Use of structural parameters in prediction of biopharmaceutics classification system

Abstract

Modeling of physicochemical and pharmacokinetic properties is important for the prediction and mechanism characterization in drug discovery and development. Biopharmaceutical Drug Disposition Classification System (BDDCS) is a four-class system based on solubility and metabolism. It is used to describe the role of transporters in pharmacokinetics and their interaction with metabolizing enzymes. Moreover, it was developed to anticipate drug disposition and potential drug-drug interactions in the liver and intestine. According to the BDDCS, drugs can be classified in four groups in terms of the extent of metabolism and solubility (high and low). Aims: In this study, structural parameters of drugs were used to develop classification based model by logistic regression for prediction of BDDCS class. Methods: Reported BDDCS class data of drugs were collected form literature. Structural descriptors were calculated by ACD/iLab software. Data was divided to training and test sets. Then, stepwise logistic regression was used for prediction of class of each drug in BDDCS system using structural parameters. Then, the validity of established models was evaluated by an external test set. Results: The results of this study showed that Clog P and Abraham solvation parameters can be used to predict class of solubility (77%) and metabolism (83%) in BDDCS system with good accuracy. Based on the developed methods for prediction solubility and metabolism class, 64% and 63% of training and test set could be predicted in correct BDDCS class, respectively. Discussion and Conclusion: Structural properties of drugs i.e. logP and Abraham solvation parameters (polarizability, hydrogen bonding acidity and basicity) can be used for prediction of BDDCS class.