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

Abstract

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.

Keywords: Artificial neural networks; Blood-to-plasma ratio; Pharmacokinetics; Quantitative structure–pharmacokinetic relationships; Volume of distribution.

Fig. 1

Three-component principal component analysis (PCA) score plots based on 11 representative molecular descriptors (n = 289). a Score plot of PCA1 (34.4%) and PCA2 (26.1%). The horizontal axis indicates the first principal component, while the vertical axis refers to the second principal component. b Score plot of PCA1 (34.4%) and PCA3 (12.5%). The horizontal axis indicates the first principal component, while the vertical axis refers to the third principal component. c Score plot of PCA2 (26.1%) and PCA3 (12.5%). The horizontal axis indicates the second principal component, while the vertical axis refers to the third principal component. Each dot represents a compound; black circle is the training set (n = 193), whereas the red circle is the test set (n = 96)

Fig. 2

Scatter plot of the training and test sets. The horizontal axis indicates the predicted Rb, while the vertical axis refers to the observed Rb. Each dot represents a compound; black circle is the training set (n = 193), whereas the red circle is the test set (n = 96). The solid line represents unity

Fig. 3

Flowchart of the modeling process for Rb prediction