Modeling of the anticancer action for radical derivatives of nitroazoles: quantitative structure-activity relationship (QSAR) study

Biological Evaluation of Arylsemicarbazone Derivatives as Potential Anticancer Agents

Fourteen arylsemicarbazone derivatives were synthesized and evaluated in order to find agents with potential anticancer activity. Cytotoxic screening was performed against K562, HL-60, MOLT-4, HEp-2, NCI-H292, HT-29 and MCF-7 tumor cell lines. Compounds 3c and 4a were active against the tested cancer cell lines, being more cytotoxic for the HL-60 cell line with IC₅₀ values of 13.08 μM and 11.38 μM, respectively. Regarding the protein kinase inhibition assay, 3c inhibited seven different kinases and 4a strongly inhibited the CK1δ/ε kinase. The studied kinases are involved in several cellular functions such as proliferation, migration, cell death and cell cycle progression. Additional analysis by flow cytometry revealed that 3c and 4a caused depolarization of the mitochondrial membrane, suggesting apoptosis mediated by the intrinsic pathway. Compound 3c induced arrest in G1 phase of the cell cycle on HL-60 cells, and in the annexin V assay approximately 50% of cells were in apoptosis at the highest concentration tested (26 μM). Compound 4a inhibited cell cycle by accumulation of abnormal postmitotic cells at G1 phase and induced DNA fragmentation at the highest concentration (22 μM).

Improved quantitative structure-activity relationship models to predict antioxidant activity of flavonoids in chemical, enzymatic, and cellular systems

Quantitative structure-activity relationship (QSAR) models are useful in understanding how chemical structure relates to the biological activity of natural and synthetic chemicals and for design of newer and better therapeutics. In the present study, 46 flavonoids and related polyphenols were evaluated for direct/indirect antioxidant activity in three different assay systems of increasing complexity (chemical, enzymatic, and intact phagocytes). Based on these data, two different QSAR models were developed using (i) physicochemical and structural (PC&S) descriptors to generate multiparameter partial least squares (PLS) regression equations derived from optimized molecular structures of the tested compounds and (ii) a partial 3D comparison of the 46 compounds with local fingerprints obtained from fragments of the molecules by the frontal polygon (FP) method. We obtained much higher QSAR correlation coefficients (r) for flavonoid end-point antioxidant activity in all three assay systems using the FP method (0.966, 0.948, and 0.965 for datasets evaluated in the biochemical, enzymatic, and whole cell assay systems, respectively). Furthermore, high leave-one-out cross-validation coefficients (q2) of 0.907, 0.821, and 0.897 for these datasets, respectively, indicated enhanced predictive ability and robustness of the model. Using the FP method, structural fragments (submolecules) responsible for the end-point antioxidant activity in the three assay systems were also identified. To our knowledge, this is the first QSAR model derived for description of flavonoid direct/indirect antioxidant effects in a cellular system, and this model could form the basis for further drug development of flavonoid-like antioxidant compounds with therapeutic potential.

Quantitative structure-activity relationships for small non-peptide antagonists of CXCR2: indirect 3D approach using the frontal polygon method

The chemokine receptor, CXCR2, plays an important role in recruiting granulocytes to sites of inflammation and has been proposed as an important therapeutic target. A number of CXCR2 antagonists have been synthesized and evaluated; however, quantitative structure-activity relationship (QSAR) models have not been developed for these molecules. Most CXCR2 antagonists can be grouped into four related categories: N,N'-diphenylureas, nicotinamide N-oxides, quinoxalines, and triazolethiols. Based on these categories, we developed a QSAR model for 59 nonpeptide antagonists of CXCR2 using a partial 3D comparison of the antagonists with local fingerprints obtained from rigid and flexible fragments of the molecules. Each compound was represented by calculated structural descriptors that encoded atomic charge, molar refraction, hydrophobicity, and geometric features. We obtained good conventional R(2) coefficients, high leave-one-out cross-validated values for the whole dataset (R(cv)(2)=0.785), as well as for the dataset divided into subsets of triazolethiol derivatives (R(cv)(2)=0.821) and joint subset of N'-diphenylureas, nicotinamide N-oxides, N,N'-diphenylureas, and quinoxaline derivatives and quinoxalines derivatives (R(cv)(2)=0.766), indicating a good predictive ability and robustness of the model. Additionally, charge distribution was found to be a significant contributor in modeling whole dataset. Using our model, structural fragments (submolecules) responsible for the antagonist activity were also identified. These data suggest the QSAR models developed here may be useful in guiding the design of CXCR2 antagonists from molecular fragments.