Analysis of Liquid Crystalline Nanoparticles by Small Angle X-Ray Diffraction: Evaluation of Drug and Pharmaceutical Additives Influence on the Internal Structure

The goal of this work was to study the liquid crystalline structure of a nanodispersion delivery system intended to be used in photodynamic therapy after loading with photosensitizers (PSs) and additives such as preservatives and thickening polymers. Polarized light microscopy and light scattering were performed on a standard nanodispersion in order to determine the anisotropy of the liquid crystalline structure and the mean diameter of the nanoparticles, respectively. Small angle X-ray diffraction (SAXRD) was used to verify the influence of drug loading and additives on the liquid crystalline structure of the nanodispersions. The samples, before and after the addition of PSs and additives, were stable over 90 days, as verified by dynamic light scattering. SAXRD revealed that despite the alteration observed in some of the samples analyzed in the presence of photosensitizing drugs and additives, the hexagonal phase still remained in the crystalline phase. (C) 2011 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 100: 2849-2857, 2011

A chemometric study on the analgesic activity of cannabinoid compounds using SDA, KNN and SIMCA methods

The supervised pattern recognition methods K-Nearest Neighbors (KNN), stepwise discriminant analysis (SDA), and soft independent modelling of class analogy (SIMCA) were employed in this work with the aim to investigate the relationship between the molecular structure of 27 cannabinoid compounds and their analgesic activity. Previous analyses using two unsupervised pattern recognition methods (PCA-principal component analysis and HCA-hierarchical cluster analysis) were performed and five descriptors were selected as the most relevants for the analgesic activity of the compounds studied: R (3) (charge density on substituent at position C(3)), Q (1) (charge on atom C(1)), A (surface area), log P (logarithm of the partition coefficient) and MR (molecular refractivity). The supervised pattern recognition methods (SDA, KNN, and SIMCA) were employed in order to construct a reliable model that can be able to predict the analgesic activity of new cannabinoid compounds and to validate our previous study. The results obtained using the SDA, KNN, and SIMCA methods agree perfectly with our previous model. Comparing the SDA, KNN, and SIMCA results with the PCA and HCA ones we could notice that all multivariate statistical methods classified the cannabinoid compounds studied in three groups exactly in the same way: active, moderately active, and inactive.