Considering the importance of quantitative structure-toxicity relationship (QSTR) studies in the field of aquatic toxicology from the viewpoint of ecological safety assessment, fish toxicity of various benzene derivatives has been modeled by the multiple regression technique using recently introduced extended topochemical atom (ETA) indices. The toxicity data have also been modeled using other selected topological descriptors and physicochemical variables, and the best ETA model has been compared to the non-ETA ones. Principal component factor analysis was used as the data preprocessing step to reduce the dimensionality of the data matrix and identify the important variables that are devoid of collinearities. All-possible-subsets regression was also applied on the parameters to cross-check the variable selection for the best model. Multiple linear regression analyses show that the best non-ETA model involves 1chi, ALogP98, and LUMO (energy) as predictor variables and the quality of the relation is as follows: n = 92, Q2 = 0.718, Ra2 = 0.730, R2 = 0.738, R = 0.859, F = 82.8 (df 3, 88), s = 0.340. On the other hand, the best ETA model has the following quality: n = 92, Q2 = 0.865, Ra2 = 0.876, R2 = 0.885, R = 0.941, F = 92.6 (df 7, 84), s = 0.230. The ETA relations showed positive contributions of molecular bulk (size), chloro and hydroxy substitutions in the benzene ring, and the simultaneous presence of methyl and nitro substitutions to the toxicity. Further, the presence of fluoro and ether functionality, amino or nitro functionality in an otherwise unsubstituted ring, and nitro functionality that is ortho to a chloro substituent decreases toxicity. An attempt to use non-ETA descriptors along with ETA ones did not improve the quality in comparison to the best ETA model. Interestingly, the ETA model developed presently for the fish toxicity is better than the previously reported models on the same data set. Thus, it appears that ETA descriptors have significant potential in QSAR/QSPR/QSTR studies, which warrants extensive evaluation.

The experimental determination of toxicological properties of commercial chemicals being costly and time consuming process, there is a need to develop mathematical predictive tool to theoretically quantify such properties. In this background, we have modeled toxicity of nitrobenzene derivatives to Tetrahymena pyriformis using extended topochemical atom (ETA) indices recently introduced by us (Roy and Ghosh, 2003). We have also modeled the toxicity data using other topological descriptors (Balaban J, kappa shape indices, connectivity indices, Wiener index) and two physicochemical variables (AlogP98, MolRef) and compared the ETA models with non-ETA ones. Principal component factor analysis was used as the data-preprocessing step to reduce the dimensionality of the data matrix and identify the important variables that are devoid of collinearities. Multiple linear regression analyses show that the best non-ETA model involves (size), halogen and additional nitro substitutions in the nitrobenzene ring and negative contributions of the substituents like methyl and hydroxymethyl groups to the toxicity. An attempt to use non-ETA descriptors along with the ETA ones slightly improves the quality in comparison to the best ETA model. Interestingly, the ETA model developed by us for the nitrobenzene toxicity is comparable to the previously reported models on the same data set (Estrada et al., 2001;Cronin et al., 1998). Thus, it appears that the ETA descriptors have significant potential in QSAR/QSPR/ QSTR studies, which warrants extensive evaluation.

Development of quantitative structure-activity relationships (QSARs) and quantitative structure-property relationships (QSPRs) has been practiced for prediction of various toxicities and other relevant properties of chemicals including drug candidates to minimize animal testing, cost and time associated with risk assessment and management processes. This communication reviews published reports of QSARs/QSPRs with Extended Topochemical Atom (ETA) indices for modeling chemical and drug induced toxicities and some physicochemical properties relevant to such toxicities. In each study, ETA models have been compared to those developed using various non-ETA models and it was found that the quality of the QSARs involving ETA parameters were comparable to those involving non-ETA parameters. ETA descriptors were also found to increase statistical quality of the models involving non-ETA parameters when used in combination. On the basis of the reported studies, it can be concluded that the ETA descriptors are sufficiently rich in chemical information to encode the structural features contributing to the toxicities and these indices may be used in combination with other topological and physicochemical descriptors for development of predictive QSAR models. Such models may be used for virtual screening and in silico prediction of toxicities, and if appropriately used, these may be proved helpful for regulatory decision support and decision making processes.

The present paper deals with modeling of the acute toxicity of 56 phenylsulfonyl carboxylates to Vibrio fischeri. Principal component factor analysis has been used as the data-preprocessing step for the selection of independent variables for the subsequent multiple regression analysis. The statistical quality of the best model using ETA descriptors is as follows: n 56, Q 2 0.726, R a 2 0.837, R 0.923, F 57.4 (df 5, 50), s 0.186, AVRES 0.136. Attempt has also been made to model the data set with different non-ETA parameters (topological indices including Wiener, Hosoya Z, molecular connectivity, kappa shape, Balaban J and E-State parameters apart from physicochemical parameters like AlogP98, MolRef and H-bond-acceptor) and the best model shows the following quality: n 56, Q 2 0.763, R a 2 0.798, R 0.903,

Quantitative structure-toxicity relationship (QSTR) study has proved to be a valuable approach in ecotoxicity estimations of acute and chronic toxicity to various organisms, and in fate estimations of physical/chemical properties, degradation, and bioconcentration. In this background, we have modeled the inhibition of 41 substituted phenols on germination rate of Cucumis sativus with extended topochemical atom (ETA) indices using different chemometric tools and compared the ETA relations with non-ETA models derived from different topological indices (Wiener W, Balaban J, Hosoya Z, Zagreb, molecular connectivity indices, E-state indices and kappa shape indices) and physicochemical parameters (AlogP98, MolRef, H_bond_acceptor, H_bond_donor, MW, logK ow and E lumo ). Different statistical tools used in this communication are stepwise regression analysis, multiple linear regression with factor analysis as the preprocessing step for variable selection (FA-MLR), multiple linear regression with genetic function approximation (GFA-MLR), partial least squares regression with factor analysis as the preprocessing step for variable selection (FA-PLS), genetic partial least squares (G/PLS) and principal component regression analysis (PCRA). The results show that the best statistical quality along with prediction statistics with ETA descriptors is found in case of GFA-MLR equation. In case of non-ETA descriptors, the best equation is obtained from PCRA considering both equation statistics and predictive ability. When combined descriptors were considered, FA-PLS gave the best equation. The best ETA equation (GFA-MLR) has better leave-one-out Q 2 value than the best non-ETA equation (PCRA). The results suggest that the ETA descriptors are sufficiently rich in chemical information to encode the structural features contributing significantly to the comparative inhibition activity of substituted phenols on germination rate of Cucumis sativus.