Predicting blood-to-plasma concentration ratios of drugs from chemical structures and volumes of distribution in humans.

Despite their importance in determining the dosing regimen of drugs in the clinic, only a few studies have investigated methods for predicting blood-to-plasma concentration ratios (Rb). This study established an Rb prediction model incorporating typical human pharmacokinetics (PK) parameters. Experimental Rb values were compiled for 289 compounds, offering reliable predictions by expanding the applicability domain. Notably, it is the largest list of Rb values reported so far. Subsequently, human PK parameters calculated from plasma drug concentrations, including the volume of distribution (Vd), clearance, mean residence time, and plasma protein binding rate, as well as 2702 kinds of molecular descriptors, were used to construct quantitative structure-PK relationship models for Rb. Among the evaluated PK parameters, logVd correlated best with Rb (correlation coefficient of 0.47). Thus, in addition to molecular descriptors selected by XGBoost, logVd was employed to construct the prediction models. Among the analyzed algorithms, artificial neural networks gave the best results. Following optimization using six molecular descriptors and logVd, the model exhibited a correlation coefficient of 0.64 and a root-mean-square error of 0.205, which were superior to those previously reported for other Rb prediction methods. Since Vd values and chemical structures are known for most medications, the Rb prediction model described herein is expected to be valuable in clinical settings.

Predicted values for human total clearance of a variety of typical compounds with differently humanized-liver mouse plasma data.

Prediction of human pharmacokinetics is important in the preclinical stage. Values for total clearance of compounds from plasma should be one of the most important pharmacokinetic parameters for predictions. Although several physiological and empirical methods including single-species allometry for prediction of values for human clearance of compounds using humanized-liver mice have been reported, further improvement of prediction accuracies would be still expected. To optimize these approaches, we proposed methods for unbound intrinsic clearance in virtually 100% humanized-liver mouse by incorporating unbound plasma fractions of compounds in differently humanized-liver mice. Comparisons of prediction accuracies of values for human clearance of 15 model compounds were performed among our current physiological and previously reported models and single-species allometry using humanized-liver mice. Incorporation of the actual unbound plasma fractions of compounds and correction of residual mice hepatocyte in humanized-liver mice showed comparable prediction accuracy to that by single-species allometry. After exclusion of 3 compounds with large species differences in values of clearance and unbound plasma fractions between mice and humans out of 15 compounds, prediction accuracies were improved in the methods investigated. The previously and present reported physiological methods could show the good prediction accuracy of values for clearance of drugs from plasma.