Showing papers by "Snezana Agatonovic-Kustrin published in 2008"
TL;DR: The quantitative results obtained for the binary crystal form mixtures clearly demonstrate the strong potential of ATR-FTIR for use in the determination of the polymorphic content not only in bulk pharmaceuticals but also in liquid formulations.
41 citations
TL;DR: It is concluded that different ratios for the tautomeric forms for S- and R-omeprazole sodium result in changes in the degree of crystallinity and are responsible for the interaction with mannitol, common excipient in formulation.
25 citations
TL;DR: Artificial neural network (ANN) QSAR models were developed that were able to predict differential relative binding affinities of a series of structurally diverse compounds with estrogenic activity and identified structural features of phytoestrogens that are responsible for selective ligand binding to ERalpha and ERbeta.
20 citations
TL;DR: The structural determinants and requirements necessary for estrogen receptors alpha and beta selectivity and ligand-receptor binding affinity and strategies likely to result in the development of a pharmacophore model that account for the differences in estrogenic effects between different ligands will be discussed.
Abstract: This review will discuss the structural determinants and requirements necessary for estrogen receptors alpha and beta selectivity and ligand-receptor binding affinity. In addition, strategies likely to result in the development of a pharmacophore model that account for the differences in estrogenic effects between different ligands will be discussed.
12 citations
TL;DR: Novel models for the prediction of separate PK parameters for a diverse set of drugs based on the retention of each drug using micellar liquid chromatography and selected theoretically-derived descriptors are developed.
Abstract: Since the majority of lead compounds identified for drug clinical trials fail to reach the market due to poor efficacy in humans or poor pharmacokinetics (PKs), the prediction of PK properties in humans plays an important role in selection of potential drug candidates. The aim of the present study was to develop novel models for the prediction of separate PK parameters for a diverse set of drugs. Prediction would be based on the retention of each drug using micellar liquid chromatography (MLC) and selected theoretically-derived descriptors. Retention time, half life (t((1/2))), and volume of distribution (Vd) for each of the 26 training drugs were extracted from literature while molecular descriptors were generated using Molecular Modeling Pro. A total of 35 molecular descriptors describing molecular size, shape and solubility were calculated from the 3D molecular structure of each compound. Artificial neural network (ANN) modeling was used to correlate the calculated descriptors and retention time with half life and volume of distribution. A sensitivity analysis procedure was used to refine the models. The final predictive models showed significant correlations with literature values of t((1/2)) and Vd: 0.854 and 0.855 respectively for the internal testing data and 0.720 and 0.827 respectively for the external validation set of compounds. Absolute predicted values were in good agreement with literature values. Analysis of descriptors in the optimum models revealed a large degree of overlap. Solubility characteristics, hydrogen bonding, and molecular size and shape were shown to play important roles in determining drug t((1/2)) and Vd. The reciprocal of retention time was also included in both optimum models attesting to the significance of this particular physicochemical parameter and the complexity of the models developed. This novel combination of theoretical and experimental data for pharmacokinetic modeling may lead to further progress in drug development.
1 citations