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Dichloroacetate (DCA) is a simple and small anticancer drug that arouses the activity of the enzyme pyruvate dehydrogenase (PDH) through inhibition of the enzyme pyruvate dehydrogenase kinases (PDK1-4). DCA can selectively promote mitochondria-regulated apoptosis, depolarizing the hyperpolarized inner mitochondrial membrane potential to normal levels, inhibit tumor growth and reduce proliferation by shifting the glucose metabolism in cancer cells from anaerobic to aerobic glycolysis. In this study, a series of DCA analogues were applied to quantitative structure–activity relationship (QSAR) analysis. A collection of chemometrics methods such as multiple linear regression (MLR), factor analysis–based multiple linear regression (FA-MLR), principal component regression (PCR), and partial least squared combined with genetic algorithm for variable selection (GA-PLS) were applied to make relations between structural characteristics and cytotoxic activities of a variety of DCA analogues. The best multiple linear regression equation was obtained from genetic algorithms partial least squares, which predict 90% of variances. Based on the resulted model, an in silico-screening study was also conducted and new potent lead compounds based on new structural patterns were designed. Molecular docking as well as protein ligand interaction fingerprints (PLIF) studies of these compounds were also investigated and encouraging results were acquired. There was a good correlation between QSAR and docking results.

A Comparative QSAR Analysis, Molecular Docking and PLIF Studies of Some N-arylphenyl-2, 2-Dichloroacetamide Analogues as Anticancer Agents