Comparative Assessment of QSAR Models for Aquatic Toxicity

Moreover, one SIDS chemical (Hexadecanoic acid, 2-sulfo-, 1-methyl ester, sodium salt(S155)), not displayed in Williams plot because there was no experimental toxicity value, isout of the applicability domain of the model according to its leverage. This prediction is notreliable.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 including only those SIDS testdata with reliable predictions according to the leverage approach, is satisfactory.N. ext = 25Q 2 ext = 75.06SDEP ext = 0.623The model predictive power is thus strongly reduced by the Y-outliers (2-Cyclohexen-1-one,3,5,5-trimethyl (S18), 2-Propenoic acid, 2-methylpropyl ester (S73) and 2-Propenoic acid,ethyl ester (S117)). If they are removed from the calculation of explained variance (Q 2 ext)and external standard deviation error of prediction (SDEP ext ), because of their suspicioustoxicity values or their possession of additional features, the model predictive powerincreases slightly:N. ext = 22Q 2 ext = 87.10SDEP ext = 0.4584.5 ConclusionsIn conclusion, having checked the model correspondence with the OECD principles it can behighlighted that, for the investigated QSAR model the OECD principles were completelyfulfilled; thus, on the basis of this information, this QSAR model could certainly be regarded assufficiently well developed to be used for regulatory purposes.In fact, it should be noted that the model was developed for a clear endpoint defined on a specificexperimental system; it shows an unambiguous algorithm which ensures the model algorithmtransparency. The applicability domain of the model was defined by the developers and themodel exhibits a satisfactory goodness-of–fit, robustness and predictivity.Finally the model has a mechanistic interpretation being the descriptor used in the modelassociated to predicted endpoint.Moreover the exercise pointed out the importance of identifying properly the model applicabilitydomain when it is applied to make predictions on the SIDS test set.In fact, the applicability domain has to be considered in all three phases of the (Q)SAR lifecycle:in the development to ensure that the domain is defined as broadly as possible, in themodel validation, to verified and eventually refined the domain and in the model application.To apply properly a QSAR model and to identify the subset of reliable predictions provided bythe model its domain has to be investigated.15