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

• Published in: Molecular diversity Vol. 25; no. 3; pp. 1261 - 1270
• Main Authors: Mamada, Hideaki, Iwamoto, Kazuhiko, Nomura, Yukihiro, Uesawa, Yoshihiro
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.08.2021; Springer Nature B.V
• Subjects: Algorithms; Biochemistry; Biomedical and Life Sciences; Databases, Pharmaceutical; Drug Monitoring; Humans; Life Sciences; Models, Theoretical; Molecular Structure; Neural Networks, Computer; Organic Chemistry; Original; Original Article; Pharmaceutical Preparations - chemistry; Pharmacokinetics; Pharmacy; Plasma; Polymer Sciences; Quantitative Structure-Activity Relationship; Structure-Activity Relationship; Tissue Distribution; Blood-to-plasma ratio; Volume of distribution; Quantitative structure–pharmacokinetic relationships; Pharmacokinetics; Artificial neural networks
• ISSN: 1381-1991; 1573-501X
• DOI: 10.1007/s11030-021-10186-7

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. Graphical abstract