Semi-correlations combined with the index of ideality of correlation: a tool to build up model of mutagenic potential
- 71 Downloads
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.
KeywordsSemi-correlation SAR Monte Carlo method Mutagenicity Ames test Index of ideality of correlation
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.
Authors have done equivalent contributions to this work.
Compliance with ethical standards
Conflict of interest
The authors confirm that this article content has no conflict of interest.
- 3.Debnath AK, Debnath G, Shusterman AJ, Hansch C (1992) A QSAR investigation of the role of hydrophobicity in regulating mutagenicity in the ames test: 1. Mutagenicity of aromatic and heteroaromatic amines in Salmonella typhimurium TA98 and TA100. Environ Mol Mutagen 19 (1): 37–52. https://doi.org/10.1002/em.2850190107
- 4.Klopman G, Rosenkranz HS (1994) Approaches to SAR in carcinogenesis and mutagenesis. Prediction of carcinogenicity/mutagenicity using MULTI-CASE. Mutat Res Fund Mol Mech Mut 305 (1): 33–46. https://doi.org/10.1016/0027-5107(94)90124-4
- 6.Debnath AK, Lopez Compadre RL, Shusterman AJ, Hansch C (1992) Quantitative structure-activity relationship investigation of the role of hydrophobicity in regulating mutagenicity in the Ames test: 2. Mutagenicity of aromatic and heteroaromatic nitro compounds in Salmonella typhimurium TA100. Environ Mol Mutagen 19(1):53–70. https://doi.org/10.1002/em.2850190108 CrossRefGoogle Scholar
- 8.Tuppurainen K, Lötjönen S, Laatikainen R, Vartiainen T, Maran U, Strandberg M, Tamm T (1991) About the mutagenicity of chlorine-substituted furanones and halopropenals. A QSAR study using molecular orbital indices. Mutat Res Fund Mol Mech Mut 247(1):97–102. https://doi.org/10.1016/0027-5107(91)90037-O CrossRefGoogle Scholar
- 13.Nendza M, Gabbert S, Kühne R, Lombardo A, Roncaglioni A, Benfenati E, Benigni R, Bossa C, Strempel S, Scheringer M, Fernández A, Rallo R, Giralt F, Dimitrov S, Mekenyan O, Bringezu F, Schüürmann G (2013) A comparative survey of chemistry-driven in silico methods to identify hazardous substances under REACH. Regul Toxicol Pharmacol 66(3):301–314. https://doi.org/10.1016/j.yrtph.2013.05.007 CrossRefGoogle Scholar
- 18.Toropova AP, Toropov AA, Veselinović AM, Veselinović JB, Benfenati E, Leszczynska D, Leszczynski J (2016) Nano-QSAR: model of mutagenicity of fullerene as a mathematical function of different conditions. Ecotoxicol Environ Saf 124:32–36. https://doi.org/10.1016/j.ecoenv.2015.09.038 CrossRefGoogle Scholar
- 20.Kumar A, Chauhan S (2017) Use of the Monte Carlo method for OECD principles-guided QSAR modeling of SIRT1 inhibitors. Arch Pharm. https://doi.org/10.1002/ardp.201600268
- 24.Trinh TX, Choi JS, Jeon H, Byun HG, Yoon TH, Kim J (2018) Quasi-SMILES-Based Nano-quantitative structure–activity relationship model to predict the cytotoxicity of multiwalled carbon nanotubes to human lung cells. Chem Res Toxicol 31(3):183–190. https://doi.org/10.1021/acs.chemrestox.7b00303 CrossRefGoogle Scholar
- 27.Toropov AA, Toropova AP, Benfenati E, Gini G, Leszczynska D, Leszczynski J (2012) Calculation of molecular features with apparent impact on both activity of mutagens and activity of anticancer agents. Anticancer Agents Med Chem 12(7):807–817. https://doi.org/10.2174/187152012802650255 CrossRefGoogle Scholar
- 28.Toropov AA, Toropova AP, Martyanov SE, Benfenati E, Gini G, Leszczynska D, Leszczynski J (2011) Comparison of SMILES and molecular graphs as the representation of the molecular structure for QSAR analysis for mutagenic potential of polyaromatic amines. Chemometr Intell Lab Syst 109(1):94–100. https://doi.org/10.1016/j.chemolab.2011.07.008 CrossRefGoogle Scholar
- 32.Kranthi Kumar K, Uma Devi B, Neeraja P (2017) Integration of in silico approaches to determination of endocrine-disrupting perfluorinated chemicals binding potency with steroidogenic acute regulatory protein. Biochem Biophys Res Commun 491(4):1007–1014. https://doi.org/10.1016/j.bbrc.2017.07.168 CrossRefGoogle Scholar
- 33.Ahlberg E, Amberg A, Beilke LD, Bower D, Cross KP, Custer L, Ford KA, Van Gompel J, Harvey J, Honma M, Jolly R, Joossens E, Kemper RA, Kenyon M, Kruhlak N, Kuhnke L, Leavitt P, Naven R, Neilan C, Quigley DP, Shuey D, Spirkl H-P, Stavitskaya L, Teasdale A, White A, Wichard J, Zwickl C, Myatt GJ (2016) Extending (Q)SARs to incorporate proprietary knowledge for regulatory purposes: a case study using aromatic amine mutagenicity. Regul Toxicol Pharmacol 77:1–12. https://doi.org/10.1016/j.yrtph.2016.02.003 CrossRefGoogle Scholar
- 34.Ford KA, Ryslik G, Chan BK, Lewin-Koh S-C, Almeida D, Stokes M, Gomez SR (2017) Comparative evaluation of 11 in silico models for the prediction of small molecule mutagenicity: role of steric hindrance and electron-withdrawing groups. Toxicol Mech Method 27(1):24–35. https://doi.org/10.1080/15376516.2016.1174761 CrossRefGoogle Scholar
- 37.Ono A, Takahashi M, Hirose A, Kamata E, Kawamura T, Yamazaki T, Sato K, Yamada M, Fukumoto T, Okamura H, Mirokuji Y, Honma M (2012) Validation of the (Q)SAR combination approach for mutagenicity prediction of flavor chemicals. Food Chem Toxicol 50(5):1538–1546. https://doi.org/10.1016/j.fct.2012.02.009 CrossRefGoogle Scholar
- 38.Gouveia DN, Costa JS, Oliveira MA, Rabelo TK, Silva AMDOE, Carvalho AA, Miguel-dos- Santos R, Lauton-Santos S, Scotti L, Scotti MT, Santos MRVD, Quintans-Júnior LJ, Albuquerque Junior RLCD, Guimarães AG (2018) α-Terpineol reduces cancer pain via modulation of oxidative stress and inhibition of iNOS. Biomed Pharmacother 105:652–661. https://doi.org/10.1016/j.biopha.2018.06.027 CrossRefGoogle Scholar