Quantitative structure-activity relationship study on potent anticancer compounds against MOLT-4 and P388 leukemia cell lines

被引:26
作者
Arthur, David Ebuka [1 ]
Uzairu, Adamu [1 ]
Mamza, Paul [1 ]
Abechi, Stephen [1 ]
机构
[1] Ahmadu Bello Univ, Dept Chem, Zaria, Kaduna State, Nigeria
关键词
QSAR method; Anticancer; paDEL descriptors; Applicability domain; Cell lines; NCI database; VARIABLE SELECTION; NEURAL-NETWORK; QSAR MODELS; CYTOTOXICITY; VALIDATION; RECEPTOR; DESCRIPTORS; INHIBITION; PREDICTION; DENSITY;
D O I
10.1016/j.jare.2016.03.010
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
A quantitative structure-activity relationship (QSAR) study was carried out on 112 anticancer compounds to develop a robust model for the prediction of anti-leukemia activity (pGI(50)) against MOLT-4 and P388 leukemia cell lines. The Genetic algorithm (GA) and multiple linear regression analysis (MLRA) were used to select the descriptors and to generate the correlation models that relate the structural features to the biological activities. The final equations consist of 15 and 10 molecular descriptors calculated using the paDEL molecular descriptor software. The GA-MLRA analysis showed that the Conventional bond order ID number of order 1 (piPC1), number of atomic composition (nAtomic), and Largest absolute eigenvalue of Burden modified matrix - n 7/weighted by relative mass (SpMax7_Bhm) play a significant role in predicting the anticancer activities of these compounds. The best QSAR model for MOLT-4 was obtained with R-2 value of 0.902, Q(LOO)(2) = 0.881 and R-pred(2)= 0.635, while for P388 cell line R-2 = 0.904, Q(LOO)(2)= 0.856 and R-pred(2)= 0.670. The Y-scrambling/randomization validation also confirms the statistical significance of the models. These models are expected to be useful for predicting the inhibitory activity (pGI50) against MOLT-4 and P388 leukemia cell lines. (C) 2016 Production and hosting by Elsevier B.V. on behalf of Cairo University.
引用
收藏
页码:823 / 837
页数:15
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