qsar prediction of physico-chemical properties and biological activities of emerging pollutants:...

QSAR prediction of physico-chemical properties QSAR prediction of physico-chemical properties

and biological activities of emerging pollutants: and biological activities of emerging pollutants:

brominated flame retardants and brominated flame retardants and

perfluorinated-chemicalsperfluorinated-chemicals

Sixth Indo-US Workshop on Mathematical Chemistry Kolkata, 8-10 January 2010

Paola GramaticaPaola Gramatica

Barun Bhhatarai, Barun Bhhatarai, Simona Kovarich and Ester PapaSimona Kovarich and Ester PapaQSAR Research Unit in Environmental Chemistry and Ecotoxicology QSAR Research Unit in Environmental Chemistry and Ecotoxicology

DBSF -University of Insubria, Varese - ItalyDBSF -University of Insubria, Varese - Italy

E-mail: E-mail: [email protected]

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

mailto:[email protected]

THE CHEMICAL UNIVERSETHE CHEMICAL UNIVERSE

More than 50.000.000 (sept.2009)

34,849,353 on the market

Regulated 247,952

EINECS100.204

TSCA

5% 5% KnownKnown data data

Environmental Environmental fate?fate?

Human effects?Human effects?

Environmental Environmental fate?fate?

Human effects?Human effects?

NEW11.000.000 / year

experimentsexperiments

EU-REACHEU-REACH

QQSSAARR

Predictive methodsPredictive methods

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

New EU-regulation:New EU-regulation:RRegistrationegistrationEEvaluationvaluationAAuthorisationuthorisationof of ChChemicalsemicals

The use of predictive QSAR models is suggested :The use of predictive QSAR models is suggested :

To highlight dangerous chemicalsTo highlight dangerous chemicals To prioritize chemicals and to focus the experimental To prioritize chemicals and to focus the experimental teststests To fill the data gapsTo fill the data gaps

Limited availability of experimental Limited availability of experimental datadata

Lack of knowledge of the properties Lack of knowledge of the properties and activities of existing substances and activities of existing substances

CComplexity of “old” regulationsomplexity of “old” regulations

Interest on development and validation Interest on development and validation

of alternative methods, such as QSARs. of alternative methods, such as QSARs.

INTRODUCTION – REACH and QSARINTRODUCTION – REACH and QSAR

in Environmental Chemistryin Environmental Chemistry

and Ecotoxicologyand Ecotoxicology

Staff Staff

Prof. Paola GramaticaProf. Paola Gramatica

Dr. Ester Papa, Ph.DDr. Ester Papa, Ph.D

Dr. Simona KovarichDr. Simona Kovarich

Dr. Jr. Mara LuiniDr. Jr. Mara Luini

Dr. Barun Bhhatarai, Ph.DDr. Barun Bhhatarai, Ph.D

(Dr. Jiazhong Li, Ph.D)(Dr. Jiazhong Li, Ph.D)


DBSF - University of InsubriaDBSF - University of InsubriaVarese - ItalyVarese - Italy


INTRODUCTION – Brominated Flame RetardantsINTRODUCTION – Brominated Flame Retardants

• Class of emerging pollutants used in a variety of consumer Class of emerging pollutants used in a variety of consumer products (plastics, polyurethane foams, textiles, electronic products (plastics, polyurethane foams, textiles, electronic equipments..) to increase fire resistancyequipments..) to increase fire resistancy

• Three most marked HPV products:Three most marked HPV products:

PBDEPBDEPolybrominated Diphenyl Ethers Polybrominated Diphenyl Ethers

O

BrBr

CH3

CH3

OHOH

Br

Br

Br

BrTBBPATBBPA

TetraBromoBisphenol-TetraBromoBisphenol-AA

Br

Br

BrBr Br

Br

HBCDHBCDHexabromocyclododecaneHexabromocyclododecane

• Levels in the environment and humans increased since theyLevels in the environment and humans increased since they came into usecame into use

• Ban of penta- and octa-BDE formulations (DecaBDE under Ban of penta- and octa-BDE formulations (DecaBDE under evaluation); HBCD in candidate list?evaluation); HBCD in candidate list?

209 possible209 possibleCONGENERSCONGENERS


Background knowledge about BFRs:Background knowledge about BFRs:

• Low water solubilityLow water solubility• High LogKow > 5High LogKow > 5• Persistence in the environmentPersistence in the environment• Liver toxicity, thyroid toxicity, developmental toxicityLiver toxicity, thyroid toxicity, developmental toxicity• Endocrine disruptorsEndocrine disruptors

The available amount of experimental data is very small and The available amount of experimental data is very small and mainly related to already banned BFRs.mainly related to already banned BFRs.

There is the need to extend knowledge about There is the need to extend knowledge about properties and ecotoxicological data for a properties and ecotoxicological data for a better understanding of BFRs behaviour and better understanding of BFRs behaviour and related risksrelated risks

INTRODUCTION – Brominated Flame RetardantsINTRODUCTION – Brominated Flame Retardants


• Perfluorinated compounds (PFCs) are Perfluorinated compounds (PFCs) are chemicals containing a long fluorinated chemicals containing a long fluorinated carbon tail attached to different functional carbon tail attached to different functional groupsgroups

• PFCs as perfluoro-octanesulfonate (PFOS), PFCs as perfluoro-octanesulfonate (PFOS), perfluoro-octanoate (PFOA) and perfluoro- perfluoro-octanoate (PFOA) and perfluoro- octane sulfonylamide (PFOSA) are stable octane sulfonylamide (PFOSA) are stable chemicals with a wide range of industrial chemicals with a wide range of industrial and consumer applicationsand consumer applications

• Degradable products of commercial PFCs Degradable products of commercial PFCs are found in environment and biota and are found in environment and biota and diPAPs (a group of PFCs used on food diPAPs (a group of PFCs used on food wrappers) was recently reported in human wrappers) was recently reported in human bloodblood

• PFCs are considered emerging pollutants PFCs are considered emerging pollutants and are believed to have potential toxic and are believed to have potential toxic effects in humans and wildlifeeffects in humans and wildlife

• PFCs along with Polyfluoro compounds are PFCs along with Polyfluoro compounds are studied for LCstudied for LC5050 inhalation toxicity of Mouse inhalation toxicity of Mouse and Ratand Rat

Predictive QSAR Predictive QSAR approaches is used to fill approaches is used to fill

the data gap and to the data gap and to predict toxicity of 250 PFCs on two of 250 PFCs on two

different species viz. different species viz. Mouse and RatMouse and Rat

R

R

R

R

R

R

R

X

R={H,F} X={-H, -OH, -SO2, -COOH,...}A: Perf luoro compounds

nX

X

F

F

X= {-F, -H, -OH, -alkyl, -aryal, -halo, -nitro,...}B: Multif luoro compounds

n

n= 1,2,3...

X

X

7

INTRODUCTION – INTRODUCTION – Perfluorinated CompoundsPerfluorinated Compounds

Aims of the Modelling StudiesAims of the Modelling Studies

Development of QSAR models for available end-points paying Development of QSAR models for available end-points paying

attention to external validation and applicability domain attention to external validation and applicability domain

analysis.analysis.

Evaluation of environmental behaviour and physico-chemical Evaluation of environmental behaviour and physico-chemical

properties of emerging pollutants: BFRs and PFCs.properties of emerging pollutants: BFRs and PFCs.

Identification of more toxic and dangerous chemicals based Identification of more toxic and dangerous chemicals based

on the studied end-points.on the studied end-points.

Prioritization of chemicals for experimental tests under Prioritization of chemicals for experimental tests under

CADASTER projectCADASTER project

Mechanistic interpretation of selected descriptors, Mechanistic interpretation of selected descriptors,

highlighting the fate, distribution and properties of chemicals.highlighting the fate, distribution and properties of chemicals.

EU-FP7EU-FP7 Project - CADASTERProject - CADASTER


To facilitate the consideration of a QSAR model To facilitate the consideration of a QSAR model for regulatory purposes, it should be associated with the for regulatory purposes, it should be associated with the

following information: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,appropriate measures of goodness of fit,

robustness and predictivity robustness and predictivity

a mechanistic interpretation, if possiblea mechanistic interpretation, if possible

--

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


METHODSMETHODS

1.1. Defined end-points of Defined end-points of Phys-chem and ToxicityPhys-chem and Toxicity

2.2. Unambiguous algorithm:Unambiguous algorithm:

• Chemical representation by theoretical molecular descriptorsChemical representation by theoretical molecular descriptors

(DRAGON) (DRAGON) selected by Genetic Algorithmsselected by Genetic Algorithms

• Statistical method Statistical method MLR regression (OLS) MLR regression (OLS)

3. Validation for model stability and predictivity (internal and 3. Validation for model stability and predictivity (internal and external validation)external validation)

4. Applicability Domain Analysis: 4. Applicability Domain Analysis:

leverage approach by Hat matrix (MLR)leverage approach by Hat matrix (MLR)

5. Interpretation of the selected molecular descriptors, if possible.5. Interpretation of the selected molecular descriptors, if possible.

Application of the OECD principles for QSAR modelsApplication of the OECD principles for QSAR models


RESULTS RESULTS

QSAR/QSPR models QSAR/QSPR models

developed for developed for

Brominated Flame RetardantsBrominated Flame Retardants

Simona Kovarich


Endpoint ModelTrainobj.

Test obj.

Desc. R2% Q2LOO%

Q2EXT

%AD%

on 243 BFR

LogKLogKOAOA

Full 30 -T(O..Br)

97.4 96.8 - 81.9 k-ANN Split 24 6 96.1 95.0 95.2 -

LogKLogKOWOW

Full 20 -T(O..Br)

96.4 95.6 - 86.0

k-ANN Split 14 6 97.1 95.9 94.7 -

MPMPFull 25 -

X2A84.4 81.9 - 95.9

k-ANN Split 20 5 82.2 78.5 93.7 -

LogPLogPLL

Full 34 -T(O..Br)

98.7 98.5 - 83.1

k-ANN Split 28 6 98.8 98.5 98.6 -

LogSLogS Full 12 - Mor23m 91.8 88.5 - 95.1

LogHLogH Full 7 - BEHe7 96.9 93.3 - 55.6

LogKp*LogKp* Full 15 - MW 94.9 93.8 - 91.4

LogHLp*LogHLp* Full 15 - T(O..Br) 94.3 92.6 - 81.9

RESULTS – QSPR modelsRESULTS – QSPR models

E. Papa, S. Kovarich, P. Gramatica, 2009. E. Papa, S. Kovarich, P. Gramatica, 2009. Development, validation and inspection of Development, validation and inspection of the applicability domain of QSPR models for physico-chemical properties of the applicability domain of QSPR models for physico-chemical properties of polybrominated diphenyl ethers.polybrominated diphenyl ethers. QSAR & Comb. Sci.,QSAR & Comb. Sci., 2828, 790-796, 790-796..

Physico-chemical and degradation PropertiesPhysico-chemical and degradation Properties

* Photodegradation

RESULTS RESULTS - - Model for Log KoaModel for Log Koa

LogKoa= 6.654 +0.222 T(O..Br)LogKoa= 6.654 +0.222 T(O..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

d

ista

nce

fro

m t

he

stru

ctu

ral

Do

mai

n (

hat

)

nona-deca

Are the predictions in the structural domain ?

90.4 % into AD90.4 % into AD

n° Obj Descriptor R2% Q2boot% Q2

EXT(rand20%) %

30 T(O..Br) 97.36 96.77 99.56

The same descriptor, i.e. The same descriptor, i.e. T(O...Br),T(O...Br), was selected was selected

as the best modeling variable for three differentas the best modeling variable for three different

properties which are related to each other properties which are related to each other

((LogPLogPLL, LogKoa, LogKoa, LogKowLogKow, LogLogHLHLpp).).

This descriptor gives a double structural information:This descriptor gives a double structural information:

its values increases according to both theits values increases according to both the numbernumber and the and the distance distance

of bromine substituentsof bromine substituents from the oxygen ether, from the oxygen ether,

on each phenyl ring. on each phenyl ring.

Thus, Thus, T(O...Br)T(O...Br) takes also into account the information related to takes also into account the information related to

thethe position of the bromine atoms on the phenyl rings.position of the bromine atoms on the phenyl rings.

RESULTS – RESULTS – Interpretation of descriptorsInterpretation of descriptors


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

Comparison with some existing models Comparison with some existing models

mono-tri

tetra-hepta

Predicted and Experimental data for 30 PBDEs Predicted and Experimental data for 30 PBDEs

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

EXT % RMSE(30 obj)

Papa et al. (2009) MLR 30 1 97.4 96.8 99.6 0.23

Xu et al. (2007) MLR 22 2 97.6 97.2 - 0.31

Chen et al. (2003) 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

D lo

g u

nit

s

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

YYPapaPapa = Predictions by our model (range Log Koa: 7.32 – 15.09) = Predictions by our model (range Log Koa: 7.32 – 15.09)YYEpisuiteEpisuite = Predictions by KoaWIN ( = Predictions by KoaWIN (DDmax = 3.33 log units; range Log Koa: 6.81-18.23)max = 3.33 log units; range Log Koa: 6.81-18.23)YYXu Xu = Predictions by Xu et al. (2007) (= Predictions by Xu et al. (2007) (DDmax =1.06 log units; range Log Koa: 7.4-15.73) max =1.06 log units; range Log Koa: 7.4-15.73)

n° bromine increase = D increase

Predictions for 209 PBDEsPredictions for 209 PBDEs

Comparison with some existing modelsComparison with some existing models


High difference with EPISUITE for highly brominated PBDEs

RESULTS RESULTS –– Environmental fate of BFRsEnvironmental fate of BFRs

5 <LogKow<7

Resistance to Photodegradation / MobilityResistance to Photodegradation / Mobility

Risk forRisk fortri-penta BDE!!tri-penta BDE!!

Endpoint Model Trainobj.

Test obj.

Desc.R2

%Q2

LOO

%Q2

EXT

%AD%

on 243 BFR

Log1/RBALog1/RBA Full 18 - RDF080v

RDF035v86.1 79.3 - 88.5

Random Split 10 8 87.2 74.0 76.8

Log1/ICLog1/IC50 50

PRPRANTANT

Full 19 - R7e+ GATS8e

85.9 81.7 - 94.2

Random Split 10 9 91.3 85.9 71.2

Log T4-Log T4-REPREP

Full 17 - qpmax MATS6v

95.2 92.9 - 97.9

Random Split 9 8 96.7 91.9 90.5

LogELogE22SULTSULT-REP-REP

Full 21 - B08[C-O] GGI7

87.6 83.6 - 100

Random Split 11 10 87.2 73.2 87.6

RESULTS – QSAR modelsRESULTS – QSAR models

Endocrine Disrupting ActivityEndocrine Disrupting Activity

RBARBA = AhR Relative Binding Affinity = EC = AhR Relative Binding Affinity = EC5050(TCDD) / EC(TCDD) / EC5050(BFR)(BFR)

PRPRANTANT = Progesterone Receptor Antagonism = Progesterone Receptor Antagonism

T4-REPT4-REP = T4-TTR Relative Competition = IC = T4-TTR Relative Competition = IC5050(T4) / IC(T4) / IC5050(BFR)(BFR)

EE22SULT-REPSULT-REP = E = E22SULT Relative Inhibition = ICSULT Relative Inhibition = IC5050(E2) / IC(E2) / IC5050(BFR)(BFR)

E. Papa, S. Kovarich, P. Gramatica, E. Papa, S. Kovarich, P. Gramatica, QSAR modeling and prediction of the QSAR modeling and prediction of the Endocrine disrupting potenciesEndocrine disrupting potencies of brominated flame retardantsof brominated flame retardants, , Submitted to Submitted to J. Chem. Inf. ModJ. Chem. Inf. Mod., 2010.

RESULTS RESULTS - - Model for LogE2SULT-REPModel for LogE2SULT-REP

LogELogE22SULT-REP = -0.56 + 2.10 B08[C-O] – 2.77 GGI7SULT-REP = -0.56 + 2.10 B08[C-O] – 2.77 GGI7

Equation of the “Split Model” (Random 50%): Equation of the “Split Model” (Random 50%):

-2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5

Log E 2SULT-REp Exp.

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

Lo

g E

2S

ULT

-RE

P P

red

.

Training set Prediction Set

6-OH-BDE-47

BDE-209

BDE-190

BDE-47

BDE-19

BDE-49BDE-28

BDE-127BDE-100 BDE-169

BDE-183BDE-155

BDE-206

4'-OH-BDE-49

3-OH-BDE-47

5-OH-BDE-47 4-OH-BDE-42

2,4,6-TBP

TBBPA

TBBPA-DBPE

2'-OH-BDE-66

PCP

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

HAT

-3

-2

-1

0

1

2

3

Res

Training set Prediction set

TBBPA-DBPE

MORE ACTIVE THAN PCP!

R2 = 0.87

Q2LOO = 0.73

Q2EXT = 0.88

RESULTS RESULTS

QSAR/QSPR models QSAR/QSPR models

developed for developed for

Per-fluorinated ChemicalsPer-fluorinated Chemicals

Barun Bhhatarai


Splitting Compounds Variables selected

R2 (%) Q2 LOO

Q2BOOT Q2 ext

R2-YScrm

Mouse Mouse

InhalationInhalation

56 56

compoundscompounds

SOM28.5%

Train: 40Test: 16 X3v; X3v;

H-048; H-048;

MLOGP; MLOGP;

F01[C-C]F01[C-C]

82.99 78.09 75.46 71.62 10.32

Random by Activity

20%

Train: 44Test: 12

77.07 71.73 69.89 85.11 8.99

Full model 79.83 76.31 75.38 - 7.05

Rat Rat

InhalationInhalation

52 52

compoundscompounds

SOM18.9%

Train: 42Test: 10 Jhetv:Jhetv:

PCR;PCR;

MLOGP; MLOGP;

B02[Cl-Cl]B02[Cl-Cl]

78.36 72.99 71.95 75.47 8.75

Random by Activity

20%

Train: 42Test: 10

80.01 75.21 74.12 66.70 9.91

Full model 78.14 73.85 73.26 - 7.64

Results: QSAR models for LCResults: QSAR models for LC5050 inhalation inhalation

21Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese (Italy)

Barun Bhhatarai and Paola Gramatica, Barun Bhhatarai and Paola Gramatica, Per- and Poly-fluoro Toxicity Per- and Poly-fluoro Toxicity (LC50 inhalation) Study in Rat and Mouse using QSAR Modeling(LC50 inhalation) Study in Rat and Mouse using QSAR Modeling , , Chem.Res. ToxicolChem.Res. Toxicol, 2010, in press, 2010, in press.

Regression plots for the models onRegression plots for the models ondatasets split by SOMdatasets split by SOM


log 1/LClog 1/LC5050 = 4.21 – 1.27 (±0.31) MlogP + 1.43 (±0.46) X3v + 0.38 (±0.13) F01[C-C] – = 4.21 – 1.27 (±0.31) MlogP + 1.43 (±0.46) X3v + 0.38 (±0.13) F01[C-C] –

1.14 (±0.37) H-048 1.14 (±0.37) H-048 n=56, s=0.72, rn=56, s=0.72, r22==79.83, F=50.5, Kx=42.34, Kxy=50.4079.83, F=50.5, Kx=42.34, Kxy=50.40Mouse Mouse

0 1 2 3 4 5 6 7

Mouse Inhalation Exp

0

1

2

3

4

5

6

7

8

Mouse

Inhala

tion P

red S

OM

Training Prediction

log 1/LClog 1/LC5050 = –12.76 + 1.87 (±0.20) Jhetv + 11.43 (±1.27) PCR – 0.60 (±0.12) MlogP – = –12.76 + 1.87 (±0.20) Jhetv + 11.43 (±1.27) PCR – 0.60 (±0.12) MlogP –

1.41 (±0.40) B02[Cl-Cl]1.41 (±0.40) B02[Cl-Cl]

n=52, s=0.82, rn=52, s=0.82, r22==78.14, F=41.99, Kx=23.55, Kxy=30.8678.14, F=41.99, Kx=23.55, Kxy=30.86RatRat

-1 0 1 2 3 4 5 6 7

Rat Inhalat ion E x p

-1

0

1

2

3

4

5

6

7

Rat

Inh

alat

ion

Pre

d S

OM

TrainingP redic t ion

Descriptor analysisDescriptor analysis

• Common descriptor characterizing Hydrophobicity was negative for both Common descriptor characterizing Hydrophobicity was negative for both speciesspecies

• JhetV and X3v have similar chemical meanings and are positive for both JhetV and X3v have similar chemical meanings and are positive for both speciesspecies

23

• B02[Cl-Cl] present for 5 of 52 compounds – fitting (?) descriptor to include all Freons


JhetvPCR

MlogPB02[Cl-Cl]

JhetvPCR

MlogPB02[Cl-Cl] MlogP

X3vF01[C-C]

H-048

MlogPX3v

F01[C-C]H-048

RATRAT

MOUSEMOUSEconventional bond-order ID number (piID) divided by the total path count

presence of heteroatom and double and triple bonds

hydrophobicityhydrophobicity

bond multiplicity, the heteroatoms and the number of atoms

total number of C-C bondpresence/absence of Cl-Cl at topological distance 02

formal oxidation number of C-atom which is the sum of the formal bond orders with electronegative atoms

Applicability Domain (AD) study on 250 PFCsApplicability Domain (AD) study on 250 PFCs

• 75.6% coverage of PFCs in Mouse model (61 compounds are out of 75.6% coverage of PFCs in Mouse model (61 compounds are out of

structural domain) and 76.8% coverage in Rat model (53 out).structural domain) and 76.8% coverage in Rat model (53 out).


•Arbitrary cutoff 0.5 (dotted lines): 11 common compounds are out of domainArbitrary cutoff 0.5 (dotted lines): 11 common compounds are out of domain

0.0 0.5 1.0 1.5 2.0 2.5 3.0

Hat V alues

0

2

4

6

8

10

12

14

Y P

red.

Com pounds S tudiedCom pounds P redic ted

P FO S A

0 .2 6 7

M ouse A D plot

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6

Hat values

-2

0

2

4

6

8

Y-P

red

C om pounds S tudied C om pounds P redic ted

0 .273 0 .5

PFO SA

R at AD plot

• Predicted compounds out of applicability domain of both Mouse and Rat model are long chain PFCs (>15-Carbon)

• They are probably extrapolated as the longest compounds in the training sets are with 7-Carbon

Focus on AD: Common Out-of-domain compoundsFocus on AD: Common Out-of-domain compounds


OS

O

NH

F F

FF

F F

FF

F F

FF

F FF

FF

4151-50-2

FF

F FFF F

FF

FF

F

FF F

F

F

FFF

307-07-3

FF

FFFF

F

FFF

F

F

FF

F F

FF

FF

F

FF F

306-91-2

FF

FF F

FF

F F

FF

F F

FF

F F

FF

F F

FF

F F

FF

FF

F

307-62-0

FF

FF

F

FFF

F FF

F

FF

F FF

F FF

51294-16-7

F F F F F

FF FF

FF F

FF

F F

FF

F F

FFFF

F

F

56523-43-4

FF

F FF

F

FF

FF

F F

FF

FF

F

F

60433-12-7

FF

F

FF

O15

O

59778-97-1

FF

F

FF

15

O

HO

FF

16517-11-6

FF

F

FF

I15

65150-94-9

FF

F

FF

I

15

F

F

F F

29809-35-6

Increasing ToxicityIncreasing Toxicity26


Toxicity TrendToxicity Trend

# 1

# 1 2 1

# 1 6 5

# 1 7 6

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

P C 1

-4

-3

-2

-1

0

1

2

3

PC

2

# 1

# 1 2 1

# 1 6 5

# 1 7 6

Exp .+Pred .=180 Common compounds=28

Exp R a t

PFOA

PFOSA

Exp Mu s

More Toxic Chemicals Predicted: by PCA analysisMore Toxic Chemicals Predicted: by PCA analysis

PFOS is under PFOS is under investigation as toxic investigation as toxic


These chemicals have been suggested to the CADASTER These chemicals have been suggested to the CADASTER Partners for experimental testsPartners for experimental tests

NF

F

FF

NF

F

FF

N

F F

N

F F

FF

376-89-6

376-53-4

98-16-8

98-46-4

N+O

-O

FF

F

H2N

FF

FO

SO F

F F

FF

F F

FFF

FF

375-81-5

O

OF F

FF

F F

FF

F F

FF

FF

F

376-27-2

O

OFF

F F

FF

F F

O

O

376-50-1

O

OHF F

FF

F F

O

HO

376-73-8

O

HOFF

F F

O

OH

377-38-8

FF

FF F

FF

O

O

F F

FF

F F

559-11-5

FF FF

F F

N

F F

FF

F FFF

F

647-12-1

O

F F

FF

F F

O

Cl Cl

678-77-3

FF

F

F FF

HO

FF

F

FFF

OH

918-21-8

O

O

F FO

O

F F

FF

424-40-8

OH

O

FF

F F

FF

F

FF

F F

FF

F F

335-67-1

1763-23-1

FF

FF F

FF FF

F F

SO

O OH

F F

FF

F F

O

OHF F

FF

F F

FF

F FF

FF

375-85-9

OS

O F

F F

FF

F F

FF

F F

FF

F

423-50-7

FF

FF

F F

F

NH2

O

F F

F

F F

FFF

423-54-1

O

O

F F

FF

F F

FF

F F

FF

F

17527-29-6

O

HOFF

F F

FF

F F

O

OH

336-08-3

O

OHF F

FF

F F

FF

FF

F

307-24-4

FF

FFF

F F

FF

F F

FF

OH

647-42-7

N

F

FF

F

F

773-82-0

F

F FF

F

FF

FF

F

FO

FF

F

813-44-5

O

F

F

FF

F

F

1187-93-5

FI FF

F

F

7783-66-6

FF

FFF

F F

FF

F F

FF

O

O

41430-70-0

PFOAPFOA

28

53 Training

41 Prediction I

Melting Point 94

Random split response

SOM split descriptor

48 Training

46 Prediction I

QSPR of Melting point: Data splittingQSPR of Melting point: Data splitting

17 compounds Prediction II


Perfluorinated chemicals

(PERFORCE)

29

Variables Train Set R2 Q2loo Q2boot RMSE train

RMSE ext

Q2ext* R2Yscr

AACAAC

F02[C-F]F02[C-F]

C-013C-013

53

Prediction I SOM

41 test 77.11 73.3571.89

40.8646.65 70.16 5.18

Prediction II17 test

71.90 25.04 91.40 5.16

48

Prediction I Response

46 test 82.85 79.3077.48

38.0748.52 72.16 5.84

Prediction II17 test

77.36 24.60 92.84 6.59

Total 111 78.45 76.82 76.60 40.3641.86(cv)

- 2.82

Results: Melting point (94+17)Results: Melting point (94+17)

AAC = mean information index on atomic correlations, information indicesF02[C-F] = frequency of C-F at topological distance 02, 2D frequency fingerprintC-013 = corresponds to CRX3 (X =electronegative atom), atom-centered fragments

*Consonni, V., et al. J. Chem. Inf. Model., 49, 1669-1678.


30

Analysis of Melting Point Model Analysis of Melting Point Model

MP = 148.81 (±18.43) AAC + 4.03 (±0.66) F02[C-F] – 14.47 (±6.88) C-013 – 269.25MP = 148.81 (±18.43) AAC + 4.03 (±0.66) F02[C-F] – 14.47 (±6.88) C-013 – 269.25 n=111n=111


-200 -150 -100 -50 0 50 100 150 200

Y-Exp

-200

-150

-100

-50

0

50

100

150

200

Y-P

red

TrainingPrediction I (SOM) Prediction II

0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18

Hat

-200

-150

-100

-50

0

50

100

150

200

250

Y-P

red

Available dataCompounds Predicted

PFOSA

PFOA

0.109


31

55 Training

50 Prediction I

Boiling Point 105

Random splitresponse

SOM splitdescriptor

53 Training

52 Prediction I

QSPR of Boiling point: Data splittingQSPR of Boiling point: Data splitting

25 compounds Prediction II

Perfluorinated chemicals

(PERFORCE)

32

VariablesVariables TrainTrain SetSet RR22 QQ22looloo QQ22boot boot RMSE RMSE

traintrainRMSE RMSE

extextQQ22extext** RR22YscrYscr

MsMs

ATS1mATS1m

nROH nROH

55

Prediction I SOM 50 test 87.50 85.25

83.16

24.78

34.54 75.71 5.73

Prediction II 25 test

86.2629.14 85.17 5.55

53


52 test 86.40 83.55

81.38

30.23

28.98 87.50 6.12

Prediction II 25 test

80.7826.20 89.53 5.35

Total 130 88.54 87.54 87.37 28.21 29.42 (cv)

- 2.41

Results: Boiling point (105+25)Results: Boiling point (105+25)

Ms = mean electro-topological state, constitutional descriptorMs = mean electro-topological state, constitutional descriptor

ATS1m = Autocorrelation of a topological structure, 2D autocorrelationsATS1m = Autocorrelation of a topological structure, 2D autocorrelations

nROH = number of OH groups, functional group countsnROH = number of OH groups, functional group counts



33

-150 -100 -50 0 50 100 150 200 250 300

Y-Exp.

-150

-100

-50

0

50

100

150

200

250

300

Y-P

red.

Training Prediction I (SOM)Prediction II

Analysis of Boiling Point ModelAnalysis of Boiling Point Model

BP = 128.43 (±5.295)ATS1m + 93.833 (±5.85)nROH – 54.23 (±4.25)Ms – 43.098BP = 128.43 (±5.295)ATS1m + 93.833 (±5.85)nROH – 54.23 (±4.25)Ms – 43.098 n=130n=130


0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16

Hat

-200

-100

0

100

200

300

400

Y-P

red

Available data Compounds Predicted

0.09

PFOA

PFOSA

34

24 Training

11 Prediction I

Vapor Pressure 35

Random splitSOM split

22 Training

13 Prediction I

QSPR of Vapor Pressure: Data splittingQSPR of Vapor Pressure: Data splitting

+ PERFORCE data


35

VariablesVariables SetSet RR22 QQ22loolooQQ22boot boot RMSE RMSE

traintrainRMSE RMSE

extextQQ22ext*ext* RR22YscrYscr

nDB;AAC;

F03[C-F]

Prediction I SOM 11 test

91.07 84.33 81.63 0.83 0.97 87.78 12.69


13 test93.75 91.23 82.13 0.64 1.14 80.36 14.08

Total 35 90.93 88.21 86.06 0.83 0.95 (cv)

- 8.95

Results: Vapor Pressure (35)Results: Vapor Pressure (35)

nDB = number of double bonds, constitutional descriptornDB = number of double bonds, constitutional descriptor

AAC = mean information index on atomic composition , information indicesAAC = mean information index on atomic composition , information indices

F03[C-F] = frequency of C-F at topological distance 03, 2D frequency F03[C-F] = frequency of C-F at topological distance 03, 2D frequency

fingerprintsfingerprints



36

Analysis of Vapour Pressure ModelAnalysis of Vapour Pressure Model

log VP = –0.642 (±0.405) nDB – 3.164 (±0.924) AAC – 0.165 (±0.025) F03[C-F] + 7.97log VP = –0.642 (±0.405) nDB – 3.164 (±0.924) AAC – 0.165 (±0.025) F03[C-F] + 7.97 n=35 n=35

-6 -4 -2 0 2 4 6

Y-Exp.

-6

-4

-2

0

2

4

6

Y-P

red.

Training Prediction (SOM)


0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

Hat

-10.0

-8.0

-6.0

-4.0

-2.0

0.0

2.0

4.0

6.0

8.0

Ypr

ed

Available dataCompounds Predicted

End End point point

DescriptorsDescriptors nn RR22 QQ22looloo QQ22bootbootRMSE RMSE traintrain

RMSE RMSE cvcv

RMSRMSEE

EPI*EPI*

(n)(n)AD%AD%

Melting Melting PointPoint

AACAACF02[C-F]F02[C-F]C-013 C-013

111 78.5 76.8 76.1 40.36 41.86 46.678(248)(248)94.794.7

Boiling Boiling PointPoint

MsMsATS1mATS1mnROH nROH

130 88.5 87.5 87.3 27.57 29.12 43.046 (290) (290) 97.997.9

Vapor Vapor PressurePressure

CIC0CIC0MATS1vMATS1v

TPSA(Tot)TPSA(Tot)35 90.9 88.2 87.1 0.83 0.95 1.12 (243)(243)

94.294.2

Summary of QSPR models on PFCs:Summary of QSPR models on PFCs:

37

* http://www.epa.gov/oppt/exposure/pubs/episuite.htm


All our models have smaller RMSE in comparison to EPISUITE modelsAll our models have smaller RMSE in comparison to EPISUITE models

ConclusionsConclusions

• Prediction of data for ~250 compounds was done for each set of Prediction of data for ~250 compounds was done for each set of

chemicals: BFRchemicals: BFRs s and PFCsand PFCs

• Applicability domain analysis also for new compounds was doneApplicability domain analysis also for new compounds was done

• QSA(P)Rs developed could be used to fill data gaps according to the QSA(P)Rs developed could be used to fill data gaps according to the

new REACH regulation, facilitating the screening and prioritization new REACH regulation, facilitating the screening and prioritization

of chemicals, reducing animal testing as well as for of chemicals, reducing animal testing as well as for designdesign of of

alternativealternative and safer and safer chemicalschemicals


•Predictive models were developed Predictive models were developed ad-hocad-hoc for several toxicity end- for several toxicity end-

points and physico-chemical propertiespoints and physico-chemical properties

•‘‘OECD principles for the validation of QSAR models, for regulatory OECD principles for the validation of QSAR models, for regulatory

applicability’ was strictly followedapplicability’ was strictly followed

•Simplicity (linear analysis, few descriptors, robust models) with Simplicity (linear analysis, few descriptors, robust models) with

external validation were usedexternal validation were used

39

Thanks for your attentionThanks for your attention !! !!

AcknowledgementsAcknowledgementsFFinancialinancial support support

by the FP7th-EU Project CADASTERby the FP7th-EU Project CADASTER



qsar prediction of physico-chemical properties and biological activities of emerging pollutants:...

Documents

dangerous chemicals

stable chemicals

prioritization of chemicals

group of pfcs

experimental tests

environmental fate

thyroid toxicity

environmentliver toxicity