Theoretically-derived molecular descriptors important in human intestinal absorption.
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.
School of Pharmaceutical Sciences, Universiti Sains Malaysia, Penang, 11800, Malaysia. nena@usm.my
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Agatonovic-Kustrin
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Beresford
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Yusof
D001185:Artificial Intelligence; D055598:Chemical Phenomena; D002627:Chemistry, Physical; D006801:Humans; D007408:Intestinal Absorption / Q000502:physiology*; D008954:Models, Biological; D016571:Neural Networks, Computer; D004364:Pharmaceutical Preparations / Q000378:metabolism; D021281:Quantitative Structure-Activity Relationship
D004364:Pharmaceutical Preparations