Advertisement

Current Pharmacology Reports

, Volume 5, Issue 4, pp 255–280 | Cite as

Ellagic Acid, Kaempferol, and Quercetin from Acacia nilotica: Promising Combined Drug With Multiple Mechanisms of Action

  • Mosab Yahya Al-NourEmail author
  • Musab Mohamed Ibrahim
  • Tilal Elsaman
Natural Products: From Chemistry to Pharmacology (C Ho, Section Editor)
  • 43 Downloads
Part of the following topical collections:
  1. Topical Collection on Natural Products: From Chemistry to Pharmacology

Abstract

The pharmacological activity of Acacia nilotica’s phytochemical constituents was confirmed with evidence-based studies, but the determination of exact targets that they bind and the mechanism of action were not done; consequently, we aim to identify the exact targets that are responsible for the pharmacological activity via the computational methods. Furthermore, we aim to predict the pharmacokinetics (ADME) properties and the safety profile in order to identify the best drug candidates. To achieve those goals, various computational methods were used including the ligand-based virtual screening and molecular docking. Moreover, pkCSM and SwissADME web servers were used for the prediction of pharmacokinetics and safety. The total number of the investigated compounds and targets was 25 and 61, respectively. According to the results, the pharmacological activity was attributed to the interaction with essential targets. Ellagic acid, Kaempferol, and Quercetin were the best A. nilotica’s phytochemical constituents that contribute to the therapeutic activities, were non-toxic as well as non-carcinogen. The administration of Ellagic acid, Kaempferol, and Quercetin as combined drug via the novel drug delivery systems will be a valuable therapeutic choice for the treatment of recent diseases attacking the public health including cancer, multidrug-resistant bacterial infections, diabetes mellitus, and chronic inflammatory systemic disease.

Keywords

A. nilotica Ellagic acid Kaempferol Quercetin Multiple mechanisms of action ADMET and computer-aided drug discovery 

Notes

Compliance with Ethical Standards

Conflict of Interest

The authors declare that having no conflict of interest.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subject performed by any of the authors

References

  1. 1.
    Islam SU, Rather LJ, Mohammad F. Acacia nilotica (L.): A review of its traditional uses, phytochemistry, and pharmacology. Sustain Chem Pharm. 2015;8:2352–5541.Google Scholar
  2. 2.
    Shakya AK. Medicinal plants: future source of new drugs. Int J Herbal Med. 2016;4:59–64.Google Scholar
  3. 3.
    Ghulam Mustafa RA, Atta A, Sharif S, Jamil A. Bioactive compounds from medicinal plants and their importance in drug discovery in Pakistan. Matrix Sci Pharma. 2017;1:17–26.CrossRefGoogle Scholar
  4. 4.
    N. C. Institute. Cancer statistics. www.cancer.gov. Accessed April 2018.
  5. 5.
    Barker JJ. Antibacterial drug discovery and structure-based design. Drug Discov Today. 2006;11:391–404.CrossRefGoogle Scholar
  6. 6.
    A. D. Association. Statistics about diabetes. www.diabetes.org, Accessed March 2018.
  7. 7.
    Straub RH, Schradin C. Chronic inflammatory systemic diseases an evolutionary trade-off between acutely beneficial but chronically harmful programs. Evol Med Public Health. 2016;2016:73–51.Google Scholar
  8. 8.
    Eweas AF, Maghrabi IA, Namarneh AI. Advances in molecular modeling and docking as a tool for modern drug discovery. Sch Res Lib Der Pharma Chem. 2014;6:211–28.Google Scholar
  9. 9.
    Saher Afshan Shaikh TJ. Sandhu G, Soni A, Jayaram B. From drug target to leads-sketching a physico-chemical pathway for lead molecule design in silico. Front Med Chem. 2011;6.Google Scholar
  10. 10.
    Banegas-Luna AJ, Ceron-Carrasco JP, Perez-Sanchez H. A review of ligand-based virtual screening web tools and screening algorithms in large molecular databases in the age of big data. Future Med Chem. 2018;10:2641–58.CrossRefGoogle Scholar
  11. 11.
    Guo ZYL, Zheng X, Hu L, Yang Y, Wang JA. A comparison of various optimization algorithms of protein-ligand docking programs by fitness accuracy. J Mol Model. 2014;20:2251–61.CrossRefGoogle Scholar
  12. 12.
    Daina A. SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep. 2017;7:42717.CrossRefGoogle Scholar
  13. 13.
    Pires DEV, Blundell TL, Ascher DB. pkCSM: predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures. J Med Chem. 2015;58:4066–72.CrossRefGoogle Scholar
  14. 14.
    Singh S, Awasthi M, Tiwari S, Pandey VP, Dwivedi UN. Computational approaches for therapeutic application of natural products in Alzheimer’s disease. Neuromethods. 2018;132:483–511.CrossRefGoogle Scholar
  15. 15.
    Wadhwa B, Mahajan P, Barik MR, Malik F, Nargotra A. Combining ligand- and structure-based in silico methods for the identification of natural product-based inhibitors of Akt1. Mol Divers. 2019.Google Scholar
  16. 16.
    Pereira F, Aires-de-Sousa J. Computational methodologies in the exploration of the marine natural product leads. Mar Drugs. 2018;16.Google Scholar
  17. 17.
    Worachartcheewan A, Prachayasittikul V, Shoombuatong W, Songtawee N, Simeon S, Prachayasittikul V, et al. Computer-aided drug design of bioactive natural products. Curr Top Med Chem. 2015;15:1780–800.CrossRefGoogle Scholar
  18. 18.
    Jones LH. An industry perspective on drug target validation. Exp Opin Drug Discovery. 2016;11:623–5.CrossRefGoogle Scholar
  19. 19.
    Xuan-Yu Meng H-XZ, Mezei M, Cui M. Molecular docking: a powerful approach for structure-based drug discovery. Curr Comput Aided Drug Des. 2011;7:146–57.CrossRefGoogle Scholar
  20. 20.
    Pires DEV, Kaminskas LM, Ascher DB. Prediction and optimization of pharmacokinetic and toxicity properties of the ligand. Methods Mol Biol. 2018;1762:271–84.CrossRefGoogle Scholar
  21. 21.
  22. 22.
    Noel MB, Boyle MO’, James CA, Morley C, Vandermeersch T, Hutchison GR. Open Babel: an open chemical toolbox. J Cheminform. 2011;3.Google Scholar
  23. 23.
    Cheeseright T, Mackey M, Rose S, Vinter A. Molecular field extrema as descriptors of biological activity: definition and validation. J Chem Inf Model. 2006;46:665–76 v. Flare, Cresset®, Litlington, Cambridgeshire, UK, http://www.cresset-group.com/flare/. CrossRefGoogle Scholar
  24. 24.
    Keiser MJ RB, Armbruster BN, Ernsberger P, Irwin JJ, Shoichet BK. Relating protein pharmacology by ligand chemistry. Nat Biotechnol. 2007;25:197–206.CrossRefGoogle Scholar
  25. 25.
    Zhi-Jiang JY, Che Y-J, Zhu MF, Wen M, Lu A-P, Cao D-S. TargetNet: a web service for predicting potential drug-target interaction profiling via multi-target SAR models. J Comput Aided Mol Des. 2016;30(5):413–24.CrossRefGoogle Scholar
  26. 26.
    T. N. Consortium. The Universal Protein Resource (UniProt). Nucleic Acids Res. 2008;36.Google Scholar
  27. 27.
    Nguyen D-T, Mathias S, et al. Pharos: collating protein information to shed light on the druggable genome. Nucleic Acids Res. 2017;45:D995–D1002.CrossRefGoogle Scholar
  28. 28.
    Li YH, Yu YY, Li XX, Zhang P, Tang J, Yang QX, et al. Therapeutic target database update 2018: enrich resource for facilitating bench-to-clinic research of targeted therapeutics. Nucleic Acids Res. 2018;46:D1121–7.Google Scholar
  29. 29.
    Anna Frenzel FG, Chmielewski W, Villunger A. Bcl2 family proteins in carcinogenesis and the treatment of cancer. Apoptosis. 2012;14:584–96.CrossRefGoogle Scholar
  30. 30.
    Kim B, Srivastava SK, Kim SH. Caspase-9 as a therapeutic target for treating cancer. Expert Opin Ther Targets. 2015;19:113–27.CrossRefGoogle Scholar
  31. 31.
    Stengel K, Zheng Y. Cdc42 in oncogenic transformation, invasion, and tumorigenesis. Cell Signal. 2011;23:1415–23.CrossRefGoogle Scholar
  32. 32.
    Montagnoli A, Moll J, Colotta F. Targeting cell division cycle 7 kinase: a new approach for cancer therapy. Clin Cancer Res. 2010;15(16):4503–8.CrossRefGoogle Scholar
  33. 33.
    Jian Kang CMS, Sutherland RL, Musgrove EA. Targeting cyclin-dependent kinase 1 (CDK1) but not CDK4/6 or CDK2 is selectively lethal to MYC-dependent human breast cancer cells. BMC Cancer. 2014;14:32.CrossRefGoogle Scholar
  34. 34.
    Lock RB, Ross WE. DNA topoisomerases in cancer therapy. Anticancer Drug Des. 1987;2:151–64.Google Scholar
  35. 35.
    Gallo D, De Stefano I, Grazia Prisco M, Scambia G, Ferrandina G. Estrogen receptor beta in cancer: an attractive target for therapy. Curr Pharm Des. 2012;18:2734–57.CrossRefGoogle Scholar
  36. 36.
    Yoshino Y, Ishioka C. Inhibition of glycogen synthase kinase-3 beta induces apoptosis and mitotic catastrophe by disrupting centrosome regulation in cancer cells. Sci Rep. 2015;5:13249.CrossRefGoogle Scholar
  37. 37.
    Farina AR, Mackay AR. Gelatinase B/MMP-9 in tumour pathogenesis and progression. Cancers (Basel). 2014;6:240–96.CrossRefGoogle Scholar
  38. 38.
    George Lund SD, Borkin D, Ni W, Grembecka J, Cierpicki T. Inhibition of CDC25B phosphatase through disruption of protein-protein interactions. ACS Chem Biol. 2015;10:390–4.CrossRefGoogle Scholar
  39. 39.
    Moretti RM, Montagnani Marelli M, Motta M, Limonata P. Role of the orphan nuclear receptor ROR alpha in the control of the metastatic behavior of androgen-independent prostate cancer. Oncol Rep. 2002;9:1139–43.Google Scholar
  40. 40.
    Lehne G. P-glycoprotein as a drug target in the treatment of multidrug-resistant cancer. Curr Drug Targets. 2000;1:85–99.CrossRefGoogle Scholar
  41. 41.
    Roskoski R Jr. The role of small molecule platelet-derived growth factor receptor (PDGFR) inhibitors in the treatment of neoplastic disorders. Pharmacol Res. 2018;129:65–83.CrossRefGoogle Scholar
  42. 42.
    Roskoski Jr R. Src protein-tyrosine kinase structure and regulation. Biochem Biophys Res Commun. 2004;324:1155–64.CrossRefGoogle Scholar
  43. 43.
    Sivonova MK, Jurecekova J, Tatarkova Z, Kaplan P, Lichardusova L, Hatok J. The role of CYP17A1 in prostate cancer development: structure, function, mechanism of action, genetic variations and its inhibition. Gen Physiol Biophys. 2017;36:487–99.CrossRefGoogle Scholar
  44. 44.
    Lehtio L, Chi NW, Krauss S. Tankyrases as drug targets. FEBS J. 2013;280:3576–93.CrossRefGoogle Scholar
  45. 45.
    Leao R, Apolonio JD, Lee D, Figueiredo A, Tabori U, Castelo-Branco P. Mechanism oh human telomerase reverse transcriptase (hTERT) regulation: clinical impact in cancer. J Biomed Sci. 2018;25:22.CrossRefGoogle Scholar
  46. 46.
    Xia Y, Shen S, Verma IM. NF-kB, an active player in human cancers. Cancer Immunol Res. 2014;2:823–30.CrossRefGoogle Scholar
  47. 47.
    Goldenberg-Furmanov M, Stein I, Pikarsky E, Rubin H, Kasem S, Wygoda M, Weinstein I, Reuveniand H, Ben-Sasson SA. Lyn is a target gene for prostate cancer sequence-based inhibition induces regression of human tumor xenografts. Cancer Res. 2004.Google Scholar
  48. 48.
    Neufeld G, Cohen T, Gengrinovitich S, Poltorak Z. Vascular endothelial growth factor (VEGF) and its receptors. FASEB J. 1999;13:9–22.CrossRefGoogle Scholar
  49. 49.
    Torsten Schwede JK, Guex N, Peitsch MC. SWISS-MODEL: an automated protein homology-modeling server. Nucleic Acids Res. 2003;31:3381–5.CrossRefGoogle Scholar
  50. 50.
    Zitko J, Dolezal M. Enoyl-acyl carrier protein reductase inhibitors:n an updated patent review. Expert Opin Ther Pat. 2016;26:1079–94.CrossRefGoogle Scholar
  51. 51.
    Hrast M, Vehar B, Turk S, Kong J, Gobec S, Janezic D. Function of the D-alanine:D-alanine ligase lid loop: a molecular modeling and bioactivity study. J Med Chem. 2012;55:6849–56.CrossRefGoogle Scholar
  52. 52.
    Craigie R. The molecular biology of HIV integrase. Future Virol. 2012;7:679–86.CrossRefGoogle Scholar
  53. 53.
    Hemmi A, et al. A Toll-like receptor recognizes bacterial DNA. Nature. 2000;408:740–5.CrossRefGoogle Scholar
  54. 54.
    Kostrewa D, Winkler F, Folkers G, Scapozza L, Perozzo R. The crystal structure of PfFabZ, the unique β-hydroxy acyl-ACP dehydratase involved in fatty acid biosynthesis of Plasmodium falciparum. Protein Sci. 2005;14:1570–80.CrossRefGoogle Scholar
  55. 55.
    Liu W, Lou C, Han C, Peng S, Yang Y, Yue J, et al. A new β-hydroxy acyl-acyl carrier protein dehydratase (fabZ) from Helicobacter pylori: molecular cloning, enzymatic characterization, and structural modeling. Biochem Biophys Res Commun. 2005;333:1078–86.CrossRefGoogle Scholar
  56. 56.
    Li J-L, Robson KJH, Chen J-L, Targett GAT, Baker DA. Pfmrk, a MO15-related protein kinase from Plasmodium falciparum. Eur J Biochem. 1996;241:805–13.CrossRefGoogle Scholar
  57. 57.
    Kumari M, Chandra S, Tiwari N, Subbarao N. 3D QSAR, pharmacophore and molecular docking studies of known inhibitors and designing of novel inhibitors for M18 aspartyl aminopeptidase of Plasmodium falciparum. BMC Struct Biol. 2016;16:12.CrossRefGoogle Scholar
  58. 58.
    Kelley LA, et al. The Phyre2 web portal for protein modeling, prediction, and analysis. Nat Protoc. 2015;10:845–58.CrossRefGoogle Scholar
  59. 59.
    Peng J, Xu J. RaptorX: exploiting structure information for protein alignment by statistical inference. Proteins. 2011;79(Suppl 10):161–71.CrossRefGoogle Scholar
  60. 60.
    Mudaliar S, Polidori D, Zambrowicz B, Henry RR. Sodium-glucose cotransporter inhibitors: effects on renal and intestinal glucose transport from bench to beside. Diabetes Care. 2015;38:2344–53.CrossRefGoogle Scholar
  61. 61.
    Srivastava SK, Ramana KV, Bhatnagar A. Role of aldose reductase and oxidative damage in diabetes and the consequent potential for therapeutic options. Endocr Rev. 2005;26:380–92.CrossRefGoogle Scholar
  62. 62.
    Paul AM, Meakin J, Benabou E, Haas ME, Bonardo B, Grino M, et al. The beta-secretase BACE1 regulates the expression of the insulin receptor in the liver. Nat Commun. 2018;9:1306.CrossRefGoogle Scholar
  63. 63.
    Tigno-Aranjuez JT, Benderitter P, Rombouts F, Deroose F, Bai X, Mattioli B, et al. In vivo inhibition of RIPK2 kinase alleviates inflammatory disease. J Biol Chem. 2014;289:29651–64.CrossRefGoogle Scholar
  64. 64.
    Shook JE, Lemcke PK, Gehrig CA, Hruby VJ, Burks RF. Antidiarrheal properties of supraspinal mu and delta and kappa opioid receptors: inhibition of diarrhea without constipation. J Pharmacol Exp Ther. 1989;249:83–90.Google Scholar
  65. 65.
    Dorsam RT, Kunapuli SP. Central role of the P2Y12 receptor in platelet activation. J Clin Invest. 2004;113:340–5.CrossRefGoogle Scholar
  66. 66.
    Colovic MB, Krstic DZ, Lazarevic-Pasti TD, Bondzic AM, Vasic VM. Acetylcholinesterase inhibitors: pharmacology and toxicology. Curr Neuropharmacol. 2013;11:315–35.CrossRefGoogle Scholar
  67. 67.
    Berman JWHM, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, et al. The Protein Data Bank. Nucleic Acids Res. 2000;28:235–42.CrossRefGoogle Scholar
  68. 68.
    Hacker K, Maas R, Kornhuber J, Fromm MF, Zolk O. Substrate-dependent inhibition of the human organic cation transporter OCT2: a comparison of metformin with experimental substrates. PLoS One. 2015;10:e0136451.CrossRefGoogle Scholar
  69. 69.
    Strognov OV, Novikov FN, Stroylov VS, Kulkov V, Chilov GG. Lead finder: an approach to improve the accuracy of protein-ligand docking, binding energy estimation, and virtual screening. J Chem Inf Model. 2008;48:2371–85.CrossRefGoogle Scholar
  70. 70.
    Cronan JE, Thomas J. Bacterial fatty acids synthesis and its relationships with polyketide synthetic pathways. Methods Enzymol. 2009;459:395–433.CrossRefGoogle Scholar
  71. 71.
    Rowlett VW, VKPS M, Karlstaedt A, Dowhan W, Taegtmeyer H, Margolin W, et al. Impact of membrane phospholipid alterations in Escherichia coli on cellular function and bacterial stress adaptation. J Bacteriol. 2017.Google Scholar
  72. 72.
    Heijenoort J. Formation of glycan chains in the synthesis of bacterial peptidoglycan. Glycobiology. 2001;11:25R–36R.CrossRefGoogle Scholar
  73. 73.
    van Schaijk BC, Kumar TR, Vos MW, Richman A, van Gemert GJ, Li T, et al. Type II fatty acid biosynthesis is essential for Plasmodium falciparum sporozoite development in the midgut of Anopheles mosquitoes. Eukaryot Cell. 2014;13:550–9.CrossRefGoogle Scholar
  74. 74.
    Qiang G, Yue S, Yang JJ, Du G, Pang X, Li X, et al. Identification of a small molecular insulin receptor agonist with potent antidiabetes activity. Diabetes. 2014;63:1394–409.CrossRefGoogle Scholar
  75. 75.
    Baker DJ, Timmons JA, Greenhaff PL. Glycogen phosphorylase inhibition in type 2 diabetes therapy: a systematic evaluation of metabolic and functional effects in rat skeletal muscles. Diabetes. 2005;54:2453–9.CrossRefGoogle Scholar
  76. 76.
    Holzer P. Opioid receptors in the gastrointestinal tract. Regul Pept. 2009;155:11–7.CrossRefGoogle Scholar
  77. 77.
    Ibrahim MM, Elsaman T, Al-Nour MY. Synthesis, anti-inflammatory activity, and in silico study of novel diclofenac and isatin conjugates. Int J Med Chem. 2018;2018:9139786.Google Scholar
  78. 78.
    Morimoto BH, Castelloe E, Fox AW. Safety pharmacology in drug discovery and development. Handb Exp Pharmacol. 2015;229:65–80.CrossRefGoogle Scholar
  79. 79.
    Doogue MP, Polasek TM. The ABCD of clinical pharmacokinetics. Ther Adv Drug Saf. 2013;4:5–7.CrossRefGoogle Scholar
  80. 80.
    Hassan M, Sallam H, Hassan Z. The role of pharmacokinetics and pharmacodynamics in early drug development with reference to the cyclin-dependent kinase (Cdk) inhibitor-roscovitine. Sultan Qaboos Univ Med J. 2011;11:165–78.Google Scholar
  81. 81.
    McCarren P, Springer C, Whitehead L. An investigation into pharmaceutically relevant mutagenicity data and the influence on Ames predictive potential. J Cheminform. 2011;3:51.CrossRefGoogle Scholar
  82. 82.
    Priest BT, Bell IM, Garcia ML. Role of hERG potassium channel assays in drug development. Channels. 2008;2:87–93.CrossRefGoogle Scholar
  83. 83.
    Qian YS, Ramamurthy S, Candasamy M, Shadab M, Kumar RG, Meka VS. Production, characterization, and evaluation of kaemferol nanosuspension for improving oral bioavailability. Curr Pharm Biotechnol. 2016;17:549–55.CrossRefGoogle Scholar
  84. 84.
    Seeram NP, Lee R, Heber D. Bioavailability of ellagic acid in pomegranate (Punica granum L.) juice. Clin Chim Acta. 2004;348:63–8.CrossRefGoogle Scholar
  85. 85.
    Kasikci MB, Bagdatlioglu N. Bioavailability of quercetin. Curr Res in Nutr Food Sci. 2016;4.Google Scholar
  86. 86.
    Hamad AWR, al Momani W, Janakat S, Oran SA. Bioavailability of ellagic acid after single dose administration using HPLC. Pak J Nutr. 2009;8:1661–4.CrossRefGoogle Scholar
  87. 87.
    Wang FM, Yao TW, Zeng S. Disposition of quercetin and kaempferol in a human following an oral administration of Ginkgo biloba. Eur J Drug Metab Pharmacokinet. 2003;28:173–7.CrossRefGoogle Scholar
  88. 88.
    Thilakarathna SH, Rupasinghe HPV. Flavonoid bioavailability and attempts for bioavailability enhancement. Nutrients. 2013;5:3367–87.CrossRefGoogle Scholar
  89. 89.
    Chen ZP, Sun J, Chen HX, Xiao YY, et al. Comparative pharmacokinetics and bioavailability studies of quercetin, kaempferol, and isorhamnetin after oral administration of Ginkgo biloba extracts, Ginkgo biloba extract phospholipid complexes and Ginkgo biloba extract solid dispersions in rats. Fitoterapia. 2010;81:1045–52.CrossRefGoogle Scholar
  90. 90.
    Wang T, Liu XH, Guan J, Ge S, Wu MB, Lin JP, et al. Advancement of multi-target drug discoveries and promising applications in the field of Alzheimer’s disease. Eur J Med Chem. 2019;169:200–23.CrossRefGoogle Scholar
  91. 91.
    Neves BJ, Braga RC, Melo-Filho CC, Teofilo J, Moreira-Filho JT, Muratov EN, et al. QSAR-based virtual screening: advances and applications in drug discovery. Front Pharmacol. 2018.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Pharmaceutical Chemistry, Faculty of PharmacyOmdurman Islamic UniversityOmdurmanSudan

Personalised recommendations