Sodium 3-(p-anilinophenylazo)benzenesulphonate

Bioaccumulation: aquatic / sediment

Link to relevant study record(s)

Reference

Endpoint:

bioaccumulation in aquatic species: fish

Type of information:

calculation (if not (Q)SAR)

Adequacy of study:

key study

Reliability:

4 (not assignable)

Rationale for reliability incl. deficiencies:

accepted calculation method

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

Specific details on test material used for the study:

- Name of test material : sodium 3-[(4-anilinophenyl)diazenyl]benzenesulfonate
- Molecular formula : C18H15N3O3S.Na
- Molecular weight: 375.383 g/mol
- Smiles notation : c1(Nc2ccccc2)ccc(\N=N\c2cc(ccc2)S(=O)(=O)[O-])cc1.[Na+]
- InChl : 1S/C18H15N3O3S.Na/c22-25(23,24)18-8-4-7-17(13-18)21-20-16-11-9-15(10-12-16)19-14-5-2-1-3-6-14;/h1-13,19H,(H,22,23,24);/q;+1/p-1/b21-20+;
- Substance type:Organic
- Physical state:Solid

Radiolabelling:

not specified

Vehicle:

not specified

Test organisms (species):

other: Fish

Route of exposure:

aqueous

Test type:

not specified

Water / sediment media type:

natural water: freshwater

Reference substance (positive control):

not specified

Key result

Type:

BCF

Value:

8.87 dimensionless

Basis:

other: Result based on the OECD principle 1-5

Calculation basis:

other: PaDEL descriptors

Remarks on result:

other: Other details not known

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.

Bioconcentration Factor
	Average	Median	Range
Experimental	-	-	-
Predicted	10.9 (2)	10.9	8.87 to 12.9

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 bioaccumulation i.e BCF for test substance sodium 3-[(4-anilinophenyl)diazenyl]benzenesulfonate was estimated to be 8.87 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 3-[(4-anilinophenyl)diazenyl]benzenesulfonate was estimated to be 8.87 dimensionless . The predicted BCF result based on the 5 OECD principles. Thus based on the result it is concluded that the test substance sodium 3-[(4-anilinophenyl)diazenyl] benzenesulfonate is non-bioaccumulative in nature.

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 3-[(4-anilinophenyl)diazenyl]benzenesulfonate was estimated to be 8.87 dimensionless . The predicted BCF result based on the 5 OECD principles. Thus based on the result it is concluded that the test substance sodium 3-[(4-anilinophenyl)diazenyl] benzenesulfonate is non-bioaccumulative in nature.

Key value for chemical safety assessment

BCF (aquatic species):

8.87 dimensionless

Additional 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 3-[(4-anilinophenyl)diazenyl]benzenesulfonate was estimated to be 8.87 dimensionless . The predicted BCF result based on the 5 OECD principles. Thus based on the result it is concluded that the test substance sodium 3-[(4-anilinophenyl)diazenyl] benzenesulfonate is non-bioaccumulative in nature.