Gopinath Ghosh

QSTR with extended topochemical atom indices. 2. Fish toxicity of substituted benzenes.

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.

Exploring QSARs with Extended Topochemical Atom (ETA) Indices for Modeling Chemical and Drug Toxicity

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.

QSTR with extended topochemical atom (ETA) indices. 12. QSAR for the toxicity of diverse aromatic compounds to Tetrahymena pyriformis using chemometric tools.

We have developed QSTR models for the toxicity of 384 diverse aromatic compounds to Tetrahymena pyriformis with recently introduced extended topochemical atom (ETA) indices and compared the ETA models with those derived from various non-ETA topological descriptors and also combined set of descriptors encompassing the ETA and non-ETA parameters. The data set was split into test (25% compounds of total data points) and training (remaining 75%) sets based on K-mean clustering technique. Different statistical analyses (factor analysis followed by multiple linear regression (FA-MLR), stepwise regression and partial least squares (PLS)) were performed with the training set compounds to develop QSTR models using the topological descriptors. All the developed models were cross-validated using leave-one-out (LOO) technique. The best models were selected on the basis of predicted R(2) values for test set compounds. The best models (based on external validation) developed from different techniques came from the combined set of descriptors. The above results indicate that the use of ETA descriptors with non-ETA descriptors improved the statistical quality of the non-ETA models. From the best models involving ETA parameters, it is observed that functionality of halogen atoms (hydrophobicity), volume parameter (bulk) and nitrogen containing functionalities (polarity) are important for developing QSTR models for the current data set. This study suggests that ETA parameters are sufficient power to encode chemical information contributing significantly to the toxicity of diverse aromatic compounds to T. pyriformis.

QSTR with extended topochemical atom (ETA) indices. 11. Comparative QSAR of acute NSAID cytotoxicity in rat hepatocytes using chemometric tools

An attempt was made to develop quantitative structure–toxicity relationships (QSTRs) for acute rat hepatocyte cytotoxicity of a series of non-steroidal anti-inflammatory drugs (NSAIDs) with extended topochemical atom (ETA) indices. The ETA models were compared with those developed with a pool of other topological indices. Finally, attempt was made to develop models from the combined pool of topological (ETA and non-ETA) descriptors. The chemometric tools used for model development were multiple linear regression with factor analysis as the variable selection tool, stepwise regression, principal component regression analysis, partial least squares (PLS), genetic function approximation (GFA) and genetic PLS (G/PLS). Use of ETA descriptors along with non-ETA ones increased the statistical quality of the non-ETA models in different techniques except stepwise regression and GFA. The best three models came from GFA, G/PLS and PLS techniques (leave-one-out Q 2 values of 0.871, 0.854 and 0.834, respectively, using combined set of descriptors except for GFA). Use of the ETA parameters suggests that the toxicity increases with bulk and degree of branching. Moreover, heteroatom count and degree of unsaturation are also important for the toxicity.

QSTR with Extended Topochemical Atom Indices. 10. Modeling of Toxicity of Organic Chemicals to Humans Using Different Chemometric Tools

In this communication, we have developed quantitative predictive models using human lethal concentration values of 26 organic compounds including some pharmaceuticals with extended topochemical atom (ETA) indices applying different chemometric tools and compared the extended topochemical atom models with the models developed from non‐extended topochemical atom ones. Extended topochemical atom descriptors were also tried in combination with non‐extended topochemical atom descriptors to develop better predictive models. The use of extended topochemical atom descriptors along with non‐extended topochemical atom ones improved equation statistics and cross‐validation quality. The best model with sound statistical quality was developed from partial least squares regression using extended topochemical atom descriptors in combination non‐extended topochemical atom ones. Finally, to check true predictability of the ETA parameters, the data set was divided into training (n = 19) and test (n = 7) sets. Partial least squares and genetic partial least squares models were developed from the training set using extended topochemical atom indices and the models were validated using the test set. The extended topochemical atom models developed from different statistical tools suggest that the toxicity increases with bulk, chloro functionality, presence of electronegative atoms within a chain or ring and unsaturation, and decreases with hydroxy functionality and branching. The results suggest that the extended topochemical atom descriptors are sufficiently rich in chemical information to encode the structural features for QSAR/QSPR/QSTR modeling.

QSTR with extended topochemical atom (ETA) indices. 13. Modelling of hERG K+ channel blocking activity of diverse functional drugs using different chemometric tools

To accelerate the drug discovery process, early prediction of human ether a-go-go (hERG) K+ channel affinity of drug candidates is becoming an important aspect. We have therefore developed quantitative structure–toxicity relationship models with extended topochemical atom (ETA) indices for hERG K+ channel blocking activity of diverse functional drugs using different chemometric tools like factor analysis followed by multiple linear regression (FA-MLR), stepwise regression and partial least squares. The data set was divided into a training set of 50 compounds and a test set of 17 compounds based on K-means clustering technique. The ETA models were compared with those developed with a pool of other topological indices. Finally, an attempt was made to develop models from the combined pool of topological (ETA and non-ETA) descriptors. It was found that on using ETA parameters along with non-ETA ones, there was a considerable increase in the quality of the models. The best model came from stepwise regression using a combined set of descriptors (Q 2 = 0.546, = 0.619). The ETA model suggests that hERG channel blocking increases with the increase of molecular bulk and electron richness and decreases with the increase of functionalities of the carboxylic acid group and the aliphatic tertiary nitrogen fragment.