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Combinatorial Chemistry & High Throughput Screening


ISSN (Print): 1386-2073
ISSN (Online): 1875-5402

Research Article

Activity Evaluation and Selection of Some Classes of Antibiotics with the use of Semi-Empirical Quantum Mechanics and Quantitative Structure- Activity Relationships Approach

Author(s): Piotr Kawczak*, Leszek Bober and Tomasz Bączek

Volume 22, Issue 2, 2019

Page: [97 - 112] Pages: 16

DOI: 10.2174/1386207322666190425144209

Price: $65


Background: A set of β-lactam antibiotics, aminoglycoside antibiotics, and tetracycline antibiotics were proposed and analyzed with the use of Quantitative Structure-Activity Relationships (QSAR) method.

Objective: The characterization of selected antimicrobial compounds in terms of both physicochemical and pharmacological on the basis of calculations of quantum mechanics and possessed biological activity data.

Methods: During the study, Multiple Linear Regression (MLR) supported with Factor Analysis (FA) and Principal Component Analysis (PCA) was made, as the types of proposed chemometric approach; the semi-empirical level of in silico molecular modeling was used for calculations and comparison of molecular descriptors both in a vacuum and in the aquatic environment.

Results: The relationships between structure and microbiological activity enabled the characterization and description of the analyzed molecules using statistically significant descriptors belonging in most cases to different structural, geometric and electronic elements defining at the same time the properties of the studied three different classes of examined antibiotics.

Conclusion: The chemometric methods used revealed the influence of some of the elements of structures examined molecules belonging to main antibiotics classes and responsible for the antimicrobial activity.

Keywords: Antibiotics, molecular modeling, structural analysis, factor analysis, principal component analysis, multiple linear regression.

Antimicrobial Resistance - World Health Organization. (Accessed on February 4, 2019).
Gualerzi, C.O.; Brandi, L.; Fabbretti, A.; Pon, C.L. Antibiotics: Targets, Mechanisms and Resistance; Wiley-VCH Verlag GmbH & Co. KGaA: Weinheim, Germany. , 2014.
Brunton, L.; Chabner, B.; Knollman, B. Goodman & Gilman’s The Pharmacological Basis of Therapeutics, 12th ed; McGraw-Hill Professional: New York, 2011.
Docquier, J.D.; Mangani, S. An update on β-lactamase inhibitor discovery and development. Drug Resist. Updat., 2018, 36, 13-29.
Foster, T.J. Can β-lactam antibiotics be resurrected to combat MRSA? Trends Microbiol., 2019, 1, 26-38.
Liu, X.; Huang, D.; Lai, C.; Zeng, G.; Qin, L.; Zhang, C.; Yi, H.; Li, B.; Deng, R.; Liu, S.; Zhang, Y. Recent advances in sensors for tetracycline antibiotics and their applications. Trends Analyt. Chem., 2018, 109, 260-274.
Wang, Q.; Li, X.; Yang, Q.; Chen, Y.; Du, B. Evolution of microbial community and drug resistance during enrichment of tetracycline-degrading bacteria. Ecotoxicol. Environ. Saf., 2019, 171, 746-752.
Mingeot-Leclercq, M.P.; Glupczynski, Y.; Tulkens, P.M. Aminoglycosides: activity and resistance. Antimicrob. Agents Chemother., 1999, 43, 727-737.
Singh, N.; Chaudhury, S.; Liu, R. AbdulHameed, M.D.M.; Tawa, G.; Wallqvist, A. QSAR classification model for antibacterial compounds and its use in virtual screening. J. Chem. Inf. Model., 2012, 52, 2559-2569.
Benveniste, R.; Davies, J. Structure-activity relationships among the aminoglycoside antibiotics: role of hydroxyl and amino groups. Antimicrob. Agents Chemother., 1973, 4, 402-409.
Cashman, D.J.; Rife, J.P.; Kellog, G.E. Which aminoglycoside ring is most important for binding? A hydropathic analysis of gentamicin, paromomycin, and analogues. Bioorg. Med. Chem. Lett., 2001, 11, 119-122.
Afshar, M.; Prescott, C.D.; Varani, G. Structure-based and combinatorial search for new RNA-binding drugs. Curr. Opin. Biotechnol., 1999, 10, 59-63.
Hermann, T.; Westhof, E. Aminoglycoside binding to the hammerhead ribozyme: a general model for the interaction of cationic antibiotics with RNA. J. Mol. Biol., 1998, 276, 903-912.
Ma, C.; Baker, N.A.; Joseph, S.; McCammon, J.A. binding of aminoglycoside antibiotics to the small ribosomal subunit: A continuum electrostatics investigation. J. Am. Chem. Soc., 2002, 124, 1438-1442.
Vaiana, A.C.; Westhof, E.; Auffinger, P. A molecular dynamics simulation study of an aminoglycoside/A-site RNA complex: Conformational and hydration patterns. Biochimie, 2006, 88, 1061-1073.
Huang, L.; Massa, L.; Karle, J. Drug target interaction energies by the kernel energy method in aminoglycoside drugs and ribosomal A site RNA targets. Proc. Natl. Acad. Sci. , 2007, 104, 4261-4266.
Bober, L.; Kawczak, P.; Bączek, T. QSAR Analysis of Compounds Exhibiting General Anesthetics’ Properties. Lett. Drug Des. Discov., 2012, 9, 595-603.
Belka, M.; Konieczna, L.; Kawczak, P.; Ciesielski, T.; Slawinski, J.; Baczek, T. The chemometric evaluation of antitumor activity of novel benzensulfonamide derivatives based on their Physio-chemical Properties. Lett. Drug Des. Discov., 2012, 3, 288-294.
Bober, L.; Kawczak, P.; Bączek, T. Pharmacological Classification and Activity Evaluation of Furan and Thiophene Amide Derivatives Applying Semi-Empirical ab initio Molecular Modeling Methods. Int. J. Mol. Sci., 2012, 13, 6665-6678.
Belka, M.; Sławinski, J.; Konieczna, L.; Kawczak, P.; Ciesielski, T.; Baczek, T. Antitumor activity of novel benzensulfonamide derivatives in view of their physiochemical properties searched by principal component analysis. Med. Chem., 2013, 9, 517-525.
Stasiak, J.; Koba, M.; Bober, L.; Kawczak, P. Baczek. T. The Comparison Between the Calculated and HPLC-Predicted Lipophilicity Parameters for Selected Groups of Drugs. Comb. Chem. High Throughput Screen., 2013, 16, 603-617.
Kawczak, P.; Bober, L.; Bączek, T. Biological Activity of Compounds Exhibiting Local Anesthetics’s Properties Evaluated by QSAR Approach. Curr. Pharm. Anal., 2014, 10, 255-262.
Kawczak, P.; Bober, L.; Bączek, T. QSPR analysis of some agonists and antagonists of α-adrenergic receptors. Med. Chem. Res., 2015, 24, 372-382.
Ciura, K.; Belka, M.; Kawczak, P.; Bączek, T.; Markuszewski, M.J.; Nowakowska, J. Combined computational-experimental approach to predict blood-brain barrier (BBB) permeation based on “green” salting-out thin layer chromatography supported by simple molecular descriptors. J. Pharm. Biomed. Anal., 2017, 143, 214-221.
Kawczak, P.; Bober, L.; Bączek, T. Activity evaluation of some psychoactive drugs with the application of QSAR/QSPR modeling methods. Med. Chem. Res., 2018, 27, 2279-2286.
Kawczak, P.; Bober, L.; Bączek, T. Application of QSAR Analysis and Different Quantum Chemical Calculation Methods in Activity Evaluation of Selected Fluoroquinolones. Comb. Chem. High Throughput Screen., 2018, 21, 468-475.
Kawczak, P. Bober. L.; Bączek, T. QSAR Analysis of Selected Antimicrobial Structures Belonging to Nitro-derivatives of Heterocyclic Compounds. Lett. Drug Des. Discov., 2018. in press
Kawczak, P. Bober. L.; Bączek, T. Evaluation of Chemotherapeutic Activity of the Selected Bases Analogues of Nucleic Acids Supported by ab initio Various Quantum Chemical Calculations. Curr. Comput. Aided Drug Des., 2019. in press
Kawczak, P.; Bober, L.; Bączek, T. The comparison of semi-empirical and ab initio molecular modeling methods in activity and properties evaluation of selected antimicrobial sulfonamides. Med. Chem. Res., 2019. in press
Chow, A.W.; Patten, V.; Bednorz, D. Susceptibility of Campylobacter fetus to twenty-two antimicrobial agents. Antimicrob. Agents Chemother., 1978, 13, 416-418.
Sabath, L.D.; Garner, C.; Wilcox, C.; Finland, M. Susceptibility of Staphylococcus aureus and Staphylococcus epidermidis to 65 Antibiotics. Antimicrob. Agents Chemother., 1976, 9, 962-969.
Allen, N.E.; Alborn, W.E., Jr; Kirst, H.A.; Toth, J.E. Comparison of aminoglycoside antibiotics with respect to uptake and lethal activity in Escherichia coli. J. Med. Chem., 1987, 30, 333-340.
HyperChem® Computational Chemistry. Part 1 Practical Guide. Part 2 Theory and Methods.; Hypercube Inc.: Waterloo, Ontario, 1996.
Dragon 7 molecular descriptors. products_dragon.php (Accessed February 4, 2019).
Todeschini, R.; Consonni, V. Molecular Descriptors for Chemoinformatics: Volume I: Alphabetical Listing/Volume II: Appendices, References, Vol. 41.; Wiley-VCH Verlag GmbH & Co. KGaA: Weinheim, , 2010.

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