overview of activities within wp 3 development and...

Overview of activities within WP 3

Development and validation of QSAR models

Overview of activities within WP 3Overview of activities within WP 3

Development and validation of QSAR modelsDevelopment and validation of QSAR models

Paola Paola GramaticaGramatica

WP3 leaderWP3 leader

QSAR Research Unit in Environmental Chemistry and QSAR Research Unit in Environmental Chemistry and EcotoxicologyEcotoxicologyDBSF DBSF --University of University of InsubriaInsubria, Varese , Varese -- ItalyItaly

EE--mail: mail: [email protected]@uninsubria.ithttp://www.qsar.ithttp://www.qsar.it

EmergingEmerging PollutantsPollutants –– CADASTER CADASTER classesclasses

TriazolesTriazoles and and BenzotriazolesBenzotriazoles

(B)(B)TAZsTAZs

PolyBrominatedPolyBrominatedDiPhenylDiPhenyl EthersEthers (PBDE) (PBDE) and and otherother BrominatedBrominatedFlameFlame RetardantsRetardants

((BFRsBFRs))

SubstitutedSubstituted musksmusks//FragrancesFragrances

PolyPoly-- and and PerfluorinatedPerfluorinatedcompoundscompounds ((PFCsPFCs))

WP3 PartnersWP3 WP3 PartnersPartners

• University of Insubria (Italy)- P. Gramatica• Linneus University (Sweden)- T. Oberg• IVL Swedish Environ. Res. Inst. (Sweden)- M. Rahmberg• IDEAConsult Ltd. (Bulgaria) - N. Jeliazkova• Helmholtz Zentrum München (Germany) - I.Tetko

WP3 TasksWP3 WP3 TasksTasks

•Task 3.1 - Preparation of the chemical structures and molecular descriptors

database for the chemicals of the four selected classes.

•Task 3.2 - Evaluation of existing QSARs.

•Task 3.3 - Gap analysis.

•Task 3.4 - Prioritization for experimental tests.

•Task 3.5 - Development of new QSARs.

•Task 3.6 - Development of multi-model approaches (consensus modeling).

•Task 3.7 - External Validation of QSAR models with experimental data from

the Project.

CADASTER CADASTER PartnersPartners at 21at 21°° SETAC meeting SETAC meeting May 2011, Milan (Italy)May 2011, Milan (Italy)

in Environmental Chemistryin Environmental Chemistry

and and EcotoxicologyEcotoxicology

Staff Staff

Prof.Prof. Paola GramaticaPaola Gramatica

Dr. Ester Papa, Dr. Ester Papa, Ph.DPh.D

Dr. Simona KovarichDr. Simona Kovarich

Dr. Mara Dr. Mara LuiniLuini

Dr. Dr. BarunBarun BhhataraiBhhatarai, , Ph.DPh.D

Dr. Dr. JiazhongJiazhong Li, Li, Ph.DPh.D

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

DBSF DBSF -- University of InsubriaUniversity of Insubria

Varese Varese -- ItalyItaly

Task 3.1 Task 3.1 -- Preparation of the chemical structures and molecular Preparation of the chemical structures and molecular descriptors database for the chemicals of the 4 selected classesdescriptors database for the chemicals of the 4 selected classes..

a) Minimization of Chemical Structures by Hyperchem

b) Calculation of DRAGON molecular descriptors

Database

c) Calculation of CADASTER online molecular descriptors

To facilitate the consideration of a QSAR model for regulatory pTo facilitate the consideration of a QSAR model for regulatory purposes, it should urposes, it should be associated with the following informationbe associated with the following information::

�� a defined endpoint a defined endpoint

�� an unambiguous algorithman unambiguous algorithm

�� a defined domain of applicabilitya defined domain of applicability

�� appropriate measures of goodness of fit, robustness and appropriate measures of goodness of fit, robustness and predictivitypredictivity

�� a mechanistic interpretation, if possiblea mechanistic interpretation, if possible

OECD Principles for QSAR models in REACHOECD Principles for QSAR models in REACH

Task 3.2 Task 3.2 -- Evaluation of existing Evaluation of existing QSARsQSARs Task 3.3 Task 3.3 -- Gap analysisGap analysis

--

• In literature, very few local models for the specific CADASTER classes, mainly

not validated and without information on their Applicability Domain (AD)

• Only general models (as EPISUITE), mainly without AD

• Very few experimental data for SIDS end-points, useful for Risk Assessment

• Need for specific local models for CADASTER chemicals

verifying external validation and AD

PrioritizationPrioritization through through structuralstructural similaritysimilarity analysisanalysis and and ranking ranking methodsmethods, , basedbased on on availableavailable toxicitytoxicity datadata

Task 3.4 Task 3.4 -- Prioritization for experimental testsPrioritization for experimental tests..

BFRsprioritization based on toxicityprofile (exp+pred ED potency) (UI), structural diversity (LNU/UI)

BFRsBFRsprioritizationprioritization basedbased on on toxicitytoxicityprofileprofile ((exp+predexp+pred ED ED potencypotency) (UI), ) (UI), structuralstructural diversitydiversity (LNU/UI) (LNU/UI)

PFCsprioritization based on toxicityprofile (exp+pred mammaliantoxicity) (UI), structuralrepresentativity (LNU/UI)

PFCsPFCsprioritizationprioritization basedbased on on toxicitytoxicityprofileprofile ((exp+predexp+pred mammalianmammaliantoxicitytoxicity) (UI), ) (UI), structuralstructuralrepresentativityrepresentativity (LNU/UI(LNU/UI))

FRAGRANCESprioritization based on toxicityprofile (exp+pred mammalian and cyto-toxicity), structural representativity (UI).

FRAGRANCESFRAGRANCESprioritizationprioritization basedbased on on toxicitytoxicityprofileprofile ((exp+predexp+pred mammalianmammalian and and cytocyto--toxicitytoxicity), ), structural structural representativityrepresentativity (UI). (UI).

(B)TAZsprioritization based on toxicityprofile (exp aquatic toxicity) (UI) and structural diversity (UI-LNU).

(B)(B)TAZsTAZsprioritizationprioritization basedbased on on toxicitytoxicityprofileprofile ((expexp aquaticaquatic toxicitytoxicity) (UI) ) (UI) and and structural diversity structural diversity (UI(UI--LNU).LNU).

and and compoundcompound’’s commercial s commercial availabilityavailability..

Task 3.4 : Task 3.4 : Prioritization for experimental tests in the ProjectPrioritization for experimental tests in the Project

PrioritizationPrioritization basedbased on on toxicitytoxicity profileprofile((exp+predexp+pred mammalianmammalian toxicitytoxicity: : ratrat and mouse and mouse oraloral and and InhalationInhalation toxicitytoxicity) )

(UI)(UI)

PFOA and PFOSHPFOA and PFOSHamongamong the more the more toxictoxic

QSAR prediction of QSAR prediction of physicophysico--chemical properties and chemical properties and

biological activitiesbiological activities

Task 3.5 Task 3.5 -- Development of new Development of new QSARsQSARs..

��DevelopmentDevelopment of QSAR of QSAR modelsmodels forfor availableavailable endend--pointspoints ((sufficientsufficient

experimentalexperimental data data forfor modelingmodeling), ), payingpaying attentionattention toto externalexternal validationvalidation

and and structuralstructural applicabilityapplicability domaindomain analysisanalysis..

��IdentificationIdentification of more of more toxictoxic and and environmentalenvironmental hazardoushazardous chemicalschemicals

basedbased on the on the studiedstudied endend--pointspoints

SummarySummary on on BFRsBFRs

XX

LNULNU

ccDragon 1.11Dragon 1.11PLSRPLSRBCFBCF

PublishedPublished + +

CAD. CAD. Dat.Dat.

CADASTER CADASTER

DatabaseDatabase

PublishedPublished

++

CADASTER CADASTER

webweb

++

CADASTER CADASTER

DatabaseDatabase

Data setData set

� ��

PublishedPublished

++

uploadeduploaded

in in CAD.CAD.

DatabaseDatabase

(243 (243 BFRsBFRs))

PredPred & AD& AD

XX

XX

FateFate

XX

XX

XX

XX

XX

PhysPhys--chemchem

SIDSSIDS

dd--eeDragon 5.5Dragon 5.5MLR/KMLR/K--NN NN

classclassED ED ActivityActivity

((~~20)20)

� �� MLRMLRHLpHLp (15)(15)

� �� MLRMLRKpKp (15)(15)

aa--bb� �� MLR MLR **logKlogKOWOW**(20)(20)

aa--bb� �� MLR MLR **logKlogKOAOA**(30)(30)

aa� �� MLR MLR WS WS * * (12)(12)

aa� �� MLR MLR **VP VP ** (34)(34)

aa--bb� �� MLR MLR **MP MP ** (25)(25)

aaDragon 5.5Dragon 5.5MLR MLR **H H ** (7)(7)

((**onon--line) UIline) UI((NNobjobj))

RefRefDescDescModel Model D 3.5D 3.5EndpointEndpoint

aa Papa, E.; Papa, E.; KovarichKovarich, S.; , S.; GramaticaGramatica P., P., QSAR Comb. Sci.QSAR Comb. Sci. 2009, 28, 7902009, 28, 790--796.796.bb Papa, E.; Papa, E.; KovarichKovarich, S.; , S.; GramaticaGramatica P., P., Molecular Informatics Molecular Informatics 2011 , 30, 2322011 , 30, 232--240 (Proceedings issue of Euro240 (Proceedings issue of Euro--QSAR2010).QSAR2010).cc ObergOberg T., T., EnvironEnviron Sci & Sci & PollutPollut ResRes 2002, 9, 4052002, 9, 405--411.411.dd Papa, E.; Papa, E.; KovarichKovarich, S.; , S.; GramaticaGramatica, P., P., Chem. Res. , Chem. Res. ToxicolToxicol. . 2010, 23, 9462010, 23, 946--954.954.ee KovarichKovarich, S.; Papa, E.; , S.; Papa, E.; GramaticaGramatica, P., , P., J.J. Hazardous Mat.Hazardous Mat. 2011,190, 1062011,190, 106--112.112.

PredictionPrediction of SIDS of SIDS endpointsendpointsand and characterizationcharacterization of of environmentalenvironmental behaviourbehaviour

* * PredictionsPredictions comparedcompared withwith EPI Suite softwareEPI Suite software

ExpExp and and PredPred data data usedused forforPrioritizationPrioritization (Del 3.4)(Del 3.4)�� experimentalexperimental testingtesting

SummarySummary on on FragrancesFragrances

Data set Data set

publishedpublished + +

CADASTER CADASTER

web / web /

DatabaseDatabase

ExpExp data data

uploadeduploaded in in

CADASTER CADASTER

web / web /

DatabaseDatabase

Data setData set

PublishedPublished

(79 (79 fragfrag))

PredPred & AD& AD

� ��

� ��

� ��

� ��

Dragon 5.5Dragon 5.5

DescriptorsDescriptors

XX

ToxTox

XX

XX

XX

PhysPhys--chemchem

SIDSSIDS

� �� CytotoxicityCytotoxicity

((~~20)20)

bb

� ��

RodentRodent

acute acute

toxicitytoxicity (23)(23)

� �� logKowlogKow** (52)(52)

� �� WSWS** (37)(37) aa

MLRMLRVPVP** (37)(37)

((* * onon--line) UIline) UI((NNobjobj))

RefRefModel Model Del 3.5Del 3.5EndpointEndpoint

aa Papa, E.; Papa, E.; LuiniLuini, M.; , M.; GramaticaGramatica, POSTER presented at SETAC, POSTER presented at SETAC--Europe 2009, Europe 2009, GGööteborgteborg, Sweden, 31 May , Sweden, 31 May –– 4 June 2009.4 June 2009.

bb Papa, E.; Papa, E.; LuiniLuini, M.; , M.; GramaticaGramatica, P. , P. SAR QSAR Environ. ResSAR QSAR Environ. Res., 2009, 20, 767., 2009, 20, 767––779.779.

PredictionPrediction of SIDS of SIDS endpointsendpoints and and

characterizationcharacterization of of environmentalenvironmental behaviourbehaviour

* * PredictionsPredictions comparedcompared withwith EPI Suite softwareEPI Suite softwareExpExp and and PredPred data data usedused forforPrioritizationPrioritization (Del 3.4)(Del 3.4)�� experimentalexperimental testingtesting

SummarySummary on on PFCsPFCs

ff

dd--ee

bb

aa

RefRef

PublishedPublished

++

CAD. webCAD. web

++

CAD. CAD.

DatabaseDatabase

Data setData set

PublishedPublished in in

respectiverespective

paperspapers

(221 (221 PFCsPFCs))

PredPred & AD& AD

LNU (PLS) LNU (PLS) refref cc

LNU, IDEA, LNU, IDEA,

HMGUHMGU

ModelsModels byby

WP3 WP3 PartnersPartners

MLR/KMLR/K--NNNN

Drag5.5Drag5.5

MLRMLR--Drag5.5Drag5.5


MLRMLR**--Drag5.5Drag5.5


MLRMLR**--Drag5.5Drag5.5


((* * onon--line) UIline) UI

Model & Model & DescDesc

XXRodentRodent acute acute

toxicitytoxicity ((~~55)55)

CMC (10)CMC (10)

ED ED activityactivity

(24)(24)

XXWSWS** (20)(20)

XXVPVP** (35)(35)

XXXXBPBP** (93)(93)

XXXXMPMP** (94)(94)

ConsCons

ensusensusToxToxPhPh--chch

SIDSSIDS

((NNobjobj))

EndpointEndpoint

aa BhhataraiBhhatarai, B. et al. (, B. et al. (WP3 partnersWP3 partners), ), Molecular InformaticsMolecular Informatics , 2011 30, 189, 2011 30, 189--204 (Proceedings issue of Euro204 (Proceedings issue of Euro--QSAR2010).QSAR2010).

bb BhhataraiBhhatarai, B.; , B.; GramaticaGramatica, P., , P., Environ. Sci. Technol.,Environ. Sci. Technol., 2010, Online first (2011 45: Special issue on 2010, Online first (2011 45: Special issue on PFCsPFCs), ), OralOral at CMTPI11at CMTPI11

CC Oberg T.; Liu T., Oberg T.; Liu T., Chemo Lab, Chemo Lab, 2011, 107, 592011, 107, 59--64.64.

dd BhhataraiBhhatarai, B.; , B.; GramaticaGramatica, P., P. ,, Molecular DiversityMolecular Diversity,, 2010, 2010, 15, 46715, 467--476. 476.

ee BhhataraiBhhatarai, B.; , B.; GramaticaGramatica P., P., Chem. Res. Chem. Res. ToxicolToxicol.,., 2010, 23, 5282010, 23, 528--539.539.

ff KovarichKovarich, S.; Papa, E., , S.; Papa, E., GramaticaGramatica, P , P SubmittedSubmitted toto SAR & QSAR SAR & QSAR EnvironEnviron. Res. Res., 2011., 2011..


ExpExp and and PredPred data data usedused forforPrioritizationPrioritization (Del 3.4)(Del 3.4)�� experimentalexperimental testingtesting


SummarySummary on (B)on (B)TAZsTAZs

bb

aa

UIUI

RefRef

in in

progressprogress

WP3WP3

PartnPartn

ersers

PublishedPublished

(351 TAZ)(351 TAZ)

Pred&ADPred&AD

in progressin progress

(MLR)(MLR)

XXLCLC5050 EarthwormEarthworm

BCFBCF

XXLCLC5050 FishFish

in progressin progress

(MLR)(MLR)

XXECEC5050 DaphniaDaphnia

XXLDLD5050 HoneybeesHoneybees

XXLDLD5050 BirdBird

ExpExp data data

uploadeduploaded

in CAD. web in CAD. web

/ Database/ Database

Drag 5.5 Drag 5.5

"pruned"pruned““

++

onon--line line descdesc..

XXECEC5050 AlgaeAlgae

� �� MLRMLRXXMPMP** (56)(56)

� �� MLRMLRXXVPVP** (33)(33)

� �� MLRMLRXXLogKowLogKow** (64)(64)PublishedPublished

++

CAD. web / CAD. web /

DatabaseDatabase

Drag 5.5Drag 5.5MLR MLR **XXWSWS** (49)(49)

DescDesc Data setData set

EcotoxEcotoxPhPh--chch

SIDSSIDS

((**onon--line) UIline) UI((NNobjobj))

Model Model D3.5D3.5EndpointEndpoint

aa BhhataraiBhhatarai, B.; , B.; GramaticaGramatica, P.,, P., Water ResearchWater Research,, 2011, 45 (3) 14632011, 45 (3) 1463--1471. 1471. OralOral at CMTPI11at CMTPI11bb KovarichKovarich S.etS.et UI UI groupgroup, , OralOral at CMTPI1at CMTPI1


ExperimentalExperimental data data usedused forforPrioritizationPrioritization (Del 3.4)(Del 3.4)�� experimentalexperimental testingtesting


A A limitedlimited amountamount of of developeddeveloped and and publishedpublished locallocal modelsmodels forfor

CADASTER CADASTER chemicalschemicals are are nownow uploadeduploaded in the CADASTER in the CADASTER

databasedatabase::

BetterBetter resultsresults thanthan EPI Suite EPI Suite

and and checkcheck of of structuralstructural AD: AD:

definitiondefinition of of interpolationinterpolation or or

extrapolationextrapolation

WP3 in WP5 : WP3 in WP5 : ModelsModels in CADASTER webin CADASTER web

�� BFRsBFRs:: VP, MP, VP, MP, logKowlogKow, , logKoalogKoa

�� PFCsPFCs:: BP, WS, VPBP, WS, VP

�� (B)(B)TAZsTAZs: WS, : WS, logKowlogKow

OngoingOngoing modellingmodelling forfor (B)TAZ are (B)TAZ are basedbased alsoalso on on

descriptorsdescriptors calculatedcalculated in the CADASTER database in the CADASTER database ��

uploadableuploadable modelsmodels

ExampleExample: : LocalLocal Model Model forfor Log Log KoaKoa of PBDEof PBDE

LogKoa=LogKoa= 6.654 +06.654 +0.222.222 T(O.T(O..Br.Br))

Experimental range of LogKoa: 7.34 (mono-BDE) – 11.96 (hepta-BDE)

1

2

7 8

10

1213

15

17

21

28

30 32

35

37

47

66

69

75

77

82

8599

100

119

126

153

154

156

183

7 8 9 10 11 12 13

LogKoa Exp.

7

8

9

10

11

12

13

Lo

gK

oa

Pre

d.

Training set

Prediction set

0

0.2

0.4

1 21 41 61 81 101 121 141 161 181 201

BDE

dis

tan

ce

fro

m t

he

str

uc

tura

l

Do

ma

in (

ha

t)

nona-deca

Are the predictions in the structural domain ?

90.4 % 90.4 % intointo ADAD

n° Obj Descriptor R2% Q2boot% Q2

EXT(rand20%) %

30 T(O..Br) 97.36 96.77 99.56

Exp. vs. Pred. data

6

7

8

9

10

11

12

13

14

15

1 2 7 8 10 12 13 15 17 21 28 30 32 35 37 47 66 69 75 77 82 85 99 100 119 126 153 154 156 183

PBDE

Lo

gK

oa

Exp. LogKoa Chen (2003) Papa (2008) Xu (2007) KoaWIN

ComparisonComparison withwith some some existingexisting modelsmodels, in , in particularparticular EPISuiteEPISuite

monomonomonomono----tritritritri

tetratetratetratetra----heptaheptaheptahepta

PredictedPredicted and and ExperimentalExperimental data data forfor 30 30 PBDEsPBDEs

Author Method N° obj. N° vars R2% Q2LOO% Q2

EXT % RMSE(30 obj)

Papa et al. MLR 30 1 97.4 96.8 99.6 0.23

Xu et al. MLR 22 2 97.6 97.2 - 0.31

Chen et al. PLS 13 10 97.9 97.5 - -

KoaWIN (Episuite) KOW/KAW 0.81

0

0.5

1

1.5

2

2.5

3

3.5

monoBDE diBDE triBDE tetraBDE pentaBDE hexaBDE heptaBDE octaBDE nonaBDE decaBDE

∆∆ ∆∆ lo

g u

nit

s

average ∆ (|YPapa- YKoaWIN|) average ∆ (|YPapa-YXu|)

YYPapaPapa = = PredictionsPredictions byby ourour model (model (rangerange Log Log KoaKoa: 7.32 : 7.32 –– 15.09)15.09)YYEpisuiteEpisuite = = PredictionsPredictions byby KoaWINKoaWIN ((DmaxDmax = 3.33 log = 3.33 log unitsunits; ; rangerange Log Log KoaKoa: 6.81: 6.81--18.23)18.23)YYXuXu = = PredictionsPredictions byby XuXu etet al.al. (2007) ((2007) (DmaxDmax =1=1.06.06 log log unitsunits; ; rangerange Log Log KoaKoa: 7.4: 7.4--15.73) 15.73)

n° bromine increase = D increase

PredictionsPredictions forfor allall the 209 the 209 PBDEsPBDEs congenerscongeners

High difference with EPISUITE for highly brominated PBDEs

ComparisonComparison withwith some some existingexisting modelsmodels, in , in particularparticular EPISuiteEPISuite

Papa, E.; Papa, E.; KovarichKovarich, S.; , S.; GramaticaGramatica P., P., Molecular Informatics Molecular Informatics 2011 , 30, 2322011 , 30, 232--240 (Proceedings issue of Euro240 (Proceedings issue of Euro--QSAR2010).QSAR2010).

to develop and propose in:

• local QSAR models• specific for the 4 classes of emerging pollutants• externally validated• verified for their structural Applicability Domain

AimAim of WP3 in CADASTER Projectof WP3 in CADASTER Project

http://www.cadaster.euhttp://www.cadaster.eu

http://www.qsar.ithttp://www.qsar.it

[email protected]@uninsubria.it

Thanks to all for your attentionThanks to all for your attention !!!!

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

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