Predicting Clearance Pathway Using Structural Parameters

Abstract

Background: The clearance, by renal elimination or hepatic metabolism, is one of the most important pharmacokinetic parameters of a drug. It allows the half-life, bioavailability and drug-drug interactions to be predicted, and it can also affect the dose regimen of a drug. Predicting the clearance pathway of new chemical candidates during drug development is vital in order to minimize the risk of possible side effects and drug interactions. Many expensive and time-consuming in vivo methods have been established to predict drugs clearance in humans, and these mainly rely on data from in vivo studies in preclinical species mainly rats, dogs and monkeys. Aim: The aim of this study is to find the relationship between clearance pathway and chemical structure. Methods: Clearance pathway of drugs was obtained from literature. Various structural descriptors (Abraham solvation parameters, Volsurf+ descriptors, topological polar surface area, number of hydrogen bond donors and acceptors, number of rotatable bonds, molecular weight, logarithm of partition coefficient (LogP), and logarithm of distribution coefficient at pH=7.4 (logD7.4)) were applied to develop a mechanistic model for predicting clearance pathway and renal clearance class.Results: The results of this study indicate that logD7.4 and number of hydrogen bond donors are the most important parameters to predict the clearance pathway of drugs. Furthermore, established models by logistic regression based on other structural parameters could help propose a prediction tool for clearance pathway. The overall prediction accuracy of all proposed models in this study is higher than 75%. Conclusion: The developed model can be used to find clearance pathways of new drug candidates with acceptable accuracy. Hydrophobicity and functional groups involved in hydrogen bond formation of a compound are the main parameters in evaluating this parameter.