Introduction of magnetic and supermagnetic nanoparticles in new approach of targeting drug delivery and cancer therapy application.

In this article, the recent applications of different types of magnetic nanoparticles such as α-Fe2O3 (hematite), γ-Fe2O3 (maghemite), Fe3O4 (magnetite), hexagonal (MFe12O19), garnet (M3Fe5O12) and spinel (MFe2O4), where M represents one or more bivalent transition metals (Mn, Fe, Co, Ni, Ba, Sr, Cu, and Zn), and different materials for coating the surface of magnetic nanoparticles like poly lactic acid (PLA), doxorubicin hydrophobic (DOX-HCL), paclitaxel (PTX), EPPT-FITC, oleic acid, tannin, 3-Aminopropyltriethoxysilane (APTES), multi-wall carbon nanotubes (CNTs), polyethylenimine (PEI) and polyarabic acid in drug delivery, biomedicine and treatment of cancer, specially chemotherapy, are reviewed. MNPs possess large surface area to volume ratios because of their nano-size, low surface charge at physiological pH and they aggregate easily in solution due to their essential magnetic nature. These materials are widely used in biology and medicine in many cases. One targeted delivery technique that has gained prominence in recent years is the use of magnetic nanoparticles. In these systems, therapeutic compounds are attached to biocompatible magnetic nanoparticles and magnetic fields generated outside the body are focused on specific targets in vivo. The fields capture the particle complex, resulting in enhanced delivery to the target site. Also, the application of brand new supermagnetic nanoparticles, like Ba,SrFe12O19, is considered and studied in this paper.

QSAR study of HCV NS5B polymerase inhibitors using the genetic algorithm-multiple linear regression (GA-MLR).

Quantitative structure-activity relationship (QSAR) study has been employed for predicting the inhibitory activities of the Hepatitis C virus (HCV) NS5B polymerase inhibitors . A data set consisted of 72 compounds was selected, and then different types of molecular descriptors were calculated. The whole data set was split into a training set (80 % of the dataset) and a test set (20 % of the dataset) using principle component analysis. The stepwise (SW) and the genetic algorithm (GA) techniques were used as variable selection tools. Multiple linear regression method was then used to linearly correlate the selected descriptors with inhibitory activities. Several validation technique including leave-one-out and leave-group-out cross-validation, Y-randomization method were used to evaluate the internal capability of the derived models. The external prediction ability of the derived models was further analyzed using modified r(2), concordance correlation coefficient values and Golbraikh and Tropsha acceptable model criteria's. Based on the derived results (GA-MLR), some new insights toward molecular structural requirements for obtaining better inhibitory activity were obtained.