title: Estimation of biliary excretion of foreign compounds using properties of molecular structure.
abstract: Biliary excretion is one of the main elimination pathways for drugs and/or their metabolites. Therefore, an insight into the structural profile of cholephilic compounds through accurate modelling of the biliary excretion is important for the estimation of clinical pharmacokinetics in early stages of drug discovery. The aim of this study was to develop quantitative structure-activity relationships as computational tools for the estimation of biliary excretion and identification of the molecular properties controlling this process. The study used percentage of dose excreted intact into bile measured in vivo in rat for a diverse dataset of 217 compounds. Statistical techniques were multiple linear regression analysis, regression trees, random forest and boosted trees. A simple regression tree model generated using the CART algorithm was the most accurate in the estimation of the percentage of bile excretion of compounds, and this outperformed the more sophisticated boosted trees and random forest techniques. Analysis of the outliers indicated that the models perform best when lipophilicity is not too extreme (log P < 5.35) and for compounds with molecular weight above 280 Da. Molecular descriptors selected by all these models including the top ten incorporated in boosted trees and random forest indicated a higher biliary excretion for relatively hydrophilic compounds especially if they are anionic or cationic, and have a large molecular size. A statistically validated molecular weight threshold for potentially significant biliary excretion was above 348 Da.
authors.affiliation: Medway School of Pharmacy, Universities of Kent and Greenwich, Chatham, Kent, ME4 4TB, UK.
authors.forename: Mohsen
authors.initials: M
authors.lastname: Sharifi
authors.forename: Taravat
authors.initials: T
authors.lastname: Ghafourian
mesh_terms: D005704:Gallbladder / Q000378:metabolism*; D008099:Liver / Q000378:metabolism*; D008956:Models, Chemical; D010599:Pharmacokinetics; D012044:Regression Analysis; D013329:Structure-Activity Relationship