qsar till ppt no 21

8/6/2019 QSAR Till Ppt No 21

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

Prof. Paola GramaticaProf. Paola GramaticaProf. Paola GramaticaQSAR and Environmental Chemistry Research UnitQSAR and Environmental Chemistry Research UnitQSAR and Environmental Chemistry Research Unit

DBSF - University of Insubria - Varese

http://www.qsar.it

DBSFDBSF -- University ofUniversity of InsubriaInsubria -- VareseVarese

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

Applicazione dellemetodologie QSAR a

problematicheambientali di inquinanti

organici

ApplicazioneApplicazione delledellemetodologiemetodologie QSAR aQSAR a

problematicheproblematicheambientaliambientali didi inquinantiinquinanti

organiciorganici

UniversitàUniversità deglidegli StudiStudi didi BolognaBologna -- DottoratoDottorato inin ChimicaChimica IndInd.. – – 17/2/ 20017/2/ 2004




QuantitativeQuantitativeQuantitative

Structure - ActivityStructureStructure -- 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 Property Activity 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 andfate 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

Q

S AR

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-CHEMICALPROPERTIES

STRUCTURE

BIOLOGICALACTIVITY

STRUCTURE/PROPERTIES

(Hansch 1964)


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


BIOLOGICALACTIVITY

(Meyer-Overton 1899-1901)Log PLog P



Classical Hansch equation:ClassicalClassical 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 relatedto 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


PHYSICOPHYSICO--CHEMICALCHEMICALPROPERTIESPROPERTIES

MM

BIOLOGICALACTIVITIES

BIOLOGICALBIOLOGICALACTIVITIESACTIVITIES

A AMOLECULAR

DESCRIPTORS

MOLECULARMOLECULAR

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-qualityexperimental 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 ofThere is a need for a “limited” number of HIGHHIGH--QUALITY QUALITY

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

HOMOGENEOUSHOMOGENEOUS

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










MM



A AMOLECULAR

DESCRIPTORS

MOLECULARMOLECULAR


DD


R1R1 R2R2

R3R3




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

Log P (orLog 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

O H

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



A AMOLECULAR

DESCRIPTORS

MOLECULARMOLECULAR


DD


R1R1 R2R2

R3R3




CHEMICALSCHEMICALS

EXPERIMENTALEXPERIMENTALDATADATA

x x11 x x22 x xnn...... YY

TRAININGSETTRAININGTRAININGSETSET

EXPLORATIVEEXPLORATIVEANALYSIS: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

MOLECULARMOLECULARDESCRIPTORSDESCRIPTORS

ChemometricMethodsChemometricChemometricMethodsMethods




DATA SETDATA SET

TRAINING SETTRAINING SETTRAINING SET TEST SETTEST SETTEST SET

REGRESSIONMODEL

EXTERNALVALIDATION

Q2EXT

PREDICTABILITYPREDICTABILITY

NEW DATANEW DATA

FITTINGR2

INTERNALVALIDATION

Q2LOO

Q2LMO

SPLITTINGSPLITTING••DimensionDimension

••ChemicalChemical compositioncomposition



MOLECULAR DESCRIPTORSMOLECULAR DESCRIPTORSMOLECULAR DESCRIPTORS


TOX

ICITY

TTOOXX

IICCIITT

YY

YY

QSARQSARMODELMODEL

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

REVERSIBLEREVERSIBLE

DECODINGDECODING

MAXIMUMMAXIMUM

PREDICTIVE POWERPREDICTIVE POWER


EXPERIMENTALEXPERIMENTALDATADATA


NEWNEWCHEMICALSCHEMICALS


??????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

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

NCPNCP New Chemicals Program (PMN with QSAR data)

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

IPSIPS Informal Priority Setting methodInformal Priority Setting method

EURAMEURAM Europe UnionEurope 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 theWHITE PAPER on the StrategyStrategy forfor a Futurea 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 tofor 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 methodsDevelopment and validation of alternative methods

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

Methods)Methods) – – JRCJRC IspraIspra

•• Inclusion in the Community legislation and OECD TestInclusion in the Community legislation and OECD TestGuidelines Programme for international recognitionGuidelines Programme for international recognition

Task Force ofTask Force of EspertsEsperts in QSARin QSAR

••Particular research efforts for developing and validatingParticular research efforts for developing and validatingmodelling (e.g. QSAR) and screening methods formodelling (e.g. QSAR) and screening methods forassessing the potential adverse effects of chemicals.assessing the potential adverse effects of chemicals.

SetubalSetubal PrinciplesPrinciples



Classi di composti studiateClassiClassi didi composticomposti studiatestudiate

POPPOP ((Persistent Organic Pollutants),Persistent Organic Pollutants), PBTPBT ((Persistent Persistent BioaccumulativeBioaccumulative Toxics)Toxics)

VOCVOC (Volatile Organic Compounds) e(Volatile Organic Compounds) e HPVHPV (High Production Volume)(High Production Volume)

PesticidiPesticidi:: insetticidiinsetticidi,, erbicidierbicidi, …, …

IdrocarburiIdrocarburi aromaticiaromatici policondensatipolicondensati (PAH)(PAH)

BifeniliBifenili policloruratipoliclorurati (PCB),(PCB), diossinediossine

BenzeniBenzeni ee fenolifenoli sostituitisostituiti

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

ListaLista didi PrioritàPriorità 11 delladella EUEU




Applicazioni in campo ambientaleApplicazioniApplicazioni in campoin campo ambientaleambientale

PredizionePredizione didi proprietàproprietà chimicochimico--fisichefisiche perper 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 èLa PCA è unauna analisianalisi esplorativaesplorativa didi datidati multivariatimultivariati..

LeLe ComponentiComponenti principaliprincipali sonosono::

••CombinazioniCombinazioni linearilineari deidei datidati originalioriginali••OrdinateOrdinate secondosecondo lele direzionidirezioni didi massimamassima varianzavarianza (PC1, PC2..(PC1, PC2..••Non correlateNon correlate••SonoSono quindiquindi nuovenuove variabilivariabili con lecon le qualiquali sisi condensacondensa e “e “puliscepulisce

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

La PCALa PCA consisteconsiste inin unauna rotazionerotazione nellonello spaziospazio deidei datidati originalioriginali

inin modomodo cheche lele singolesingole componenticomponenti sianosiano tratra loroloro ortogonaliortogonali

II datidati vengonovengono cosìcosì ““vistivisti” in un” in un diversodiverso sistemasistema didi riferimentoriferimentoSecondoSecondo visualivisuali controllatecontrollate perper qualitàqualità ee quantitàquantitàdell’informazionedell’informazione rappresentatarappresentata



PERSISTENCE

ATMOSPHERICDEGRADATION REACTIONS

Reaction rate constants for the degradation by

Tropospheric Oxidants: OH OH ••,, NO NO 3 3

•• radicals and Ozone radicals and Ozone

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



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 molecularorbital (nucleophilicity of molecules)

•nBnz: number of aromatic rings

•MATS1m: 2D-autocorrelation of Moran

(atomic distribution)

-logk(NO3) experimental

- l o g k ( N O 3 ) p r e d i c t e d

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 Q2

LMO(50%) Q2ext 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

P

C 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

)

Atmospheric Persistence

GLOBAL ATMOSPHERIC PERSISTENCEGLOBAL ATMOSPHERIC PERSISTENCE

INDEX (ATPIN)INDEX (ATPIN)Principal Component Analysis

Cum. E.V.% = 95.3% (PC1 = 80.9%)

PC1 = ATPIN

P

C 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)

Atmospheric Persistence

““enlargedenlarged

”” 399 VOCs

65 VOCs


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



ATPIN da PCA

A T P I N c a l c o l a t o

-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 setTest 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 0.42255 174 3 HOMO nBnz BEHe4 93.7 93.5 93.4 92.7 0.41

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

ATMOSPHERIC PERSISTENCE INDEXATMOSPHERIC PERSISTENCE INDEXPC1 score = ATPINATPIN (399 chemicals experimental + predicted dataexperimental + predicted data)

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

•HOMO: highest occupied molecularorbital (nucleophilicity of molecules)

•nBnz: number of aromatic rings

•BEHe4: weighted by electronegativity(charge distribution)




Ranking of pesticides for environmentalRanking 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

logKowlogS

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

P C 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 environmentalClustering of pesticides for environmental

distribution in 4distribution in 4 a priori a priori classesclasses


4 1

1 2

3 3

1 1

4 3

1 6 3 5

2 5 1

4 8

4 5

2 8

5 4

5 0

3 6

3 8

3 9

3 7

4 6 7 1 5

3 4 9

4 7

4 4 6 5

4 0

3 2

2 7

3 1

1 4

3 4 9 8 1

0 1 5 4 1

3 4 2 2

3 5

2 9

2 5

2 6

1 8

1 9

3 0

2 0

1 7

2 3

2 2

2 4

2 1

0.00

33.33

66.67

100.00

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

2: Volatile comp. 4:Sorbed comp.

DENDROGRAMSimilarity



Definition of 4Definition of 4 a priori a priori classes of pesticidesclasses 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

P C 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

30

3132

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.

LeachingLeaching Sorption

Solubility

Volatility



Classification of 54 pesticides forClassification of 54 pesticides for

environmental distributionenvironmental distribution


1 2 3 4

1NHD

2.53J

317.69MW

Classification Tree

DESCRIPTORS:DESCRIPTORS:DESCRIPTORS:DESCRIPTORS:

MW: molecularMW: molecularMW: molecularMW: molecularweight (size)weight (size)weight (size)weight (size)

nHDnHDnHDnHD: number of : number of : number of : number of

donor atoms indonor atoms indonor atoms indonor atoms inHydrogen bondsHydrogen bondsHydrogen bondsHydrogen 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



WQO - Water Quality ObjectivesWQOWQO -- Water Quality ObjectivesWater Quality Objectives



WQO Water Quality ObjectivesWQOWQO 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 CARTCLASSIFICAZIONE 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 INTOXICITY 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

dir dir --WHIM descriptor WHIM descriptor Exp. toxicity

C a l c . t o x i c i t y

-1

0

1

2

3

4

5

6

7

-1 0 1 2 3 4 5 6 7

Daphnia

PC1

P C 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

P C 2

-2.5

-1.5

-0.5

0.5

1.5

2.5

-5 -3 -1 1 3 5Low toxicity High toxicity

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--220220




PC1

P C 2

-2.5

-1.5

-0.5

0.5

1.5

2.5

-5 -3 -1 1 3 5Low toxicity High toxicity

T. Daphnia

T. Fish

T. AlgaeExperimental

Predicted

I II III

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



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

water, soil

pp ,,

water, soilwater, soil


SOIL

Experimental half life

P r e d i c t e d h a l f l i f e

-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%)

S O L U

B L E S a n d V O L A T I L E S

PC 1

P C 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

P C 2

-4

-3

-2

-1

0

1

2

3

4

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

ground W

air

half-lifehalf-life

half-lifehalf-lifesoil

surf.W.

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

S O R B E D

PERSISTENCE

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

PC1= 9.22 + 3.14 AACPC1= 9.22 + 3.14 AAC-- 6.326.32 EE2s2s – – 17.49 E1e17.49 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

P r e d i c t e d P C 1 s c o r e s

-4

-3

-2

-1

0

1

2

3

4

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




PC 1

P C 2

-4

-3

-2

-1

0

1

2

3

4

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

ground W

airhalf-lifehalf-life

half-lifehalf-lifesoil

surf.W.

PERSISTENCE

S O L U B L E S a n d V O L A T I L E

S

S O R B E D




AcknowledgementsAcknowledgementsAcknowledgements

Dr. Ester Papa

Dr. Pamela Pilutti Dr. Francesca Battaini

Dr. Fulvio Villa

Dr.Dr. Ester PapaEster Papa

Dr. PamelaDr. Pamela Pilutti Pilutti Dr. FrancescaDr. Francesca Battaini Battaini

Dr.Dr. FulvioFulvio VillaVilla

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

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

qsar till ppt no 21

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