Comparative Assessment of QSAR Models for Aquatic Toxicity

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It has to be pointed out that, while high leverage chemicals in the QSAR model training setreinforce the model itself, the test chemicals with high leverage values greater than thewarning value have unreliable predicted data, being the result of substantial extrapolation ofthe model.Several other SIDS chemicals have unreliable predictions according to their leverage values(1,2,3-Propanetriol (S2), Ethene,chloro (S12), 2-Cyclohexen-1-one,3,5,5-trimethyl- (S18),1,6-Octadien-3-ol, 3,7-dimethyl (S19), 2-Propenamide (S23), 2-Pyrrolidinone, 1-ethenyl(S37), 1,2-Benzenedicarbonitrile (S43), Propanoic acid, 2-methyl-, anhydride (S54), 2-Propenoic acid, 2-ethylhexyl ester (S68), 2-Butenal, 3-methyl (S79), Cyclohexene (S90), 5-Hepten-2-one,6-methyl- (S92), 2-Propanol, 1,1'-oxybis (S93), 1-Propene (S96), 1,6-Octadien-3-ol, 3,7-dimethyl-, acetate (S99), Phenol, 2,6-bis(1,1-dimethylethyl)-4-methyl-(S115), 3,5,9-Undecatrien-2-one, 6,10-dimethyl- (S118), 2-Propenoic acid, butyl ester(S119), 1-Hexadecen-3-ol, 3,7,11,15-tetramethyl- (S126), 1,2,4-Benzenetricarboxylic acid(S127), 5-Isobenzofurancarboxylic acid, 1,3-dihydro-1,3-dioxo (S128), 2-Buten-1-ol, 3-methyl (S129), 1,4-Benzenediamine, N-(1,3-dimethylbutyl)-N'-phenyl- (S133), 1,3,5-Triazine-2,4,6(1H,3H,5H)-trione, 1,3,5-tris(2-hydroxyethyl)- (S135), HCFC 141b (S143),Cyclohexanol, 5-methyl-2-(1-methylethyl)-, [1R-(1alpha,2beta,5alpha)]- (S144), 2-Propenoicacid, 2-(dimethylamino)ethyl ester (S147), Cyclohexanemethanamine, 5-amino-1,3,3-trimethyl- (S149), 1,2,4-Benzenetricarboxylic acid, tris(2-ethylhexyl) ester (S152),Hexadecanoic acid, 2-sulfo-, 1-methyl ester, sodium salt (S155), 2H-Pyran, 3,4-dihydro-2-methoxy (S157), 2-Benzothiazolesulfenamide, N,N-dicyclohexyl- (S159), 2,6-Octadienal,3,7-dimethyl (S161), Benzene, 1,4-dimethyl-2-(1-phenylethyl)- (S163), Benzenepropanoicacid, 3,5-bis(1,1-dimethylethyl)-4-hydroxy-, methyl ester (S165), 1,4-Benzenedicarboxylicacid, bis(2-ethylhexyl) ester (S166), Cyclohexanamine, 4,4'-methylenebis[2-methyl- (S167),Benzene, bis(1-methylethyl)- (S171), Benzene, 1,1'-oxybis-, pentabromo deriv. (S174)).These are not displayed in Williams plot because their experimental toxicity values aremissing. These are outside the applicability domain of the model according to their leverageand thus their predictions are not reliable.Then 22 SIDS chemicals were identified as strong outliers (Formaldehyde (S1), 1,2-Propanediol (S3), Formamide, N,N-dimethyl- (S8), 2-Butanol (S21), 2-Propenoic acid, 2-methyl-, methyl ester (S32), 1,2-Benzenedicarboxylic acid, dibutyl ester (S35), Butanamide,N-(2-methylphenyl)-3-oxo- (S45), 1,2-Ethanediamine (S76), 2,4-Pentanediol, 2-methyl-(S78), 2-Propanol, 1-methoxy (S81), 2-Butyne-1,4-diol (S89), 1,2-Benzenediol (S103), 2,4-Pentanedione (S108), Acetic acid, butyl ester (S110), Phosphoric-acid-tributyl-ester- (S112),2-Propenoic acid, ethyl ester (S117), Acetic-acid-ethyl-ester (S120), 2-Propanol, 1-phenoxy(S132), Phosphonic acid, dimethyl ester (S137), 1-Propanol, 2-phenoxy (S156), Phenol,nonyl- (S169), Phenol, 4-nonyl-, branched (S177). These chemicals are outliers only in theY-response space, since they are inside the X-AD of the model: either their toxicity valuesare wrong or the model is lacking in some additional feature.Evaluation of predictive performanceThe prediction capability of the model in terms of explained variance (Q 2 ext) and externalstandard deviation error of prediction (SDEP ext ), evaluated by including only those SIDS testdata with reliable predictions according to the leverage approach, is satisfactory.26
N. ext = 39Q 2 ext = 48.98SDEP ext = 1.144The model predictive power is strongly reduced by the Y-outliers: Formaldehyde (S1), 1,2-Propanediol (S3), Formamide, N,N-dimethyl- (S8), 2-Butanol (S21), 2-Propenoic acid, 2-methyl-, methyl ester (S32), 1,2-Benzenedicarboxylic acid, dibutyl ester (S35), Butanamide,N-(2-methylphenyl)-3-oxo- (S45), 1,2-Ethanediamine (S76), 2,4-Pentanediol, 2-methyl-(S78), 2-Propanol, 1-methoxy (S81), 2-Butyne-1,4-diol (S89), 1,2-Benzenediol (S103), 2,4-Pentanedione (S108), Acetic acid, butyl ester (S110), Phosphoric-acid-tributyl-ester- (S112),2-Propenoic acid, ethyl ester (S117), Acetic-acid-ethyl-ester (S120), 2-Propanol, 1-phenoxy(S132), Phosphonic acid, dimethyl ester (S137), 1-Propanol, 2-phenoxy (S156), Phenol,nonyl- (S169), Phenol, 4-nonyl-, branched (S177). If these outliers are removed from thecalculation of the explained variance (Q 2 ext) and external standard deviation error ofprediction (SDEP ext ), because of their suspicious toxicity values or their possession ofadditional feature, the model predictive power increases slightly:N. ext = 17Q 2 ext = 89.43SDEP ext = 0.3985.5 ConclusionsIn conclusion, it should be noted that generally, a QSAR model would either aim to have a broadapplicability, sacrificing to some extent the level of predictivity, or the model would aim to havenarrow applicability, but with greater predictivity.Since the model analyzed was intended to be a global model, developed to have a broadapplicability and to make predictions for chemicals acting with different modes of toxic action athe model could exhibit better predictive performance If trained with a wider training dataset. Infact, the a group contribution approach Is expected to provide better results if applied to morediverse training dataset.The atom-type electrotopological state (E-state) indices used as structural parameters to developthe model are attractive theoretical descriptors, because they can be calculated easily, rapidly andare error free, and thus not affected by variability.The investigated QSAR model fulfills the OECD principles; in fact, it was developed for a clearendpoint defined on a specific experimental system; it shows an unambiguous algorithm whichensures the model algorithm transparency. The applicability domain of the model was defined bythe developers and the model exhibits a satisfactory goodness-of–fit, robustness and predictivity.Finally the model has a mechanistic interpretation being the descriptors used in the modelassociated to predicted endpoint.27
Page 3 and 4: EUROPEAN COMMISSIONDIRECTORATE GENE
Page 8 and 9: 7.2 Model comparison by ratio of QS
Page 12 and 13: 5. Estimation of predictive ability
Page 14 and 15: 4. MIXED MODE OF ACTION QSAR4 EVALU
Page 16: modified; SDEC = Standard Deviation
Page 19 and 20: away each chemical was from the PC
Page 21 and 22: Dotplot of Log KowMOA-SetAChECNSENM
Page 23 and 24: 54Model descriptor space (LogKow -
Page 25 and 26: Moreover, one SIDS chemical (Hexade
Page 28 and 29: The following fitness regression pa
Page 30 and 31: The first two chemicals (1-amino-2,
Page 32 and 33: • Internal validation:2The model
Page 34 and 35: Multidimensional scaling0.200.15S1S
Page 38 and 39: 6. TERRAQSAR FHM QSAR 6 EVALUATION6
Page 40 and 41: The TerraQSAR TM FHM program estima
Page 42 and 43: 32• Outlier detection:Residual pl
Page 44 and 45: 8TerraQSAR NN model SIDS prediction
Page 46 and 47: 7. COMPARATIVE ANALYSIS OF THE MODE
Page 48 and 49: factor of 1, 10 and 100, respective
Page 50 and 51: TGD - PN model30N. chemicals2520151
Page 52 and 53: N Model30N. chemicals2520151050< 0.
Page 54 and 55: Mixed model30N. chemicals2520151050
Page 56 and 57: E-State modelN. chemicals3020100< 0
Page 58 and 59: ACKNOWLEDGEMENTSThe authors would l
Page 60 and 61: Kaiser, K.L.E., and S.P. Niculescu.
Page 63 and 64: TABLESTable I - SIDS test data.ID C
Page 65 and 66: Table I - SIDS test data (continued
Page 73 and 74: Table II - Mixed model (QSAR4) trai
Page 75 and 76: Table II - Mixed model (QSAR4) trai
Page 77 and 78: Table IV - QSAR 4 predictions for t
Page 79 and 80: Table IV - QSAR 4 predictions for t
Page 81 and 82: Table V - E-state indices model (QS
Page 83 and 84: Table V - E-state indices model (QS
Page 85 and 86: Table VII - SIDS chemicals not suit
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Table VIII -QSAR 5 predictions for
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Table VIII -QSAR 5 predictions for
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Table IX - TerraQSAR (QSAR6) traini
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Table X -TerraQSAR predictions for
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Table XI - Model performance compar
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APPENDIX I: TERMINOLOGY AND STATIST
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nTSS = ∑( yi − y)i=1This is the
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manipulating X to give the so-calle
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where y ˆi/ idenotes the response
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correlation, i.e. collinearity with
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Mission of the JRCThe mission of th
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Comparative Assessment of QSAR Models for Aquatic Toxicity

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