Predicting carcinogenicity of diverse chemicals using probabilistic neural network modeling approaches

Gupta, Shikha; Rai, Premanjali

doi:10.1016/J.TAAP.2013.06.029

Title: Predicting carcinogenicity of diverse chemicals using probabilistic neural network modeling approaches

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Abstract

Robust global models capable of discriminating positive and non-positive carcinogens; and predicting carcinogenic potency of chemicals in rodents were developed. The dataset of 834 structurally diverse chemicals extracted from Carcinogenic Potency Database (CPDB) was used which contained 466 positive and 368 non-positive carcinogens. Twelve non-quantum mechanical molecular descriptors were derived. Structural diversity of the chemicals and nonlinearity in the data were evaluated using Tanimoto similarity index and Brock–Dechert–Scheinkman statistics. Probabilistic neural network (PNN) and generalized regression neural network (GRNN) models were constructed for classification and function optimization problems using the carcinogenicity end point in rat. Validation of the models was performed using the internal and external procedures employing a wide series of statistical checks. PNN constructed using five descriptors rendered classification accuracy of 92.09% in complete rat data. The PNN model rendered classification accuracies of 91.77%, 80.70% and 92.08% in mouse, hamster and pesticide data, respectively. The GRNN constructed with nine descriptors yielded correlation coefficient of 0.896 between the measured and predicted carcinogenic potency with mean squared error (MSE) of 0.44 in complete rat data. The rat carcinogenicity model (GRNN) applied to the mouse and hamster data yielded correlation coefficient and MSE of 0.758, 0.71 and 0.760, 0.46, respectively. Themore »« less

Authors:

Gupta, Shikha; Rai, Premanjali ^[1]

Academy of Scientific and Innovative Research, Council of Scientific and Industrial Research, New Delhi (India)

Publication Date:: Tue Oct 15 00:00:00 EDT 2013

OSTI Identifier:: 22285439

Resource Type:: Journal Article

Journal Name:: Toxicology and Applied Pharmacology

Additional Journal Information:: Journal Volume: 272; Journal Issue: 2; Other Information: Copyright (c) 2013 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved.; Country of input: International Atomic Energy Agency (IAEA); Journal ID: ISSN 0041-008X

Country of Publication:: United States

Language:: English

Subject:: 60 APPLIED LIFE SCIENCES; ACCURACY; CARCINOGENS; CLASSIFICATION; DATASETS; HAMSTERS; MICE; NEURAL NETWORKS; PESTICIDES; PROBABILISTIC ESTIMATION; RATS

Citation Formats


                    Singh, Kunwar P., E-mail: kpsingh_52@yahoo.com, Environmental Chemistry Division, CSIR-Indian Institute of Toxicology Research, Post Box 80, Mahatma Gandhi Marg, Lucknow 226 001, Gupta, Shikha, Rai, Premanjali, and Environmental Chemistry Division, CSIR-Indian Institute of Toxicology Research, Post Box 80, Mahatma Gandhi Marg, Lucknow 226 001. Predicting carcinogenicity of diverse chemicals using probabilistic neural network modeling approaches.  United States: N. p., 2013. 
        Web.  doi:10.1016/J.TAAP.2013.06.029.

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                    Singh, Kunwar P., E-mail: kpsingh_52@yahoo.com, Environmental Chemistry Division, CSIR-Indian Institute of Toxicology Research, Post Box 80, Mahatma Gandhi Marg, Lucknow 226 001, Gupta, Shikha, Rai, Premanjali, & Environmental Chemistry Division, CSIR-Indian Institute of Toxicology Research, Post Box 80, Mahatma Gandhi Marg, Lucknow 226 001. Predicting carcinogenicity of diverse chemicals using probabilistic neural network modeling approaches.  United States.  https://doi.org/10.1016/J.TAAP.2013.06.029

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                    Singh, Kunwar P., E-mail: kpsingh_52@yahoo.com, Environmental Chemistry Division, CSIR-Indian Institute of Toxicology Research, Post Box 80, Mahatma Gandhi Marg, Lucknow 226 001, Gupta, Shikha, Rai, Premanjali, and Environmental Chemistry Division, CSIR-Indian Institute of Toxicology Research, Post Box 80, Mahatma Gandhi Marg, Lucknow 226 001. 2013.  
        "Predicting carcinogenicity of diverse chemicals using probabilistic neural network modeling approaches".  United States.  https://doi.org/10.1016/J.TAAP.2013.06.029.

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@article{osti_22285439,

  title        = {Predicting carcinogenicity of diverse chemicals using probabilistic neural network modeling approaches},

  author       = {Singh, Kunwar P., E-mail: kpsingh_52@yahoo.com and Environmental Chemistry Division, CSIR-Indian Institute of Toxicology Research, Post Box 80, Mahatma Gandhi Marg, Lucknow 226 001 and Gupta, Shikha and Rai, Premanjali and Environmental Chemistry Division, CSIR-Indian Institute of Toxicology Research, Post Box 80, Mahatma Gandhi Marg, Lucknow 226 001},

  abstractNote = {Robust global models capable of discriminating positive and non-positive carcinogens; and predicting carcinogenic potency of chemicals in rodents were developed. The dataset of 834 structurally diverse chemicals extracted from Carcinogenic Potency Database (CPDB) was used which contained 466 positive and 368 non-positive carcinogens. Twelve non-quantum mechanical molecular descriptors were derived. Structural diversity of the chemicals and nonlinearity in the data were evaluated using Tanimoto similarity index and Brock–Dechert–Scheinkman statistics. Probabilistic neural network (PNN) and generalized regression neural network (GRNN) models were constructed for classification and function optimization problems using the carcinogenicity end point in rat. Validation of the models was performed using the internal and external procedures employing a wide series of statistical checks. PNN constructed using five descriptors rendered classification accuracy of 92.09% in complete rat data. The PNN model rendered classification accuracies of 91.77%, 80.70% and 92.08% in mouse, hamster and pesticide data, respectively. The GRNN constructed with nine descriptors yielded correlation coefficient of 0.896 between the measured and predicted carcinogenic potency with mean squared error (MSE) of 0.44 in complete rat data. The rat carcinogenicity model (GRNN) applied to the mouse and hamster data yielded correlation coefficient and MSE of 0.758, 0.71 and 0.760, 0.46, respectively. The results suggest for wide applicability of the inter-species models in predicting carcinogenic potency of chemicals. Both the PNN and GRNN (inter-species) models constructed here can be useful tools in predicting the carcinogenicity of new chemicals for regulatory purposes. - Graphical abstract: Figure (a) shows classification accuracies (positive and non-positive carcinogens) in rat, mouse, hamster, and pesticide data yielded by optimal PNN model. Figure (b) shows generalization and predictive abilities of the interspecies GRNN model to predict the carcinogenic potency of diverse chemicals. - Highlights: • Global robust models constructed for carcinogenicity prediction of diverse chemicals. • Tanimoto/BDS test revealed structural diversity of chemicals and nonlinearity in data. • PNN/GRNN successfully predicted carcinogenicity/carcinogenic potency of chemicals. • Developed interspecies PNN/GRNN models for carcinogenicity prediction. • Proposed models can be used as tool to predict carcinogenicity of new chemicals.},

  doi          = {10.1016/J.TAAP.2013.06.029},

  url          = {https://www.osti.gov/biblio/22285439},
  journal      = {Toxicology and Applied Pharmacology},

  issn         = {0041-008X},

  number       = 2,

  volume       = 272,

  place        = {United States},

  year         = {Tue Oct 15 00:00:00 EDT 2013},

  month        = {Tue Oct 15 00:00:00 EDT 2013}

}

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