Tetrakis(2-ethylhexyl) benzene-1,2,4,5-tetracarboxylate

Bioaccumulation: aquatic / sediment

Link to relevant study record(s)

Reference

Endpoint:

bioaccumulation in aquatic species: fish

Type of information:

(Q)SAR

Remarks:

CAESAR (VEGA)

Adequacy of study:

supporting study

Study period:

April 2016

Reliability:

2 (reliable with restrictions)

Rationale for reliability incl. deficiencies:

results derived from a valid (Q)SAR model, but not (completely) falling into its applicability domain, with adequate and reliable documentation / justification

Justification for type of information:

The model provides a quantitative prediction of bioconcentration factor (BCF) in fish, given in
log(L/kg). It is implemented inside the VEGA online platform, accessible at:http://www.vega-qsar.eu/
The model extends the original CAESAR model, freely available at: http://www.caesarproject.
eu/software/
The validity of the model has been evaluated in accordance with the OECD validation principles (OECD, 2004; Worth et al, 2005; OECD, 2007). See attached report.

Qualifier:

according to guideline

Guideline:

other: CAESAR Model C (VEGA)

Version / remarks:

Two models, Model A and Model B, have been used to build hybrid model, Model C. In the proposed approach, the outputs of the individual models (Model A and B) were used as inputs of the hybrid model. Model A was developed by Radial Basis Function Neural Networks (RBFNN) using an heuristic method to select the optimal descriptors; Model B was developed by RBFNN using genetic algorithm for the descriptors selection. RBFNN was used with a Matlab function for building the models. An in-house software made as a PC-Windows Excel macro was used to combine Models A and B within the Model C. Model A used an heuristic method to select the optimal descriptors and Model B used genetic algorithm for the descriptors selection.

Principles of method if other than guideline:

Two models, Model A and Model B, have been used to build hybrid model, Model C. In the proposed approach, the outputs of the individual models (Model A and B) were used as inputs of the hybrid model. Model A was developed by Radial Basis Function Neural Networks (RBFNN) using an heuristic method to select the optimal descriptors; Model B was developed by RBFNN using genetic algorithm for the descriptors selection. RBFNN was used with a Matlab function for building the models. An in-house software made as a PC-Windows Excel macro was used to combine Models A and B within the Model C. Model A used an heuristic method to select the optimal descriptors and Model B used genetic algorithm for the descriptors selection. Full reference and details of the used formulas can be found in:
Zhao, C., Boriani, E., Chana, A., Roncaglioni,A., Benfenati, E. A new hybrid system of QSAR models for predicting bioconcentration factors (BCF). Chemosphere (2008), 73, 1701-1707.
Lombardo A, Roncaglioni A, Boriani E, Milan C, Benfenati E. Assessment and validation of the CAESAR predictive model for bioconcentration factor (BCF) in fish. Chemistry Central Journal (2010), 4 (Suppl 1).
The descriptors used are the following:
- Moriguchi octanol-water partition coefficient (MlogP).
- Moran autocorrelation of lag 5, weighted by atomic van der Waals volumes (MATS5V): molecular descriptor calculated from the molecular graph by summing the products of atom weights of the terminal atoms of all paths of the considered path length (the lag).
- Number of chlorine atoms (Cl-089), Cl attached to carbon (sp2).
- Second highest eigenvalue of Burden matrix, weighted by atomic polarizabilities (BEHp2).
- Geary autocorrelation of lag 5, weighted by atomic van der Waals volumes (GATS5V): molecular descriptor calculated from the molecular graph by summing the products of atom weights of the terminal atoms of all paths of the considered path length (the lag).
- Solvation connectivity index chi-0 (XOSolv): molecular descriptor designed for modeling solvation entropy and describing dispersion interactions in solution.
- Sum of all -Cl groups E-state values in molecule (SsCl).
- Absolute eigenvalues sum from electronegativity weighted distance matrix (Aeige).
The descriptors were calculated, in the original CAESAR version, by means of dragonX software and are now entirely calculated by an in-house software module in which they are implemented as described in: R. Todeschini and V. Consonni, Molecular Descriptors for Chemoinformatics, Wiley-VCH, 2009.

GLP compliance:

no

Specific details on test material used for the study:

CAS number 3126-80-5
EC number: 221-508-0

Key result

Type:

other: Log BCF

Value:

0.6 L/kg

Type:

BCF

Value:

4 L/kg

Details on results:

Prediction is 0.6 log(L/kg), but the result may be not reliable. A check of the information given in the following section should be done, paying particular attention to the following issues:
- 2 descriptor(s) for this compound have values outside the descriptor range of the compounds of the training set.
The following relevant fragments have been found: Moiety (SMILES:
O=Cc1ccccc1) (SR 01); Carbonyl residue (SR 02); >C=O group (PG 09)

Validity criteria fulfilled:

yes

Conclusions:

Estimated BCF is 4 L/kg

Executive summary:

The estimated BCF using CAESAR model for tetrakis(2-ethylhexyl) benzene-1,2,4,5-tetracarboxylate is 4 L/kg (Log BCF = 0.6)

Description of key information

The estimated bioconcentration factor Log BCF using BCFBAF model from EPI Suite Software v4.1 for tetrakis(2-ethylhexyl) benzene-1,2,4,5-tetracarboxylate is 0.5

The test substance has a low bioaccumulation potential.

Key value for chemical safety assessment

BCF (aquatic species):

3.16 L/kg ww

Additional information

Supporting QSARs for tetrakis(2-ethylhexyl) benzene-1,2,4,5-tetracarboxylate:

The estimated bioconcentration factor Log BCF using Arnot-Gobas model from EPI Suite Software v4.1 is Log BCF (upper trophic) = -0.048 (BCF = 0.8944 L/kg wet-wt)

The estimated bioaccumulation factor Log BAF using Arnot-Gobas model from EPI Suite Software v4.1 is Log BAF (upper trophic) = 0.898 (BAF = 7.899 L/kg wet-wt)

The substance has a low bioaccumulation potential.

The estimated BCF using CAESAR model is 4 L/kg (Log BCF = 0.6)