Exploring Lumacaftor: a novel antibacterial approach for treating E. coli-induced urinary tract infections

Authors

  • Anjali G. Department of Microbiology, Vignan P. G. College, Guntur, Andhra Pradesh, India
  • Lakshmi M. Department of Microbiology, Vignan P. G. College, Guntur, Andhra Pradesh, India
  • Deepika Srija B. Department of Microbiology, Vignan P. G. College, Guntur, Andhra Pradesh, India
  • Ushaswini M. Department of Microbiology, Vignan P. G. College, Guntur, Andhra Pradesh, India
  • Neeraja K. Department of Microbiology, Vignan P. G. College, Guntur, Andhra Pradesh, India
  • Venkateswarlu T. C. Department of Biotechnology and Bioinformatics, Vignan University, Guntur, Andhra Pradesh, India
  • Anuradha V. Department of Chemistry, Vignan P. G. College, Guntur, Andhra Pradesh, India
  • Meenakshi Kante Department of Microbiology, Vignan P. G. College, Guntur, Andhra Pradesh, India

DOI:

https://doi.org/10.18203/2320-6012.ijrms20252399

Keywords:

Urinary tract infection, Nitrofurantoin, Nitroreductases, Molecular dynamics simulations

Abstract

Background: Urinary tract infections (UTIs) are primarily caused by Escherichia coli (E. coli), among the most common bacterial infections internationally. Oxygen-insensitive NADPH nitro reductase (NFSA), a vital protein in E. coli, plays a major role in the development of UTIs. Present NFSA-inhibiting drugs, such as nitrofurantoin, have limitations, including adverse effects, reduced effectiveness, and confront against drug-resistant strains. This study suggests Lumacaftor as a potential lead compound to target NFSA, given its promising pharmacological profile.

Methods: The study recognized NFSA as the important target protein and performed virtual screening of compounds from the Drug Bank database via AutoDock Vina. The pharmacokinetics of the most excellent candidates were evaluated, and molecular dynamics simulations (100 ns) carried out using Desmond further validated the strength and binding efficacy of the preferred leads.

Results: The molecular docking study noted Lumacaftor as the most capable NFSA inhibitor, attained a docking score of -10.02 kcal/mol, indicating the strongest expected binding affinity among the screened compounds. The predicted pharmacokinetic properties of Lumacaftor, Phthalocyanine, and Nitrofurantoin exposed key differences that carry the suitability of Lumacaftor as a potential NFSA inhibitor. Furthermore, Lumacaftor revealed a relatively low average ligand RMSD in relation to the protein (5.146 Å) and itself (2.073 Å), suggestive of stable binding within the active site of the NFSA protein.

Conclusions: This study successfully acknowledged and validated NFSA as a therapeutic target for E. coli causing UTIs, addressing the limitations of current treatments, including antibiotic resistance and reduced pharmacological efficiency.

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References

Flores-Mireles AL, Walker JN, Caparon M, Hultgren SJ. Urinary tract infections: epidemiology, mechanisms of infection and treatment options. Nat Rev Microbiol. 2015;13(5):269-84. DOI: https://doi.org/10.1038/nrmicro3432

Medina M, Castillo-Pino E. An introduction to the epidemiology and burden of urinary tract infections. Ther Adv Urol. 2019;11:1756287219832172. DOI: https://doi.org/10.1177/1756287219832172

Collaborators GBD 2019 Antimicrobial Resistance. Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. Lancet. 2022;399(10325):629-55. DOI: https://doi.org/10.1016/S0140-6736(21)02724-0

Foxman B. The epidemiology of urinary tract infection. Nat Rev Urol. 2010;7(12):653-60. DOI: https://doi.org/10.1038/nrurol.2010.190

Ronald A. The etiology of urinary tract infection: traditional and emerging pathogens. Am J Med. 2002;113(1A):14S-9S. DOI: https://doi.org/10.1016/S0002-9343(02)01055-0

Zenno S, Koike H, Kumar AN, Jayaraman R, Tanokura M. Biochemical characterization of NfsA, the Escherichia coli major nitroreductase exhibiting a high amino acid sequence homology to Frp, a Vibrio harveyi flavin oxidoreductase. J Bacteriol. 1996;178(15):4508-14. DOI: https://doi.org/10.1128/jb.178.15.4508-4514.1996

Zhou Y, Zhou Z, Zheng L, Gong Z, Li Y, Jin Y, Huang Y, Chi M. Urinary Tract Infections Caused by Uropathogenic Escherichia coli: Mechanisms of Infection and Treatment Options. Int J Mol Sci. 2023;24(13):10537. DOI: https://doi.org/10.3390/ijms241310537

Wishart DS, Guo AC, Oler E, Wang F, An C, Nguyen L, et al. DrugBank 6.0: the DrugBank knowledgebase for 2024. Nucleic Acids Res. 2024;52(D1):D848-54. DOI: https://doi.org/10.1093/nar/gkad976

Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, et al. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J Comput Chem. 2009;30(16):2785-91. DOI: https://doi.org/10.1002/jcc.21256

O'Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR. Open Babel: An open chemical toolbox. J Cheminform. 2011;3:33. DOI: https://doi.org/10.1186/1758-2946-3-33

Eberhardt J, Santos-Martins D, Tillack AF, Forli S. AutoDock Vina 1.2.0: New docking methods, expanded force field, and Python bindings. J Chem Inf Model. 2021;61(8):3891-8. DOI: https://doi.org/10.1021/acs.jcim.1c00203

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;7:42717.

Pires DE, Blundell TL, Ascher DB. pkCSM: predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures. J Med Chem. 2015;58(9):4066-72.

Schrödinger Release 2025-2: Desmond Molecular Dynamics System, D. E. Shaw Research, New York, NY, 2024. Maestro-Desmond Interoperability Tools, Schrödinger, LLC, New York, NY. 2025.

Bowers KJ, Chow E, Xu H, Dror RO, Eastwood MP, Gregersen BA, et al. Scalable algorithms for molecular dynamics simulations on commodity clusters. Proceedings of the ACM/IEEE Conference on Supercomputing (SC06). Tampa, FL. 2006. DOI: https://doi.org/10.1109/SC.2006.54

Sastry GM, Adzhigirey M, Day T, Annabhimoju R, Sherman W. Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments. J Comput Aided Mol Des. 2013;27(3):221-34. DOI: https://doi.org/10.1007/s10822-013-9644-8

Jorgensen WL, Maxwell DS, Tirado-Rives J. Development and testing of the OPLS all-atom force field on conformational energetics and properties of organic liquids. J Am Chem Soc. 1996;118(45):11225-36. DOI: https://doi.org/10.1021/ja9621760

Kumar S, Sharma PK, Singh R, Malik R, Singh P, Kumar S. A Molecular Docking and Dynamics Simulation Approach to Explore the Potential of Natural Compounds as Inhibitors of SARS-CoV-2 Main Protease. J Biomol Struct Dyn. 2023;41(6):1-15.

Pires DEV, Blundell TL, Ascher DB. pkCSM: Predicting Small-Molecule Pharmacokinetic and Toxicity Properties Using Graph-Based Signatures. J Med Chem. 2015;58(9):4066-72. DOI: https://doi.org/10.1021/acs.jmedchem.5b00104

Alam, A., et al. (2021). Computational analysis and identification of NF-κB pathway inhibitors using molecular docking and dynamics. Journal of Biomolecular Structure and Dynamics, 39(15), 5291–5301.

Singh, A., et al. (2020). Phthalocyanines as versatile therapeutic agents: A review. Bioorganic Chemistry, 96, 103611. DOI: https://doi.org/10.1016/j.bioorg.2020.103611

Pushpakom S, Iorio F, Eyers PA, Escott KJ, Hopper S, Wells A, et al. Drug repurposing: progress, challenges and recommendations. Nat Rev Drug Discov. 2019;18(1):41-58. DOI: https://doi.org/10.1038/nrd.2018.168

Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev. 2001;46(1-3):3-26. DOI: https://doi.org/10.1016/S0169-409X(96)00423-1

Veber DF, Johnson SR, Cheng HY, Smith BR, Ward KW, Kopple KD. Molecular properties that influence the oral bioavailability of drug candidates. J Med Chem. 2002;45(12):2615-23. DOI: https://doi.org/10.1021/jm020017n

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;7:42717. DOI: https://doi.org/10.1038/srep42717

Karplus M, McCammon JA. Molecular dynamics simulations of biomolecules. Nat Struct Biol. 2002;9(9):646-52. DOI: https://doi.org/10.1038/nsb0902-646

Hollingsworth SA, Dror RO. Molecular dynamics simulation for all. Neuron. 2018;99(6):1129-43. DOI: https://doi.org/10.1016/j.neuron.2018.08.011

Hospital A, Goñi JR, Orozco M, Gelpí JL. Molecular dynamics simulations: advances and applications. Adv Appl Bioinform Chem. 2015;8:37-47. DOI: https://doi.org/10.2147/AABC.S70333

Durrant JD, McCammon JA. Molecular dynamics simulations and drug discovery. BMC Biol. 2011;9:71. DOI: https://doi.org/10.1186/1741-7007-9-71

Klebe G. Drug Design: Methodology, Concepts, and Mode-of-Action. Springer. 2015.

Wang, J., et al. (2019). Water-mediated interactions contribute to the specificity and affinity of protein–ligand binding. Journal of Chemical Theory and Computation, 15(1), 211–224.

Bellacchio E. Exploring the Mechanism of Activation of CFTR by Curcuminoids: An Ensemble Docking Study. Int J Mol Sci. 2023;25(1):552. DOI: https://doi.org/10.3390/ijms25010552

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Published

2025-07-30

How to Cite

G., A., M., L., B., D. S., M., U., K., N., T. C., V., V., A., & Kante, M. (2025). Exploring Lumacaftor: a novel antibacterial approach for treating E. coli-induced urinary tract infections. International Journal of Research in Medical Sciences, 13(8), 3299–3308. https://doi.org/10.18203/2320-6012.ijrms20252399

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Original Research Articles