2-amino-2',5-dichlorobenzophenone

Adsorption / desorption

Administrative data

Endpoint:

adsorption / desorption

Remarks:

adsorption/desorption

Type of information:

(Q)SAR

Remarks:

Migrated phrase: estimated by calculation

Adequacy of study:

supporting study

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
Estimation Programs Interface Suite™ for Microsoft® Windows v 4.10. US EPA, United States Environmental Protection Agency, Washington, DC, USA.

2. MODEL (incl. version number)
KOCWIN Program (v2.00) - MCI method

3. SMILES OR OTHER IDENTIFIERS USED AS INPUT FOR THE MODEL
O=C(c(c(N)ccc1CL)c1)c(c(ccc2)CL)c2

4. SCIENTIFIC VALIDITY OF THE (Q)SAR MODEL
[Explain how the model fulfils the OECD principles for (Q)SAR model validation. Consider attaching the QMRF or providing a link]
- Defined endpoint:
log Koc – soil adsorption coefficient of organic compounds.
- Unambiguous algorithm:
log Koc  =  0.5213 MCI  +  0.60 + ΣPfN  
MCI – molecular connectivity index, ΣPfN - summation of the products of all applicable correction factor coefficients available in the data set multiplied by the number of times (N) that factor is counted for the structure.
- Defined domain of applicability:
Currently, there is no universally accepted definition of model domain. The training set of the model contains diverse molecules, so that the fragment library is 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: 32-665 g/mol
Log Kow limits: -2.11-9.10
- Appropriate measures of goodness-of-fit and robustness and predictivity:
for the statistics, training data set has been split up into two subsets: the one containing non-polar substances with no fragments subjected to corrections (i.e. those with ΣPfN = 0) and the one containing the remaining ones. For the non-polar set: N = 69 compounds, correlation coefficient R2= 0.967, standard deviation sd = 0.247 and average deviation ad = 0.199. For the second set: N = 447 compounds, correlation coefficient R2= 0.9, standard deviation sd = 0.34 and average deviation ad = 0.273. For the external validation data set: N = 158 compounds, correlation coefficient R2= 0.85, standard deviation sd = 0.583 and average deviation ad = 0.459. For the 516 compounds in the training set, 93% are within 0.6 log units and 100% within 1 log unit.
- Mechanistic interpretation:
The methodology and relationship between the forst order molecular connectivity index (MCI) and adsorption coefficient is outlined in the reference paper: Meylan, W., P.H. Howard and R.S. Boethling, "Molecular Topology/Fragment Contribution Method for Predicting Soil Sorption Coefficients", Environ. Sci. Technol. 26: 1560-7 (1992). MCI was initially successfully used to predict soil sorption coefficients for non-polar organics, and the developed new estimation method based on MCIand series of statistically derived fragment contribution factors made it useful also for the polar ones.

5. APPLICABILITY DOMAIN
[Explain how the substance falls within the applicability domain of the model]
- Descriptor domain:
The substance is in the molecular weight range and in the logKow range of the compounds in the training set.
- Structural and mechanistic domains:
the correction factors for the substance do not exceed the maximum number in any individual compound of the training set.
- Similarity with analogues in the training set:
the most similar substance within the training and validation set is 2-chlorobenzamide (CAS 609-66-5) with a Dice index >50%. According to the model documentation this substance has a measured logKoc of 1.51 and an estimated logKoc of 1.45 (MCI method) indicating that the QSAR model works well for the most similar substance.

6. ADEQUACY OF THE RESULT
[Explain how the prediction fits the purpose of classification and labelling and/or risk assessment]
The organic carbon dependent sorption behavior is important to describe the sorption of organic non-ionic substances on sediment/soil/sludge. As the substance is organic and non-ionisable the prediction of the logKoc is not only reliable but also relevant for exposure and risk assessment purposes.

Data source

Reference

Reference Type:

study report

Title:

Unnamed

Year:

2014

Report date:

2014

Materials and methods

Test guideline

Qualifier:

according to guideline

Guideline:

other: QSAR calculation

Results and discussion

Adsorption coefficient

Type:

log Koc

Value:

3.167

Remarks on result:

other: calculated with EPIWIN 4.1 (Koc Estimate from MCI)

Applicant's summary and conclusion

Conclusions:

The estimated Log Koc is 3.1667 (calculated with EPIWIN 4.1 (Koc Estimate from MCI)).