Research Article
Topological index Nclass as a factor determining the antibacterial activity of quinolones against Escherichia coli
Published Online:4 Oct 2019https://doi.org/10.4155/fmc-2019-0073
Abstract
Aim: Due to antibiotic resistance and the lack of investment in antimicrobial R&D, quantitative structure–activity relationship (SAR) methods appear as an ideal approach for the discovery of new antibiotics. Result & methodology: Molecular topology and linear discriminant analysis were used to construct a model to predict activity against Escherichia coli. This model establishes new SARs, of which, molecular size and complexity (Nclass), stand out for their discriminant power. This model was used for the virtual screening of the Index Merck database, with results showing a high success rate as well as a moderate restriction. Conclusion: The model efficiently finds new active compounds. The topological index Nclass can act as a filter in other quantitative structure–activity relationship models predicting activity against E. coli.
Papers of special note have been highlighted as: • of interest; •• of considerable interest
References
- 1. Round table: are new antibiotics needed? Antimicrob. Agents Chemother. 5, 1107–1114 (1965).
- 2. World Health Organization. United Nations meeting on antimicrobial resistance. http://www.who.int/bulletin/volumes/94/9/16-020916.pdf • Emphasizes the threat antibiotic resistance is and will increasingly become for the public health worldwide.
- 3. World Health Organization. WHO priority pathogen list for R&D of new antibiotics. http://www.who.int/medicines/publications/WHO-PPL-Short_Summary_25Feb-ET_NM_WHO.pdf?ua=1
- 4. The Escherichia coli K-12 gntP gene allows E. coli F-18 to occupy a distinct nutritional niche in the streptomycin-treated mouse large intestine. Infect. Immun. 64(9), 3497–3503 (1996).
- 5. . Diarrheagenic Escherichia coli. Clin. Microbiol. Rev. 11, 142–201 (1998).
- 6. Colistin- and carbapenem-resistant Escherichia coli harboring mcr-1 and blaNMD-5. causing a complicated urinary tract infection in a patient from the United States. MBio 7(4), e01191–e01196 (2016).
- 7. World Health Organization. 10 Facts on antimicrobial resistance. http://www.who.int/features/factfiles/antimicrobial_resistance/en
- 8. . The crisis of no new antibiotics – what is the way forward? Lancet Infect. Dis. 12(3), 249–253 (2012). • Highlights the current crisis regarding investment on antibiotic development.
- 9. . History and objectives of quantitative drug design. In: Comprehensive Medicinal Chemistry, Vol. 4. Hansch CSammes PGTaylor JB (Eds). Pergamon Press, Oxford, UK, 1–30 (1990).
- 10. Integrating virtual screening and combinatorial chemistry for accelerated drug discovery. Comb. Chem. High Throughput Screen. 14(6), 475–487 (2011). •• Highlights the importance of quantitative structure–activity relationship for the discovery of new drugs in a time and cost-effective manner.
- 11. . Deep learning and virtual drug screening. Future Med. Chem. 10(21), 2557–2567 (2018).
- 12. . Lead- and drug-like compounds: the rule-of-five revolution. Drug Discov. Today Technol. 1, 337–341 (2004).
- 13. Molecular properties that define the activities of antibiotics in Escherichia coli and Pseudomonas aeruginosa. ACS Infect. Dis. 4(8), 1223–1234 (2018).
- 14. . Application of QSAR analysis and different quantum chemical calculation methods in activity evaluation of selected fluoroquinolones. Comb. Chem. High Throughput Screen. 21(7), 468–475 (2018).
- 15. Virtual screening approach of bacterial peptide deformylase inhibitors result in new antibiotics. Mol. Inf. 37(3), 1700087 (2018).
- 16. Screening, synthesis, and QSAR research on cinnamaldehyde-amino acid Schiff base compounds as antibacterial agents. Molecules 23(11), E3027 (2018).
- 17. . Synthesis, characterization, antibacterial and antioxidant potency of N-substituted-2-sulfanylidene-1,3-thiazolidin-4-one derivatives and QSAR study. Med. Chem.
doi:10.2174/1573406415666181205163052 (2018) (Epub ahead of print). - 18. . MOLCONN-Z software. Eastern Nazarene College, Quincy, MA, USA. (1995). http://www.molconn.com/index.html
- 19. DESMOL13 software. Unidad de Investigación de Diseño de Fármacos y Conectividad Molecular. Facultad de Farmacia, Universidad de Valencia, Spain (2000). https://www.uv.es/galvez/research-task.html
- 20. . BMDP statistical software. University of California. CA, USA (1990). www.statsols.com
- 21. Topological pattern for the search of new active drugs against methicillin resistant Staphylococcus aureus. Eur. J. Med. Chem. 138, 807–815 (2017).
- 22. . Prediction of molecular volume and surface of alkanes by molecular topology. J. Chem. Inf. Comput. Sci. 43(3), 1231–1239 (2003).
- 23. . Charge Indexes. New topological descriptors. J. Chem. Inf. Comput. Sci. 34(3), 520–525 (1994).
- 24. Synthesis and antibacterial activity of new fluoroquinolones containing a substituted N-(phenethyl)piperazine moiety. Bioorg. Med. Chem. Lett. 16(1), 3499–3503 (2006).
- 25. . The meaning of molecular connectivity: a bimolecular accessibility model. Croat. Chem. Acta. 75(2), 371–382 (2003).
- 26. . On the importance of topological descriptors in understanding structure–property relationships. J. Comput. Aided Mol. Des. 22, 441–460 (2008).
- 27. . Multivariate design and modeling in QSAR. Chemomr. Intell. Lab. 34, 1–19 (1996).
- 28. . Best practices for QSAR model development, validation and exploitation. Mol. Inform. 29, 476–488 (2010).
- 29. . New hypoglycaemic agents selected by molecular topology. Int. J. Pharm. 278, 111–118 (2004).
- 30. . Topological model for the search of new antibacterial drugs. 158 theoretical candidates. Curr. Comput. Aided Drug Des. 11, 336–345 (2015).
