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Environmental fate & pathways

Bioaccumulation: aquatic / sediment

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Reference
Endpoint:
bioaccumulation in aquatic species: fish
Type of information:
calculation (if not (Q)SAR)
Adequacy of study:
weight of evidence
Reliability:
4 (not assignable)
Rationale for reliability incl. deficiencies:
secondary literature
Justification for type of information:
Data is from authoritative 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
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
Vehicle:
not specified
Test organisms (species):
other: Fish
Route of exposure:
aqueous
Test type:
other: predicted data
Water / sediment media type:
natural water: freshwater
Reference substance (positive control):
not specified
Key result
Type:
BCF
Value:
3.65 dimensionless
Basis:
other: Result based on the OECD principle 1-5
Calculation basis:
other: PaDEL descriptors
Remarks on result:
other: other details not available

Prediction based on following 5 OECD principles:

OECD Principle 1 (Defining the endpoint):

The original data collected from the PhysProp database (685 chemicals) has 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 (618 chemicals).

 

Then, QSAR-ready structures were generated by standardizing all chemical structures and removing duplicates, inorganic and metallo-organic chemicals (608 chemicals).

 

The curated outlier-free Physprop data inculding PBDEs additional data (626 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 4.Defining the algorithm - OECD Principle 2 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 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=0.85 RMSE=0.53

Robustness - Statistics obtained by leave-many-out cross-validation: Performance in 5-fold cross-validation: Q2=0.84 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=0.83 RMSE=0.64

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.Several publications reported a general bilinear

correlation pattern between BCF and logKow [Section 9.2 ref 3-6]. In our model, we used descriptors related to lipophilicity with different

methods and encoding different information (XLogP, CrippenLogP and ALogP) Since the number of H-bond acceptor atoms explains the tendency of polar chemicals towards aquatic partitioning two related descriptors were selected (nHBAcc, LipinskiFailures).The number of acidic groups (nAcid) is also a descriptor that encodes information about the partitioning of a chemical between the orgainc and water phases. Factors that increase intermolecular interactions (hydrogen bonding and polarity) lower the bioconcentration factor by making molecules remain in the aqueous phase, or cause binding to membranes and thereby hinder penetration into the organism.Dearden and Shinnawei demonstrated that the electronic properties

(polarizability and electronegativity) are of high significance to BCF modeling. Such information is encodd in these descriptors (minsC, naasC

and ATSC1s). van de Waals volume, here encoded in (ATSC0v) was shown to be of utility to BCF modeling by Papa et al.

Validity criteria fulfilled:
not specified
Conclusions:
The bioaccumulation factor i.e BCF for test substance sodium 2-phenylacetate was estimated to be 3.65 dimensionless.
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 bioaccumulation i.e BCF for test substance sodium 2-phenylacetate (CAS no.114 -70 -5) was estimated to be 3.65 dimensionless . The predicted BCF result based on the 5 OECD principles. Thus based on the result it is concluded that the test substance sodium 2-phenylacetate is non-bioaccumulative in nature, because the bioconcentration factor in fish is less than 2000.

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 bioaccumulation i.e BCF for test substance sodium 2-phenylacetate (CAS no.114 -70 -5) was estimated to be 3.65 dimensionless . The predicted BCF result based on the 5 OECD principles. Thus based on the result it is concluded that the test substance sodium 2-phenylacetate is non-bioaccumulative in nature, because the bioconcentration factor in fish is less than 2000.

Key value for chemical safety assessment

BCF (aquatic species):
3.65 dimensionless

Additional information

Predicted data for the target compound sodium 2-phenylacetate (CAS No: 114-70-5) and supporting weight of evidence studies for its read across substance were reviewed for the bioaccumulation 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 bioaccumulation i.e BCF for test substance sodium 2-phenylacetate (CAS no.114 -70 -5) was estimated to be 3.65 dimensionless. The predicted BCF result is based on the 5 OECD principles.

 

In the supporting weight of evidence study from HSDB ( Hazardous Substance Data Bank, 2017).The BCF value of read across chemical 2-phenylacetic acid (CAS no. 103-82-2) was estimated was 3 dimensionless by using log Kow of 1.41 and regression derived equation.

 

In another supporting weight of evidence study from same source as mentioned above (HSDB) The BCF value for read across chemical benzyl acetate (CAS 140-11-4) estimated was 18 dimensionless by using a log Kow of 1.96 and a regression derived equation.

 

On the basis of above results for target chemical sodium 2-phenylacetate (CompTox Chemistry Dashboard, 2017) and for its read across substance from authoritative database HSDB, it can be concluded that the BCF value of test substance sodium 2-phenylacetate ranges from 3 to 18 dimensionless which does not exceed the bioconcentration threshold of 2000, indicating that the chemical sodium 2-phenylacetate is not expected to bioaccumulate in the food chain.