Quantitative structure—activity relationships for the …Quantitative structure—activity...

Journal of Environmental Science and Health Part A (2007) 42, 573–590Copyright C© Taylor & Francis Group, LLCISSN: 1093-4529 (Print); 1532-4117 (Online)DOI: 10.1080/10934520701244326

Quantitative structure—activity relationshipsfor the prediction of relative in vitro potencies (REPs)for chloronaphthalenes

TOMASZ PUZYN1, JERZY FALANDYSZ1, PAUL D. JONES2 and JOHN P. GIESY2,3,4

1Department of Environmental Chemistry and Ecotoxicology, University of Gdansk, Faculty of Chemistry, Gdansk, Poland2Department of Veterinary Biomedical Sciences and Toxicology Centre, University of Saskatchewan, Saskatoon,Saskatchewan, Canada3Zoology Department, National Food Safety and Toxicology Centre and, Centre for Integrative Toxicology,Michigan State University, East Lansing, Michigan, USA4Department of Biology and Chemistry, City University of Hong Kong, Kowloon, SAR, China

Chloronaphthalenes (CNs), due to their structural similarities to 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) and the other “dioxin-like”compounds, can bind to the aryl hydrocarbon receptor (AhR) and induce a wide range of pleotrophic effects. Relative potencyof individual dioxin analogues can be measured relative to that of TCDD. Relative effects potencies (REP) can be based on manyresponses, including in vivo and in vitro responses. Both in vivo and in vitro tests, based on either indigenous responses such as theinduction of ethoxyresorufin O-deethylase (EROD) or exogenous reporter genes under the control of the AhR such as luciferase canbe used to determine REP values. Here we used measured REP values determined for CNs in two assays. Both assays are based onH4IIE rat hepatoma cells. The H4IIE assay is based on expression of the endogenous reporter gene (CYP-1A) that codes for theexpression of EROD and the H4IIE-luc assay which is based on the exogenous reporter gene (luciferase) transfected into the H4IIEcell line. Experimentally determined REP were available for only 17 and 18 of the 75 possible choronaphthalene congeners, for theH4IIE and H4IIE-luc assays, respectively. For this reason computational models were developed to allow prediction of the relativepotencies of the other CN congeners. Predictive relationships were based on quantum chemical descriptors obtained from DensityFunctional Theory (DFT) calculations (B3LYP/6–311++G∗∗). The final models were found by means of a hybrid method combininga genetic algorithm and artificial neural networks. REP values estimated for individual CNs based on the H4IIE assay ranged from4.3 × 10−9 to 3.2 × 10−2 while those based on the H4IIE-luc assay ranged from 4.0 × 10−8 to 1.8 × 10−3. CN congeners nos. 66,67, 70 and 73 were exhibited the greatest REP values in both assays. The 1,2,3,5,6,8-hexaCN congener (no. 68) had a REP value thatwas 10-fold less. The remaining congeners had REP values that were less or did not cause sufficient up-regulation of the monitoredgenes to allow for the calculation of a REP. Interactions of CNs with the AhR could be affected by three possible factors: molecularsize, steric interactions and electrostatic interactions. These findings are discussed relative to the use of consensus TCDD equivalencyfactors’ (TEFs) for use in risk assessments of CNs for regulatory purposes.

Keywords: Chloronaphthalenes, REPs, EROD, H4IIe, H4IIE-luc, luciferase, QSAR, dioxin-like compounds, PCNs.

Introduction

Chloronaphthalenes (CNs) are a class containing 75individual compounds (congeners) differing by a degreeof chlorination (from mono- to octachloronaphthalene)and position of Cl substitution.[1−3] When substituted with

Address correspondence to Jerzy Falandysz, Department of En-vironmental Chemistry & Ecotoxicology, University of Gdansk,18 Sobieskiego Str., PL 80-952, Gdansk, Poland; E-mail:[email protected] December 29, 2006.

more than one chlorine atom, these compounds are referredto as polychlorinated napthalenes (PCNs). Chloronaph-thalenes are relatively well known persistent organic pol-lutants and have been intensively studied.[4−6] CNs havebeen released into the environment from use of techni-cal CN mixtures and as a byproduct in chlorobiphenylformulations. CNs has been used for numerous applica-tions, including electro energetic equipment, like trans-formers and capacitors. Although CNs are formed also inthermal processes such as combustion, incineration etc.,the main sources of the compounds are related to hu-man activities.[1,7−11] CNs have been found in many envi-ronmental matrices, including wildlife and humans.[1,5,12,13]

574 Puzyn et al.

Technical CN formulations, such as the Halowaxes, can betoxic to biota.[14]

The critical (occurring at the least concentration) mech-anism of toxic action for CNs is similar to that of “dioxin-like” compounds. Due to their structural similarities to2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), can bind tothe aryl hydrocarbon receptor (AhR). The AhR is a cyto-plasmic receptor that after formation of the CN-AhR com-plex and binding the nuclear translocator protein (ARNT),is translocated to the nucleus, where after some transfor-mations the complex interacts with specific DNA regions,known as dioxin response elements (DREs) that control theexpression of many genes that are indicative of exposure to“dioxin-like compounds.”[15−17]

Based on the knowledge of the toxic mechanism of ac-tion, several in vitro bioassays based on mammalian cellcultures have been developed. These assays allow the deter-mination of relative potency values (REP) for compoundsthat can cause AhR-mediated effects by comparing theamount of the chemical required to cause the same levelof response (such as gene induction) as the reference com-pound, TCDD.[18] These assays can be based on both en-dogenous and exogenous reporter genes. Both of the assaysused to generate measured REP values for CNs that wereused in the Quantitative Structure– Activity Relationship(QSAR) models developed in this study are based on H4IIErat hepatoma cells. One commonly used assay is the H4IIEassay, which is based on the expression of (CYP-1A1),which is the gene that codes for the enzyme that catalyzesthe de-alkylation of ethoxy-resorufin (7-ehoxyrezorufin O-deethylaze (EROD).[19,20] The H4IIE assay has several lim-itations to its use.[20,21]

Some of the inherent limitations of using endogenous re-porter genes are situations, such as the conditions wherethe ligand of interest is a suicide substrate for the reportergene or where the ligands are only partial agonists for theAhR. In such cases, the results of the H4IIE assay areunreliable.[17,20,21] Alternatively, the use of H4IIE-luc as-say is more sensitive and avoids some of H4IIE assay. TheH4IIE assay is a genetically engineered version of the H4IIEcells, into which an exogenous gene that codes for the en-zyme luciferase. Luciferase is the enzyme that produces lightin firefly tails. This gene has been inserted under the con-trol of a DRE.[21] Synthesis of luciferin in response to ex-posure to AhR-active compounds results in changes in theproduction of light that can be a sensitive measure underappropriate conditions.[22]

REP values have been determined for 18 chloronaph-thalene congeners in the H4IIE (EROD) assay, while REPvalues have been determined for 17 congeners by use ofthe H4IIE-luc (luciferase) assay.[17,19] However, risk assess-ments of CN mixtures has been limited by the lack of REPvalues for the other CN congeners.[23−29] Thus, in this study,we developed quantitative relationships, based on the struc-ture of the congeners, to predict REP values for those con-geners for which REPs were not available. These QSARs

were based on the assumption that differences in REP val-ues are a function of the molecular structure and that apredictive relationship, based on first and second princi-ples can be developed that would be predictive of the mag-nitude of the REP. The descriptors applied were calculatedfrom quantum-chemical Density Functional Theory, andthe final QSAR models were developed based on a hybridmethod that made use of both a genetic algorithm and arti-ficial neural networks (GA-ANN). Several examples of theuse of QSAR to estimate REP values for CNs are availablein the literature.[30,31]

Both studies were based on molecular descriptors calcu-lated based on lower level of quantum-chemical theory andused linear predictive relationships. However, since the re-lationships between molecular structure and REP values ofCNs are non-linear, and the fact that the magnitudes of dif-ferences in values of molecular descriptors were small themethodology applied in this study was expected to providemore accurate results. The aims of the presented study wereto: (i) to predict REP values of all individual CNs based onthe GA-ANN hybrid approach with molecular descriptorsfrom DTF/6–311++G∗∗ calculations; (ii) to compare theREP values obtained from the predictive relationships withvalues measured in vitro in previous studies; (iii) to provideguidance on the use of REP values for CN congeners to befurther evaluated in vitro and in vivo and the use of REPvalues in risk assessments and; (iv) to propose first-evertoxic equivalency factors (TEFs) for all of chloronaphtha-lene congeners.

Materials and methods

The predictive and validation steps were conducted in sev-eral phases. The same modeling strategy was used with boththe data from the H4IIE and H4II-luc assays. The empiricaldata from both in vitro assays was used to develop the pre-dictive relationships then predicted values were comparedto the empirical data (Table 1). For both assays CN con-geners, for which experimental data was available, were di-vided into two sets: a training set (TS) and an independentvalidation set (VS). Optimized predictive relationships wereused to make reliable predictions for each of non-in vitro-tested CN congeners and, in this way, we finally obtained acomplete activity data table for all 75 congeners. The appli-cability domain of the model was evaluated by use of princi-pal component analysis of the rotated feature (descriptor)space and ranges of available empirical data.[32−34] Thesedata ranged from 2.1 × 10−3 to 3.1 ×10−9 and from 3.9 ×10−3 to 1.0 × 10−7 for H4IIE and H4II-luc, respectively.

In the first stage of the study 40 molecular descrip-tors (Table 2 and Appendix) were calculated for each ofthe congeners. These quantum-mechanical computationswere conducted at the level of Density Fucntional The-ory by use of the Gaussian 03 software package.[35] Weused one of the most advanced DFT hybrid functional

The prediction of relative potencies for chloronaphthalenes 575

Table 1. Experimental and estimated REP values of activity of CNs based on the H4IIE (EROD) and H4IIE-luc assays.

H4IIE EROD H4IIE-luc

CN Congener In vitroa,b In silicoc In silicod In vitrob,e In silicof In silicoc In silicod

1 1-chloronaphthalene 1.1 × 10−07TS 8.9 × 10−10 9.5 × 10−08 I 7.2 × 10−12 4.1 × 10−08∗

2 2-chloronaphthalene 1.1 × 10−08 1.0 × 10−08 1.0 × 10−07TS I 9.5 × 10−10 1.5 × 10−07

3 1,2-dichloronaphthalene 2.1 × 10−06 2.3 × 10−07 I 3.5 × 10−07 2.2 × 10−07

4 1,3-dichloronaphthalene 2.5 × 10−11 3.2 × 10−08 I 5.9 ×10−09 1.2 × 10−07

5 1,4-dichloronaphthalene 3.1 × 10−09VS 5.0 × 10−08 4.3 × 10−09 2.0 × 10−07TS I 9.1 × 10−09 1.5 × 10−07


7 1,6-dichloronaphthalene 1.3 × 10−07 2.8 × 10−08 I 4.1 × 10−08 4.0 × 10−08∗

8 1,7-dichloronaphthalene 7.1 × 10−11 6.0 × 10−08 I 7.4 × 10−09 9.3 × 10−08∗




12 2,7-dichloronaphthalene 2.6 × 10–07TS 6.9 × 10−07 3.5 × 10−07 4.2 × 10−07VS I 5.1 × 10−07 4.9 × 10−07

13 1,2,3-trichloronaphthalene 6.0 × 10−06 4.2 × 10−08 I 6.9 × 10−06 9.1 × 10−07




17 1,2,7-trichloronaphthalene 6.2 × 10−07TS 1.1 × 10−07 6.6 × 10−07 I 1.1 × 10−06 1.7 × 10−07










27 1,2,3,4-tetrachloronaphthalene 4.1 × 10−05 9.1 × 10−07 I 2.8 × 10−05 2.3 × 10−06







34 1,2,4,7-tetrachloronaphthalene 3.5 × 10−07TS 2.6 × 10−07 4.7 × 10−07 5.8 × 10−07TS I 3.4 × 10−06 1.3 × 10−06






40 1,2,6,8-tetrachloronaphthalene 4.0 × 10−07 1.2 × 10−07 1.6 × 10−05TS I 4.7 × 10−06 1.4 × 10−05


42 1,3,5,7-tetrachloronaphthalene 3.5 × 10−06TS 2.5 × 10−06 1.2 × 10−06 5.8 × 10−06TS I 4.0 × 10−05 3.2 × 10−06





47 1,4,6,7-tetrachloronaphthalene 5.4 × 10−06 1.2 × 10−08 A 1.7 × 10−05 3.0 × 10−07

48 2,3,6,7-tetrachloronaphthalene 3.5 × 10−04VS 5.5 × 10−04 2.3 × 10−04 I 3.3 × 10−04 1.0 × 10−05

49 1,2,3,4,5-pentachloronaphthalene 3.2 × 10−05 3.6 × 10−07 I 6.3 × 10−05 7.9 × 10−07

50 1,2,3,4,6-pentachloronaphthalene 1.7 × 10−05 4.2 × 10−05 A 6.0 × 10−05 3.0 × 10−05




54 1,2,3,6,7-pentachloronaphthalene 7.6 × 10−05TS 4.4 × 10−05 2.8 × 10−05 1.7 × 10−04TS A 1.3 × 10−04 5.5 × 10−05

55 1,2,3,6,8-pentachloronaphthalene 3.9 × 10−06TS 2.8 × 10−05 7.1 × 10−06 I 9.1 × 10−05 6.8 × 10−05

56 1,2,3,7,8-pentachloronaphthalene 2.2 × 10–05TS 2.0 × 10−04 2.3 × 10−05 4.6 × 10−05VS I 1.8 × 10−04 5.6 × 10−05

57 1,2,4,5,6-pentachloronaphthalene 1.6 × 10−06VS 5.4 × 10−06 1.5 × 10−06 3.5 × 10−06VS I 2.9 × 10−05 1.5 × 10−06

(Continued on next page)

576 Puzyn et al.

Table 1. Experimental and estimated REP values of activity of CNs based on the H4IIE (EROD) and H4IIE-luc assays. (Continued)

H4IIE EROD H4IIE-luc

CN Congener In vitroa,b In silicoc In silicod In vitrob,e In silicof In silicoc In silicod



60 1,2,4,6,7-pentachloronaphthalene 3.9 × 10−07TS 5.0 × 10−06 1.3 × 10−06 2.6 × 10−05TS A 1.0 × 10−04 2.8 × 10−05

61 1,2,4,6,8-pentachloronaphthalene 3.9 × 10−07VS 2.3 × 10−06 2.9 × 10−07 I 6.0 × 10−05 1.3 × 10−05


63 1,2,3,4,5,6-hexchloronaphthalene 1.1 × 10−04 2.2 × 10−05 A 2.3 × 10−04 2.2 × 10−05

64 1,2,3,4,5,7-hexchloronaphthalene 4.4 × 10−05 1.1 × 10−04 A 3.8 × 10−04 1.0 × 10−05

65 1,2,3,4,5,8-hexchloronaphthalene 1.6 × 10−04 1.3 × 10−05 I 2.7 × 10−04 8.9 × 10−08

66 1,2,3,4,6,7-hexchloronaphthalene 6.3 × 10−04TS 2.1 × 10−04 6.9 × 10−04 3.9 × 10−03TS A 8.3 × 10−04 2.9 × 10−03

67 1,2,3,5,6,7-hexchloronaphthalene 2.9 × 10−04VS 4.9 × 10−04 1.0× 10−03 1.0 × 10−03TS A 1.4 × 10−03 1.7 × 10−03

68 1,2,3,5,6,8-hexachloronaphthalene 4.4 × 10−04TS 6.0 × 10−05 2.7 × 10−04 1.5 × 10−04VS A 4.5 × 10−04 1.1 × 10−04

69 1,2,3,5,7,8-hexachloronaphthalene 5.8 × 10−05 8.3 × 10−07 A 4.3 × 10−04 1.5 × 10−04

70 1,2,3,6,7,8-hexachloronaphthalene 2.1 × 10−03TS 7.8 × 10−04 2.8 × 10−03 5.9 × 10−04TS A 8.1 × 10−04 7.1 × 10−04

71 1,2,4,5,6,8-hexachloronaphthalene 4.8 × 10−05 4.3 × 10−05 I 3.8 × 10−04 1.6 × 10−07

72 1,2,4,5,7,8-hexachloronaphthalene 2.6 × 10−05 1.0 × 10−04 I 2.9 × 10−04 8.9 × 10−08

73 1,2,3,4,5,6,7-heptachloronaphthalene 4.6 × 10−04TS 1.2 × 10−04 3.8 × 10−04 1.0 × 10−03TS A 9.3 × 10−04 1.8 × 10−03

74 1,2,3,4,5,6,8-heptachloronaphthalene 3.6 × 10−05 2.7 × 10−03∗ 1.0 × 10−07VS I 5.2 × 10−04 1.0 × 10−07

75 1,2,3,4,5,6,7,8-octachloronaphthalene 1.0 × 10−03 3.2× 10−02∗ 1.0 × 10−07VS I 3.2 × 10−03 8.7 × 10−08∗

TSTraining set.VSValidation set.∗High uncertainty due to extrapolation outside of the model’s domain.aVilleneuve et al.[17]

bVilleneuve et al.[49]

cFalandysz and Puzyn[30]

dThis study.eBlankenship et al.[15]

fOlivero-Verbel et al.[31]; I = Inactive, A = Active.

B3LYP and relatively large 6–311++G∗∗ basis set. Thisfunctional (B3LYP) is a linear combination of exchange-correlation energy from the Local Spin Density Ap-proximation (LSDA), exchange energy difference betweenHartree Fock and LSDA, Becke’s exchange energy withgradient correction (1988) and correlation energy with aLee-Young-Parr correction. The Pople style basis set 6–311++G∗∗ is a triple split valence basis, where the coreorbitals are a contraction of six primitive Gaussian-typefunctions (PGTOs). The valence split into three functions,represented by three, one, and one PGTOs, respectively. Todevelop better descriptions of the systems, diffuse and po-larization functions were added for hydrogen, carbon andchlorine atoms.[36] In such studies the 6–311++G∗∗ basis setwas found to be the optimal solution, due to both relativehigh accuracy and low computation time.

The following quantum-chemical and thermo-dynamicaldescriptors were used: valence angle between C1 and C8(CCC(1–8), valence angle between C4 and C5 (CCC(4–5), dipole moment (D), mean polarizability (A), maximalpositive and negative partial Mulliken’s charge (MaxQ+and MaxQ-), energy of the highest occupied molecular or-bital (HOMO), energy of the lowest unoccupied molecu-lar orbital (LUMO), molecular hardness (Hard), ioniza-tion potential (IP), electron affinity (EA), total energy of

the molecule (Et), standard enthalpy of formation (dH),standard Gibbs free energy of formation (dG), heat ca-pacity (Cv), entropy (S), molecular refraction (MR), molarvolume (MR), solvent accessible molecular surface area inwater (SASw), solvent accessible molecular volume in wa-ter (SAVw), total electrostatic energy of solvatation in water(TEESolw), polarized solute – solvent interaction energy inwater (PolSSw), cavitation energy in water (CEw), disper-sion energy in water (DEw), total non-electrostatic energyof solvatation in water (TNEw), solvent accessible molecu-lar surface area in octanol (SASo), solvent accessible molec-ular volume in octanol (SAVo), total electrostatic energy ofsolvatation in octanol (TEESolo), polarized solute-solventinteraction energy in octanol (PolSSo), cavitation energyin octanol (CEo), dispersion energy in octanol (DEo),total non-electrostatic energy of solvatation in octanol(TNEo).

Because octanol is not a standard solvent included in theGaussian 03 package, we characterized it using the dielec-tric constant εoct = 10.3 and solvent radius r = 3.250 A.Mean polarizability was calculated as the mean eigenvaluefrom diagonalization of the polarizability tensor. Ionizationpotential was determined as the difference between totalenergy of fully optimized molecular cation and the neu-tral molecule. The electron affinity used in this study was


Table 2. The list of the descriptors used∗.

No. Symbol Description Unit

1. nCla Total number of Cl atoms —2. nClalphaa Number of Cl atoms in alpha positions —3. nClbetaa Number of Cl atoms in beta positions —4. nClp1a Number of Cl atoms present in the first aromatic ring —5. nClp2a Number of Cl atoms present in the second aromatic ring —6. CCC(1–8)b Valence angle between C1 and C8 Degree7. CCC(4–5)b Valence angle between C4 and C5 Degree8. Db Dipole moment Debye9. Ab Mean polarizability calculated from elements αxx, αyy, i αzz of diagonalized tensor A3

10. MaxQ+b Maximal positive Mulliken charge —11. MaxQ-b Maximal negative Mulliken charge —12. HOMOb Energy of the highest occupied molecular orbital Hartree13. LUMOb Energy of the lowest unoccupied molecular orbital Hartree14. Hardb Molecular hardness Hartree15. CHBBb Hydrogen bonding basicity Hartree × 10−3

16. CHBAb Hydrogen bonding acidity Hartree × 10−3

17. IPb Ionization potential eV18. EAb Electron affinity eV19. Etb Total energy of the molecule Hartree20. dHb Standard enthalpy of formation kJ mol−1

21. dGb Gibbs free energy of formation kJ mol−1

22. Cvb Heat capacity (v = const.) kJ mol−1

23. Sb Entropy J mol−1 K−1

24. MR Molecular refraction A3

25. MVolb Molar volume26 SASwc Solvent accessible molecular surface area in water A2

27. SAVwc Solvent accessible molecular volume in water A3

28. TEESolwc Total electrostatic energy of solvatation in water Hartree29. PolSSwc Polarized solute – solvent interaction energy in water kJ mol−1

30. CEwc Energy of cavitation in water kJ mol−1

31. DEwc Dispersion energy in water kJ mol−1

32. TNEwc Total non-electrostatic energy of solvatation in water kJ mol−1

33. SASod Solvent accessible molecular surface area in octanol A2

34. SAVod Solvent accessible molecular volume in octanol A3

35. TEESolod Total electrostatic energy of solvatation in octanol Hartree36. PolSSod Polarized solute – solwent interaction energy in octanol kJ mol−1

37. CEod Energy of cavitation in octanol kJ mol−1

38. DEod Dispersion energy in octanol kJ mol−1

39. TNEod Solvent accessible molecular volume in octanol kJ mol−1

40. T(Cl-Cl)a Sum of topological distances between Cl..Cl —

aTopological descriptor; bquantum-chemical descriptor calculated in vaccuo (B3LYP/6–311++G∗∗); cquantum-chemical descriptor calculated inwater (PCM model, B3LYP/6–311++G∗∗ ); dquantum-chemical descriptor calculated in octanol (PCM model, B3LYP/6–311++G; dielectricconstant εoct = 10.3, solvent radius r = 3.250 A); ∗A data matrix presenting values of 40 molecular descriptors calculated for the 75 possible CNcongeners is available from the corresponding author.

calculated as the difference between the energy of molecularanion and the corresponding neutral molecule. Thermody-namic descriptors were calculated based on frequency anal-ysis using the algorithm proposed by Ochterski.[37] Topo-logical descriptors, calculated using DRAGON softwareincluded: total number of Cl atoms (nCl), number of Clatoms in alpha and beta positions (nClalpha and nClbeta),number of chlorine atoms in the first and the second aro-matic ring (nClp1 and nClp2), hydrogen-bonding basicity

and acidity (CHBB and CHBA), sum of topological dis-tances between chlorine atoms (T(Cl-Cl).[38,39]

In the second phase of the study, autoscaling was usedto make the contribution of each of the 40 variables equalin the final model. Internal correlations between descrip-tors and class homogeneity were investigated by use of aninter-correlation matrix and principal component analysis(PCA), which is a standard chemometrical tool used to re-duce redundancy of the correlated parameters.[40−42]

578 Puzyn et al.

During the third phase, predictive relationships betweenthe structure represented by molecular descriptors and theREP values determined in the H4IIE and H4IIE-luc assayswere investigated. The predictive relationships were devel-oped from the data in the training set and the predictivepower was assessed by use of the data in the validation set.The predictive relationships were based on artificial neuralnetwork (ANN) models, followed by optimization of thenumber and composition of input variables. The optimiza-tion was carried out by use of a genetic algorithm (GA). Be-cause the both mathematical procedures are complicated,here we present only simplified descriptions.

The artificial neural network (ANN) technique is basedon a mathematical imitation of the functioning of the mam-malian nervous system. Each of the artificial neurons is asummation of the weighted input signals. The neuron pro-cesses the information using the transformation functionand results in a final signal that is transferred to the otherneural cells. In this way, signals are transferred and pro-cessed though the net and artificial neural networks areable to model even very complicated and non-linear phe-nomena. Before predictions begin, the neural network mustfirst be “trained”. During training, signal weights connect-ing to individual input signals are matched. Because of theissue of “overfitting,” the networks developed in our studywere trained using only data in the data set designated asthe training set. Simultaneously, we monitored the error ofprediction by use of the empirical results in the validationdata set. The training process was continued if both theerror of prediction in the based on the training set and val-idation set was decreasing. The process was stopped whenthe error in the validation set increased significantly. Neuralnetworks used in this research were trained by use of twosupervised learning techniques the back-propagation (BP)and coupled gradient algorithm (CG).[43,44]

The second artificial intelligence technique, a genetic al-gorithm, solves optimization problems by use of an evo-lutionary process resulting in a best (fittest) solution (sur-vivor). The mathematical strategy is based on the principlesof Darwinian evolution theory. The algorithm starts withan initial “population” that represents a set of possible so-lutions given by numerically expressed “chromosomes.” Inthe case of variable selection, each “chromosome” is as-signed a string of 0 and 1 values that indicates if an in-dependent variable is included in the model or not. Thefirst set of “chromosomes” (first “population”) is selectedrandomly. Solutions from the first “population” are recom-bined with each other, and the result of this “crossing-over”creates a new “population.” Solutions from the new “pop-ulation” characterized by the best fitness, according to the“swindling roulette rulel,” are more likely (have a greaterprobability) to reproduce. From time to time a “mutation”operator is included, numerically by exchanging of 0 to 1at randomly selected “chromosomes” in the “population”.This procedure was repeated until one of the conditionswas met: (i) after finite number of iteration or (ii) until the

number of the same chromosomes in the population ex-ceeded a threshold of 60%. Controlling parameters of thealgorithm were set as follows: the number of chromosomesin each generation was 100; the maximal number of gener-ations was 100; crossing-over coefficient was 0.3; mutationcoefficient was set as 1. The neural networks had been train-ing by means of the back-propagation (BP) method duringthe first 50 epochs. After them, the learning process wascontinued using coupled gradient algorithm (CG).[45]

The error of predictions in the training and validation setswere expressed as RMSEt (root mean square error of train-ing) and RMSEv (root mean square error of validation),respectively. The values of both errors were calculated fromEquation 1.

RMSE =√∑n

i=1(yi − yi)2

n(1)

where: yi—ith estimated value of the dependent variable(REP); yi—ith observed (empirically measured) value ofthe dependent variable; n-the number of compounds in thetraining or validation set, respectively.

REP values based on the H4IIE and H4IIE-luc assayswere estimated for each of 75 CNs, including the con-geners for which no REP values were available. The pre-dicted results were then compared not only to empiricalresults, but also to results previously predicted with otherQSAR models. Based on the sets of descriptors selectedby the GA, we also inferred potential mechanisms of CN-AhR binding, and determined that this is the key deter-minant of the relative potencies of CN acting through theAhR-mediated mode of toxic action. Additionally, the first-ever toxic equivalency factors (TEFs) were proposed for allcongeners.

Results and discussion

Molecular descriptors

This study confirmed applicability of quantum-chemicaldescriptors calculated at the level of B3LYP/6–311++G∗∗in such QSAR studies dedicated on a set of structurallysimilar compounds (congeners), like chloronaphthalenes.It is because, in case of each descriptor (i.e., dipole mo-ment), the standard deviation of its values calculated forall 75 congeners were always about 3 times greater than theabsolute error of calculation (i.e. dipole moment) by theB3LYP/6311++G∗∗ method. In other words, applicationof this quantum-mechanical method resulted in descriptorsvery accurate discriminating relatively small differences invalues of the descriptor (i.e., dipole moment) between con-geners. In effect, REP values could be effectively predictedfrom the molecular descriptor applied. A data matrix pre-senting values of 40 molecular descriptors calculated forthe 75 possible CN congeners is shown in the Appendix.


Fig. 1. A projection of the molecular feature space on the plane restricted by the first (VW1) and the third (VW3) rotated factor.

Principal component analysis (PCA) used for multidi-mensional visualization of these data confirmed their ho-mogeneity. In the linear map, which is a projection of themolecular feature space on the plane restricted by the firstand the third rotated factor (after VARIMAX rotation)(Fig. 1), those CNs, which are the most toxic in vivo andhad the greatest REP values based on the H4IIE and H4IIE-luc assays are grouped in the top-right corner on the plot.The first varivector (x-axis, V1), followed by the loadingvalues of individual descriptors (data not shown), can beinterpret as the size of a molecule. The value of V1 dis-tinguished the homologue groups of chloronaphthalenes.The third factor (y-axis, V3), which is influenced mainlyby the ionization potential and presence of chlorine atomsin beta positions, separated CNs inside individual homo-logue groups. This result suggests that ionization potentialas much as the number of chlorine atoms in beta positionsseems to be an appropriate molecular parameter to pre-dict REP values of CNs acting through an AhR-mediatedmechanism of action.

Predicted REP values based on the H4IIE assay

The three-layer architecture of the best network chosenfrom the final generation is presented (Fig. 2). The modelis characterized by relatively low values of the root meansquare errors of prediction in the training and validationsets (RMSEt = 0.253 and RMSEv = 0.267), respectively.

An additional important parameter characterizing the net-work was the quotient of the standard deviation of the resid-uals (se) and responses of the model (sy). The values of thequotient se/sy were 0.17 and 0.14 for the training and vali-dation sets, respectively. Because of the fact that these valuesare near 0, the network characterizes by good quality andexplains a significant part of the information in the data set.A strong correlation was observed between the emperical(measured in vitro) and predicted REP values of CNs asdetermined in the H4IIE bioassay (Fig. 3). The correlationcoefficient “r” for the training set was 0.985, while that of

Fig. 2. Architecture of the artificial neural network used for pre-diction of REP values based on the H4IIE (EROD) assay.

580 Puzyn et al.

Fig. 3. A plot of predicted vs. observed (experimental) REP values based on the H4IIE (EROD) assay.

the validation set was 0.991. These observations confirmthat the predictive relationships developed to predict REPvalues from the H4IIE bioassay were accurate.

Predicted REP values based on the H4IIE-luc assay

The artificial neural network selected to predict REP val-ues based on the H4IIE-luc assay (Fig. 4) were charac-terized by RMSEt = 0.230 and RMSEv = 0.180. Similarly,values of the quotient se/sy were 0.14 for both, the train-

Fig. 4. Architecture of the artificial neural network used for pre-diction of REP values based on the H4IIE-luc assay.

ing and the validation set. In this case, there was also highcorrelation observed between in silico and in vitro results.The correlation coefficients were rt = 0.990 for the trainingand rv = 0.990 for the validation set, respectively. The plotof observed vs. predicted values of the response in the lu-ciferase bioassay is presented (Fig. 5). All of these featuresqualified the network as the predictive relationship able toaccurately estimate REP values based on H4II-luc assay forchloronaphthalenes.

Correlation between predicted REP values

The relationship between the predicted REP data sets wasassessed by plotting the predicted REP values for each con-gener (Fig. 6). Congeners 65, 71, 72, 74 and 75 were not in-cluded in this analysis since their predicted values for REPH4IIE-luc did not appear to be accurately predicted by themodel. When the predicted REP values for the differentcongeners were compared, the general trends in the REPwith increasing congener number were similar for the twoassay systems indicating that both systems, are able to ac-curately predict the relative potency of the different con-geners. However, in general the potencies predicted usingthe H4IIE data set were lower, with higher REP values, thanthose predicted by the H4IIE-luc data set.

Development of TEF values

For use in risk assessment of chemicals active at the Ah-receptor the TEF approach has proven very effective. The


Fig. 5. A plot of predicted vs. observed (experimental) REP values based on the H4IIE-luc assay.

TEF value for each chemical relates its biological potencyto that of the most potent agonist of the receptor, 2,3,7,8-TCDD. To date TEF values have been defined for themost active chemicals PCDDs PCDFS and PCBs.[18] De-velopment of TEFs for other compounds will allow forassessment of the relative toxicological contributions ofeach compound of class to the overall toxicity of chemi-cal mixtures.

To develop TEF values for CNs we selected the high-est predicted REP value for each compound and expressed

Fig. 6. Comparison of Predicted REP values for different PCNcongeners. H4IIE = filled squares and dashed line, H4IIE-luc =empty squares and solid line. Lines are linear best-fit.

that potency relative to 2,3,7,8-TCDD. The TEF valueswere then rounded to the next highest order of magnitudeto simplify the TEF (Table 3). While TEF values derivedfrom the two assay systems were in general agreement insome cases TEFs were different by greater than an order ofmagnitude. To ensure the protective nature of the TEFs thelargest of the two TEFs was selected in these cases.

The REP values determined for the CNs, either by in vitrobioassay or the predicted values of the CNs were compa-rable to REP values reported by other researchers and alsofor other compounds such as non- and mono-ortho chloro-biphenyls, which express AhR-mediated activity.[15,17,30,31]

The earlier predicted REP values were developed by use of

Table 3. Proposed TEF values, telative to 2,3,7,8-TCDD for PCNcongeners.

Congeners TEF

75∗ 0.166 70 74∗ 73 67 0.0168 48∗ 69∗ 64∗ 72∗ 0.00155 56 54 71∗ 50 52 60∗ 41 63 31∗ 51 62 40∗ 45 61∗ 65∗ 0.000118∗ 53∗ 29 38∗ 35∗ 22∗ 42 30 36 58∗ 27 33∗ 44 9∗ 23∗ 57

37 34 21∗0.00001

26 13∗ 28∗ 49 43∗ 39 11∗ 17 32∗ 59 12 14∗ 15 16 10∗47∗ 6∗ 19∗ 3 20 24 2∗ 5∗ 4

0.000001

1 8 7 25 46 1 × 10−07

∗Indicates greater than 1 order of magnitude of uncertainty between assaysystems. The greater of the two values was used for the TEF.

582 Puzyn et al.

principal component regression or discriminant analysis.The predictive models used quantum-chemical descriptorscalculated at the semi-empirical PM3 or B3LYP densityfunctional level with 6–31G* basis set. In the earlier studiesCN congeners exhibiting measurable REP values were nos.47, 50, 51, 52, 54, 60, 63, 64, 66, 67, 68, 69, 70 and 74 or48, 54, 56, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74 and75. When the GA-ANN model was applied, two additionalCNs, nos. 55 and 62, were identified.[30,31] The GA-ANNmodel used in those studies utilizes molecular descriptorscalculated at the highest level of quantum-mechanical the-ory and are based on a larger data set, than other QSARapproaches. Both of the models that were applied previ-ously were cross-validated but not externally validated.[30,31]

Therefore the predictions made by use of the GA-ANNmodel are considered less reliable. The differences betweenthe observed and predicted REP values were much less inthe current study than in previous predictions of REP val-ues for CNs (Table 1). This observation confirms that im-plementation of the GA-ANN technique is useful for pre-dicting REPs for AhR-ligands.

Based on the battery of descriptors selected by GA thatwere used in the final predictive relationships some general-ities about the more predictive molecular properties of REPvalues of CNs can be made. These seem to be three primaryclasses of descriptors that were useful in predicting REPvalues. One was size and volume of a molecule. The sec-ond (represented by CCC(4-5) is related to the planarityof a molecule, while the third group represents descrip-tors related to the substitution pattern of chlorine atoms.Those congeners, which have more chlorine atoms in β posi-tions (2,3,6 and 7), which are characterized with the great-est ionization potentials, exhibit the greatest REP values.Substitution pattern of chlorine atoms also determines thedistribution of the partial Mulliken charges. It appears forthis analysis that interactions of chloronaphthalenes withthe AhR are affected by the following three primary fac-tors: size of the molecule, steric interactions and, electro-static interactions, which seems to be the most importantparameter.

In our studies REP values determined by use of the twoassays were similar. This observation is similar to the re-sults of other studies that have observed strong correla-tions between REP values from the H4IIE and H4IIE-lucassays.[46] However, there were some differences between theREP values determined for individual congeners by use ofthe H4IIE and H4IIE-luc assays. The H4IIE assay, whichuses changes in expression of the endogenous reporter gene(CYP-1 A) that codes for EROD activity is standard andone of the most used assays to determine the potency ofindividual AhR-active compounds and mixtures.[20] Whilethe REP values based on the H4IIE and H4IIE-luc as-says, were similar for CNs they can be different for othercompounds, such as PCBs, can inhibit 7-ethoxyresorufinO-deethylase, which leads to lesser induction.[46,47] Also,

the H4IIE assay is sensitive to oxidative stress and resultsare dependent on the species or cell type [47]. Since theH4IIE-luc assay is not based on EROD activity it does nothave these limitations. It is faster and not as sensitive toinhibition.

The relatively great REP values predicted for CNsnos. 74 and 75 based on the results of the H4IIE assayare not in agreement with other observations. Although,these congeners have been reported to be toxic, basedon our understanding of the molecular descriptors thatpredict REP values these compounds should not exhibitsuch great REP values of congeners nos. 66, 67, 70 and73.[48] The most probable reason of this likely artefact isextrapolation (prediction outside of the predictive rela-tionship’s domain). This extrapolation was necessary dueto the lack of experimental data for the more chlorinatedchloronaphthalene congeners.

TCDD Equivalency Factors (TEFs) are consensus valuesderived from studies of several species and or end-points.Collection of this information for the complete set of 75possible CNs for which no in vivo or in vitro information iscurrently available would be time-consuming and costly. Es-timated REPs values, such as those reported here were usedto develop preliminary TEFs for chloronaphthalenes. Al-though it is generally preferable to develop TEFs based on avariety of in vitro and in vivo endpoints the TEFs presentedhere represent a first approximation of values for CNs.[18]

These TEFs are of particular relevance for the compari-son of toxicological contributions from the different CNcongeners and the additive toxicity of different CN mix-tures. In addition, during development of the TEFs valueswere rounded to the next highest order of magnitude mak-ing the TEFs protective rather than predictive. The TEFsdeveloped ranged to values as low as 1 × 10−7, which isconsiderably lower than values applied to PCDD/Fs andPCBs. While such low TEF values may seem toxicologi-cally irrelevant it needs to be remembered that PCNs havethe potential to occur at environmental concentrations sev-eral orders of magnitude greater than PCDD/Fs. Thereforeeven PCNs with relatively low TEFs may be toxicologicallyrelevant at environmental concentrations when comparedto PCDD/Fs. Even so it may simply assessment somewhatto group all congeners together that have a TEF of 1 × 10−6

or less and give them a TEF of 1 × 10−6, thereby equatingppt of PCDD/F to ppm of PCNs.

Acknowledgments

This study was supported by the Ministry of Educationand Science under Grant no. KBN 1128/T09/2003/24 andDS/8250-4-0092-6. Computations were conducted usingcomputers in the Academic Computer Center in GdanskTASK. Dr. Tomasz Puzyn is the recipient of a fellowshipfrom the Foundation for the Polish Science.


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14

(Con

tinu

edon

next

page

)

587

AP

PE

ND

IX.V

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.171

516

3.84

54−1

2.13

3615

1.71

1812

19−1

.63

174.

47−3

1.13

143.

3972

7.94

1704

.93

−176

4.79

30−0

.334

716

7.90

39−1

2.09

1815

5.85

4014

20−1

.55

176.

98−3

1.46

145.

6073

7.79

1726

.74

−176

4.79

41−0

.209

217

0.12

14−1

2.17

5415

7.90

4216

21−2

.05

176.

98−3

1.34

145.

6973

7.72

1726

.63

−176

4.79

39−0

.418

417

0.07

96−1

2.13

3615

7.98

7816

22−2

.93

172.

72−3

1.17

141.

5971

9.39

1687

.09

−176

4.78

05−0

.711

316

6.23

03−1

2.13

3615

4.09

6714

23−2

.55

170.

08−3

1.05

139.

0870

9.29

1664

.59

−176

4.77

87−0

.502

116

4.01

28−1

2.13

3615

1.87

9214

24−1

.76

174.

56−3

1.17

143.

4372

8.29

1705

.74

−176

4.79

36−0

.292

916

7.98

76−1

2.09

1815

5.89

5816

25−2

.89

174.

77−3

1.13

143.

6472

8.01

1705

.81

−176

4.79

06−0

.711

316

8.11

31−1

2.09

1815

6.02

1414

26−2

.80

177.

19−3

1.51

145.

7373

7.49

1726

.66

−176

4.79

11−0

.502

117

0.20

51−1

2.21

7315

7.98

7816

27−2

.34

179.

16−3

3.81

145.

3973

9.54

1760

.46

−222

4.39

79−0

.627

617

0.62

35−1

3.09

5915

7.52

7622

28−2

.09

181.

71−3

3.30

148.

4575

0.72

1785

.04

−222

4.40

30−0

.502

117

2.92

47−1

3.01

2215

9.91

2526

29−2

.09

184.

26−3

3.68

150.

6276

0.57

1806

.75

−222

4.40

41−0

.334

717

5.10

04−1

3.13

7816

2.00

4529

30−2

.59

184.

18−3

3.51

150.

7176

0.36

1806

.34

−222

4.40

40−0

.585

817

5.05

86−1

3.05

4116

2.00

4528

31−3

.64

179.

66−3

3.35

146.

3674

1.15

1764

.42

−222

4.38

87−0

.962

317

1.04

19−1

3.05

4115

7.94

6025

32−2

.09

179.

79−3

3.35

146.

4874

2.68

1767

.60

−222

4.39

25−0

.460

217

1.16

74−1

3.05

4115

8.11

3427

33−1

.26

184.

26−3

3.51

150.

7976

1.61

1808

.67

−222

4.40

72−0

.167

417

5.18

41−1

3.01

2216

2.13

0030

34−1

.30

184.

22−3

3.51

150.

7576

1.47

1808

.34

−222

4.40

74−0

.209

217

5.14

22−1

3.01

2216

2.13

0029

35−2

.34

179.

54−3

3.35

146.

2374

1.61

1764

.91

−222

4.39

09−0

.543

917

0.95

82−1

3.05

4115

7.90

4226

36−1

.88

181.

84−3

3.56

148.

3275

1.11

1785

.88

−222

4.40

46−0

.334

717

3.00

84−1

3.13

7815

9.87

0630

(Con

tinu

edon

next

page

)

589

AP

PE

ND

IX.V

alue

sof

mol

ecul

arde

scri

ptor

sno

s.31

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tinu

ed)

2930

3132

3334

3536

3738

3940

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Ew

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NE

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Cl)

37−1

.34

184.

14−3

3.51

150.

6776

1.19

1807

.72

−222

4.40

75−0

.209

217

5.10

04−1

3.05

4116

2.04

6329

38−2

.43

179.

58−3

3.43

146.

1974

1.72

1765

.14

−222

4.39

16−0

.502

117

1.00

01−1

3.09

5915

7.90

4228

39−2

.76

184.

43−3

3.56

150.

9276

1.28

1808

.61

−222

4.40

51−0

.627

617

5.26

78−1

3.05

4116

2.21

3730

40−2

.93

182.

17−3

3.56

148.

6675

1.85

1787

.77

−222

4.39

34−0

.669

417

3.21

76−1

3.09

5916

0.12

1729

41−4

.35

179.

33−3

3.43

145.

9873

9.95

1761

.52

−222

4.38

87−1

.171

517

0.74

90−1

3.09

5915

7.65

3126

42−0

.63

186.

56−3

3.51

153.

0977

1.54

1830

.14

−222

4.41

02−0

.041

817

7.23

42−1

2.97

0416

4.26

3830

43−1

.59

182.

21−3

3.39

148.

8775

3.03

1789

.89

−222

4.39

62−0

.292

917

3.34

31−1

3.01

2216

0.33

0929

44−1

.63

186.

82−3

3.68

153.

2277

1.64

1831

.01

−222

4.40

79−0

.251

017

7.40

16−1

3.05

4116

4.34

7531

45−1

.59

184.

81−3

3.68

151.

2176

3.17

1812

.58

−222

4.39

79−0

.209

217

5.60

25−1

3.09

5916

2.50

6630

46−2

.34

177.

61−3

3.39

144.

3173

3.37

1747

.36

−222

4.37

79−0

.418

416

9.20

10−1

3.55

6215

5.64

4828

47−1

.67

184.

43−3

3.39

151.

0876

2.23

1810

.15

−222

4.40

76−0

.376

617

5.30

96−1

2.97

0416

2.33

9230

48−2

.59

187.

02−3

3.89

153.

1877

1.34

1830

.90

−222

4.40

53−0

.418

417

7.48

53−1

3.47

2516

4.01

2832

49−2

.43

186.

44−3

5.52

150.

9676

3.15

1841

.63

−268

3.99

90−0

.585

817

5.68

62−1

3.97

4616

1.75

3442

50−1

.42

191.

17−3

5.69

155.

5678

3.25

1885

.86

−268

4.01

59−0

.251

017

9.91

20−1

3.93

2716

5.97

9346

51−1

.55

191.

42−3

5.69

155.

7778

4.11

1888

.01

−268

4.01

73−0

.292

918

0.12

12−1

3.93

2716

6.14

6648

52−0

.84

193.

76−3

5.69

158.

1179

4.24

1909

.90

−268

4.02

01−0

.083

718

2.21

32−1

3.84

9016

8.32

2348

53−1

.92

189.

20−3

5.52

153.

7277

4.78

1867

.42

−268

4.00

41−0

.418

417

8.11

29−1

3.93

2716

4.22

2046

54−1

.97

194.

05−3

5.86

158.

2479

4.38

1910

.94

−268

4.01

79−0

.334

718

2.38

06−1

3.97

4616

8.40

6050

55−2

.01

191.

79−3

5.86

156.

0278

5.06

1890

.25

−268

4.00

59−0

.334

718

0.41

41−1

4.01

6416

6.39

7748

56−3

.47

189.

03−3

5.73

153.

3477

3.24

1864

.24

−268

4.00

12−0

.836

817

7.94

55−1

4.01

6416

3.92

9146

57−1

.67

189.

28−3

5.73

153.

5977

5.20

1868

.37

−268

4.00

52−0

.292

917

8.19

66−1

4.01

6416

4.18

0248

58−0

.75

191.

88−3

5.69

156.

2378

6.34

1892

.74

−268

4.00

96−0

.041

818

0.49

78−1

3.93

2716

6.60

6948

59−1

.80

187.

02−3

5.61

151.

4676

5.72

1847

.70

−268

3.98

94−0

.334

717

6.18

82−1

3.97

4616

2.17

1846

60−0

.92

194.

10−3

5.69

158.

4579

5.49

1912

.98

−268

4.02

11−0

.125

518

2.46

42−1

3.84

9016

8.61

5250

61−0

.96

191.

63−3

5.69

156.

0278

5.37

1890

.25

−268

4.00

80−0

.125

518

0.28

86−1

3.93

2716

6.39

7748

62−1

.97

188.

87−3

5.69

153.

2277

3.67

1864

.52

−268

4.00

33−0

.418

417

7.86

18−1

3.97

4616

3.88

7346

63−1

.88

195.

81−3

7.87

157.

9979

5.28

1941

.34

−314

3.61

11−0

.376

618

2.63

16−1

4.89

5016

7.73

6669

64−0

.84

198.

53−3

7.82

160.

7580

6.91

1966

.98

−314

3.61

58−0

.083

718

5.05

83−1

4.81

1417

0.24

7070

65−1

.88

193.

59−3

7.74

155.

9078

5.96

1920

.85

−314

3.59

48−0

.376

618

0.62

33−1

4.89

5016

5.77

0167

66−0

.84

201.

04−3

7.91

163.

2281

7.21

1990

.13

−314

3.62

96−0

.083

718

7.23

40−1

4.93

6917

2.33

9073

67−0

.88

201.

00−3

7.82

163.

2681

7.07

1989

.89

−314

3.62

99−0

.083

718

7.19

22−1

4.76

9517

2.42

2673

68−0

.92

198.

91−3

7.82

161.

1380

8.14

1970

.47

−314

3.61

75−0

.125

518

5.30

94−1

4.81

1417

0.49

8072

69−1

.17

198.

53−3

7.82

160.

7580

6.74

1966

.89

−314

3.61

55−0

.209

218

5.01

65−1

4.81

1417

0.20

5171

70−2

.34

198.

70−3

8.03

160.

7580

6.42

1966

.58

−314

3.61

35−0

.418

418

5.10

02−1

4.93

6917

0.16

3372

71−1

.00

196.

48−3

7.87

158.

6679

8.16

1948

.11

−314

3.60

08−0

.083

718

3.17

55−1

4.85

3216

8.28

0571

72−1

.05

196.

36−3

7.87

158.

5379

7.65

1946

.82

−314

3.60

05−0

.125

518

3.05

00−1

4.85

3216

8.19

6870

73−0

.92

205.

43−3

9.96

165.

5682

8.15

2043

.53

−360

3.22

33−0

.083

718

9.70

26−1

5.69

0017

4.01

2610

074

−0.9

620

2.92

−40.

0016

2.97

817.

9020

20.0

8−3

603.

2055

−0.0

837

187.

5269

−15.

7737

171.

7532

9875

−0.8

420

9.33

−42.

1716

7.23

837.

5320

91.6

9−4

062.

8097

−0.0

418

191.

8782

−16.

6523

175.

2259

132

590

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