The aquatic toxicity values of 57 esters, with experimental and predicted LC50 in fish, EC50 in Daphnia and seaweed and IGC in Entosiphon sulcatum, were studied in the Principal Component space. The first component was found to be the most important with 61.8% of explained variance and can be considered as a general index of aquatic toxicity. In order to have a fast method to rank the esters according to their aquatic toxicity, the PC1 was modeled by theoretical molecular descriptors. The best model, selected by Genetic Algorithm, was verified for stability and predictivity by internal and external validation.

Gramatica, P., Battaini, F., Gramatica, P., Battaini, F., Papa, E.Papa, E.

QSAR and Environmental Chemistry Research Unit, University of Insubria, Varese (Italy).QSAR and Environmental Chemistry Research Unit, University of Insubria, Varese (Italy).

Web: http://dipbsf.uninsubria.it/qsar/ e-mail:Web: http://dipbsf.uninsubria.it/qsar/ e-mail: paola.gramatica@uninsubria.it

QSAR PREDICTION OF AQUATIC TOXICITY OF ESTERSQSAR PREDICTION OF AQUATIC TOXICITY OF ESTERS

INTRODUCTIONINTRODUCTION

A large number of compounds (more than 100,000) are currently in common use, and

about 2,000 new ones appear each year. No data are available for the majority of these

compounds so we have no understanding of their environmental fate, their behavior or

effects [1]. This general lack of knowledge has led to the European Commission adopting

a “White Paper on a strategy for a future Community Policy for Chemicals” [2]. This

Directive requires, at the latest by the end of 2005, physico-chemicals data and toxicity

data for HPV (High Production Volume) compounds with production volume of 1,000

tonnes/year. Among the HPV compounds the class of esters is one of the largest and

environmentally most “interesting”. Some esters, i.e. phthalates, are known for their

weak carcinogenic and estrogenic effects [3], thus, there is a need to identify these

compounds to assess their potential health hazard and their impact on the environment.

The aim of our research was to develop “local” QSAR (Quantitative Structure-Activity

Relationship) models to rapidly predict the toxicity of esters. As this prediction is based

simply on knowing molecular structure, the approach could be applied usefully to new

chemicals, even those not yet synthesised, if they belong to the chemical domain of the

training set. In this case it is possible to reduce the cost and the time needed for

experimental data.

RESULTS AND DISCUSSIONRESULTS AND DISCUSSION

The more relevant molecular descriptors, calculated by the DRAGON software, were select by Genetic Algorithm (GA – Variable Subset Selection). For each end-points the best model was validated with more validation techniques:

• Leave-one-out using QUIK rule (Q Under Influence of K (18)) to avoid chance correlation.

• Strongest validation using leave-many-out procedure (15-30%).

• Y scrambling ( permutation testing by recalculating models for randomly reordered response ).

The models were not all validated externally owing to the small sets studied (14-30 obj.). The reliability of the

predictions was always checked by the leverage approach in order to verify the chemical domain of the models.

The regression lines of the fish and Daphnia models are reported (outliers and influential chemicals are

highlighted). Table 1 shows the performance of the best models for each end-point.

ABSTRACTABSTRACT

Esters are an important class of industrial chemicals, for which the EU-Directive “White Paper on a strategy for a

future Community Policy for Chemicals” requires toxicity data by, at the latest, the end of 2005. The object of the

study was to develop QSAR models to rapidly predict the aquatic toxicity of esters. Unfortunately the experimental

toxicity data are not known for a large number of these compounds or, if known, the data are not all homogeneous,

hindering an accurate and comparable evaluation of the toxicological behaviour of the considered compounds.

Different theoretical molecular descriptors (1D-constitutional, 2D-topological, and different 3D-descriptors) are

calculated by the DRAGON software. The Genetic Algorithm (GA-Variable Subset Selection) is used to select the

more relevant molecular descriptors in the modelling by Ordinary Least Squares (OLS) regression. The studied end-

points are: LC50 in Pimephales promelas, EC50 in Daphnia magna and in seaweed, IGC50 in Entosiphon sulcatum

and chronic toxicity in Daphnia magna. The best models were validated for their predictive performance using

leave-one-out (Q2LOO=70-90%), leave-many-out (30% of perturbation, Q2

LMO=70-90%) and the scrambling of the

responses. The models were not all externally validated owing to the small dimension (14-30) of the studied sets.

The reliability of the predictions was always checked by the leverage approach in order to verify the chemical

domain of the models. A PCA model, based on four acute toxicity end-points, has been proposed to evaluate the

trend of aquatic toxicity for the studied esters. The PC1 score is also modelled by theoretical molecular descriptors

(Q2LOO=89%, Q2

LMO=88%): this last model can be used as an evaluative method for screening esters according to their

aquatic toxicity, just starting from their molecular structure.

End-point Species N.obj. Variables R2 Q2 Q215% Q230%LC50 Fish 30 DP02 n=CH2 82.5 79.2 79.7 78.8EC50 Daphnia 30 TI1 Jhetv GATS1v 85.1 80.8 80.2 78.2EC50 Seaweed 12 DIPp H8u 96 93.5 92.9 88.1EC50 Pseudomonas 13 GATS5e R2v+ 92.5 86.5 85.9 83.7IGC Entosiphon 18 Me Xindex 91.5 87.7 88.1 87.9IGC Scenedesmus 17 AAC Jhetm 89.6 81.9 82.4 81.1IGC Pseudomonas 15 GATS1e R5u+ 83.4 74.3 73.7 71.6

LOEC Daphnia 13 BELm4 94 90.1 91.2 90.1NOEL Daphnia 14 BELm4 91.4 86.9 87.3 85.5

Principal Component AnalysisCum.Ev% = 82% (PC1 = 61.8%)

PC1

PC

2

1

2

3

4

5

6

7

89

10

11

12

13

14

15 16

17

18

19 2021

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37 38

39

40

41

42

43

44

45

46

4748

4950

51

52

53

54

55

56

57

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

-3.5 -3.0 -2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

FishSeaweed

Daphnia

Entosiphon

AQUATIC TOXICITY= - 5.76 - 0.39 TI2 + 4.99 GATS1v - 2.74 DISPp

AQUATIC TOXICITY from PCA

AQ

UA

TIC

TO

XIC

ITY

cal

cula

ted

-4.0

-2.5

-1.0

0.5

2.0

3.5

-4.0 -2.5 -1.0 0.5 2.0 3.5

test (14 obj.)traingin (43 obj.)

Log(1/EC50)= 14.4 - 0.03 TI1 - 1.4 Jhetv - 7.1 GATS1v

Experimental Log(1/EC50)

Pre

dic

ted

Lo

g(1

/EC

50

)

-1.5

-0.5

0.5

1.5

2.5

3.5

-1.5 -0.5 0.5 1.5 2.5 3.5

methyl acrylate 

butyl benzyl phthalate

glycerol trienanthate

Log(1/LC50) = - 2.4 + 0.7 DP02 + 1.1 n=CH2

Experimental Log(1/LC50)

Pre

dic

ted

Lo

g(1

/LC

50

)

-0.9

-0.4

0.1

0.6

1.1

1.6

2.1

2.6

-0.9 -0.4 0.1 0.6 1.1 1.6 2.1 2.6

diethyl phthalate

MOLECULAR DESCRIPTORS

The molecular structure of the studied compounds was described using several molecular descriptors calculated by the DRAGON software [8]:

descriptors 0D – costitutional descriptors (atoms and group counts)

descriptors 1D – functional groups, atom centered fragments and empirical descriptors

descriptors 2D – BCUTs, Galvez indices from the adjacency matrix, walk counts, various autocorrelations from the molecular graph and topological descriptors.

descriptors 3D – Randic molecular profiles from the geometry matrix, WHIMs, GETAWAY and geometrical descriptors

CHEMOMETRIC METHODS

Multiple Linear Regression analysis and variable selection were performed by the software MOBY DIGS [9] using the Ordinary Least Square Regression (OLS) method and GA-VSS (Genetic Algorithm-Variable

Subset Selection) [10]. All the calculations were performed using the leave-one-out (LOO) and leave-many-out (LMO) procedures and the response scrambling for the internal validation of the models.

External validation [11-12] was performed on a validation set obtained with the splitting at 75% of the original data set by Experimental Design procedure, applying the software DOLPHIN of Todeschini et al [13].

Tools of regression diagnostics as residual plots and Williams plots were used to check the quality of the best models and define their applicability regarding to the chemical domain, using the chemometric

package SCAN [14]. RMS (residual mean squares) are also reported for model comparison with ECOSAR [15].

EXPERIMENTAL DATA

The studied end-points are: LC50 in Pimephales promelas, EC50 in Daphnia magna, in Pseudomonas and in seaweed, IGC50 in Entosiphon sulcatum, in Scenedesmus and in Pseudomonas. Also studied was the

chronic toxicity of phthalates in Daphnia magna. The experimental data were taken from literature [4-7], reported in mmol/L and transformed in logarithmic units.

MATERIALS & METHODSMATERIALS & METHODS

Log (1/EC50) in Daphnia magna

Log (1/LC50) in Fish

For comparison purposes the RMS (Residual Mean Squares) values are reported only for LC50 in fish and EC50 in seaweed as the other end-points are not included in the ECOSAR software. The ECOSAR models for LC50 in fish and our new models show similar performance; but the EPA model for EC50 in seaweed has the biggest RMS (tab.2). This result appears particulary satisfactory considering that EPIWIN model was obtained on a training set bigger than our data set. End-Point Species Obj. training Variables RMS from our model RMS from ECOSAR

LC50 Fish 30 DP02 n=CH2 0.31 0.38EC50 Seaweed 12 DIPp H8u 0.13 3.47

Tab.1 – Model Performances

Tab.2 – Comparison of models

Aquatic Toxicity

n.obj=43 R2=91.5% Q2=89.9% Q2

LMO30%=89.9% Q2EXT=95.6%

CONCLUSIONSCONCLUSIONS

New predictive “local” models for ecotoxicity end-points of esters are proposed.

These models are based only on theoretical molecular descriptors selected by Genetic

Algorithm.

All models have good predictive power, verified by internal validation techniques.

Principal Component Analysis has been used to propose an esters ranking for global

aquatic toxicity for 4 acute toxicity end-points (LC50 in fish, EC50 in Daphnia magna and in

seaweed, IGC in Entosiphon sulcatum).

The PC1 score highlights the global trend of aquatic toxicity and is modelled by

theoretical molecular descriptors. This model can be used for the screening and ranking

of esters according to their global toxicity, just starting from their structure.

The application of those models reduces animal testing and minimises the time and money

needed for experimental data.

REFERENCESREFERENCES

[1] Gramatica P., Fine Chemicals and Intermediates technologies (Chemistry Today), 1991, 18-24;

[2] http://europa.eu.int/comm/environmental/chemicals/whitepaper.htm;

[3] Thomsen M. and al. Chemophere, 1999, 38, 2613-2624.[4] Cash G.G.and Clements R.G., SAR and QSAR in Environmental Research, 1996, 5, 113-124;

[5] European Commission – Joint Research Centre IUCLID CD-ROM, 2000;

[6] Verschueren K., Handbook of Environmental Data on Organic Chemicals, 1983, 2th Edition, Van Nostrand Reinhold

[7] Rhodes J.E. and al., Environmental Toxicology and Chemistry, 1995, 14, 1967-1976

[8] Todeschini R., Consonni V. and Pavan E. 2002. DRAGON – Software for the calculation of molecular descriptors, rel. 1.12 for

Windows. Free download available at http://www.disat.unimib/chm.;

[9] Todeschini, R., 2001. Moby Digs - Software for multilinear regression analysis and variable subset selection by Genetic Algorithm, rel. 2.3 for

Windows, Talete srl, Milan (Italy);

[10] Leardi, R.; Boggia, R.; Terrile, M.,. J. Chemom., 1992, 6, 267-281;

[11] Wold, S. Eriksson, L. Chemometric Methods in Molecular Design, 1995, VCH, Germany, 309-318;

[12] Golbraikh, A. Tropsha, A., J. Mol. Graph and Mod., 2002, 20, 269-276.

[13] Todeschini, R. and Mauri, A., 2000; DOLPHIN- Software for Optimal Distance-based Experimental Design rel 1.1 for Windows, Talete srl, Milan

(Italy);

[14] SCAN- Software for Chemometric Analysis, rel. 1.1 for Windows, Jerll. Inc., Standard, CA, 1992;

[15] ECOSAR in EPIWIN-EPI Suite 2001, Ver.3.10, Environmental Protection Agency (http://www.epa.gov)