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Semi-correlations combined with the index of ideality of correlation: a tool to build up model of mutagenic potential

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Abstract

Mutagenicity is the ability of a substance to induce mutations. This hazardous ability of a substance is decisive from point of view of ecotoxicology. The number of substances, which are used for practical needs, grows every year. Consequently, methods for at least preliminary estimation of mutagenic potential of new substances are necessary. Semi-correlations are a special case of traditional correlations. These correlations can be named as “correlations along two parallel lines.” This kind of correlation has been tested as a tool to predict selected endpoints, which are represented by only two values: “inactive/active” (0/1). Here this approach is used to build up predictive models for mutagenicity of large dataset (n = 3979). The so-called index of ideality of correlation (IIC) has been tested as a statistical criterion to estimate the semi-correlation. Three random splits of experimental data into the training, invisible-training, calibration, and validation sets were analyzed. Two models were built up for each split: the first model based on optimization without the IIC and the second model based on optimization where IIC is involved in the Monte Carlo optimization. The statistical characteristics of the best model (calculated with taking into account the IIC) n = 969; sensitivity = 0.8050; specificity = 0.9069; accuracy = 0.8648; Matthews’s correlation coefficient = 0.7196 (using IIC). Thus, the use of IIC improves the statistical quality of the binary classification models of mutagenic potentials (Ames test) of organic compounds.

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Acknowledgements

APT and AAT are grateful for the contribution of the EU project LIFE-COMBASE (LIFE15 ENV/ES/000416). DL and JL were supported by the NSF CREST Interdisciplinary Nanotoxicity Center Grant # HRD-1547754.

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Authors and Affiliations

  1. Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa 19, Milano, Italy

    Alla P. Toropova & Andrey A. Toropov

  2. Department of Chemistry, Faculty of Medicine, University of Niš, Niš, Serbia

    Aleksandar M. Veselinović & Jovana B. Veselinović

  3. Department of Civil and Environmental Engineering, Interdisciplinary Nanotoxicity Center, Jackson State University, 1325 Lynch St, Jackson, MS, 39217-0510, USA

    Danuta Leszczynska

  4. Department of Chemistry and Biochemistry, Interdisciplinary Nanotoxicity Center, Jackson State University, 1400 J. R. Lynch Street, P.O. Box 17910, Jackson, MS, 39217, USA

    Jerzy Leszczynski

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  1. Alla P. Toropova
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  2. Andrey A. Toropov
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  3. Aleksandar M. Veselinović
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11010_2018_3419_MOESM1_ESM.xlsx

Supplementary material 1 Supplementary materials section contains technical details of the best model: (i) distribution into the training, invisible training, calibration, and validation sets; (ii) observed and predicted values of the categorical endpoint; (iii) domain of applicability; and (iv) correlation weights used to calculated the DCW(T*, N*). (XLSX 346 KB)

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Toropova, A.P., Toropov, A.A., Veselinović, A.M. et al. Semi-correlations combined with the index of ideality of correlation: a tool to build up model of mutagenic potential. Mol Cell Biochem 452, 133–140 (2019). https://doi.org/10.1007/s11010-018-3419-4

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