Applicazione delle metodologie QSAR a problematiche ...dipbsf.uninsubria.it/qsar/education/Mat Didattico/MaterialeCorsi... · Prof. Paola Gramatica -QSAR Research Unit -DBSF -University

Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese (Italy)

Prof. Paola GramaticaProf. Paola GramaticaProf. Paola Gramatica

QSAR and Environmental Chemistry Research UnitQSAR and Environmental Chemistry Research UnitQSAR and Environmental Chemistry Research Unit

DBSF - University of Insubria - Varese

http://www.qsar.it

DBSF DBSF -- University of University of InsubriaInsubria -- VareseVarese

http://http://www.qsar.itwww.qsar.it

Applicazione dellemetodologie QSAR a

problematicheambientali di inquinanti

organici

ApplicazioneApplicazione delledellemetodologiemetodologie QSAR a QSAR a

problematicheproblematicheambientaliambientali didi inquinantiinquinanti

organiciorganici

Università Università deglidegli StudiStudi didi Bologna Bologna -- DottoratoDottorato in in ChimicaChimica IndInd..–– 17/2/ 200417/2/ 2004


QuantitativeQuantitativeQuantitative

Structure - ActivityStructure Structure -- ActivityActivity

RelationshipsRelationshipsRelationships

QSARQSARQSAR

Quantitative Structure-Activity Relationships (QSAR)

and

Quantitative Structure-Property Relationships (QSPR)

Quantitative StructureQuantitative Structure--Activity Relationships (QSAR)Activity Relationships (QSAR)

andand

Quantitative StructureQuantitative Structure--Property Relationships (QSPR)Property Relationships (QSPR)

“The structure of a chemical influences its properties and biological activity”

“Similar compounds behave similarly”

(Hansch 1964)

Activity or PropertyActivity or Property = f= f (Structure)(Structure)


PREDICTED DATAPREDICTED DATAPREDICTED DATA

It is possible to find a relationship (f) between Structure and behavior

(Activity or Property) of a chemical


ChemicalsChemicals

EnvironmentalEnvironmentalfate and fate and behaviorbehavior

Biological ActivityBiological Activity

toxicity

mutagenicity

carcinogenicity

endocrine disrupt.

degradation

persistence

bioaccumulation

partitioning

PhysicoPhysico--chemicalchemicalpropertiesproperties

Natural productsNatural products XenobioticsXenobiotics

Synthesis

THE CHEMICAL UNIVERSETHE CHEMICAL UNIVERSETHE CHEMICAL UNIVERSETHE CHEMICAL UNIVERSETHE CHEMICAL UNIVERSETHE CHEMICAL UNIVERSETHE CHEMICAL UNIVERSETHE CHEMICAL UNIVERSETHE CHEMICAL UNIVERSETHE CHEMICAL UNIVERSETHE CHEMICAL UNIVERSETHE CHEMICAL UNIVERSE

QSAR

22.000.000 in C.A.S.

100.000 on market

EINECS TSCA

5%

known

data

Environmental fate?Environmental fate?Environmental fate?

Human effects?Human effects?Human effects?

NEW1.000.000 / year

NEW2.000 / year


experiments

(Q)SAR History(Q)SAR History(Q)SAR History

Alkane m.p. and b.p.(Cros, 1863)

Alcohol water solubility

n.Cn.CM.WM.W..

n.Cn.CM.WM.W..

PHYSICO-CHEMICAL PROPERTIES

STRUCTURE

BIOLOGICAL ACTIVITY

STRUCTURE/PROPERTIES

(Hansch 1964)


Alcohol toxicity part. part. coeffcoeff..oil/wateroil/water

PHYSICO-CHEMICAL PROPERTIES

BIOLOGICAL ACTIVITY

(Meyer-Overton 1899-1901)

Log PLog P

Classical Hansch equation:Classical Classical HanschHansch equation:equation:

“Toxicity” = a + b logP + c E + d S


logPor log Kow, partition coefficient between octanol and water:

hydrophobicity term

Eelectronic term

Ssteric term related to bulk and shape

The possibility of the chemical to interact with the target and to be active

The probability or ability of the chemical to reach the target site

CongenericityCongenericity principleprinciple

CHEMICALSCHEMICALSCHEMICALS

M: experimental measures of properties

A: experimental measures of activities

D: theoretical procedures for descriptors

R1, R2, R3: mathematical relationships

PHYSICO-CHEMICALPROPERTIES

PHYSICOPHYSICO--CHEMICALCHEMICALPROPERTIESPROPERTIES

MM

BIOLOGICAL ACTIVITIES

BIOLOGICAL BIOLOGICAL ACTIVITIESACTIVITIES

AAMOLECULAR DESCRIPTORSMOLECULAR MOLECULAR DESCRIPTORSDESCRIPTORS

DD


R1R1 R2R2

R3R3

THE 3 NECESSITIES:THE 3 NECESSITIES:THE 3 NECESSITIES:

GOOD INPUT DATAGOOD INPUT DATA

MEANINGFUL STRUCTURAL INFORMATIONMEANINGFUL STRUCTURAL INFORMATION

PREDICTIVE MODELSPREDICTIVE MODELS


High-quality experimental dataexperimental data as input data to find the Structure-Activity

Relation

Good representation of the chemical structure: molecular descriptorsmolecular descriptors

Quantitative modelsQuantitative models with validated predictivepredictive performances

Experimental data setExperimental data setExperimental data set

The models will only be as good as the data used to develop themThe models will only be as good as the data used to develop them!!

“Garbage in, garbage out”“Garbage in, garbage out”

There is a need for a “limited” number of There is a need for a “limited” number of HIGHHIGH--QUALITYQUALITY

experimental dataexperimental data on which to develop QSAR models!on which to develop QSAR models!


NEEDS FOR EXPERIMENTAL DATA:NEEDS FOR EXPERIMENTAL DATA:

AS NUMEROUS AS POSSIBLEAS NUMEROUS AS POSSIBLE

CORRECTCORRECT

REPRESENTATIVEREPRESENTATIVE

HOMOGENEOUS HOMOGENEOUS

(ideally, same lab, same method)(ideally, same lab, same method)








MM




DD


R1R1 R2R2

R3R3


The “magic” molecular descriptorThe “magic” molecular descriptorThe “magic” molecular descriptor

Log P (or Log P (or KowKow))

“ 35 years of (“ 35 years of (ab)usingab)using of log P for everything modelling is enough! “of log P for everything modelling is enough! “

(R. Schwarzenback)SETAC 2000

FROM PARTITION PROPERTY TO ACTUAL MOLECULAR STRUCTURE...FROM PARTITION PROPERTY TO ACTUAL MOLECULAR STRUCTURE...

molecularfragments

C log PSoftware

OH

Cl

Bioconcentration

Sorption

Water solubility

Toxicity

MOLECULAR

DESCRIPTORS

MOLECULAR

DESCRIPTORS. .· ·

··· ·

· ···

· ·

.

.

.

...

. .C

C

C

C

C C

C C

CC

CC

C l C l

C l C l

H

H

H

H

H

H

. .· ·

··· ·

· ···

· ·

.

.

.

...

. .

1D1D1D

3D3D3D

2D2D2D

ClCl

Cl Cl

Cl Cl

ClCl

H

H

H

H

H

H


0D0D0D

Representation of a chemical by numerical indicesRepresentation of a chemical by numerical indices








MM




DD


R1R1 R2R2

R3R3


CHEMICALSCHEMICALS

EXPERIMENTAL EXPERIMENTAL DATADATA

xx11 xx22 xxnn...... YY

TRAINING SET

TRAINING TRAINING SETSET

EXPLORATIVE EXPLORATIVE ANALYSIS:ANALYSIS:

-- Principal Component Analysis- Cluster Analysis

Quantitative modelsQuantitative modelsforfor

qualitative responsesqualitative responses

CLASSIFICATION METHODS:CLASSIFICATION METHODS:

-- Classification Tree (CART)

- Discriminant Analysis

- Neural Networks

REGRESSION METHODS:REGRESSION METHODS:

-- Multivariate Linear Regression(MLR)

-- Partial Least Squares Regression(PLS)

Quantitative modelsQuantitative modelsforfor

quantitative responsesquantitative responses

MOLECULAR MOLECULAR DESCRIPTORSDESCRIPTORS

Chemometric

Methods

Chemometric Chemometric

MethodsMethods


DATA SETDATA SET

TRAINING SETTRAINING SETTRAINING SET TEST SETTEST SETTEST SET

REGRESSION MODEL

EXTERNAL VALIDATION

Q2EXT

PREDICTABILITYPREDICTABILITY

NEW DATANEW DATA

FITTINGR2

INTERNAL VALIDATION

Q2LOO

Q2LMO

SPLITTINGSPLITTING

••DimensionDimension

••Chemical Chemical compositioncomposition

MOLECULAR DESCRIPTORSMOLECULAR DESCRIPTORSMOLECULAR DESCRIPTORS


TOXICITY

TTOOXXIICCIITTYY

YY

QSAR QSAR MODELMODEL

INFORMATION

SELECTIONSELECTIONSELECTIONModel with

relevant information

Y = f ( selected descriptors)


LIMITATIONS OF QSAR MODELSLIMITATIONS OF QSAR MODELSLIMITATIONS OF QSAR MODELS

Statistical qualityFitting RFitting R22

Predictability QPredictability Q22

Outliers

Chemical domain

Exp. responseExp. response

PredPred. response. response

Prediction reliability

CHEMICALSCHEMICALS

MODELMODEL

Y XX

FITTINGFITTING

REVERSIBLE REVERSIBLE

DECODINGDECODING

MAXIMUMMAXIMUM

PREDICTIVE POWERPREDICTIVE POWER


EXPERIMENTALEXPERIMENTALDATADATA

MOLECULARMOLECULARDESCRIPTORSDESCRIPTORS

NEWNEWCHEMICALSCHEMICALS

MOLECULARMOLECULARDESCRIPTORSDESCRIPTORS

??????PREDICTIONPREDICTION


APPLICATIONS of QSAR PREDICTIONSAPPLICATIONS of QSAR PREDICTIONSAPPLICATIONS of QSAR PREDICTIONS

•Filling of data gaps

•Validation of experimental data

•Screening, ranking and priority setting

•Highlighting chemicals of concern (also before their synthesis)

PRIORITY LISTSPRIORITY LISTSPRIORITY LISTS

Optimize industry resource allocation Minimize animal testing


ENVIRONMENTAL PARAMETERSENVIRONMENTAL PARAMETERSENVIRONMENTAL PARAMETERSPriority setting / Risk Assessment

ParametersParameters Quality of QSAR modelsQuality of QSAR models

Physico-chemical data

Environmental fate and pathways

Ecotoxicity

Mammalian toxicity

OPTIMUM

MEDIUM

MEDIUM-HIGH

HIGH

m.p.; b.p.; vapour pressure; Henry law constant; water solubility; partition coefficients (Kow, Koc, …).

chemical-, photo- and bio-degradation; bioaccumulation; compartment partitioning.

algae; Daphnia; fish; ….

skin-, eyes-, oral-, inhalation acute toxicity; mutagenicity; carcinogenicity; toxicity to reproduction system;...

QSAR in U.S.QSAR in U.S.QSAR in U.S.Since 1979/80 wide use and “abuse”Since 1979/80 wide use and “abuse”

EPA / OPPTEPA / OPPT OfficOffice of Pollution Prevention and Toxics

TSCA TSCA Toxic Substances Control Act inventory (~75.000 chem.)

NCP NCP New Chemicals Program (PMN with QSAR data)

QSAR in E.U.QSAR in E.U.QSAR in E.U.

IPS IPS Informal Priority Setting methodInformal Priority Setting method

EURAM EURAM Europe Union Europe Union RAnkingRAnking MethodMethod

EC Regulation on Evaluation and Control of Risks of EC Regulation on Evaluation and Control of Risks of

Existing SubstancesExisting Substances

Since 1992/93 but, so far, limited useSince 1992/93 but, so far, limited use


WHITE PAPER on the WHITE PAPER on the StrategyStrategy forfor a Future a Future

ChemicalsChemicals PolicyPolicy (FebFeb 2001)2001)

••Same regulation for new and existing chemicals (1:15)Same regulation for new and existing chemicals (1:15)

••Responsibility from authorities to industries for testing and riResponsibility from authorities to industries for testing and risk sk assessmentassessment

••REACH systemREACH system: Registration Evaluation Authorisation : Registration Evaluation Authorisation

of Chemicalsof Chemicals

-- RegistrationRegistration by companies for > 1 t prod (30.000) by companies for > 1 t prod (30.000) into 2005 1000 t (HPV), into 2012 allinto 2005 1000 t (HPV), into 2012 all

-- EvaluationEvaluation of information by authorities for > 100 t (5000)of information by authorities for > 100 t (5000)into 2008into 2008

-- AuthorisationAuthorisation for carcinogenic, mutagenic, toxic to for carcinogenic, mutagenic, toxic to

reproduction and POPsreproduction and POPs


QSAR in WHITE PAPERQSAR in WHITE PAPERQSAR in WHITE PAPER

Art. 3.2Art. 3.2 ….”to keep animal testing to a minimum”….”to keep animal testing to a minimum”

•• Development and validation of alternative methods Development and validation of alternative methods

ECVAM (ECVAM (EuropEurop. Centre for Validation of Alternative . Centre for Validation of Alternative

Methods) Methods) ––JRC JRC IspraIspra

•• Inclusion in the Community legislation and OECD Test Inclusion in the Community legislation and OECD Test Guidelines Programme for international recognitionGuidelines Programme for international recognition

Task Force of Task Force of EspertsEsperts in QSAR in QSAR

••Particular research efforts for developing and validating Particular research efforts for developing and validating modelling (e.g. QSAR) and screening methods for modelling (e.g. QSAR) and screening methods for assessing the potential adverse effects of chemicals.assessing the potential adverse effects of chemicals.

SetubalSetubal PrinciplesPrinciples

Classi di composti studiateClassiClassi didi composticomposti studiatestudiate

POP POP ((Persistent Organic Pollutants),Persistent Organic Pollutants), PBT PBT ((Persistent Persistent BioaccumulativeBioaccumulative Toxics)Toxics)

VOC VOC (Volatile Organic Compounds) e (Volatile Organic Compounds) e HPV HPV (High Production Volume)(High Production Volume)

PesticidiPesticidi: : insetticidiinsetticidi, , erbicidierbicidi, …, …

IdrocarburiIdrocarburi aromaticiaromatici policondensatipolicondensati (PAH)(PAH)

BifeniliBifenili policloruratipoliclorurati (PCB), (PCB), diossinediossine

BenzeniBenzeni e e fenolifenoli sostituitisostituiti

ProdottiProdotti IndustriaIndustria ChimicaChimica ItalianaItaliana (per FEDERCHIMICA)(per FEDERCHIMICA)

ListaLista didi PrioritàPriorità 1 1 delladella EUEU


Applicazioni in campo ambientaleApplicazioniApplicazioni in campo in campo ambientaleambientale

PredizionePredizione didi proprietàproprietà chimicochimico--fisichefisiche per per studistudi didi ripartizioneripartizioneneinei comparticomparti ambientaliambientali::

PredizionePredizione didi parametriparametri didi persistenzapersistenza ambientaleambientale::

PredizionePredizione didi attivitàattività biologichebiologiche::

- bioconcentrazione (BCF)- volatilità (log Koa, log H, Vp)- coefficiente di adsorbimento nel suolo (log Koc)-- indiciindici didi mobilitàmobilità (leaching…….)(leaching…….)

- reattività atmosferica (costanti di velocità di reazione

con radicali OH, NO3 ed O3)

- indici di persistenza ambientale (emivite)

- biodegradabilità …….

- tossicità- mutagenicità ……


AnalisiAnalisi delledelle ComponentiComponenti PrincipaliPrincipali (PCA)(PCA)

La PCA è La PCA è unauna analisianalisi esplorativaesplorativa didi datidati multivariatimultivariati..

Le Le ComponentiComponenti principaliprincipali sonosono::••CombinazioniCombinazioni linearilineari deidei datidati originalioriginali••Ordinate Ordinate secondosecondo le le direzionidirezioni didi massimamassima varianzavarianza (PC1, PC2..)(PC1, PC2..)••Non correlateNon correlate••SonoSono quindiquindi nuovenuove variabilivariabili con le con le qualiquali sisi condensacondensa e “e “puliscepulisce” ”

l’informazionel’informazione contenutacontenuta neinei datidati originalioriginali••RappresentanoRappresentano macroproprietàmacroproprietà dell’insiemedell’insieme deidei datidati originalioriginali

La PCA La PCA consisteconsiste in in unauna rotazionerotazione nellonello spaziospazio deidei datidati originalioriginaliin in modomodo cheche le le singolesingole componenticomponenti sianosiano tratra loroloro ortogonaliortogonali

I I datidati vengonovengono cosìcosì ““vistivisti” in un ” in un diversodiverso sistemasistema didi riferimentoriferimentoSecondoSecondo visualivisuali controllatecontrollate per per qualitàqualità e e quantitàquantitàdell’informazionedell’informazione rappresentatarappresentata

PERSISTENCE

ATMOSPHERIC

DEGRADATION REACTIONS

Reaction rate constants for the degradation by

Tropospheric Oxidants:

OHOH••,, NONO33•• radicals and Ozoneradicals and Ozone

Paola Gramatica - QSAR and Environmental Chemistry Research UnitDBSF - INSUBRIA University - (Varese - ITALY)

GLOBAL ATMOSPHERIC PERSISTENCEINDEX (ATPIN) 65 VOCs

GLOBAL ATMOSPHERIC PERSISTENCEGLOBAL ATMOSPHERIC PERSISTENCE

INDEX (ATPIN) 65 INDEX (ATPIN) 65 VOCsVOCs

ATPIN=20.37+1.94HOMO-2.34nBnz-0.29AMW+0.27DELS

ATPIN from PCA

AT

PIN

calc

ula

ted

-5

-4

-3

-2

-1

0

1

2

3

4

-5 -4 -3 -2 -1 0 1 2 3 4

1254

255

45

228

PC1 score = PC1 score = ATPINATPIN

(Atmospheric Persistence Index)(Atmospheric Persistence Index)

65 objects 4 variables65 objects 4 variables

RR22=93.76% Q=93.76% Q22LOOLOO=92.53=92.53

QQ22LMO(50%)LMO(50%)=91.44% RMS=0.163=91.44% RMS=0.163

DESCRIPTORS :(in order of significance)DESCRIPTORS :(in order of significance)

•• HOMO: highest occupied molecular orbital HOMO: highest occupied molecular orbital ((nucleophilicitynucleophilicity))

•• nBnznBnz: number of aromatic rings: number of aromatic rings

•• AMW: average molecular weight AMW: average molecular weight

•• DELS: molecular DELS: molecular electropologicalelectropological variation variation (charge distribution)(charge distribution)

Principal Component AnalysisCum.E.V.%=95.1% (PC1=88.3%)

PC1=ATPIN

PC

2

1

20

21

22

29

31

3441

45 66

81

82

87

94

95

97

114

115

116

126

134135

141

142

143

144145

165 166

167

172

184

186

197

216

218

227

228229

233

241

243

247

254

255

297

298

301

305

316

325

337

349

370

385

388419

456

459

461466468

474

484 485501

-1.4

-1.0

-0.6

-0.2

0.2

0.6

1.0

-5 -4 -3 -2 -1 0 1 2 3 4

-logkOH

-logkNO3

-logkO3

Atmospheric Persistence


Example: QSAR Models for Degradation byExample: QSAR Models for Degradation by

NONO33•• ((114 chemicals)114 chemicals)

DESCRIPTORS (in order of significance):DESCRIPTORS (in order of significance):

•HOMO: highest occupied molecular

orbital (nucleophilicity of molecules)

•nBnz: number of aromatic rings

•MATS1m: 2D-autocorrelation of Moran

(atomic distribution)

-logk(NO3) experimental

-lo

gk

(NO

3)

pre

dic

ted

8

10

12

14

16

18

8 10 12 14 16 18

Training set

Test set

Gramatica et al., Atmos. Environ. 2003, 37, 3115-3124.

Obj.Tr. Obj.Test Var.N. VARIABLES R2

Q2LOO Q

2LMO(50%) Q

2ext RMS

114 3 HOMO nBnz MATS1m 92.9 92.3 92.1 0.58

77 37 3 HOMO nBnz MATS1m 90.3 91.2 89.6 95.9 0.59


Principal Component AnalysisCum.E.V.%=95.1% (PC1=88.3%)

PC1=ATPIN

PC

2

-1.4

-1.0

-0.6

-0.2

0.2

0.6

1.0

-5 -4 -3 -2 -1 0 1 2 3 4

-logk(OH)

-logk(NO3)

-logk(O3)


GLOBAL ATMOSPHERIC PERSISTENCEGLOBAL ATMOSPHERIC PERSISTENCE

INDEX (ATPIN)INDEX (ATPIN)

Principal Component AnalysisCum. E.V.% = 95.3% (PC1 = 80.9%)

PC1 = ATPIN

PC

2

-2

-1

0

1

2

-5 -3 -1 1 3 5

Exp. + Pred. (399 obj.)

Exp. (65 obj.)

-log k(OH)

-log k(NO3)

-log k(O3)


““enlargedenlarged” ” 399 VOCs65 VOCs


P. Gramatica et al., SAR &QSAR Env Res., 13, 2002, 743-753.

ATPIN da PCA

AT

PIN

calc

ola

to

-4.5

-3.5

-2.5

-1.5

-0.5

0.5

1.5

2.5

3.5

4.5

-4.5 -3.5 -2.5 -1.5 -0.5 0.5 1.5 2.5 3.5 4.5

Training set

Test set

Obj.Tr. Obj.Test Var.N. VARIABILI R2 Q2LOO Q2

LMO(50%) Q2ext RMS

399 3 HOMO nBnz BEHe4 93.3 93.2 93.2 0.42

255 174 3 HOMO nBnz BEHe4 93.7 93.5 93.4 92.7 0.41

QSAR Modelling of “enlarged” GLOBAL QSAR Modelling of “enlarged” GLOBAL

ATMOSPHERIC PERSISTENCE INDEXATMOSPHERIC PERSISTENCE INDEX

PC1 score = ATPINATPIN (399 chemicals experimental + predicted dataexperimental + predicted data)

DESCRIPTORS (in order of significance):DESCRIPTORS (in order of significance):

•HOMO: highest occupied molecular

orbital (nucleophilicity of molecules)

•nBnz: number of aromatic rings

•BEHe4: weighted by electronegativity(charge distribution)


Ranking of pesticides for environmental Ranking of pesticides for environmental

distribution, based on PCAdistribution, based on PCA


CLA 1 = MEDIUM COMP.CLA 2 = VOLATILE COMP.

CLA 3 = SOLUBLE COMP.CLA 4 = SORBED COMP.

43210-1-2-3-4

3

2

1

0

-1

-2

log H

log vp

logKow logS

logKoc

54

53

52

51

50

49

48

47

46

45

44

43

42

41

40

39

3837

3635

34

33

3231

30

29

28

27

26

25

24

23

2221

20 19

18

17

16

15

14

13

12

11

10

98

7

65

4

3

2

1

PC1

PC

2

Principal Component Analysis (PCA) ON CHEMICAL-PHYSICAL PROPERTIES OF 54 PESTICIDES

Cum. E.V. = 94.6% (PC1 = 70.1%)

Sorption

Solubility

Volatility

Clustering of pesticides for environmental Clustering of pesticides for environmental

distribution in 4 distribution in 4 a prioria priori classesclasses


4112331143163 525148452854503638393746715349474465 403227311434981015413422 35292526181930201723222421

0.00

33.33

66.67

100.00

1 23 43: Soluble comp. 1: Not-volatile/medium comp.

2: Volatile comp. 4:Sorbed comp.

DENDROGRAMSimilarity

Definition of 4 Definition of 4 a prioria priori classes of pesticides classes of pesticides

for environmental distributionfor environmental distribution


PCA ON CHEMICAL-PHYSICAL PROPERTIES OF 54 PESTICIDES

Cum. E.V. = 94.6% (PC1 = 70.1%)

PC 1

PC

2

1

2

3

4

56

7

89

10

11

12

13

14

15

16

17

18

1920

2122

23

24

25

26

27

28

29

3031

32

33

34

3536

37 38

39

40

41

42

43

44

45

46

47

48

49

50

51

52

53

54

-3

-2

-1

0

1

2

3

4

-5 -3 -1 1 3 5

CLA 3

CLA 2

CLA 1

CLA 4

CLA 1 = NOT-volatile COMP.CLA 2 = VOLATILE COMP.

CLA 3 = SOLUBLE COMP.CLA 4 = SORBED COMP.

LeachingLeachingSorption

Solubility

Volatility

Classification of 54 pesticides for Classification of 54 pesticides for

environmental distributionenvironmental distribution


1 2 3 4

1NHD

2.53J

317.69MW

Classification Tree

DESCRIPTORS:DESCRIPTORS:DESCRIPTORS:DESCRIPTORS:

MW: molecular MW: molecular MW: molecular MW: molecular weight (size) weight (size) weight (size) weight (size)

nHDnHDnHDnHD: number of : number of : number of : number of donor atoms in donor atoms in donor atoms in donor atoms in Hydrogen bonds Hydrogen bonds Hydrogen bonds Hydrogen bonds

J: J: J: J: BalabanBalabanBalabanBalabantopological indextopological indextopological indextopological index

Not Volatile/Med. comp. Sorbed comp.Soluble comp.Volatile comp.

P.Gramatica,..Int. J. Environ. Anal. Chem. 84, 65-74, 2004

Environmental Partitioning for 135 pesticidesEnvironmental Partitioning for 135 pesticides

The “FOURThe “FOUR--LEAVES” approachLEAVES” approach((P.GramaticaP.Gramatica and and A.DiA.Di GuardoGuardo, , ChemosphereChemosphere, 2002, 47, 9547, 2002, 47, 9547--956956))

PC1 score = PC1 score = LIN LIN Leaching IndexLeaching Index PC2 score = PC2 score = VIN VIN Volatility IndexVolatility Index


LIN

VIN

1

2

34

5

6

7

8

9

10

11

12

13

1415

16

17

18

19

20

21

22

23

24

25

26

2728

29

30

31

32

33

34

35

36

37

3839

40

41

42

4344

45

46

47 4849

50

51

52 53

54

5556

57

5859

60

6162

6364

6566

67

68

69

70

71

72 73

74

75 76

77

7879

80

8182 83

84

85

86

87

88

89

90

91

92

93

94

95

96

97

98

99

100

101

102

103104

105

106107

108

109

110111112

113

114

115

116117

118119

120

121

122123

124 125

126

127

128

129

130

131

132

133

134

135135

-5

-4

-3

-2

-1

0

1

2

3

4

5

-5 -4 -3 -2 -1 0 1 2 3 4 5

log VP

log Sw

log H

log Kow

log Koc

LeachingLeaching

Environmental Partitioning Indexes RegressionEnvironmental Partitioning Indexes Regression

P. P. GramaticaGramatica and A. and A. DiDi GuardoGuardo, , ChemosphereChemosphere, 2002, 47, 9547, 2002, 47, 9547--956956..

LIN (Leaching Index)LIN (Leaching Index) ==

--3.04 3.04 -- 0.96 0.96 nXnX -- 2.28 nNO+3.42 Ms 2.28 nNO+3.42 Ms

-- 1.74 ICR 1.74 ICR -- 0.45 0.45 nSnS


LIn calculated

LIn

ex

pe

rim

en

tal

1

2

3

4 5

6

7

8

910

11

12

1314

15

16

17

18

19

20

21

22

23

24

2526

27

28

29

30

31

3233

34

35

36

37

38

39

40

41 42

43 44

4546

4748

4950

51

52

53

54

55

56

57

58

59

60

61

62

63

64

65

66

67

68

69

7071

72

73

7475

76

77

78

79

80

81

82

838485

86

87

88

89

90

91

92

93

94

95

96

97

9899

100

101

102

103

104105106

107

108

109110

111112113114

115

116

117

118

119120

121122

123124

125

126127

128129

130

131

132

133134

135

-4

-2

0

2

4

6

-4 -2 0 2 4 6

VIn calculated

VIn

e

xp

eri

me

nta

l

1

2

34

5

6

7

8

9

10

11

12

13

14 15

16

17

18

19

20

21

22

23

24

25

26

2728

29

30

31

32

33

34

35

36

37

38

39

40

41

42

4344

45

46

4748

49

50

51

52 53

54

55

56

57

5859

60

61

62

6364

65

66

67

68

69

70

71

7273

74

7576

77

7879

80

818283

84

85

86

87

88

89

90

91

92

93

94

95

96

97

98

99

Y respon

101

102

103104

105

106107

108

109

110

111

112

113

114

115

116

117

118119

120

121

122

123

124125

126

127

128

129

130

131

132

133

134

135

-4

-2

0

2

4

-4 -2 0 2 4

nn= 135 R= 135 R22= 87.0% Q= 87.0% Q22LOOLOO= 85.8% = 85.8%

QQ22LMOLMO= 85.7% SDEP= 0.68 SDEC= 0.65= 85.7% SDEP= 0.68 SDEC= 0.65

VIN (Volatility Index)VIN (Volatility Index) = =

2.352.35--1.58HY1.58HY--0.230.2300XXvv--0.28nBM0.28nBM--0.40nCIC0.40nCIC--

1.42ASP+0.97 IVDE1.42ASP+0.97 IVDE

n=135 Rn=135 R22= 77.1% Q= 77.1% Q22LOOLOO= 74.1% = 74.1%

QQ22LMOLMO= 72.6% SDEP= 0.66 SDEC=0.63= 72.6% SDEP= 0.66 SDEC=0.63

WQO - Water Quality ObjectivesWQO WQO -- Water Quality ObjectivesWater Quality ObjectivesEEC Priority List 1 toxicity test on algae, Daphnia, fishEEC Priority List 1 toxicity test on algae, EEC Priority List 1 toxicity test on algae, DaphniaDaphnia, fish, fish

ZM1 72

CHI0 5.13

Assigned classI II III

CLASSIFICAZIONE CART CLASSIFICAZIONE CART

Obj. n.: 125 Selected var.: ZM1 - CHI0

NoModel ER: 40.8% ER: 7.2% cvER: 15.2%

ZM1: Zagreb index CHI0: connectivity indexZM1: Zagreb index CHI0: connectivity index


TOXICITY IN TOXICITY IN DAPHNIADAPHNIA

Log 1/EC50 = - 3.57 + 4.05 nP - 0.39 nHA + 1.02 IDM + 0.67 E1m

n = 94 R2 = 84.2% Q2LOO = 82.1% Q2

LMO = 81.7%

nPnP: n. of phosphorous atoms : n. of phosphorous atoms nHAnHA: n. of H bond acceptors : n. of H bond acceptors

IDM: mean inf. cont. on the dist. IDM: mean inf. cont. on the dist. magnmagn. E1m: . E1m: distribuzdistribuz. . atomicaatomica

dirdir--WHIM descriptorWHIM descriptorExp. toxicity

Ca

lc. to

xic

ity

-1

0

1

2

3

4

5

6

7

-1 0 1 2 3 4 5 6 7

Daphnia

PC1

PC

2

-2.5

-1.5

-0.5

0.5

1.5

2.5

-5 -3 -1 1 3 5

Low toxicity High toxicity

T. Daphnia

T. Fish

T. Algae

Experimental

PC1

PC

2

-2.5

-1.5

-0.5

0.5

1.5

2.5

-5 -3 -1 1 3 5


T. Daphnia

T. Fish

T. AlgaeExperimental

Predicted

I II III

PRINCIPAL COMPONENT ANALYSIS (PCA) PRINCIPAL COMPONENT ANALYSIS (PCA)

- all toxicity data available for 37 chemicals (E.V.: 90%)

- experimental + predicted data for 97 chemicals (E.V.: 93.7%)

EcotoxEcotox and Environ Safety, 49, (2001) 206and Environ Safety, 49, (2001) 206--220 220


PC1

PC

2

-2.5

-1.5

-0.5

0.5

1.5

2.5

-5 -3 -1 1 3 5


T. Daphnia

T. Fish

T. AlgaeExperimental

Predicted

I II III

SCREENING of POPs for overall persistence based on half-life in air,

water, soil

SCREENING of SCREENING of POPsPOPs for overall persistence based on halffor overall persistence based on half--life in air, life in air,

water, soilwater, soil


SOIL

Experimental half life

Pre

dic

ted h

alf life

-0.2

0.2

0.6

1.0

1.4

1.8

2.2

-0.2 0.2 0.6 1.0 1.4 1.8 2.2

HALFHALF--LIFE in SOILLIFE in SOIL

Log h.l. soil = - 3.46 + 0.58 IDM + 0.99 E2m + 0.48 G2e

obj.= 30 R2 = 83.2% Q2LOO = 77.8% Q2

LMO = 76.9%

IDM : mean inf. index on distance magnitudeE2m- G2e : directional WHIMs

PRINCIPAL COMPONENT ANALYSIS (PCA) PRINCIPAL COMPONENT ANALYSIS (PCA)

all half-life data available for 29 chemicals (Cum. E.V.: 87.6%)

SO

LU

BL

ES

an

d V

OL

AT

ILE

S

PC 1

PC

2

-2.5

-1.5

-0.5

0.5

1.5

2.5

3.5

-4 -3 -2 -1 0 1 2 3 4PC 1

PC

2

-4

-3

-2

-1

0

1

2

3

4

-4 -3 -2 -1 0 1 2 3 4

groundW

air

half-lifehalf-life

half-lifehalf-lifesoil

surf.W.

experimental + predicted data for 91 chemicals (Cum. E.V.: 79.5%)

SO

RB

ED

PERSISTENCE

OVERALL PERSISTENCE INDEX (PC1) OVERALL PERSISTENCE INDEX (PC1)

PC1= 9.22 + 3.14 AACPC1= 9.22 + 3.14 AAC-- 6.32 6.32 EE2s2s –– 17.49 E1e 17.49 E1e –– 0.16 Tm0.16 Tm

objobj. = 91 R. = 91 R22 = 85.1 Q= 85.1 Q22LOOLOO = 82.6 Q= 82.6 Q22

LMOLMO = 82.2 = 82.2

PC 1

PC 1 scores

Pre

dic

ted

PC

1 s

co

res

-4

-3

-2

-1

0

1

2

3

4

-4 -3 -2 -1 0 1 2 3 4


PC 1

PC

2

-4

-3

-2

-1

0

1

2

3

4

-4 -3 -2 -1 0 1 2 3 4

groundW

air

half-lifehalf-life

half-lifehalf-lifesoil

surf.W.

PERSISTENCE

SO

LU

BL

ES

an

d V

OL

AT

ILE

SS

OR

BE

D

“SCREENING” dei POP per il Long Range Transport (LRT)“SCREENING” “SCREENING” deidei POP per POP per ilil Long Range Transport (LRT)Long Range Transport (LRT)


Principal Component Analysis

PC1= 74.6%

PC

2

1

2

3

4

567

8

910

1112

1314

1516

1718

19

20

2122

23

24

25

26

27

28

2930

3132

33

34

35

363738

39

4041

42

43

44

45

4647

48

49

50

51

5253

54

55

56575859

6061

62

6364

656667

68

69

70

71

72

737475

76 77

78

79

80

81

-2.5

-1.5

-0.5

0.5

1.5

2.5

-7 -5 -3 -1 1 3 5

mp

logKow

logKoa

logVP

logH

logSolMW

MOBILITYMOBILITY

LRT potential for 81 chemicals

LRT=0.08-0.04nCl+0.06X2-0.10SPAM+0.44E1u

LRT index from MCDM (utility function)

LR

T p

ote

nti

al p

red

icte

d

1 234

56

7 8 9 10

11 12

13 1415

16 17181920

21

22 2324 25262728

29 30 313233

34

35

3637

38 3940

41 4243

4445

46

4748

49

5051

52

53

5455

56

57

5859

6061

62 6364

6566

676869

70

7172

73

74

75

76

77

78

79

80

81

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.2 0.4 0.6 0.8 1.0

ID

UT

ILIT

Y

1

2

34

5

6

7

89

10

11

1213

1415

16

17

1819

20

21

22

2324

2526

2728

29

30

31

3233

34

35

3637

38

39

40

41

42

43

4445

464748

49

5051

52

53

54

5556

57

5859

6061

6263

6465666768

69

70

71

72

73

74

75

76

77

78

79

80

81

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0 10 20 30 40 50 60 70 80

High LRT potential (Class 1)

Medium LRT potential (Class 2)

Low LRT potential (Class 3)

Fx fi i

i

k

x( ) ( )= ∗

=

∑λ1

MCDM: MCDM:

MulticriteriaMulticriteria DecisionDecision

MakingMaking

Per avere Per avere mimimomimimo potenziale di LRT:potenziale di LRT:

•• Minimizzare la persistenza Minimizzare la persistenza (PC1 in PCA delle (PC1 in PCA delle emiviteemivite, fig. precedente), fig. precedente)

•• Minimizzare la mobilità Minimizzare la mobilità (PC1 in PCA delle proprietà chimico(PC1 in PCA delle proprietà chimico--fisiche,fisiche,es. a lato)es. a lato)

Q2LOO=86.4%,

Q2LMO =85.7%

Funzione di utilitàFunzione di utilità

Regressione Regressione

eeClassificazioneClassificazione

CART (CART (nCnC, E1u) :, E1u) :

MRMRcvcv== 6.2%6.2%

No Model 42%No Model 42%


AcknowledgementsAcknowledgementsAcknowledgements

Dr. Ester Papa

Dr. Pamela Pilutti

Dr. Francesca Battaini

Dr. Fulvio Villa

Dr.Dr. Ester Papa Ester Papa

Dr. Pamela Dr. Pamela PiluttiPilutti

Dr. Francesca Dr. Francesca BattainiBattaini

Dr. Dr. FulvioFulvio VillaVilla

http://http://dipbsf.uninsubria.it/qsardipbsf.uninsubria.it/qsar

http://http://www.qsar.itwww.qsar.it

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Applicazione delle metodologie QSAR a problematiche ...dipbsf.uninsubria.it/qsar/education/Mat Didattico/MaterialeCorsi... · Prof. Paola Gramatica -QSAR Research Unit -DBSF -University