Prediction of the first-pass metabolism of drugs after oral intake based on structural parameters and physicochemical properties

Abstract

Background: Oral first-pass metabolism is one of the crucial factors, which plays a key role in a drug’s pharmacokinetic profile. Prediction of oral first-pass metabolism based on chemical structural parameters can be useful in the drug-designing process. Developing an orally administered drug with an acceptable pharmacokinetic profile is necessary for reducing cost and time, consumed in preclinical studies to evaluate the extent of first-pass metabolism in a candidate compound. Objective: The primary end-point of this study is to estimate the first-pass metabolism of an orally administered drug. Methods: A data set of compounds with reported first-pass metabolism was collected. Moreover, human intestinal absorption percentage and oral bioavailability data were extracted from the literature to propose a classification system to split the drugs based on the extent of first-pass metabolism. In the following, various structural parameters were calculated for each compound. The relations between the structural and physicochemical values of each compound with its belonging class were obtained using logistic regression. Results: Initial analysis showed that compounds with LogD7.4>1 or Rugosity factor>1.5 are more likely to have high first-pass metabolism. Four different models were introduced, which can predict the class of oral first-pass metabolism with an acceptable error. Overall accuracies of models were in the range of 72% (model with simple descriptors) to 78% (models with complex descriptors). Although the models with simple descriptors have lower accuracy compared to complex models, they are more interpretable and easier to utilize for the researchers. Conclusion: All this considered, a novel classification of drugs based on the extent of oral first-pass metabolism was introduced, and mechanistic models were developed to fit the candidate compounds in one of the proposed classes.