f.consolaro 1, p.gramatica 1, h.walter 2 and r.altenburger 2 1 qsar research unit - dbsf -...

F.ConsolaroF.Consolaro11, P.Gramatica, P.Gramatica11, H.Walter, H.Walter22 and R.Altenburger and R.Altenburger22

11QSAR Research Unit - DBSF - University of Insubria - VARESE - ITALY QSAR Research Unit - DBSF - University of Insubria - VARESE - ITALY 22UFZ Centre for Environmental Research - LEIPZIG - GERMANYUFZ Centre for Environmental Research - LEIPZIG - GERMANY

e-mail: fedec@mailserver.unimi.it Web: http://fisio.dipbsf.uninsubria.it/dbsf/qsar/QSAR.html e-mail: fedec@mailserver.unimi.it Web: http://fisio.dipbsf.uninsubria.it/dbsf/qsar/QSAR.html

INTRODUCTIONINTRODUCTIONEnvironmental exposure situations are often characterized by a multitude of heterogeneous chemicals with different mechanisms of action and type of effect. The EEC priority List 1 (Council Directive 76/464/EEC) consists of heterogeneous environmental chemicals with mostly unknown or unspecific modes of action, so it was used to select components for mixture experiments in the EEC PREDICT (Prediction and Assessment of the Aquatic Toxicity of Mixtures of Chemicals) project. A list of 202 compounds was studied for structural similarity to identify the most representative and dissimilar chemicals and to find an objective method to group them on the basis of their structural aspects. These chemicals have been then tested for their algal toxicity and the experimental results have been modelled by the already cited molecular descriptors. The comparison with analogous models obtained on congeneric environmental chemicals will be discussed.

STRUCTURAL DESCRIPTION OF COMPOUNDSSTRUCTURAL DESCRIPTION OF COMPOUNDSMolecular descriptors represent the way chemical information contained in the molecular structure is transformed and coded. Among the theoretical descriptors, the best known, obtained simply from the knowledge of the formula, are: molecular weight and count descriptors (1D-descriptors, i. e. counting of bonds, atoms of different kind, presence or counting of functional groups and fragments, etc.). Graph-invariant descriptors (2D-descriptors, including both topological and information indices), are obtained from the knowledge of the molecular topology. WHIM molecular descriptors [1] contain information about the whole 3D-molecular structure in terms of size, symmetry and atom distribution. All these indices are calculated from the (x,y,z)-coordinates of a three-dimensional structure of a molecule, usually from a spatial conformation of minimum energy: 37 non-directional (or global) and 66 directional WHIM descriptors are obtained. A complete set of about two hundred molecular descriptors has been obtained [2].[1] Todeschini R. and Gramatica P.; Quant.Struct.-Act.Relat. 1997, 16, 113-119

[2] Todeschini R. and Consonni V. - DRAGON - Software for the calculation of the molecular descriptors., Talete srl, Milan (Italy) 2000. Download: http://www.disat.unimib.it/chm.

CHEMOMETRIC METHODSCHEMOMETRIC METHODSSeveral chemometric analyses have been applied to the compounds (represented by molecular descriptors) to group the more similar ones, in accordance with a multivariate structural approach, and with the final aim to highlight the structurally most dissimilar compounds. The analyses performed are:

Hierarchical Cluster Analysis:Hierarchical Cluster Analysis: hierarchical clustering was performed with the aim of finding clusters of the studied compounds in high dimensional space, using molecular descriptors as variables. Different distance metrics (Euclidean, Manhattan, Pearson) and different linkages (Complete, average, single, etc.) were used and compared to find the best way to cluster these compounds.

Principal Component Analysis (PCA):Principal Component Analysis (PCA): this analysis was used to calculate just a few components from a large number of variables. These components allow the highlighting of the distribution of the compounds according to structure, and find the similarity between compounds assigned to the same cluster.

Kohonen Maps:Kohonen Maps: this is an additional way of mapping similar compounds by using the so-called “self-organized topological feature maps”, which are maps that preserve the topology of a multidimensional representation within a toroidal two-dimensional representation. The position of the compounds in this map shows the similarity level of the structure of the EEC List 1 compounds.

100

Similarity

Dendrogram of hierarchical cluster analysis.Euclidean distance - complete linkage.Variables = first 10 structural principal components

Benzene derivatives (2) Chloroaliphatic compounds (7)

DDT - PCBs (11)Organo-phosphates (12)

Phen.-Triaz. (10)

PAH (15)

Chlorinated aliphatics (9)

0

PCA on all molecular descriptors for 202 EEC List 1 compounds

Cum. E.V. = 47.4%

PC 1

PC

2

1

2

3

5

6

7

8

9

10

11

13

14

15

16

17

1819

20

21

22

23

2425

26b

2728

2930

3132

32b32c

32d32e

32f

32g

32h32i

3334

3536

37

38

39

4041

4242b

42c42d42e

43

44

45

46

46b46c

4747b

47c

47d

48

49

50

5252b

52c

52d

52e

52f

5354

55

5656b

58

5960

61

62

63

63b63c

63d

63e

63f

64

64b

64c

64d

64e

64f6565b

65c

65d

66

67tr

67cs

6868c68d

68e

69

70

71

72

73

74

75

76

77

7879

80

81

8282b

83

84

85

85b

85c

86

87

88

89

90

91 9394tr

94cs

95

96

9798

99

99b

99c99d

99e

99f

99g100

101

101a

101b101c

101d101e

101f101g101h101i

101l101m101n101o101p101q101r

102

103

104105

106

107

108

109

110

111

112

113

114

115

116

117b117c

118

119

120

121

122122b122c

122d

122e

122f

123

124

125

126127

128

129

129b129c

130s

133s

134s

135s

-16

-12

-8

-4

0

4

8

12

-40 -30 -20 -10 0 10 20

CluPCec=1CluPCec=2CluPCec=3CluPCec=4CluPCec=5CluPCec=6CluPCec=7CluPCec=8CluPCec=9CluPCec=10CluPCec=11CluPCec=12CluPCec=13CluPCec=14CluPCec=15CluPCec=16

Group 17

Group 18

Group 19

Group 20

KOHONEN MAP

RO

W

1

2

3

5

6

7

8

9

10

11

13

14

15

16

17

18

19

20

21

22

23

24

25 026b

27

2829

30

31

32

032b

032c

032d

032e

032f

032g

032h

032i

33

34 35

36

37

38

39

40

4142

042b

042c

042d042e

43

44

45

46046b

046c

47047b

047c047d

48

49

50

52 052b

052c

052d

052e052f

53

54

55

56056b

58

59

60

61

62

63063b

063c

063d

063e

063f

64

064b

064c

064d

064e

064f

65 065b

065c

065d

66

067atr067azc

68 068c

068d

068e

6970

71

72

73

74

75

76

77

7879

80

81

82

082b

83

84

85 085b

085c

86

87

88

89

90

9193094atr

094cs

95

96

9798

99 099b099c099d

099e

099f 099g

100

101

101a

101b101c

101d

101e

101f

101g101h

101i101l

101m101n

101o

101p101q

101r

102

103

104105

106

107

108

109

110 111

112

113

114

115

116

117b

117c

118

119

120

121

122

122b

122c

122d

122e

122f

123

124

125

126127

128

129129b

129c

130s

133s

134s135s

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

CL=1CL=2CL=3CL=4CL=5CL=6CL=7CL=8CL=9CL=10CL=11CL=12CL=13CL=14CL=15CL=16

Cl=19

Cl=20

Cl=18

Cl=17

These different chemometric approaches have shown that the structurally most dissimilar compounds are:

N. Substance Chemical Class

1 atrazine Triazine2 biphenyl Aromate3 chloralhydrat Chlorinated aliphatics4 2,4,5-trichlorophenol Benzene derivative5 fluoranthene PAH6 lindane HCH7 naphthalene PAH8 parathion Organophosphate9 phoxime Organophosphate10 tributyltin chloride Organotin11 triphenyltin chloride Organotin

REGRESSION MODELSREGRESSION MODELSQSAR models were developed by Ordinary Least Square regression (OLS) method. The selection of the best subset variables for modelling the algal toxicity of the studied compounds was done by a Genetic Algorithm (GA-VSS) approach and all the calculations have been performed by using the leave-one-out (LOO) and leave-more-out (LMO) procedures and the scrambling of the responses for the validation of the models.

RR22 = 78 Q = 78 Q22LOOLOO = 62.1 Q = 62.1 Q22

LMOLMO = 61.7 SDEP = 0.751 SDEC = 0.573 = 61.7 SDEP = 0.751 SDEC = 0.573

nO is the number of O atoms and IDE is the mean information content on the distance equality.

A QSAR model has been obtained, with acceptable fitting properties but without an adequate predictive capability. This is probably due to the presence of structurally dissimilar and with unknown mechanism of action chemicals.

HETEROGENEOUS HETEROGENEOUS COMPOUNDSCOMPOUNDS

CONGENERIC COMPOUNDS CONGENERIC COMPOUNDS (NITROBENZENES)(NITROBENZENES)

HETEROGENEOUS + HETEROGENEOUS + CONGENERIC COMPOUNDSCONGENERIC COMPOUNDS

RR22 = 93.9 Q = 93.9 Q22LOOLOO = 91.8 Q = 91.8 Q22

LMOLMO = 87.5 SDEP = 0.342 SDEC = 0.296 = 87.5 SDEP = 0.342 SDEC = 0.296

CONCLUSIONSCONCLUSIONSThe chemometric analyses here applied have been turned up to be very useful in ranking the studied chemicals in according to their structural similarity or dissimilarity.

In modelling of structural heterogeneous compounds with unknown mode of action, not very satisfactory QSAR models have been obtained.

The role of specific parameters, such as directional WHIMs, capable to describe particular molecular features relevant for explaining the specific mode of action, is always important in QSAR models for congeneric chemicals. Increasing heterogeneity increases the role of structural and topological descriptors, accounting for general molecular features, not related to specific mode of action.

nOH is the number of OH groups, Sp is the sum of polarizabilities and Ds is the 3D-WHIM considering the global electrotopological distribution.

The information explained by these descriptors are related to the electronic distribution of the molecular atoms and are more specific in respect to the mode of action than the selected descriptors in the heterogeneous set models.

The quality of this model is very satisfactory both in fitting and in prediction.

nO is the number of O atoms, IDDM is the mean information content on the distance degree magnitude while E1e is a directional 3D-WHIM descriptor of atomic distribution weighted on the electronegativity.

Here are selected a topological descriptor (IDDM) that probably represents the heterogeneous compounds and a 3D-WHIM descriptor (E1e) that probably represents the homogeneous compounds.

The performances of this model are satisfactory, considering that the data set is composed by structurally different compounds and that for many of them the mechanism of action is unknown.

RR22 = 77 Q = 77 Q22LOOLOO = 69.7 Q = 69.7 Q22

LMOLMO = 69.7 SDEP = 0.709 SDEC = 0.619 = 69.7 SDEP = 0.709 SDEC = 0.619

Regression model for 11 selected compounds

Log 1/EC50 = -5.14 -0.52 nO +2.12 IDE

experimental Log1/EC50

pre

dic

ted

Lo

g1

/EC

50

1

2

3

4

5

67

8

9

1011

-4

-3

-2

-1

0

1

2

-4 -3 -2 -1 0 1 2

Model for 19 nitrobenzenes

Log1/EC50 = -7.87 -2.96 nOH +0.10 Sp +13.25 Ds

experimental Log1/EC50

pre

dic

ted

Lo

g1/

EC

50

1

2

34

5

6 7

8

9

10

11

12

13 14

15

16

1718

19

-3.5

-2.5

-1.5

-0.5

0.5

1.5

2.5

-3.5 -2.5 -1.5 -0.5 0.5 1.5 2.5

Model for 19 nitrobenzenes + 11 heterogeneous compounds

Log1/EC50 = -20.27 -0.55 nO +3.87 IDDM +11.44 E1e

experimental Log1/EC50

pre

dic

ted

Lo

g1/

EC

50

1

2

3

4567

8

910

1112

13

14

15

16

17

18

19

20

21

22

23

24

25

2627

28

29

30

-4

-3

-2

-1

0

1

2

-4 -3 -2 -1 0 1 2

NitrobenzenesHeterogeneous compounds

RANKING OF “EEC PRIORITY LIST 1” FOR STRUCTURAL SIMILARITYRANKING OF “EEC PRIORITY LIST 1” FOR STRUCTURAL SIMILARITYAND MODELLING OF ALGAL TOXICITYAND MODELLING OF ALGAL TOXICITY