A quantitative structure-human intestinal absorption relationship was developed using artificial neural network (ANN) modeling. A set of 86 drug compounds and their experimentally-derived intestinal absorption values used in this study was gathered from the literature and a total of 57 global molecular descriptors, including constitutional, topological, chemical, geometrical and quantum chemical descriptors, calculated for each compound. A supervised network with radial basis transfer function was used to correlate calculated molecular descriptors with experimentally-derived measures of human intestinal absorption. A genetic algorithm was then used to select important molecular descriptors. Intestinal absorption values (IA%) were used as the ANN's output and calculated molecular descriptors as the inputs. The best genetic neural network (GNN) model with 15 input descriptors was chosen, and the significance of the selected descriptors for intestinal absorption examined. Results obtained with the model that was developed indicate that lipophilicity, conformational stability and inter-molecular interactions (polarity, and hydrogen bonding) have the largest impact on intestinal absorption.
A simple high-performance liquid chromatographic method was developed for the determination of omeprazole in human plasma. Omeprazole and the internal standard, chloramphenicol, were extracted from alkalinized plasma samples using dichloromethane. The mobile phase was 0.05 M Na2HPO4-ACN (65:35, v/v) adjusted to pH 6.5. Analysis was run at a flow rate of 1.0 ml/min at a detection wavelength of 302 nm. The method was specific and sensitive with a detection limit of 2.5 ng/ml at a signal-to-noise ratio of 4:1. The limit of quantification was set at 5 ng/ml. The calibration curve was linear over a concentration range of 5-1280 ng/ml. Mean recovery value of the extraction procedure was about 96%, while the within and between day coefficient of variation and percent error values of the assay method were all less than 14%.