PURPOSE: To develop a statistical model for predicting effect of food on the extent of absorption (area under the curve of time-plasma concentration profile, AUC) of drugs based on physicochemical properties. MATERIALS AND METHODS: Logistic regression was applied to establish the relationship between the effect of food (positive, negative or no effect) on AUC of 92 entries and physicochemical parameters, including clinical doses used in the food effect study, solubility (pH 7), dose number (dose/solubility at pH 7), calculated Log D (pH 7), polar surface area, total surface area, percent polar surface area, number of hydrogen bond donor, number of hydrogen bond acceptors, and maximum absorbable dose (MAD). RESULTS: For compounds with MAD >or= clinical dose, the food effect can be predicted from the dose number category and Log D category, while for compounds with MAD < clinical dose, the food effect can be predicted from the dose number category alone. With cross validation, 74 out of 92 entries (80%) were predicted into the correct category. The correct predictions were 97, 79 and 68% for compounds with positive, negative and no food effect, respectively. CONCLUSIONS: A logistic regression model based on dose, solubility, and permeability of compounds is developed to predict the food effect on AUC. Statistically, solubilization effect of food primarily accounted for the positive food effect on absorption while interference of food with absorption caused negative effect on absorption of compounds that are highly hydrophilic and probably with narrow window of absorption.
PURPOSE: To develop a statistical model for predicting effect of food on the extent of absorption (area under the curve of time-plasma concentration profile, AUC) of drugs based on physicochemical properties. MATERIALS AND METHODS: Logistic regression was applied to establish the relationship between the effect of food (positive, negative or no effect) on AUC of 92 entries and physicochemical parameters, including clinical doses used in the food effect study, solubility (pH 7), dose number (dose/solubility at pH 7), calculated Log D (pH 7), polar surface area, total surface area, percent polar surface area, number of hydrogen bond donor, number of hydrogen bond acceptors, and maximum absorbable dose (MAD). RESULTS: For compounds with MAD >or= clinical dose, the food effect can be predicted from the dose number category and Log D category, while for compounds with MAD < clinical dose, the food effect can be predicted from the dose number category alone. With cross validation, 74 out of 92 entries (80%) were predicted into the correct category. The correct predictions were 97, 79 and 68% for compounds with positive, negative and no food effect, respectively. CONCLUSIONS: A logistic regression model based on dose, solubility, and permeability of compounds is developed to predict the food effect on AUC. Statistically, solubilization effect of food primarily accounted for the positive food effect on absorption while interference of food with absorption caused negative effect on absorption of compounds that are highly hydrophilic and probably with narrow window of absorption.
Authors: Annette Bak; Dennis Leung; Stephanie E Barrett; Seth Forster; Ellen C Minnihan; Andrew W Leithead; James Cunningham; Nathalie Toussaint; Louis S Crocker Journal: AAPS J Date: 2014-11-15 Impact factor: 4.009