In Silico Exploring of the Antibiotic Adjuvant Potential of some Natural Ligands in Carbapenem-Resistance Acinetobacter baumannii
,
Abstract
Background: A. baumannii is a gram-negative pathogen that has become one of the most important challenges in the world due to its high antibiotic resistance, and today many efforts are being made to treat infections caused by it. In recent years, there have been many concerns about increasing resistance to the beta-lactam antibiotic, carbapenem. Because resistance to these antibiotics greatly narrows the treatment options for the infections. The main source of carbapenem resistance in A. baumannii is the production of class D carbapenemase enzymes.
Methods: In this study, 27 plant ligands that have been shown to have antibacterial effects against A. baumannii and other resistant bacteria were selected. The chemical structure of the ligands and the three-dimensional structure of carbapenemase OXA-58 were extracted. The requirements of oral consumption of ligands were examined and ligand and OXA-58 docking were performed. 9 ligands including baicalein, berberine, curcumin, ellagic acid, epicatechin, honokiol, magnolol, norwogonin, and thymol, which met the requirements of Rule 5 and had better binding affinity than 6-alpha- hydroxymethyl penicillanate were selected. Redocking with a focus on the active position was performed by AutoDock software.
Results: The amino acids involved in the hydrogen bonding of an antibiotic-representative ligand to the receptor were identified. Ligands that bind to at least one of these amino acids at the binding site by hydrogen bond were selected. Pharmacological and toxicity studies were performed and finally, the epicatechin ligand was introduced as the best ligand.
Conclusion: Plant ligands can be further investigated as promising antibiotic adjuvants and used in the future.
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6. Landman D, Georgescu C, Martin DA, Quale J. Polymyxins revisited. Clin Microbiol Rev. 2008; 21(3):449-65.
7. Viehman JA, Nguyen MH, Doi Y. Treatment options for carbapenem-resistant and extensively drug-resistant Acinetobacter baumannii infections. Drugs. 2014; 74(12):1315-33.
8. Organization WH. Combat drug resistance, no action today no cure tomorrow, world health day 2011. 2011.
9. Codjoe FS, Donkor ES. Carbapenem Resistance: A Review. Med Sci (Basel). 2017; 6(1).
10. Ramirez MS, Bonomo RA, Tolmasky ME. Carbapenemases: Transforming Acinetobacter baumannii into a Yet More Dangerous Menace. Biomolecules. 2020; 10(5).
11. Evans BA, Amyes SG. OXA β-lactamases. Clin Microbiol Rev. 2014; 27(2):241-63.
12. Bush K, Jacoby GA. Updated functional classification of beta-lactamases. Antimicrob Agents Chemother. 2010; 54(3):969-76.
13. Kumar V, Yasmeen N, Pandey A, Chaudhary AA, Alawam AS, Rudayni HA, et al. Antibiotic adjuvants: synergistic tool to combat multi-drug resistant pathogens. Frontiers in Cellular and Infection Microbiology. 2023; 13.
14. Narendrakumar L, Chakraborty M, Kumari S, Paul D, Das B. β-Lactam potentiators to re-sensitize resistant pathogens: Discovery, development, clinical use and the way forward. Frontiers in Microbiology. 2023; 13:1092556.
15. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, et al. The Protein Data Bank. Nucleic Acids Res. 2000; 28(1):235-42.
16. Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, Meng EC, et al. UCSF Chimera--a visualization system for exploratory research and analysis. J Comput Chem. 2004; 25(13):1605-12.
17. Kim S, Chen J, Cheng T, Gindulyte A, He J, He S, et al. PubChem 2023 update. Nucleic Acids Research. 2022; 51(D1):D1373-D80.
18. O'Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR. Open Babel: An open chemical toolbox. Journal of Cheminformatics. 2011; 3(1):33.
19. Forli S, Huey R, Pique ME, Sanner MF, Goodsell DS, Olson AJ. Computational protein-ligand docking and virtual drug screening with the AutoDock suite. Nat Protoc. 2016; 11(5):905-19.
20. Hassan NM, Alhossary AA, Mu Y, Kwoh C-K. Protein-Ligand Blind Docking Using QuickVina-W With Inter-Process Spatio-Temporal Integration. Scientific Reports. 2017; 7(1):15451.
21. Dallakyan S, Olson AJ. Small-molecule library screening by docking with PyRx. Methods Mol Biol. 2015; 1263:243-50.
22. Morris GM, Goodsell DS, Halliday RS, Huey R, Hart WE, Belew RK, et al. Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. Journal of computational chemistry. 1998; 19(14):1639-62.
23. 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.
24. 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.
25. Pollastri MP. Overview on the Rule of Five. Current protocols in pharmacology. 2010; 49(1):9.12. 1-9.. 8.
26. Daina A, Michielin O, Zoete V. SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Scientific Reports. 2017; 7(1):42717.
27. Banerjee P, Eckert AO, Schrey AK, Preissner R. ProTox-II: a webserver for the prediction of toxicity of chemicals. Nucleic Acids Res. 2018; 46(W1):W257-w63.
28. Pantsar T, Poso A. Binding Affinity via Docking: Fact and Fiction. Molecules. 2018; 23(8).
29. Bulusu G, Desiraju GR. Strong and Weak Hydrogen Bonds in Protein–Ligand Recognition. Journal of the Indian Institute of Science. 2020; 100(1):31-41.
30. Lipinski CA. Lead- and drug-like compounds: the rule-of-five revolution. Drug Discov Today Technol. 2004; 1(4):337-41.
31. Ghose AK, Viswanadhan VN, Wendoloski JJ. A Knowledge-Based Approach in Designing Combinatorial or Medicinal Chemistry Libraries for Drug Discovery. 1. A Qualitative and Quantitative Characterization of Known Drug Databases. Journal of Combinatorial Chemistry. 1999; 1(1):55-68.
32. Oprea TI. Property distribution of drug-related chemical databases. J Comput Aided Mol Des. 2000; 14(3):251-64.
33. Brown RD, Hassan M, Waldman M. Combinatorial library design for diversity, cost efficiency, and drug-like character. J Mol Graph Model. 2000; 18(4-5):427-37, 537.
34. Veber DF, Johnson SR, Cheng H-Y, Smith BR, Ward KW, Kopple KD. Molecular Properties That Influence the Oral Bioavailability of Drug Candidates. Journal of Medicinal Chemistry. 2002; 45(12):2615-23.
35. Egan WJ, Merz KM, Jr., Baldwin JJ. Prediction of drug absorption using multivariate statistics. J Med Chem. 2000; 43(21):3867-77.
36. Muegge I, Heald SL, Brittelli D. Simple Selection Criteria for Drug-like Chemical Matter. Journal of Medicinal Chemistry. 2001; 44(12):1841-6.
37. Martin YC. A Bioavailability Score. Journal of Medicinal Chemistry. 2005; 48(9):3164-70.
38. Hua S. Advances in oral drug delivery for regional targeting in the gastrointestinal tract-influence of physiological, pathophysiological and pharmaceutical factors. Frontiers in pharmacology. 2020; 11:524.
39. Warren KE. Beyond the Blood:Brain Barrier: The Importance of Central Nervous System Pharmacokinetics for the Treatment of CNS Tumors, Including Diffuse Intrinsic Pontine Glioma. Front Oncol. 2018; 8:239.
40. Lynch T, Price A. The effect of cytochrome P450 metabolism on drug response, interactions, and adverse effects. Am Fam Physician. 2007; 76(3):391-6.
41. Yu YQ, Yang X, Wu XF, Fan YB. Enhancing Permeation of Drug Molecules Across the Skin via Delivery in Nanocarriers: Novel Strategies for Effective Transdermal Applications. Front Bioeng Biotechnol. 2021; 9:646554.
42. Potts RO, Guy RH. Predicting skin permeability. Pharm Res. 1992; 9(5):663-9.
43. Gadaleta D, Vuković K, Toma C, Lavado GJ, Karmaus AL, Mansouri K, et al. SAR and QSAR modeling of a large collection of LD50 rat acute oral toxicity data. Journal of Cheminformatics. 2019; 11(1):1-16.
44. Lin Z, Will Y. Evaluation of drugs with specific organ toxicities in organ-specific cell lines. Toxicol Sci. 2012; 126(1):114-27.
45. Chapter 4 Mechanisms of immunotoxic effects. In: Descotes J, editor. Immunotoxicology of Drugs and Chemicals: an Experimental and Clinical Approach. 1: Elsevier; 2004. p. 127-62.
46. Hsu KH, Su BH, Tu YS, Lin OA, Tseng YJ. Mutagenicity in a Molecule: Identification of Core Structural Features of Mutagenicity Using a Scaffold Analysis. PLoS One. 2016; 11(2):e0148900.
47. Qari SH, Alrefaei AF, Ashoor AB, Soliman MH. Genotoxicity and Carcinogenicity of Medicinal Herbs and Their Nanoparticles. Nutraceuticals. 2021; 1(1):31-41.
48. Judson RS, Magpantay FM, Chickarmane V, Haskell C, Tania N, Taylor J, et al. Integrated Model of Chemical Perturbations of a Biological Pathway Using 18 In Vitro High-Throughput Screening Assays for the Estrogen Receptor. Toxicol Sci. 2015; 148(1):137-54.
49. Gopal J, Muthu M, Paul D, Kim D-H, Chun S. Bactericidal activity of green tea extracts: the importance of catechin containing nano particles. Scientific Reports. 2016; 6(1):19710.
50. Tsuchiya H. Stereospecificity in membrane effects of catechins. Chem Biol Interact. 2001; 134(1):41-54.
51. Ibitoye OB, Ajiboye TO. Catechin potentiates the oxidative response of Acinetobacter baumannii to quinolone-based antibiotics. Microb Pathog. 2019; 127:239-45.
52. Osterburg A, Gardner J, Hyon SH, Neely A, Babcock G. Highly antibiotic-resistant Acinetobacter baumannii clinical isolates are killed by the green tea polyphenol-epigallocatechin-3-gallate. Clin Microbiol Infect. 2009; 15(4):341-6.
53. Betts JW, Wareham DW. In vitro activity of curcumin in combination with epigallocatechin gallate versus multidrug-resistant Acinetobacter baumannii. BMC Microbiol. 2014; 14:172.
54. Ćurković-Perica M, Hrenović J, Kugler N, Goic-Barišić I, Tkalec M. Antibacterial Activity of Pinus pinaster Bark Extract and its Components Against Multidrug-resistant Clinical Isolates of Acinetobacter baumannii. Croatica Chemica Acta. 2015; 88:133-7.
55. Betts JW, Hornsey M, Wareham DW, La Ragione RM. In vitro and In vivo Activity of Theaflavin-Epicatechin Combinations versus Multidrug-Resistant Acinetobacter baumannii. Infect Dis Ther. 2017; 6(3):435-42.
56. Lee S, Razqan GS, Kwon DH. Antibacterial activity of epigallocatechin-3-gallate and its synergism with β-lactam antibiotics sensitizing carbapenem-associated multidrug resistant clinical isolates of Acinetobacter baumannii. Phytomedicine. 2017; 24:49-55.
2. Howard A, O'Donoghue M, Feeney A, Sleator RD. Acinetobacter baumannii: an emerging opportunistic pathogen. Virulence. 2012; 3(3):243-50.
3. Moreira Silva G, Morais L, Marques L, Senra V. Acinetobacter community-acquired pneumonia in a healthy child. Rev Port Pneumol. 2012; 18(2):96-8.
4. Santajit S, Indrawattana N. Mechanisms of Antimicrobial Resistance in ESKAPE Pathogens. BioMed Research International. 2016; 2016:2475067.
5. Levy-Blitchtein S, Roca I, Plasencia-Rebata S, Vicente-Taboada W, Velásquez-Pomar J, Muñoz L, et al. Emergence and spread of carbapenem-resistant Acinetobacter baumannii international clones II and III in Lima, Peru. Emerging Microbes & Infections. 2018; 7(1):1-9.
6. Landman D, Georgescu C, Martin DA, Quale J. Polymyxins revisited. Clin Microbiol Rev. 2008; 21(3):449-65.
7. Viehman JA, Nguyen MH, Doi Y. Treatment options for carbapenem-resistant and extensively drug-resistant Acinetobacter baumannii infections. Drugs. 2014; 74(12):1315-33.
8. Organization WH. Combat drug resistance, no action today no cure tomorrow, world health day 2011. 2011.
9. Codjoe FS, Donkor ES. Carbapenem Resistance: A Review. Med Sci (Basel). 2017; 6(1).
10. Ramirez MS, Bonomo RA, Tolmasky ME. Carbapenemases: Transforming Acinetobacter baumannii into a Yet More Dangerous Menace. Biomolecules. 2020; 10(5).
11. Evans BA, Amyes SG. OXA β-lactamases. Clin Microbiol Rev. 2014; 27(2):241-63.
12. Bush K, Jacoby GA. Updated functional classification of beta-lactamases. Antimicrob Agents Chemother. 2010; 54(3):969-76.
13. Kumar V, Yasmeen N, Pandey A, Chaudhary AA, Alawam AS, Rudayni HA, et al. Antibiotic adjuvants: synergistic tool to combat multi-drug resistant pathogens. Frontiers in Cellular and Infection Microbiology. 2023; 13.
14. Narendrakumar L, Chakraborty M, Kumari S, Paul D, Das B. β-Lactam potentiators to re-sensitize resistant pathogens: Discovery, development, clinical use and the way forward. Frontiers in Microbiology. 2023; 13:1092556.
15. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, et al. The Protein Data Bank. Nucleic Acids Res. 2000; 28(1):235-42.
16. Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, Meng EC, et al. UCSF Chimera--a visualization system for exploratory research and analysis. J Comput Chem. 2004; 25(13):1605-12.
17. Kim S, Chen J, Cheng T, Gindulyte A, He J, He S, et al. PubChem 2023 update. Nucleic Acids Research. 2022; 51(D1):D1373-D80.
18. O'Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR. Open Babel: An open chemical toolbox. Journal of Cheminformatics. 2011; 3(1):33.
19. Forli S, Huey R, Pique ME, Sanner MF, Goodsell DS, Olson AJ. Computational protein-ligand docking and virtual drug screening with the AutoDock suite. Nat Protoc. 2016; 11(5):905-19.
20. Hassan NM, Alhossary AA, Mu Y, Kwoh C-K. Protein-Ligand Blind Docking Using QuickVina-W With Inter-Process Spatio-Temporal Integration. Scientific Reports. 2017; 7(1):15451.
21. Dallakyan S, Olson AJ. Small-molecule library screening by docking with PyRx. Methods Mol Biol. 2015; 1263:243-50.
22. Morris GM, Goodsell DS, Halliday RS, Huey R, Hart WE, Belew RK, et al. Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. Journal of computational chemistry. 1998; 19(14):1639-62.
23. 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.
24. 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.
25. Pollastri MP. Overview on the Rule of Five. Current protocols in pharmacology. 2010; 49(1):9.12. 1-9.. 8.
26. Daina A, Michielin O, Zoete V. SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Scientific Reports. 2017; 7(1):42717.
27. Banerjee P, Eckert AO, Schrey AK, Preissner R. ProTox-II: a webserver for the prediction of toxicity of chemicals. Nucleic Acids Res. 2018; 46(W1):W257-w63.
28. Pantsar T, Poso A. Binding Affinity via Docking: Fact and Fiction. Molecules. 2018; 23(8).
29. Bulusu G, Desiraju GR. Strong and Weak Hydrogen Bonds in Protein–Ligand Recognition. Journal of the Indian Institute of Science. 2020; 100(1):31-41.
30. Lipinski CA. Lead- and drug-like compounds: the rule-of-five revolution. Drug Discov Today Technol. 2004; 1(4):337-41.
31. Ghose AK, Viswanadhan VN, Wendoloski JJ. A Knowledge-Based Approach in Designing Combinatorial or Medicinal Chemistry Libraries for Drug Discovery. 1. A Qualitative and Quantitative Characterization of Known Drug Databases. Journal of Combinatorial Chemistry. 1999; 1(1):55-68.
32. Oprea TI. Property distribution of drug-related chemical databases. J Comput Aided Mol Des. 2000; 14(3):251-64.
33. Brown RD, Hassan M, Waldman M. Combinatorial library design for diversity, cost efficiency, and drug-like character. J Mol Graph Model. 2000; 18(4-5):427-37, 537.
34. Veber DF, Johnson SR, Cheng H-Y, Smith BR, Ward KW, Kopple KD. Molecular Properties That Influence the Oral Bioavailability of Drug Candidates. Journal of Medicinal Chemistry. 2002; 45(12):2615-23.
35. Egan WJ, Merz KM, Jr., Baldwin JJ. Prediction of drug absorption using multivariate statistics. J Med Chem. 2000; 43(21):3867-77.
36. Muegge I, Heald SL, Brittelli D. Simple Selection Criteria for Drug-like Chemical Matter. Journal of Medicinal Chemistry. 2001; 44(12):1841-6.
37. Martin YC. A Bioavailability Score. Journal of Medicinal Chemistry. 2005; 48(9):3164-70.
38. Hua S. Advances in oral drug delivery for regional targeting in the gastrointestinal tract-influence of physiological, pathophysiological and pharmaceutical factors. Frontiers in pharmacology. 2020; 11:524.
39. Warren KE. Beyond the Blood:Brain Barrier: The Importance of Central Nervous System Pharmacokinetics for the Treatment of CNS Tumors, Including Diffuse Intrinsic Pontine Glioma. Front Oncol. 2018; 8:239.
40. Lynch T, Price A. The effect of cytochrome P450 metabolism on drug response, interactions, and adverse effects. Am Fam Physician. 2007; 76(3):391-6.
41. Yu YQ, Yang X, Wu XF, Fan YB. Enhancing Permeation of Drug Molecules Across the Skin via Delivery in Nanocarriers: Novel Strategies for Effective Transdermal Applications. Front Bioeng Biotechnol. 2021; 9:646554.
42. Potts RO, Guy RH. Predicting skin permeability. Pharm Res. 1992; 9(5):663-9.
43. Gadaleta D, Vuković K, Toma C, Lavado GJ, Karmaus AL, Mansouri K, et al. SAR and QSAR modeling of a large collection of LD50 rat acute oral toxicity data. Journal of Cheminformatics. 2019; 11(1):1-16.
44. Lin Z, Will Y. Evaluation of drugs with specific organ toxicities in organ-specific cell lines. Toxicol Sci. 2012; 126(1):114-27.
45. Chapter 4 Mechanisms of immunotoxic effects. In: Descotes J, editor. Immunotoxicology of Drugs and Chemicals: an Experimental and Clinical Approach. 1: Elsevier; 2004. p. 127-62.
46. Hsu KH, Su BH, Tu YS, Lin OA, Tseng YJ. Mutagenicity in a Molecule: Identification of Core Structural Features of Mutagenicity Using a Scaffold Analysis. PLoS One. 2016; 11(2):e0148900.
47. Qari SH, Alrefaei AF, Ashoor AB, Soliman MH. Genotoxicity and Carcinogenicity of Medicinal Herbs and Their Nanoparticles. Nutraceuticals. 2021; 1(1):31-41.
48. Judson RS, Magpantay FM, Chickarmane V, Haskell C, Tania N, Taylor J, et al. Integrated Model of Chemical Perturbations of a Biological Pathway Using 18 In Vitro High-Throughput Screening Assays for the Estrogen Receptor. Toxicol Sci. 2015; 148(1):137-54.
49. Gopal J, Muthu M, Paul D, Kim D-H, Chun S. Bactericidal activity of green tea extracts: the importance of catechin containing nano particles. Scientific Reports. 2016; 6(1):19710.
50. Tsuchiya H. Stereospecificity in membrane effects of catechins. Chem Biol Interact. 2001; 134(1):41-54.
51. Ibitoye OB, Ajiboye TO. Catechin potentiates the oxidative response of Acinetobacter baumannii to quinolone-based antibiotics. Microb Pathog. 2019; 127:239-45.
52. Osterburg A, Gardner J, Hyon SH, Neely A, Babcock G. Highly antibiotic-resistant Acinetobacter baumannii clinical isolates are killed by the green tea polyphenol-epigallocatechin-3-gallate. Clin Microbiol Infect. 2009; 15(4):341-6.
53. Betts JW, Wareham DW. In vitro activity of curcumin in combination with epigallocatechin gallate versus multidrug-resistant Acinetobacter baumannii. BMC Microbiol. 2014; 14:172.
54. Ćurković-Perica M, Hrenović J, Kugler N, Goic-Barišić I, Tkalec M. Antibacterial Activity of Pinus pinaster Bark Extract and its Components Against Multidrug-resistant Clinical Isolates of Acinetobacter baumannii. Croatica Chemica Acta. 2015; 88:133-7.
55. Betts JW, Hornsey M, Wareham DW, La Ragione RM. In vitro and In vivo Activity of Theaflavin-Epicatechin Combinations versus Multidrug-Resistant Acinetobacter baumannii. Infect Dis Ther. 2017; 6(3):435-42.
56. Lee S, Razqan GS, Kwon DH. Antibacterial activity of epigallocatechin-3-gallate and its synergism with β-lactam antibiotics sensitizing carbapenem-associated multidrug resistant clinical isolates of Acinetobacter baumannii. Phytomedicine. 2017; 24:49-55.
| Files | ||
| Issue | Vol 12 No 1 (2024) | |
| Section | Original Articles | |
| DOI | https://doi.org/10.18502/jmb.v12i1.15022 | |
| Keywords | ||
| Acinetobacter baumannii Carbapenem Phyto-ligand Antibiotic adjuvant Epicatechin | ||
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How to Cite
1.
Zadeh Hosseingholi E, Molavi G, Mohammadi MS. In Silico Exploring of the Antibiotic Adjuvant Potential of some Natural Ligands in Carbapenem-Resistance Acinetobacter baumannii. J Med Bacteriol. 2024;12(1):69-85.
