Advertisement

Medicinal Chemistry Research

, Volume 24, Issue 5, pp 1884–1892 | Cite as

Probing the origins of anticancer activity of chrysin derivatives

  • Apilak Worachartcheewan
  • Chanin Nantasenamat
  • Chartchalerm Isarankura-Na-Ayudhya
  • Virapong Prachayasittikul
Original Research

Abstract

Chrysin is a derivative of flavonoid, a natural product commonly found in plants. It has been shown to afford a wide variety of pharmacological activities particularly anticancer properties. In this study, 21 chrysin derivatives with anticancer activities against human gastric adenocarcinoma (SGC-7901) and human colorectal adenocarcinoma (HT-29) cell lines were employed for quantitative structure–activity relationship (QSAR) investigation. Molecular structures were geometrically optimized at the B3LYP/6-311++g(d,p) level and their quantum chemical and molecular properties were obtained from Gaussian 09 and Dragon softwares, respectively. Significant descriptors for modeling the anticancer activities of SGC-7901 (i.e., SIC2, Mor11e, P2p, HTp, and R5e+) and HT-29 (i.e., L/Bw, BIC2, and Mor19p) cell lines were deduced from stepwise multiple linear regression (MLR) method. QSAR models were constructed using MLR and their predictivities were verified via internal (i.e., leave one-out cross-validation; LOO-CV) and external sets. The predictive performance was evaluated from their squared correlation coefficients (R 2 and Q 2) and root mean square error (RMSE). Results indicated good correlation between experimental and predicted anticancer activities as deduced from statistical parameters of internal and external sets as follows: R Tr 2  = 0.8778, RMSETr = 0.0854, Q CV 2  = 0.7315, RMSECV = 0.1375, Q Ext 2  = 0.7324, and RMSEExt = 0.1168 for QSAR models of SGC-7901 while R Tr 2  = 0.8201, RMSETr = 0.1293, Q CV 2  = 0.6829, RMSECV = 0.1735, Q Ext 2  = 0.8486, and RMSEExt = 0.1179 for QSAR models of HT-29. Furthermore, the obtained QSAR models provided pertinent insights on the structure–activity relationship of investigated compounds where molecular properties such as shape, electronegativities and polarizabilities were crucial for anticancer activity. The knowledge gained from the constructed QSAR models could serve as guidelines for the rational design of novel chrysin derivatives with potent anticancer activity.

Keywords

Chrysin Cytotoxicity QSAR Multiple linear regression Data mining 

Notes

Acknowledgments

This research project is supported by the annual budget grant of Mahidol University (B.E. 2556–2558). A. W. is thankful for Mahidol University Talent Management Program. Partial support is gratefully acknowledged from Office of the Higher Education Commission and Mahidol University under the National Research Universities Initiative.

Supplementary material

44_2014_1260_MOESM1_ESM.ppt (116 kb)
Supplementary material 1 (PPT 118 kb)

References

  1. Bae Y, Lee S, Kim SH (2011) Chrysin suppresses mast cell-mediated allergic inflammation: involvement of calcium, caspase-1 and nuclear factor-κB. Toxicol Appl Pharmacol 254:56–64CrossRefPubMedGoogle Scholar
  2. Batra P, Sharma AK (2013) Anti-cancer potential of flavonoids: recent trends and future perspectives. 3 Biotech 3:439–459CrossRefPubMedCentralGoogle Scholar
  3. Cragg GM, Grothaus PG, Newman DJ (2009) Impact of natural products on developing new anti-cancer agents. Chem Rev 109:3012–3043CrossRefPubMedGoogle Scholar
  4. Cushnie TP, Lamb AJ (2005) Antimicrobial activity of flavonoids. Int J Antimicrob Agents 26:343–356CrossRefPubMedGoogle Scholar
  5. DenningtonII R, Keith T, Millam J, Eppinnett K, Hovell WL, Gilliland R (2003) GaussView, Version 3.09. Semichem Inc, Shawnee Mission, KSGoogle Scholar
  6. Drews J (2007) Drug discovery: a historical perspective. Science 287:1960–1964CrossRefGoogle Scholar
  7. Duchowicz PR, Bennardi DO, Bacelo DE, Bonifazi EL, Rios-Luci C, Padrón JM, Burton G, Misico RI (2014) QSAR on antiproliferative naphthoquinones based on a conformation-independent approach. Eur J Med Chem 77:176–184CrossRefPubMedGoogle Scholar
  8. Eriksson L, Johansson E (1996) Multivariate design and modeling in QSAR. Chemometr Intell Lab Syst 34:1–19CrossRefGoogle Scholar
  9. Frisch MJ, Trucks GW, Schlegel HB, Scuseria GE, Robb MA, Cheeseman JR, Scalmani G, Barone V, Mennucci B, Petersson GA, Nakatsuji H, Caricato M, Li X, Hratchian HP, Izmaylov AF, Bloino J, Zheng G, Sonnenberg JL, Hada M, Ehara M, Toyota K, Fukuda R, Hasegawa J, Ishida M, Nakajima T, Honda Y, Kitao O, Nakai H, Vreven T, Montgomery JA, Peralta JE, Ogliaro F, Bearpark M, Heyd JJ, Brothers E, Kudin KN, Staroverov VN, Kobayashi R, Normand J, Raghavachari K, Rendell A, Burant JC, Iyengar SS, Tomasi J, Cossi M, Rega N, Millam JM, Klene M, Knox JE, Cross JB, Bakken V, Adamo C, Jaramillo J, Gomperts R, Stratmann RE, Yazyev O, Austin AJ, Cammi R, Pomelli C, Ochterski JW, Martin RL, Morokuma K, Zakrzewski VG, Voth GA, Salvador P, Dannenberg JJ, Dapprich S, Daniels AD, Farkas O, Foresman JB, Ortiz JV, Cioslowski J, Fox DJ (2009) Gaussian 09, Revision A.1. Wallingford, ConnecticutGoogle Scholar
  10. Heim KE, Tagliaferro AR, Bobilya DJ (2002) Flavonoid antioxidants: chemistry, metabolism and structure-activity relationships. J Nutr Biochem 13:572–584CrossRefPubMedGoogle Scholar
  11. Ibrahim AK, Youssef AI, Arafa AS, Ahmed SA (2013) Anti-H5N1 virus flavonoids from Capparis sinaica Veill. Nat Prod Res 27:2149–2153CrossRefPubMedGoogle Scholar
  12. Ishihara M, Yokote Y, Sakagami H (2006) Quantitative structure-cytotoxicity relationship analysis of coumarin and its derivatives by semiempirical molecular orbital method. Anticancer Res 26:2883–2886PubMedGoogle Scholar
  13. Ishihara M, Kawase M, Westman G, Samuelsson K, Motohashi N, Sakagami H (2007) Quantitative structure-cytotoxicity relationship analysis of phenoxazine derivatives by semiempirical molecular-orbital method. Anticancer Res 27:4053–4057PubMedGoogle Scholar
  14. Kandaswami C, Lee LT, Lee PP, Hwang JJ, Ke FC, Huang YT, Lee MT (2005) The antitumor activities of flavonoids. In Vivo 19:895–909PubMedGoogle Scholar
  15. Karelson M, Lobanov VS, Katritzky AR (1996) Quantum-chemical descriptors in QSAR/QSPR studies. Chem Rev 96:1027–1044CrossRefPubMedGoogle Scholar
  16. Khachatoorian R, Arumugaswami V, Raychaudhuri S, Yeh GK, Maloney EM, Wang J, Dasgupta A, French SW (2012) Divergent antiviral effects of bioflavonoids on the hepatitis C virus life cycle. Virology 433:346–355CrossRefPubMedCentralPubMedGoogle Scholar
  17. Kubo I, Kinst-Hori I, Chaudhuri SK, Kubo Y, Sánchez Y, Ogura T (2000) Flavonols from Heterotheca inuloides: tyrosinase inhibitory activity and structural criteria. Bioorg Med Chem 8:1749–1755CrossRefPubMedGoogle Scholar
  18. Mohammed HA, Ba LA, Burkholz T, Schumann E, Diesel B, Zapp J, Kiemer AK, Ries C, Hartmann RW, Hosny M, Jacob C (2011) Facile synthesis of chrysin-derivatives with promising activities as aromatase inhibitors. Nat Prod Commun 6:31–34PubMedGoogle Scholar
  19. Nantasenamat C, Isarankura-Na-Ayudhya C, Naenna T, Prachayasittikul V (2007a) Quantitative structure-imprinting factor relationship of molecularly imprinted polymers. Biosens Bioelectron 22:3309–3317CrossRefPubMedGoogle Scholar
  20. Nantasenamat C, Isarankura-Na-Ayudhya C, Tansila N, Naenna T, Prachayasittikul V (2007b) Prediction of GFP spectral properties using artificial neural network. J Comput Chem 28:1275–1289CrossRefPubMedGoogle Scholar
  21. Nantasenamat C, Isarankura-Na-Ayudhya C, Prachayasittikul V (2010) Advances in computational methods to predict the biological activity of compounds. Expert Opin Drug Discov 5:633–654CrossRefPubMedGoogle Scholar
  22. Nantasenamat C, Worachartcheewan A, Prachayasittikul S, Isarankura-Na-Ayudhya C, Prachayasittikul V (2013a) QSAR modeling of aromatase inhibitory activity of 1-substituted 1,2,3-triazole analogs of letrozole. Eur J Med Chem 69:99–114CrossRefPubMedGoogle Scholar
  23. Nantasenamat C, Li H, Mandi P, Worachartcheewan A, Monnor T, Isarankura-Na-Ayudhya C, Prachayasittikul V (2013b) Exploring the chemical space of aromatase inhibitors. Mol Divers 17:661–677CrossRefPubMedGoogle Scholar
  24. Newman DJ, Cragg GM (2007) Natural products as sources of new drugs over the last 25 years. J Nat Prod 70:461–477CrossRefPubMedGoogle Scholar
  25. Nijveldt RJ, van Nood E, van Hoorn DE, Boelens PG, van Norren K, van Leeuwen PA (2001) Flavonoids: a review of probable mechanisms of action and potential applications. Am J Clin Nutr 74:418–425PubMedGoogle Scholar
  26. Parr RG, Pearson RG (1983) Absolute hardness: companion parameter to absolute electronegativity. J Am Chem Soc 105:7512–7516CrossRefGoogle Scholar
  27. Parr RG, Donnelly RA, Levy M, Palke WE (1978) Electronegativity: the density functional viewpoint. J Chem Phys 68:3801–3807CrossRefGoogle Scholar
  28. Parr RG, Szentpaly Lv, Liu S (1999) Electrophilicity Index. J Am Chem Soc 121:1922–1924CrossRefGoogle Scholar
  29. Pietta PG (2000) Flavonoids as antioxidants. J Nat Prod 63:1035–1042CrossRefPubMedGoogle Scholar
  30. Rathee P, Chaudhary H, Rathee S, Rathee D, Kumar V, Kohli K (2009) Mechanism of action of flavonoids as anti-inflammatory agents: a review. Inflamm Allergy Drug Targets 8:229–235CrossRefPubMedGoogle Scholar
  31. Sathiavelu J, Senapathy GJ, Devaraj R, Namasivayam N (2009) Hepatoprotective effect of chrysin on prooxidant-antioxidant status during ethanol-induced toxicity in female albino rats. J Pharm Pharmacol 61:809–817CrossRefPubMedGoogle Scholar
  32. Serafini M, Peluso I, Raguzzini A (2010) Flavonoids as anti-inflammatory agents. Proc Nutr Soc 69:273–278CrossRefPubMedGoogle Scholar
  33. Sun LP, Chen AL, Hung HC, Chien YH, Huang JS, Huang CY, Chen YW, Chen CN (2012) Chrysin: a histone deacetylase 8 inhibitor with anticancer activity and a suitable candidate for the standardization of Chinese propolis. J Agric Food Chem 60:11748–11758CrossRefPubMedGoogle Scholar
  34. Takahashi T, Kokubo R, Sakaino M (2004) Antimicrobial activities of eucalyptus leaf extracts and flavonoids from Eucalyptus maculata. Lett Appl Microbiol 39:60–64CrossRefPubMedGoogle Scholar
  35. Thanikaivelan P, Subramanian V, Raghava Rao J, Unni Nair B (2000) Application of quantum chemical descriptor in quantitative structure activity and structure property relationship. Chem Phys Lett 323:59–70CrossRefGoogle Scholar
  36. Tropsha A, Gramatica P, Gombar VK (2003) The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models. QSAR Comb Sci 22:69–77CrossRefGoogle Scholar
  37. Wang J, Qiu J, Dong J, Li H, Luo M, Dai X, Zhang Y, Leng B, Niu X, Zhao S, Deng X (2011) Chrysin protects mice from Staphylococcus aureus pneumonia. J Appl Microbiol 111:1551–1558CrossRefPubMedGoogle Scholar
  38. Witten IH, Frank E, Hall MA (2011) Data mining: practical machine learning tools and techniques, 3rd edn. Morgan Kaufmann, San FranciscoGoogle Scholar
  39. Woo KJ, Jeong YJ, Park JW, Kwon TK (2004) Chrysin-induced apoptosis is mediated through caspase activation and Akt inactivation in U937 leukemia cells. Biochem Biophys Res Commun 325:1215–1222CrossRefPubMedGoogle Scholar
  40. Worachartcheewan A, Nantasenamat C, Isarankura-Na-Ayudhya C, Prachayasittikul V (2013a) QSAR study of amidino bis-benzimidazole derivatives as potent anti-malarial agents against Plasmodium falciparum. Chem Pap 67:1462–1473CrossRefGoogle Scholar
  41. Worachartcheewan A, Nantasenamat C, Isarankura-Na-Ayudhya C, Prachayasittikul V (2013b) Predicting antimicrobial activities of benzimidazole derivatives. Med Chem Res 22:5418–5430CrossRefGoogle Scholar
  42. Worachartcheewan A, Nantasenamat C, Owasirikul W, Monnor T, Naruepantawart O, Janyapaisarn S, Prachayasittikul S, Prachayasittikul V (2014) Insights into antioxidant activity of 1-adamantylthiopyridine analogs using multiple linear regression. Eur J Med Chem 73:258–264CrossRefPubMedGoogle Scholar
  43. Zhang S, Yang X, Morris ME (2004a) Flavonoids are inhibitors of breast cancer resistance protein (ABCG2)-mediated transport. Mol Pharmacol 65:1208–1216CrossRefPubMedGoogle Scholar
  44. Zhang T, Chen X, Qu L, Wu J, Cui R, Zhao Y (2004b) Chrysin and its phosphate ester inhibit cell proliferation and induce apoptosis in Hela cells. Bioorg Med Chem 12:6097–6105CrossRefPubMedGoogle Scholar
  45. Zheng X, Meng WD, Xu YY, Cao JG, Qing FL (2003) Synthesis and anticancer effect of chrysin derivatives. Bioorg Med Chem Lett 13:881–884CrossRefPubMedGoogle Scholar
  46. Zheng X, Zhao FF, Liu YM, Yao X, Zheng ZT, Luo X, Liao DF (2010) Synthesis and preliminary biological evaluation of chrysin derivatives as potential anticancer drugs. Med Chem 6:6–8CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Apilak Worachartcheewan
    • 1
  • Chanin Nantasenamat
    • 1
    • 2
  • Chartchalerm Isarankura-Na-Ayudhya
    • 2
  • Virapong Prachayasittikul
    • 2
  1. 1.Center of Data Mining and Biomedical Informatics, Faculty of Medical TechnologyMahidol UniversityBangkokThailand
  2. 2.Department of Clinical Microbiology and Applied Technology, Faculty of Medical TechnologyMahidol UniversityBangkokThailand

Personalised recommendations