The Role of Oxidative Stress Modulators in Breast Cancer.

BACKGROUND: Latest data from International Agency for Cancer Research shows that breast cancer is the leading cancer site in women and is the leading cause of death among female cancers. Induction of reactive oxygen species (ROS) and oxidative stress as a consequence of impaired balance between prooxidants and antioxidants are suggested to be involved in induction and progression of breast cancer. Cancer cells are found to exhibit higher levels of ROS compared to normal cells. However increased antioxidant defence which balances the oxidative status within the cancer cells suggests that high ROS levels may prevent tumorigenesis via various mechanisms. These contradictory roles of ROS and oxidative stress in breast cancer let scientists investigate potential oxidative stress modulators as anticancer strategies. CONCLUSIONS: In the present review we address the mechanisms of ROS production in breast cancer cells, the role of impaired oxidative status as well as the benefits of introducing oxidative stress modulators in therapeutic strategies in breast cancer. This review is focusing more on melatonin which we have been working on during the last decade. Our data, in accordance with the literature, suggest an important role for melatonin in breast cancer prevention and adjuvant therapy.

An application of CIFAP for predicting the binding affinity of Chk1 inhibitors derived from 2-aminothiazole-4-carboxamide.

Investigation of protein-ligand interactions obtained from experiments has a crucial part in the design of newly discovered and effective drugs. Analyzing the data extracted from known interactions could help scientists to predict the binding affinities of promising ligands before conducting experiments. The objective of this study is to advance the CIFAP (compressed images for affinity prediction) method, which is relevant to a protein-ligand model, identifying 2D electrostatic potential images by separating the binding site of protein-ligand complexes and using the images for predicting the computational affinity information represented by pIC50 values. The CIFAP method has 2 phases, namely, data modeling and prediction. In data modeling phase, the separated 3D structure of the binding pocket with the ligand inside is fitted into an electrostatic potential grid box, which is then compressed through 3 orthogonal directions into three 2D images for each protein-ligand complex. Sequential floating forward selection technique is performed for acquiring prediction patterns from the images. In the prediction phase, support vector regression (SVR) and partial least squares regression are used for testing the quality of the CIFAP method for predicting the binding affinity of 45 CHK1 inhibitors derived from 2-aminothiazole-4-carboxamide. The results show that the CIFAP method using both support vector regression and partial least squares regression is very effective for predicting the binding affinities of CHK1-ligand complexes with low-error values and high correlation. As a future work, the results could be improved by working on the pose of the ligands inside the grid.