Comparative Assessment of QSAR Models for Aquatic Toxicity

5. Estimation of predictive ability by internal validation techniques (cross-validation,bootstrap, response randomization).6. Evaluation of QSAR applicability domains by making predictions of SIDS test data:checking the domain of applicability with respect to descriptor ranges and any structuralrules defining the group of substances for which the models are valid.7. Application of the models to the SIDS chemicals8. Evaluation of predictive performance in terms of explained variance (Q 2 ext) and theprediction reliability (order of magnitude between estimated and experimental data).Predictive performance was assessed for the full set of SIDS substances, and for subsetsbased on different hypotheses about the applicability domain.9. Comparative analysis of the model quality.2. SIDS TOXICITY DATA SELECTIONThe experimental toxicity values were available for 32 SIDS chemicals; interval values wereprovided for 4 chemicals and open intervals (>) for 6 chemicals. All the measured effectconcentrations expressed as “>” were disregarded, since these values were difficult to comparewith QSAR predictions.In order to provide a deeper and more realistic further evaluation/validation of the selectedmodels the AQUIRE (AQUatic toxicity Information REtrieval) database developed by the U.S.EPA Mid-Continent Ecology Division, Duluth, MN (MED-Duluth)(http://www.epa.gov/ecotox/) was investigated to fill in the experimental missing values of theSIDS data.The AQUIRE database provided experimental toxicity values of 25 SIDS missing values. Sincethe database gave more than one value for each chemical the average value was used to fill in thedata gaps. Thus the final integrated SIDS dataset was made of 57 experimental toxicity data outthe 177 SIDS chemicals. The 177 SIDS chemicals investigated in this study, their toxicity interms of LogLC50(mol/l), their logKow values and their mechanism of action are listed in TableI.The mechanism of toxic action (MOA) of the SIDS chemicals was studied and identified bycomparing three classification schemes and developing a consensus classification scheme basedon a majority principle according to which each chemical has been classified belonging to theclass more represented among the classifications compared and following the precautionaryprinciple according to which all chemicals with a MOA differently interpreted by theclassification schemes were classified as potentially specifically reactive chemicals. The detailsof the three classification schemes compared together with the consensus classification schemeare illustrated in the European Commission Report EUR 21749 EN (Pavan, M. et al. 2005).2