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Singh K.P.,Academy of Scientific and Innovative Research | Singh K.P.,Indian Institute of Toxicology Research | Gupta S.,Academy of Scientific and Innovative Research | Gupta S.,Indian Institute of Toxicology Research | Basant N.,Kanban Systems Pvt. Ltd.
Chemometrics and Intelligent Laboratory Systems | Year: 2015

The permeability of the molecules through the cultured Caco-2 cells is an established in vitro method for the assessment of the absorption of oral drugs. The computational approach for predicting the cellular permeability of molecules may potentiate the screening of new drugs. In this study, gradient boosted tree (GBT) approach based qualitative and quantitative structure-activity relationship (SAR) models have been established for binary classification (moderate-poor and highly permeable) and permeability prediction of molecules using the Caco-2 cell dataset. The structural diversity of the chemicals and nonlinear structure in the considered data were tested by the similarity index and Brock-Dechert-Scheinkman statistics. The external predictive power of the developed SAR models was evaluated through the internal and external validation procedures recommended in QSAR literature. In complete data, the qualitative SAR model rendered classification accuracy of 99.26%, while the quantitative SAR model yielded a correlation (R2) of 0.917 between the measured and predicted permeability values with the mean squared error (MSE) of 0.08. The results suggest for the appropriateness of the developed SAR models to reliably predict the cellular permeability of diverse chemicals in Caco-2 cells and can be useful tools for initial screening of molecules in the drug development process. © 2014 Elsevier B.V.


Basant N.,Kan Ban Systems Pvt. Ltd. | Gupta S.,Indian Institute of Toxicology Research | Singh K.P.,Indian Institute of Toxicology Research
Journal of Chemical Information and Modeling | Year: 2015

A comprehensive safety evaluation of chemicals should require toxicity assessment in both the aquatic and terrestrial test species. Due to the application practices and nature of chemical pesticides, the avian toxicity testing is considered as an essential requirement in the risk assessment process. In this study, tree-based multispecies QSAR (quantitative-structure activity relationship) models were constructed for predicting the avian toxicity of pesticides using a set of nine descriptors derived directly from the chemical structures and following the OECD guidelines. Accordingly, the Bobwhite quail toxicity data was used to construct the QSAR models (SDT, DTF, DTB) and were externally validated using the toxicity data in four other test species (Mallard duck, Ring-necked pheasant, Japanese quail, House sparrow). Prior to the model development, the diversity in the chemical structures and end-point were verified. The external predictive power of the QSAR models was tested through rigorous validation deriving a wide series of statistical checks. Intercorrelation analysis and PCA methods provided information on the association of the molecular descriptors related to MW and topology. The S36 and MW were the most influential descriptors identified by DTF and DTB models. The DTF and DTB performed better than the SDT model and yielded a correlation (R2) of 0.945 and 0.966 between the measured and predicted toxicity values in test data array. Both these models also performed well in four other test species (R2 > 0.918). ChemoTyper was used to identify the substructure alerts responsible for the avian toxicity. The results suggest for the appropriateness of the developed QSAR models to reliably predict the toxicity of pesticides in multiple avian test species and can be useful tools in screening the new chemical pesticides for regulatory purposes. (Graph Presented). © 2015 American Chemical Society.


Gupta S.,Indian Institute of Toxicology Research | Basant N.,Kanban Systems Pvt. Ltd. | Singh K.P.,Indian Institute of Toxicology Research
Combustion Theory and Modelling | Year: 2015

This study reports linear chemometric methods for identification of explosives and prediction of their characteristic parameters using a set of descriptors derived for 244 chemicals. Linear discriminant analysis and k-means clustering methods were developed for discriminating the ideal, non-ideal, and non-explosives, whereas logistic regression and partial least squares regression methods were developed for predicting the detonation parameters. Classification models yielded misclassification of 10.66 and 9.84% in complete data, and regression models predicted detonation velocity and pressure with correlation values of 0.876, 0.861, 0.879 and 0.836 in complete data. These models can be used for predicting the behaviour of new chemicals. © 2015 Taylor & Francis.


Gupta S.,Indian Institute of Toxicology Research | Basant N.,Kan Ban Systems Pvt. Ltd. | Singh K.P.,Indian Institute of Toxicology Research
SAR and QSAR in Environmental Research | Year: 2015

The hazardous dose of a chemical (HD50) is an emerging and acceptable test statistic for the safety/risk assessment of chemicals. Since it is derived using the experimental toxicity values of the chemical in several test species, it is highly cumbersome, time and resource intensive. In this study, three machine learning-based QSARs were established for predicting the HD50 of chemicals in warm-blooded species following the OECD guidelines. A data set comprising HD50 values of 957 chemicals was used to develop SDT, DTF and DTB QSAR models. The diversity in chemical structures and nonlinearity in the data were verified. Several validation coefficients were derived to test the predictive and generalization abilities of the constructed QSARs. The chi-path descriptors were identified as the most influential in three QSARs. The DTF and DTB performed relatively better than SDT model and yielded r2 values of 0.928 and 0.959 between the measured and predicted HD50 values in the complete data set. Substructure alerts responsible for the toxicity of the chemicals were identified. The results suggest the appropriateness of the developed QSARs for reliably predicting the HD50 values of chemicals, and they can be used for screening of new chemicals for their safety/risk assessment for regulatory purposes. © 2015 Taylor & Francis.


Gupta S.,Academy of Scientific and Innovative Research | Gupta S.,Indian Institute of Toxicology Research | Basant N.,Kanban Systems Pvt. Ltd. | Singh K.P.,Academy of Scientific and Innovative Research | Singh K.P.,Indian Institute of Toxicology Research
SAR and QSAR in Environmental Research | Year: 2015

In this study, structure–activity relationship (SAR) models have been established for qualitative and quantitative prediction of the blood–brain barrier (BBB) permeability of chemicals. The structural diversity of the chemicals and nonlinear structure in the data were tested. The predictive and generalization ability of the developed SAR models were tested through internal and external validation procedures. In complete data, the QSAR models rendered ternary classification accuracy of >98.15%, while the quantitative SAR models yielded correlation (r2) of >0.926 between the measured and the predicted BBB permeability values with the mean squared error (MSE) <0.045. The proposed models were also applied to an external new in vitro data and yielded classification accuracy of >82.7% and r2 > 0.905 (MSE < 0.019). The sensitivity analysis revealed that topological polar surface area (TPSA) has the highest effect in qualitative and quantitative models for predicting the BBB permeability of chemicals. Moreover, these models showed predictive performance superior to those reported earlier in the literature. This demonstrates the appropriateness of the developed SAR models to reliably predict the BBB permeability of new chemicals, which can be used for initial screening of the molecules in the drug development process. © 2015, © 2015 Taylor & Francis.


Basant N.,KanbanSystems Pvt. Ltd. | Gupta S.,Indian Institute of Toxicology Research | Singh K.P.,Indian Institute of Toxicology Research
Journal of Molecular Liquids | Year: 2015

The ionic liquids (ILs) constitute a group of novel chemicals that have potential industrial applications. Designing of safer ILs is among the priorities of the chemists and toxicologists today. Computational approaches have been considered appropriate methods for prior safety assessment of the chemicals. The present study is an attempt to investigate the chemical attributes of a wide variety of ILs towards their inhibitory potential of acetyl cholinesterase enzyme (AChE) through the development of predictive qualitative and quantitative structure-activity relationship (SAR) models in light of the OECD principles. Here, machine learning based cascade correlation network (CCN) and support vector machine (SVM) SAR models were established for qualitative and quantitative prediction of the AChE inhibition potential of ILs. Diversity and nonlinearity of the considered dataset were evaluated. The CCN and SVM models were constructed using simple descriptors and validated with external data. Predictive power of these SAR models was established through deriving several stringent parameters recommended for QSAR studies. The developed SAR models exhibited better statistical confidence than those in the previously reported studies. The models identified the structural elements of the ILs responsible for the AChE inhibition, and hence could be useful tools in designing of safer and green ILs. © 2015 Published by Elsevier B.V.


Gupta S.,Indian Institute of Toxicology Research | Basant N.,Kanban Systems Pvt. Ltd. | Singh K.P.,Indian Institute of Toxicology Research
Environmental Science and Pollution Research | Year: 2015

Safety assessment and designing of safer ionic liquids (ILs) are among the priorities of the chemists and toxicologists today. Computational approaches have been considered as appropriate methods for prior safety assessment of chemicals and tools to aid in structural designing. The present study is an attempt to investigate the chemical attributes of a wide variety of ILs towards their cytotoxicity in leukemia rat cell line IPC-81 through the development of nonlinear quantitative structure-activity relationship (QSAR) models in the light of the OECD principles for QSAR development. Here, the cascade correlation network (CCN), probabilistic neural network (PNN), and generalized regression neural networks (GRNN) QSAR models were established for the discrimination of ILs in four categories of cytotoxicity and their end-point prediction using few simple descriptors. The diversity and nonlinearity of the considered dataset were evaluated through computing the Euclidean distance and Brock-Dechert-Scheinkman statistics. The constructed QSAR models were validated with external test data. The predictive power of these models was established through a variety of stringent parameters recommended in QSAR literature. The classification QSARs rendered the accuracy of >86 %, and the regression models yielded correlation (R2) of >0.90 in test data. The developed QSAR models exhibited high statistical confidence and identified the structural elements of the ILs responsible for their cytotoxicity and, hence, could be useful tools in structural designing of safer and green ILs. © 2015, Springer-Verlag Berlin Heidelberg.


Singh K.P.,Academy of Scientific and Innovative Research | Singh K.P.,Indian Institute of Toxicology Research | Gupta S.,Academy of Scientific and Innovative Research | Gupta S.,Indian Institute of Toxicology Research | Basant N.,KanbanSystems Pvt. Ltd.
RSC Advances | Year: 2014

Ionic liquids (ILs) due to their unique characteristics have attained much importance for future applications, although they may pose environmental risks to aquatic ecosystems that have to be assessed. This paper presents a novel computational approach for estimating the toxicity of ILs in multiple test species of different trophic levels in accordance with the OECD guidelines. Here, ensemble learning based global structure-activity relationship (SAR) models were established for qualitative (two- and four-category) and quantitative toxicity predictions of ILs in Vibrio fischeri and successfully applied to the algae and daphnia species. Diversity and nonlinearity of the considered datasets were evaluated using the Tanimoto similarity index, Kruskal-Wallis and Brock-Dechert-Scheinkman statistics. The gradient boosted tree (GBT) and bagged decision tree (BDT) SAR models were constructed using simple descriptors and validated by stringent statistical tests. In V. fischeri, algae and daphnia data, the classification accuracies rendered by GBT and BDT models were 84.44-100% (two-category) and 92.23-98.74% (four-category), while the two models yielded correlations (R2) of 0.857-0.982 between the measured and predicted toxicity values with mean squared errors of 0.21-0.04. The SARs also identified structural elements of ILs responsible for their toxicities. The successful results obtained in three test species of different trophic levels reveal that the proposed approach can be useful as a screening tool to easily aid, from the early stages of the processes, the design of aquatic environmentally friendly ILs. © 2014 The Royal Society of Chemistry.


Singh K.P.,Academy of Scientific and Innovative Research | Singh K.P.,Indian Institute of Toxicology Research | Singh K.P.,Kanban Systems Pvt. Ltd. | Gupta S.,Academy of Scientific and Innovative Research | And 3 more authors.
Chemical Research in Toxicology | Year: 2014

Pesticides are designed toxic chemicals for specific purposes and can harm nontarget species as well. The honey bee is considered a nontarget test species for toxicity evaluation of chemicals. Global QSTR (quantitative structure-toxicity relationship) models were established for qualitative and quantitative toxicity prediction of pesticides in honey bee (Apis mellifera) based on the experimental toxicity data of 237 structurally diverse pesticides. Structural diversity of the chemical pesticides and nonlinear dependence in the toxicity data were evaluated using the Tanimoto similarity index and Brock-Dechert-Scheinkman statistics. Probabilistic neural network (PNN) and generalized regression neural network (GRNN) QSTR models were constructed for classification (two and four categories) and function optimization problems using the toxicity end point in honey bees. The predictive power of the QSTR models was tested through rigorous validation performed using the internal and external procedures employing a wide series of statistical checks. In complete data, the PNN-QSTR model rendered a classification accuracy of 96.62% (two-category) and 95.57% (four-category), while the GRNN-QSTR model yielded a correlation (R2) of 0.841 between the measured and predicted toxicity values with a mean squared error (MSE) of 0.22. The results suggest the appropriateness of the developed QSTR models for reliably predicting qualitative and quantitative toxicities of pesticides in honey bee. Both the PNN and GRNN based QSTR models constructed here can be useful tools in predicting the qualitative and quantitative toxicities of the new chemical pesticides for regulatory purposes. © 2014 American Chemical Society.


Singh K.P.,Academy of Scientific and Innovative Research | Singh K.P.,Indian Institute of Toxicology Research | Gupta S.,Academy of Scientific and Innovative Research | Gupta S.,Indian Institute of Toxicology Research | Basant N.,KanbanSystems Pvt. Ltd.
Chemosphere | Year: 2015

High concentrations of pharmacological active compounds (PACs) detected in global drinking water resources and their toxicological implications in aquatic life has become a matter of concern compelling for the development of reliable QSTRs (qualitative/quantitative structure-toxicity relationships) for their risk assessment. Robust QSTRs, such as decision treeboost (DTB) and decision tree forest (DTF) models implementing stochastic gradient boosting and bagging algorithms were established by experimental toxicity data of structurally diverse PACs in daphnia using molecular descriptors for predicting toxicity of new untested compounds in multiple test species. Developed models were rigorously validated using OECD recommended internal and external validation procedures and predictive power tested with external data of different trophic level test species (algae and fish). Classification QSTRs (DTB, DTF) rendered accuracy of 98.73% and 97.47%, respectively in daphnia and 84.38%, 85.94% (algae), 78.46% and 79.23% (fish). On the other hand, the regression QSTRs (DTB, DTF) yielded squared correlation coefficient values of 0.831, 0.852 (daphnia), 0.534, 0.556 (algae) and 0.620, 0.637 (fish). QSTRs developed in this study passed the OECD validation criteria and performed better than reported earlier for predicting toxicity of PACs, and can be used for screening the new untested compounds for regulatory purpose. © 2014 Elsevier Ltd.

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