Quantitative structure–activity relationship study to predict the antibacterial activity of gemini quaternary ammonium surfactants against Escherichia coli

Ely Setiawan Mudasir Mudasir   

Open Access   

Published:  Jun 19, 2022


Gemini quaternary ammonium surfactants (GQAS) have a unique structure built of two conventional surfactants connected by a spacer group. In previous studies, it has been found that GQAS have potency as antimicrobial agents. Thus, we developed a quantitative structure–activity relationship (QSAR) model to predict the antibacterial activity of GQAS. A dataset containing 57 GQAS with antibacterial activity against Escherichia coli was chosen from the literature. After optimizing all structures of these compounds using the ab initio 6-311G basis sets at the Hartree–Fock level theory, the molecular descriptors were calculated using the Mordred program. The genetic algorithm (GA) and multiple linear regressions (MLR) were used for generating two QSAR models with different splitting techniques. The predictive powers of the obtained models were discussed using the leave-one-out (LOO) cross-validation and external test set. The best GA-MLR models were obtained with reliable value of R2 = 0.891, Q2 LOO = 0.851, lack-of-fit = 0.116, root mean square error (RMSEtrain) = 0.267, R2 test = 0.834, and RMSEtest = 0.269. The GA-MLR methods were used to develop models that possess good predictive ability based on both internal and external validation parameters. The design of new molecules was done, and the antibacterial activity could be predicted using the resulting model with 16 compounds that showed potential as antibacterial agents.

Keyword:     Multiple linear regression molecular descriptors genetic algorithm cationic surfactants gemini quaternary ammonium GA-MLR.


Setiawan E, Mudasir M. Quantitative structure–activity relationship study to predict the antibacterial activity of gemini quaternary ammonium surfactants against Escherichia coli. J Appl Pharm Sci, 2022; 12(07):099–105.

Copyright: © The Author(s). This is an open-access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Abadi RSK, Alizadehdakhel A, Paskiabei ST. A DFT and QSAR study of several sulfonamide derivatives in gas and solvent. J Korean Chem Soc, 2016; 60:225-34. https://doi.org/10.5012/jkcs.2016.60.4.225

Andrzejewska W, Wilkowska M, Chrab?szczewska M, Kozak M. The study of complexation between dicationic surfactants and the DNA duplex using structural and spectroscopic methods. RSC Adv, 2017; 7:26006-18. https://doi.org/10.1039/C6RA24978G

Bari SB, Haswani NG. Design, synthesis and molecular docking study of thienopyrimidin-4(3H)-thiones as antifungal agents. J Saudi Chem Soc, 2017; 21:S264-74. https://doi.org/10.1016/j.jscs.2014.02.011

Brycki B, Szulc A, Koenig H, Kowalczyk I, Pospieszny T, Górka S. Effect of the alkyl chain length on micelle formation for bis(N-alkylN,N-dimethylethylammonium)ether dibromides. Comptes Rendus Chim, 2019; 22:386-92. https://doi.org/10.1016/j.crci.2019.04.002

Bunton CA, Robinson L, Stam MF, Schaak J. Catalysis of nucleophilic substitutions by micelles of dicationic detergents. J Org Chem, 1971; 36:2346-50. https://doi.org/10.1021/jo00815a033

Cassani S, Gramatica P. Identification of potential PBT behavior of personal care products by structural approaches. Sustain Chem Pharm, 2015; 1:19-27. https://doi.org/10.1016/j.scp.2015.10.002

?iri? Zdravkovi? S, Pavlovi? M, Apostlovi? S, Kora?evi? G, Šalinger Martinovi? S, Stanojevi? D, Sokolovi? D, Veselinovi? AM. Development and design of novel cardiovascular therapeutics based on Rho kinase inhibition-in silico approach. Comput Biol Chem, 2019; 79:55-62. https://doi.org/10.1016/j.compbiolchem.2019.01.007

Daina A, Michielin O, Zoete V. SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep, 2017; 7:1-13. https://doi.org/10.1038/srep42717

Devinsky F, Lacko I, Bittererova F, Mlynarcik D. Quaternary ammonium salts XVIII. Preparation and relationship between structure, IR spectral characteristics, and antimicrobial activity of some new bis-quaternary isosters of 1, 5-pentanediammonium dibromides. Chem Pap, 1987; 41:803-14.

Devinsky F, Lacko I, Mlynarcik D, Racansky V, Krasnec L. Relationship Between critical micelle concentrations and minimum inhibitory concentrations for some non-aromatic quaternary ammonium salts and amine oxides. Tenside Deterg, 1985; 22:10-5. https://doi.org/10.1515/tsd-1985-220105

Gadaleta D, Mangiatordi GF, Catto M, Carotti A, Nicolotti O. Applicability domain for QSAR models. Int J Quant Struct Relat, 2016; 1:45-63. https://doi.org/10.4018/IJQSPR.2016010102

Gramatica P, Cassani S, Chirico N. QSARINS-chem: insubria datasets and new QSAR/QSPR models for environmental pollutants in QSARINS. J Comput Chem, 2014; 35:1036-44. https://doi.org/10.1002/jcc.23576

Gramatica P, Chirico N, Papa E, Cassani S, Kovarich S. QSARINS: a new software for the development, analysis, and validation of QSAR MLR models. J Comput Chem, 2013; 34:2121-32. https://doi.org/10.1002/jcc.23361

Kamiya Y, Omura A, Hayasaka R, Saito R, Sano I, Handa K, Ohori J, Kitajima M, Shono F, Funatsu K, Yamazaki H. Prediction of permeability across intestinal cell monolayers for 219 disparate chemicals using in vitro experimental coefficients in a pH gradient system and in silico analyses by trivariate linear regressions and machine learning. Biochem Pharmacol, 2021; 192:114749. https://doi.org/10.1016/j.bcp.2021.114749

Kovalishyn V, Grouleff J, Semenyuta I, Sinenko VO, Slivchuk SR, Hodyna D, Brovarets V, Blagodatny V, Poda G, Tetko IV, Metelytsia L. Rational design of isonicotinic acid hydrazide derivatives with antitubercular activity: machine learning, molecular docking, synthesis and biological testing. Chem Biol Drug Des, 2018; 92:1272-8. https://doi.org/10.1111/cbdd.13188

Melville JL, Hirst JD. TMACC: interpretable correlation descriptors for quantitative structure-activity relationships. J Chem Inf Model, 2007; 47:626-34. https://doi.org/10.1021/ci6004178

Moriwaki H, Tian YS, Kawashita N, Takagi T. Mordred: a molecular descriptor calculator. J Cheminform, 2018; 10:1-14. https://doi.org/10.1186/s13321-018-0258-y

Negm NA, El Farargy AF, Youssif MA, Mohamed S, Said MM. Antibacterial evaluation of cationic surfactants. Househ Pers Care Today, 2014; 9:48-53.

Pérez L, Garcia MT, Ribosa I, Vinardell MP, Manresa A, Infante MR. Biological properties of arginine-based gemini cationic surfactants. Environ Toxicol Chem, 2002; 21:1279-85. https://doi.org/10.1002/etc.5620210624

Piccione D, Mirabelli S, Minto N, Bouklas T. Difficult but not impossible: in search of an anti-candida vaccine. Curr Trop Med Rep, 2019; 6:42-9. https://doi.org/10.1007/s40475-019-00173-2

Prabhakar Y, Rawal R, Gupta M, Solomon V, Katti S. Topological descriptors in modeling the HIV inhibitory activity of 2-aryl-3- pyridylthiazolidin-4-ones. Comb Chem High Throughput Screen, 2005; 8:431-7. https://doi.org/10.2174/1386207054546531

Puzyn T, Leszczynski J, Cronin MTD. Recent advances in QSAR studies. Methods and applications. Springer, Dordrecht, The Netherlands; New York, NY, pp 3 -11, 2010. https://doi.org/10.1007/978-1-4020-9783-6

Roy K, Kar S, Das RN. A primer on QSAR/QSPR modeling: fundamental concepts. SpringerBriefs in Molecular Science, Springer, Cham, Switzerland, 2015. https://doi.org/10.1007/978-3-319-17281-1

Setiawan E, Wijaya K, Mudasir M. Generic QSPR study for predicting critical micelle concentration of gemini cationic surfactants using the online chemical modeling environment (OCHEM). In: AIP Conference Proceedings, 2021a, vol. 2349, pp 020027. https://doi.org/10.1063/5.0051623

Setiawan E, Wijaya K, Mudasir M. QSAR modeling for predicting the antifungal activities of gemini imidazolium surfactants against Candida albicans using GA-MLR methods. J Appl Pharm Sci, 2021b; 11:022-7.

Shukla D, Tyagi VK. Cationic gemini surfactants: a review. J Oleo Sci, 2006; 55:381-90. https://doi.org/10.5650/jos.55.381

Tiwari P, Singh VK. Computer assisted drug designing : quantitative structure activity relationship studies on mono- and bisthiazolium salts having potent antimalarial activity. Int J Sci Res Publ, 2017; 7:213-35.

Tyagi S, Tyagi VK. Novel cationic gemini surfactants and methods for determination of their antimicrobial activity-review. Tenside Surfactants Deterg, 2014; 51:379-86. https://doi.org/10.3139/113.110319

Veerasamy R, Rajak H, Jain A, Sivadasan S, Varghese CP, Agrawal RK. Validation of QSAR models-strategies and importance. Int J Drug Des Discovery, 2011; 2:511-9.

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