures present in the unknown (alkyl, ether, alcohols). A comparison of experimental and predicted values for the analogues was performed to decide on the preferred model for the prediction. Based on analogy of the sorption process with the octanol water partitioning, preference was given to value calculated with the Log Kow model.

Confidence intervals are derived based on the error histogram provided by the developer that indicate that 95% of the substances in the training dataset are predicted within +/- 0.7 units of the experimental error.

 

Name

SMILES(major component, 85%)

Log Koccalc

Koccalc

Bisphenol A diglycidil ether

c1(C(C)(C)c2ccc(OC3CO3)cc2)ccc(OCC2CO2)cc1

-1.29 ± 0.7

0.05 (0.01–0.26)

 

The predicted value is considered reliable for the purpose of environmental modelling and the uncertainty is comparable to the situation where experimental data for only one or a small number of different soils has been tested.