Abstract
Aim and Objective: Esters are of great importance in industry, medicine, and space studies. Therefore, studying the toxicity of esters is very important. In this research, a Quantitative Structure–Activity Relationship (QSAR) model was proposed for the prediction of aquatic toxicity (log 1/IGC50) of aliphatic esters towards Tetrahymena pyriformis using molecular descriptors.
Materials and Methods: A data set of 48 aliphatic esters was separated into a training set of 34 compounds and a test set of 14 compounds. A large number of molecular descriptors were calculated with Dragon software. The Genetic Algorithm (GA) and Multiple Linear Regression (MLR) methods were used to select the suitable descriptors and to generate the correlation models that relate the chemical structural features to the biological activities.
Results: The predictive powers of the MLR models are discussed by using Leave-One-Out (LOO) cross-validation and external test set. The best QSAR model is obtained with R2 value of 0.899, Q2 LOO =0.928, F=137.73, RMSE=0.263.
Conclusion: The predictive ability of the GA-MLR model with two selected molecular descriptors is satisfactory and it can be used for designing similar group and predicting of toxicity (log 1/IGC50) of ester derivatives.
Keywords: Molecular descriptors, toxicity of aliphatic esters, GA-MLR, Tetrahymena pyriformis, validation, QSAR models.
[1]
Hansch, C.; Hoekman, D.; Leo, A.; Weininger, D.; Selassie, C.D. Chem-bioinformatics: comparative QSAR at the interface between chemistry and biology. Chem. Rev., 2002, 102(3), 783-812. [http://dx.doi.org/10.1021/cr0102009]. [PMID: 11890757].
[2]
Ren, S.; Kim, H. Comparative assessment of multiresponse regression methods for predicting the mechanisms of toxic action of phenols. J. Chem. Inf. Comput. Sci., 2003, 43(6), 2106-2110. [http://dx.doi.org/10.1021/ci034092y]. [PMID: 14632462].
[3]
Salassie, C.D.; Verma, R.P.; Kapur, S.; Shusterman, A.J.; Hansch, C. QSAR for the cytotoxicity of 2-alkyl or 2,6-dialkyl, 4-X-phenols: the nature of the radical reaction. J. Chem. Soc. Perkin Trans, 2002, 2, 1112-1117. [http://dx.doi.org/10.1039/b201478e].
[4]
Karelson, M.; Lobanov, V.S.; Katritzky, A.R. Quantum-chemical descriptors in QSAR/QSPR studies. Chem. Rev., 1996, 96(3), 1027-1044. [http://dx.doi.org/10.1021/cr950202r]. [PMID: 11848779].
[5]
Platts, J.A. Theoretical prediction of hydrogen bond basicity. Phys. Chem. Chem. Phys., 2000, 2, 3115-3120. [http://dx.doi.org/10.1039/b003026k].
[6]
Hemmateenejad, B.; Mehdipour, A.R.; Miri, R.; Shamsipur, M. Comparative QSAR studies on toxicity of phenol derivatives using quantum topological molecular similarity indices. Chem. Biol. Drug Des., 2010, 75(5), 521-531. [http://dx.doi.org/10.1111/j.1747-0285.2010.00960.x]. [PMID: 20486939].
[7]
Vlaia, V.; Olariui, T.; Vlaia, L.; Butur, M.; Ciubotariu, C.; Medeleanu, M.; Ciubotariui, D. Quantitative structure-activity relationship (QSAR). IV. Analysis of the toxicity of aliphatic esters by means of weighted holistic invariant molecular (WHIM) descriptors. Farmacia, 2009, 57(4), 511-522.
[8]
Blaha, L.; Damborsky, J.; Nemec, M. QSAR for acute toxicity of saturated and unsaturated halogenated compounds. Chemosphere, 1998, 36, 1345-1365. [http://dx.doi.org/10.1016/S0045-6535(97)10020-0].
[9]
Randić, M. The connectivity index 25 years after. J. Mol. Graph. Model., 2001, 20(1), 19-35. [http://dx.doi.org/10.1016/S1093-3263(01)00098-5]. [PMID: 11760000].
[10]
Randi’c, M. On characterization of molecular branching. J. Am. Chem. Soc., 1975, 97, 6609-6615. [http://dx.doi.org/10.1021/ja00856a001].
[11]
Balaban, A.T. Highly discriminating distance-based topological index. Chem. Phys. Lett., 1982, 89, 399-804. [http://dx.doi.org/10.1016/0009-2614(82)80009-2].
[12]
Bonchev, D.; Trinajsti’c, N. Chemical information theory: Structural aspects. Int. J. Quantum Chem. Quantum Chem. Symp, 1982, 16, pp. 463-480.
[13]
Pandith, A.H.; Giri, S.; Chattaraj, P.K. A comparative study of two quantum chemical descriptors in predicting toxicity of aliphatic Compounds towards Tetrahymena pyriformis. Org. Chem. Int., 2010, 2010, 1-17. [http://dx.doi.org/10.1155/2010/545087].
[14]
Schultz, T.W.; Sinks, G.D.; Bearden, A.P. QSARs in aquatic toxicology: A mechanism of action approach comparing toxic potency to Pimephales promelas, Tetrahymena pyriformis, and Vibrio fischeri. In: Comparative QSAR; Devillers, J., Ed.; Taylor and Francis: London, 1998; pp. 52-109.
[15]
Bogaerts, P.; Bohatier, J.; Bonnemoy, F. Use of the ciliated protozoan Tetrahymena pyriformis for the assessment of toxicity and quantitative structure--activity relationships of xenobiotics: comparison with the Microtox test. Ecotoxicol. Environ. Saf., 2001, 49(3), 293-301. [http://dx.doi.org/10.1006/eesa.2001.2074]. [PMID: 11440483].
[16]
Schultz, T.W. Tetratox: Tetrahymena pyriformis population growth impairment endpointa surrogate for fish lethality. Toxicol. Methods, 1997, 7, 289-309. [http://dx.doi.org/10.1080/105172397243079].
[18]
Walter, H.; Consolaro, F.; Gramatica, P.; Scholze, M.; Altenburger, R. Mixture toxicity of priority pollutants at no observed effect concentrations (NOECs). Ecotoxicology, 2002, 11(5), 299-310. [http://dx.doi.org/10.1023/A:1020592802989]. [PMID: 12463676].
[19]
Vighi, M.; Altenburger, R.; Arrhenius, A.; Backhaus, T.; Bödeker, W.; Blanck, H.; Consolaro, F.; Faust, M.; Finizio, A.; Froehner, K.; Gramatica, P.; Grimme, L.H.; Grönvall, F.; Hamer, V.; Scholze, M.; Walter, H. Water quality objectives for mixtures of toxic chemicals: problems and perspectives. Ecotoxicol. Environ. Saf., 2003, 54(2), 139-150. [http://dx.doi.org/10.1016/S0147-6513(02)00047-7]. [PMID: 12550091].
[20]
Moriwaki, H.; Tian, Y.S.; Kawashita, N.; Takagi, T. Mordred: a molecular descriptor calculator. J. Cheminform., 2018, 10(1), 4. [http://dx.doi.org/10.1186/s13321-018-0258-y]. [PMID: 29411163].
[22]
Todeschini, R.; Consonni, V. Molecular Descriptors for Chemoinformatics. Alphabetical Listing, 2nd ed; Wiley-VCH: Weinheim, 2009, Vol. 1, pp. 1-252. [http://dx.doi.org/10.1002/9783527628766]
[23]
Todeschini, R.; Consonni, V. Handbook of Molecular Descriptors; Wiley-VCH: Weinheim, 2000. [http://dx.doi.org/10.1002/9783527613106]
[24]
Bonchev, D.; Trinajsti’c, N. Chemical information theory: Structural aspects. Int. J. Quantum Chem.: Quantum Chem. Symp, 1982, 16, 463-480.
[25]
Weisberg, S. Applied Linear Regression, 3rd ed; John & Sonc, Inc.: Hoboken, 2005. [http://dx.doi.org/10.1002/0471704091]
[26]
Chatterje, S.; Hadi, A.S. Regression Analysis by Example., John Wiley & Sonc. 2006.
[http://dx.doi.org/[http://dx.doi.org/10.1002/0470055464]]
[http://dx.doi.org/[http://dx.doi.org/10.1002/0470055464]]
[28]
Dohoo, I.R.; Ducrot, C.; Fourichon, C.; Donald, A.; Hurnik, D. An overview of techniques for dealing with large numbers of independent variables in epidemiologic studies. Prev. Vet. Med., 1997, 29(3), 221-239. [http://dx.doi.org/10.1016/S0167-5877(96)01074-4]. [PMID: 9234406].
[29]
Leardi.; R. Application of genetic algorithm–PLS for feature selection in spectral data sets. J. Chemometr., 2000, 14, 643-655. [http://dx.doi.org/10.1002/1099-128X(200009/12)14:5/6<643:AID-CEM621>3.0.CO;2-E].
[30]
González, M.P.; Terán, C.; Saíz-Urra, L.; Teijeira, M. Variable selection methods in QSAR: an overview. Curr. Top. Med. Chem., 2008, 8(18), 1606-1627. [http://dx.doi.org/10.2174/156802608786786552]. [PMID: 19075770].
[31]
Pourbasheer, E.; Ahmadpour, S.; Zare-Dorabei, R.; Nekoei, M.M. Quantitative structure activity relationship study of p38a MAP kinase inhibitors. Arab. J. Chem., 2017, 10(1), 33-34. [http://dx.doi.org/10.1016/j.arabjc.2013.05.009].
[32]
Yoo, W.; Mayberry, R.; Bae, S.; Singh, K.; Peter, Q. He.; Lillard, Jr. J.W. A study of effects of multicollinearity in the multivariable analysis. Int. J. Appl. Sci. Technol., 2014, 4, 9-19. [PMID: 25664257].
[33]
Reisfeld, B.; Mayeno, A.N. computational toxicology.. On the
development and validation of QSAR models. Springer:
Science+Business Media.. 2013, 499-529.
[34]
Craney, T.A.; Surles, J.G. Model-Dependent Variance Inflation Factor Cutoff Values. Qual. Eng., 2002, 14, 391-403. [http://dx.doi.org/10.1081/QEN-120001878].
[35]
Gramatica, P. Principles of QSAR models validation: Internal and external. QSAR Comb. Sci., 2007, 26, 694-701. [http://dx.doi.org/10.1002/qsar.200610151].
[36]
Chatterjee, S.; Simonoff, J.S. Handbook of Regression Analysis; John Wiley & Sons, 2013, pp. 81-109. [http://dx.doi.org/10.1002/9781118532843.ch5]
[37]
Basak, S.C. Use of molecular complexity indices in predictive pharmacology and toxicology: A QSAR approach. Med. Sci. Res., 1987, 15, 605-609.
[38]
Basak, S.C. Information theoretic indices of neighborhood complexity and their applications.Topological indices and related descriptors in QSAR and QSPR; Devillers, J; Balaban, A.T., Ed.; Gordon and Breach Science Publishers: Amsterdam, 1999, pp. 563-593.
[39]
Basak, S.C. Mathematical descriptors for the prediction of property, bioactivity, and toxicity of chemicals from their structure: a chemical-cum-biochemical approach. Curr. Comput. Aided Drug Des., 2013, 9(4), 449-462. [http://dx.doi.org/10.2174/15734099113096660041]. [PMID: 24138422].
[40]
Kapur, S.; Shusterman, A.; Verma, R.P.; Hansch, C.; Selassie, C.D. Toxicology of benzyl alcohols: A QSAR analysis. Chemosphere, 2000, 41(10), 1643-1649. [http://dx.doi.org/10.1016/S0045-6535(00)00019-9]. [PMID: 11057692].
[41]
Bundy, J.G.; Morriss, A.W.; Durham, D.G.; Campbell, C.D.; Paton, G.I. Development of QSARs to investigate the bacterial toxicity and biotransformation potential of aromatic heterocylic compounds. Chemosphere, 2001, 42(8), 885-892. [http://dx.doi.org/10.1016/S0045-6535(00)00178-8]. [PMID: 11272910].
[42]
Ren, S.; Frymier, P.D. Estimating the toxicities of organic chemicals to bioluminescent bacteria and activated sludge. Water Res., 2002, 36(17), 4406-4414. [http://dx.doi.org/10.1016/S0043-1354(02)00153-7]. [PMID: 12420944].
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