Probing the origins of anticancer activity of chrysin derivatives
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.
KeywordsChrysin Cytotoxicity QSAR Multiple linear regression Data mining
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.
- DenningtonII R, Keith T, Millam J, Eppinnett K, Hovell WL, Gilliland R (2003) GaussView, Version 3.09. Semichem Inc, Shawnee Mission, KSGoogle Scholar
- 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
- Witten IH, Frank E, Hall MA (2011) Data mining: practical machine learning tools and techniques, 3rd edn. Morgan Kaufmann, San FranciscoGoogle Scholar