Sodium phenylacetate

Adsorption / desorption

Link to relevant study record(s)

Reference

Endpoint:

adsorption / desorption, other

Type of information:

calculation (if not (Q)SAR)

Adequacy of study:

key study

Reliability:

4 (not assignable)

Rationale for reliability incl. deficiencies:

secondary literature

Justification for type of information:

Data is from CompTox Chemistry Dashboard database.

Qualifier:

according to guideline

Guideline:

other: Refer below principle

Principles of method if other than guideline:

Prediction done using OPERA (OPEn (quantitative) structure-activity Relationship Application) V1.02 model in which calculation based on PaDEL descriptors (calculate molecular descriptors and fingerprints of chemical)

GLP compliance:

not specified

Type of method:

other: PaDEL descriptors

Media:

soil

Specific details on test material used for the study:

- Name( IUPAC): sodium 2-phenylacetate
- Name of test material (as cited in study report): Sodium phenylacetate
- Molecular formula C8H8O2.Na
- Molecular weight: 158.1313 g/mol
- Smiles notation : c1(ccccc1)CC(=O)[O-].[Na+]
- InChl: 1S/C8H8O2.Na/c9-8(10)6-7-4-2-1-3-5-7;/h1-5H,6H2,(H,9,10);/q;+1/p-1
- Substance type: Organic
- Physical state: Liquid

Radiolabelling:

not specified

Analytical monitoring:

not specified

Key result

Type:

Koc

Value:

40.5 L/kg

Remarks on result:

other: Log Koc=1.6074 Result based on the OECD principle 1-5

Transformation products:

not specified

Prediction based on following 5 OECD principles:

OECD Principle 1 (Defining the endpoint):

The original data collected from the PHYSPROP database (788 chemicals) have undergone a series of processes to curate the chemical structures and remove duplicates, obvious outliers and erroneous entries. This procedure also included a consistency check to ensure only good quality data is used for the development of the QSAR model (750 chemicals).

 

Then, QSAR-ready structures were generated by standardizing all chemical structures and removing duplicates, inorganic and metallo-organic chemicals (735 chemicals). The descriptions of KNIME workflows that were developed for the purpose of the cleaning and standardization of the data are available in the papers [ref 1 and ref 4 Section 2.7].

 

The curated outlier-free experimental data (729 chemicals) was divided into training and validation sets before the machine learning and modeling steps.

 

OECD Principle 2(Defining the algorithm):

Type of model:

QSAR model using PaDEL descriptors

 

Explicit algorithm:

Distance weighted k-nearest neighbors (kNN) This is a refinement of the classical k-NN classification algorithm where the contribution of each of the k neighbors is weighted according to their distance to the query point, giving greater weight to closer neighbors.The used distance is the Euclidean distance. kNN is an unambiguous algorithm that fulfills the transparency requirements of OECD principle 2 with an optimal compromise between 4.Defining the algorithm - OECD Principle 2 model complexity and performance.

 

OECD Principle 3(Defining the applicability domain):

Method used to assess the applicability domain:The applicability domain of the model is assessed in two independent levels using two different distance-based methods. First, a global applicability domain is determined by means of the leverage approach that checks whether the query structure falls within the multidimensional chemical space of the whole training set. The leverage of a query chemical is proportional to its Mahalanob is distance measure from the centroid of the training set. The leverages of a given dataset are obtained from the diagonal values of the hat matrix. This approach is associated with a threshold leverage that corresponds 5.Defining the applicability domain - OECD Principle 3 to 3*p/n where p is the number of model variables while n is the number of training compounds. A query chemical with leverage higher than the threshold is considered outside the AD and can be associated with unreliable prediction. The leverage approach has specific limitations, in particular with respects to gaps within the descriptor space of the model or at the boundaries of the training set. To obviate such limitations, a second tier of applicability domain assessement was added. This comprised a local approach which only investigated the vicinity of the query chemical. This local approach provides a continuous index ranging from 0 to 1 which is different from the first approach which only provides Boolean answers (yes/no). This local AD-index is relative to the similarity of the query chemical to its 5 nearest neighbors in the p dimensional space of the model. The higher this index, the more the prediction is likely to be reliable.

 

OECD Principle 4 (Internal validation):

Availability of the training set: Yes

Statistics for goodness-of-fit: Performance in training: R2=0.81 RMSE=0.54

Robustness - Statistics obtained by leave-many-out cross-validation: Performance in 5-fold cross-validation: Q2=0.81 RMSE=0.55

 

OECD Principle 4(External validation):

Availability of the external validation set: Yes

.Predictivity - Statistics obtained by external validation: Performance in test: R2=0.71 RMSE=0.61

Experimental design of test set:

The structures are randomly selected to represent 25% of the available data keeping a similar normal distrubution of LogKoc vlaues in both training and test sets using the Venetian blinds method.

 

OECD Principle 5 (Providing a mechanistic interpretation):

Mechanistic basis of the model: The model descriptors were selected statistically but they can also be mechanistically interpreted. KOC is the ratio between the concentration of a chemical adsorbed by the soil normalized to soil organic carbon and the concentration dissolved in the soil water. Thussoil sorption is closely related to water solubility and logP. Therefore, the chemical features which determine the soil sorption are similar to those related to water solubility and logP. In particular, size related descriptors since larger compounds tend to have higher soil sorption because they do have lower water solubility. Also elctronic profile descriptors related to charges and to charge distribution are of high importance: the presence of active functional group next to carbon leads to better water solubility, likewise higher polarity leads to better water solubility.

Validity criteria fulfilled:

not specified

Conclusions:

From CompTox Chemistry Dashboard using OPERA (OPEn (quantitative) structure-activity Relationship Application) V1.02 model in which calculation based on PaDEL descriptors (calculate molecular descriptors and fingerprints of chemical) the adsorption coefficient i.e KOC for test substance sodium 2-phenylacetate was estimated to be 40.5 L/kg and log Koc= 1.6074.

Executive summary:

From CompTox Chemistry Dashboard using OPERA (OPEn (quantitative) structure-activity Relationship Application)  V1.02 model in which calculation based on PaDEL descriptors (calculate molecular descriptors and fingerprints of chemical)  the adsorption coefficient i.e KOC for test substance sodium 2 -phenylacetate (CAS no. 114-70-5) was estimated to be 40.5 L/kg (log Koc = 1.6074) .The predicted KOC result based on the 5 OECD principles. Thus based on the result it is concluded that the test substance Sodium 2 -phenylacetate has a low sorption to soil and sediment and therefore have moderate migration potential to ground water.

Description of key information

From CompTox Chemistry Dashboard using OPERA (OPEn (quantitative) structure-activity Relationship Application)  V1.02 model in which calculation based on PaDEL descriptors (calculate molecular descriptors and fingerprints of chemical)  the adsorption coefficient i.e KOC for test substance sodium 2 -phenylacetate (CAS no.114-70-5) was estimated to be 40.5 L/kg (log Koc =1.6074) .The predicted KOC result based on the 5 OECD principles. Thus based on the result it is concluded that the test substance Sodium 2 -phenylacetate has a low sorption to soil and sediment and therefore have moderate migration potential to ground water.

Key value for chemical safety assessment

Koc at 20 °C:

40.5

Additional information

Various predicted data for the target compound of sodium 2-phenylacetate (CAS No: 114-70-5) and its supporting weight of evidence study for it’s read cross substance were reviewed for the adsorption end point which are summarized as below:

In a prediction done from CompTox Chemistry Dashboard using OPERA (OPEn (quantitative) structure-activity Relationship Application) V1.02 model in which calculation based on PaDEL descriptors (calculate molecular descriptors and fingerprints of chemical) the adsorption coefficient i.e KOC for test substance sodium 2 –phenylacetate (CAS no.114-70-5) was estimated to be 40.5 L/kg (log Koc =1.6074). The predicted KOC result based on the 5 OECD principles. Thus based on the result it is concluded that the test substance Sodium 2 –phenylacetate has a low sorption to soil and sediment and therefore have moderate migration potential to ground water.

 

In a supporting study from authoritative database HSDB (Hazardous Substance Data Bank, 2017) the Koc values for read across chemical 2 phenylacetic acid (CAS no. 103-82-2) obtained were 31, 26 and 28 dimensionless and log Koc are 1.49, 1.41 and 1.44 in three different types of soil namely podzol, alfisol and sediment from lake constance respectively. These log Koc values suggests that 2 phenylacetic acid is expected to have negligible sorption to soil and sediment and therefore have rapid migration potential to ground water.

 

On the basis of above overall results for target chemical sodium 2-phenylacetate (CAS No: 114-70-5) (from Comptox Chemistry Dashboard, 2017), it can be concluded that the Koc value of test substance sodium 2-phenylacetate ranges from 26 to 40.5 and log Koc ranges from 1.41 to 1.60 indicating that the test chemical sodium 2-phenylacetate has negligible to low sorption to soil and sediment and therefore have rapid to moderate migration potential to groundwater.

[LogKoc: 1.6074]