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

QSAR prediction of physico-chemical properties and biological activities of emerging pollutants: brominated flame retardants and perfluorinated-chemicals. Paola Gramatica Barun Bhhatarai, Simona Kovarich and Ester Papa QSAR Research Unit in Environmental Chemistry and Ecotoxicology

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

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  1. QSAR prediction of physico-chemical properties and biological activities of emerging pollutants: brominated flame retardants and perfluorinated-chemicals Paola Gramatica Barun Bhhatarai, Simona Kovarich and Ester Papa QSAR Research Unit in Environmental Chemistry and Ecotoxicology DBSF -University of Insubria, Varese - Italy E-mail: paola.gramatica@uninsubria.it http://www.qsar.it Sixth Indo-US Workshop on Mathematical Chemistry Kolkata, 8-10January 2010

  2. NEW 11.000.000 / year QSAR TSCA EINECS 100.204 5% Known data experiments Predictive methods EU-REACH Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese (Italy) THE CHEMICAL UNIVERSE More than 50.000.000 (sept.2009) 34,849,353 on the market Regulated 247,952 Environmental fate? Human effects?

  3. Interest on development and validation of alternative methods, such as QSARs. INTRODUCTION – REACH and QSAR New EU-regulation: Registration Evaluation Authorisation of Chemicals Limited availability of experimental data Lack of knowledge of the properties and activities of existing substances Complexity of “old” regulations • The use of predictive QSAR models is suggested : • To highlight dangerous chemicals • To prioritize chemicals and to focus the experimental tests • To fill the data gaps

  4. QSAR Research Unit http://www.qsar.it DBSF - University of Insubria Varese - Italy Staff Prof. Paola Gramatica Dr. Ester Papa, Ph.D Dr. Simona Kovarich Dr. Jr. Mara Luini Dr. Barun Bhhatarai, Ph.D (Dr. Jiazhong Li, Ph.D) in Environmental Chemistry and Ecotoxicology Brominated Flame Retardants Fragrances Perfluoroalkylate Substances Triazoles & Benzotriazoles Endocrine Disruptors

  5. TBBPA TetraBromoBisphenol-A PBDE Polybrominated Diphenyl Ethers HBCD Hexabromocyclododecane Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese (Italy) INTRODUCTION – Brominated Flame Retardants • Class of emerging pollutants used in a variety of consumer products (plastics, polyurethane foams, textiles, electronic equipments..) to increase fire resistancy • Three most marked HPV products: 209 possible CONGENERS • Levels in the environment and humans increased since they • came into use • Ban of penta- and octa-BDE formulations (DecaBDE under • evaluation); HBCD in candidate list?

  6. Thereis the needtoextendknowledgeaboutproperties and ecotoxicological data for a betterunderstandingofBFRsbehaviour and relatedrisks Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese (Italy) INTRODUCTION – Brominated Flame Retardants • Background knowledgeaboutBFRs: • Low water solubility • High LogKow > 5 • Persistence in the environment • Livertoxicity, thyroidtoxicity, developmentaltoxicity • Endocrine disruptors The available amount of experimental data is very small and mainly related to already banned BFRs.

  7. Perfluorinated compounds (PFCs) are chemicals containing a long fluorinated carbon tail attached to different functional groups PFCs as perfluoro-octanesulfonate (PFOS), perfluoro-octanoate (PFOA) and perfluoro- octane sulfonylamide (PFOSA) are stable chemicals with a wide range of industrial and consumer applications Degradable products of commercial PFCs are found in environment and biota and diPAPs (a group of PFCs used on food wrappers) was recently reported in human blood PFCs are considered emerging pollutants and are believed to have potential toxic effects in humans and wildlife PFCs along with Polyfluoro compounds are studied for LC50 inhalation toxicity of Mouse and Rat Predictive QSAR approachesisused to fill the data gap and to predicttoxicityof 250 PFCs on twodifferentspeciesviz. Mouse and Rat Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese (Italy) INTRODUCTION – Perfluorinated Compounds

  8. Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese (Italy) Aims of the Modelling Studies EU-FP7Project - CADASTER • Development of QSAR models for available end-points paying attention to external validation and applicability domain analysis. • Evaluation of environmental behaviour and physico-chemical properties of emerging pollutants: BFRs and PFCs. • Identification of more toxic and dangerous chemicals based on the studied end-points. • Prioritization of chemicals for experimental tests under CADASTER project • Mechanistic interpretation of selected descriptors, highlighting the fate, distribution and properties of chemicals.

  9. Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese (Italy) OECD Principles for QSAR models in REACH To facilitate the consideration of a QSAR model for regulatory purposes, it should be associated with the following information: • a defined endpoint • an unambiguous algorithm • a defined domain of applicability • appropriate measures of goodness of fit, robustness and predictivity • a mechanistic interpretation, if possible -

  10. Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese (Italy) METHODS Application of the OECD principles for QSAR models • Defined end-points of Phys-chem and Toxicity • Unambiguous algorithm: • Chemical representation by theoretical molecular descriptors • (DRAGON) selected by Genetic Algorithms • Statistical method  MLR regression (OLS) • 3. Validation for model stability and predictivity (internal and external validation) • 4. Applicability Domain Analysis: • leverage approach by Hat matrix (MLR) • 5. Interpretation of the selected molecular descriptors, if possible.

  11. Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese (Italy) RESULTS QSAR/QSPR models developed for Brominated Flame Retardants Simona Kovarich

  12. RESULTS – QSPR models Physico-chemical and degradation Properties * Photodegradation E. Papa, S. Kovarich, P. Gramatica, 2009. Development, validation and inspection of the applicability domain of QSPR models for physico-chemical properties of polybrominated diphenyl ethers.QSAR & Comb. Sci.,28, 790-796.

  13. nona-deca 90.4 % into AD Are the predictions in the structural domain ? RESULTS - Model for Log Koa LogKoa= 6.654 +0.222 T(O..Br) Experimental range of LogKoa: 7.34 (mono-BDE) – 11.96 (hepta-BDE)

  14. Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese (Italy) RESULTS – Interpretation of descriptors The same descriptor, i.e. T(O...Br), was selected as the best modeling variable for three different properties which are related to each other (LogPL, LogKoa, LogKow, LogHLp). This descriptor gives a double structural information: its values increases according to both thenumberand the distance of bromine substituentsfrom the oxygen ether, on each phenyl ring. Thus, T(O...Br) takes also into account the information related to theposition of the bromine atoms on the phenyl rings.

  15. Comparison with some existing models Predicted and Experimental data for 30 PBDEs tetra-hepta mono-tri

  16. n° bromine increase = D increase Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese (Italy) Comparison with some existing models Predictions for 209 PBDEs YPapa = Predictions by our model (range Log Koa: 7.32 – 15.09) YEpisuite = Predictions by KoaWIN (Dmax = 3.33 log units; range Log Koa: 6.81-18.23) YXu = Predictions by Xu et al. (2007) (Dmax =1.06 log units; range Log Koa: 7.4-15.73) High difference with EPISUITE for highly brominated PBDEs

  17. Risk for tri-penta BDE!! Resistance to Photodegradation / Mobility RESULTS –Environmental fate of BFRs 5 <LogKow<7

  18. RESULTS – QSAR models Endocrine Disrupting Activity RBA = AhR Relative Binding Affinity = EC50(TCDD) / EC50(BFR) PRANT = Progesterone Receptor Antagonism T4-REP = T4-TTR Relative Competition = IC50(T4) / IC50(BFR) E2SULT-REP = E2SULT Relative Inhibition = IC50(E2) / IC50(BFR) E. Papa, S. Kovarich, P. Gramatica, QSAR modeling and prediction of the Endocrine disrupting potenciesof brominated flame retardants, Submitted to J. Chem. Inf. Mod., 2010.

  19. MORE ACTIVE THAN PCP! RESULTS - Model for LogE2SULT-REP Equation of the “Split Model” (Random 50%): LogE2SULT-REP = -0.56 + 2.10 B08[C-O] – 2.77 GGI7 R2 = 0.87 Q2LOO = 0.73 Q2EXT = 0.88

  20. Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese (Italy) RESULTS QSAR/QSPR models developed for Per-fluorinated Chemicals Barun Bhhatarai

  21. Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese (Italy) Results: QSAR models for LC50 inhalation Barun Bhhatarai and Paola Gramatica, Per- and Poly-fluoro Toxicity (LC50 inhalation) Study in Rat and Mouse using QSAR Modeling, Chem.Res. Toxicol, 2010, in press.

  22. log 1/LC50 = 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 n=56, s=0.72, r2=79.83, F=50.5, Kx=42.34, Kxy=50.40 Mouse log 1/LC50 = –12.76 + 1.87 (±0.20) Jhetv + 11.43 (±1.27) PCR – 0.60 (±0.12) MlogP – 1.41 (±0.40) B02[Cl-Cl] n=52, s=0.82, r2=78.14, F=41.99, Kx=23.55, Kxy=30.86 Rat Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese (Italy) Regression plots for the models ondatasets split by SOM

  23. Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese (Italy) Descriptor analysis RAT bond multiplicity, the heteroatoms and the number of atoms Jhetv PCR MlogP B02[Cl-Cl] conventional bond-order ID number (piID) divided by the total path count MOUSE hydrophobicity MlogP X3v F01[C-C] H-048 presence of heteroatom and double and triple bonds presence/absence of Cl-Cl at topological distance 02 total number of C-C bond formal oxidation number of C-atom which is the sum of the formal bond orders with electronegative atoms • Common descriptor characterizing Hydrophobicity was negative for both species • JhetV and X3v have similar chemical meanings and are positive for both species • B02[Cl-Cl] present for 5 of 52 compounds – fitting (?) • descriptor to include all Freons

  24. Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese (Italy) Applicability Domain (AD) study on 250 PFCs • 75.6% coverage of PFCs in Mouse model (61 compounds are out of structural domain) and 76.8% coverage in Rat model (53 out). • Arbitrary cutoff 0.5 (dotted lines): 11 common compounds are out of domain

  25. Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese (Italy) Focus on AD: Common Out-of-domain compounds • 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

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

  27. These chemicals have been suggested to the CADASTER Partners for experimental tests Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese (Italy) More Toxic Chemicals Predicted: by PCA analysis PFOA PFOS is under investigationastoxic 27

  28. Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese (Italy) QSPR ofMeltingpoint: Data splitting Melting Point 94 SOM splitdescriptor Randomsplitresponse 48 Training 53 Training 41 Prediction I 46 Prediction I Perfluorinated chemicals (PERFORCE) 17compoundsPrediction II 28

  29. Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese (Italy) Results: Meltingpoint (94+17) *Consonni, V., et al. J. Chem. Inf. Model., 49, 1669-1678. AAC = mean information index on atomic correlations, information indices F02[C-F] = frequency of C-F at topological distance 02, 2D frequency fingerprint C-013 = corresponds to CRX3 (X =electronegative atom), atom-centered fragments

  30. Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese (Italy) Analysis of Melting Point Model MP = 148.81 (±18.43) AAC + 4.03 (±0.66) F02[C-F] – 14.47 (±6.88) C-013 – 269.25 n=111

  31. Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese (Italy) QSPR ofBoilingpoint: Data splitting Boiling Point 105 SOM split descriptor Randomsplit response 53 Training 55 Training 50 Prediction I 52 Prediction I Perfluorinated chemicals (PERFORCE) 25compoundsPrediction II

  32. Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese (Italy) Results: Boilingpoint (105+25) *Consonni, V., et al. J. Chem. Inf. Model., 49, 1669-1678. Ms = mean electro-topological state, constitutional descriptor ATS1m = Autocorrelation of a topological structure, 2D autocorrelations nROH = number of OH groups, functional group counts

  33. Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese (Italy) Analysis of Boiling Point Model BP = 128.43 (±5.295)ATS1m + 93.833 (±5.85)nROH – 54.23 (±4.25)Ms – 43.098 n=130

  34. Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese (Italy) QSPR of Vapor Pressure: Data splitting + PERFORCE data Vapor Pressure35 SOM split Randomsplit 22 Training 24 Training 11 Prediction I 13 Prediction I 34

  35. Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese (Italy) Results: Vapor Pressure (35) *Consonni, V., et al. J. Chem. Inf. Model., 49, 1669-1678. nDB = number of double bonds, constitutional descriptor AAC = mean information index on atomic composition , information indices F03[C-F] = frequency of C-F at topological distance 03, 2D frequency fingerprints

  36. Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese (Italy) Analysis of Vapour Pressure Model log VP = –0.642 (±0.405) nDB – 3.164 (±0.924) AAC – 0.165 (±0.025) F03[C-F] + 7.97 n=35

  37. Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese (Italy) Summary of QSPR models on PFCs: * http://www.epa.gov/oppt/exposure/pubs/episuite.htm All our models have smaller RMSE in comparison to EPISUITE models

  38. Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese (Italy) Conclusions • Predictive models were developed ad-hoc for several toxicity end-points and physico-chemical properties • ‘OECD principles for the validation of QSAR models, for regulatory applicability’ was strictly followed • Simplicity (linear analysis, few descriptors, robust models) with external validation were used • Prediction of data for ~250 compounds was done for each set of chemicals: BFRs and PFCs • Applicability domain analysis also for new compounds was done • QSA(P)Rs developed could be used to fill data gaps according to the new REACH regulation, facilitating the screening and prioritization of chemicals, reducing animal testing as well as for design of alternative and safer chemicals

  39. Prof. Paola Gramatica - QSAR Research Unit - DBSF - University of Insubria - Varese (Italy) Acknowledgements Financialsupport by the FP7th-EU Project CADASTER http://www.qsar.it Thanks for your attention !!

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