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Quantitative structure–retention relationship(QSRR) method was used to model reversed-phase high-performance liquid chromatography (RP-HPLC) separation of 18 selected amino acids. Retention data for phenylthiocarbamyl (PTC) amino acids derivatives were obtained using gradient elution on ODS column with mobile phase of varying acetonitrile, acetate buffer and containing 0.5 ml/l of triethylamine (TEA). Molecular structure of each amino acid was encoded with 36 calculated molecular descriptors. The correlation between the molecular descriptors and the retention time of the compounds in the calibration set was established using the genetic neural network method. A genetic algorithm (GA) was used to select important molecular descriptors and supervised artificial neural network (ANN) was used to correlate mobile phase composition and selec...
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.
Quantitative structure–retention relationship(QSRR) method was used to model reversed-phase high-performance liquid chromatography (RP-HPLC) separation of 18 selected amino acids. Retention data for phenylthiocarbamyl (PTC) amino acids derivatives were obtained using gradient elution on ODS column with mobile phase of varying acetonitrile, acetate buffer and containing 0.5 ml/l of triethylamine (TEA). Molecular structure of each amino acid was encoded with 36 calculated molecular descriptors. The correlation between the molecular descriptors and the retention time of the compounds in the calibration set was established using the genetic neural network method. A genetic algorithm (GA) was used to select important molecular descriptors and supervised artificial neural network (ANN) was used to correlate mobile phase composition and selec...
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.
Microemulsions, being thermodynamically stable systems, with low viscosity and elegant in appearance have attracted interest not only for the delivery of single drug substances with low water solubility but for the stabilization of drugs in combination due to their preferential solubility in either the water or oil phases. Microemulsion design involves the solubilisation of an optimum amount of the dispersed phase in the continuous phase, utilizing the minimum amount of surfactant/mixture of surfactants/cosurfactants. It is the choice of the surfactant/surfactant mixture and/or cosurfactants, which poses the greatest challenge in the design of a thermodynamically stable microemulsion formulation. This paper will present a strategy for choosing surfactants to achieve a stable, dilutable microemulsion formulation for oral administration....
Recent reports that a wide variety of natural and man-made compounds are capable of competing with natural hormones for estrogen receptors serve as timely examples of the need to advance screening techniques to support human health and ascertain ecological risk. Quantitative structure–activity relationships (QSARs) can potentially serve as screening tools to identify and prioritize untested compounds for further empirical evaluations. Computer-based QSAR molecular models have been used to describe ligand–receptor interactions and to predict chemical structures that possess desired pharmacological characteristics. These have recently included combined and differential relative binding affinities of potential estrogenic compounds at estrogen receptors (ER) α and β. In the present study, artificial neural network (ANN) QSAR models were de...
Microemulsions, being thermodynamically stable systems, with low viscosity and elegant in appearance have attracted interest not only for the delivery of single drug substances with low water solubility but for the stabilization of drugs in combination due to their preferential solubility in either the water or oil phases. Microemulsion design involves the solubilisation of an optimum amount of the dispersed phase in the continuous phase, utilizing the minimum amount of surfactant/mixture of surfactants/cosurfactants. It is the choice of the surfactant/surfactant mixture and/or cosurfactants, which poses the greatest challenge in the design of a thermodynamically stable microemulsion formulation. This paper will present a strategy for choosing surfactants to achieve a stable, dilutable microemulsion formulation for oral administration....
Recent reports that a wide variety of natural and man-made compounds are capable of competing with natural hormones for estrogen receptors serve as timely examples of the need to advance screening techniques to support human health and ascertain ecological risk. Quantitative structure–activity relationships (QSARs) can potentially serve as screening tools to identify and prioritize untested compounds for further empirical evaluations. Computer-based QSAR molecular models have been used to describe ligand–receptor interactions and to predict chemical structures that possess desired pharmacological characteristics. These have recently included combined and differential relative binding affinities of potential estrogenic compounds at estrogen receptors (ER) α and β. In the present study, artificial neural network (ANN) QSAR models were de...
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