Multivariate Quantitative Structure–Pharmacokinetic Relationships (QSPKR) Analysis of Adenosine A1 Receptor Agonists in rat

Abstract

The aim of this study was to investigate the feasibility of a quantitative structure-pharmacokinetic relationships (QSPKR) method based on contemporary three-dimensional (3D) molecular characterization and multivariate statistical analysis. For this purpose, the programs SYBYL/CoMFA, GRID, and Pallas, in combination with the multivariate statistical technique principal component analysis were employed to generate a total of 16 descriptor variables for a series of 12 structurally related adenosine A1 receptor agonists. Subsequently, the multivariate regression method, partial least squares, was used to predict clearance (CL), volume of distribution (VdSS) and protein binding (fraction unbound, fU). The QSPKR models obtained could account for most of the variation in CL, VdSS, and fU (R2 = 0.82, 0.61 and 0.78, respectively). Cross-validation confirmed the predictive ability of the models (Q2 = 0.59, 0.41 and 0.62 for CL, VdSS, and fU, respectively). In conclusion, we have developed a multivariate 3D QSPKR model that could adequately predict overall pharmacokinetic behavior of adenosine A1 receptor agonists in rat. This methodology can also be used for other classes of compounds and may facilitate the further integration of QSPKR in drug discovery and preclinical development.

Prediction of Biliary Excretion in Rats and Humans Using Molecular Weight and Quantitative Structure–Pharmacokinetic Relationships

“…A model with four parameters predicted CL b fairly well with a R 2 of 0.689. Its prediction power was also demonstrated by internal validation generating a Q 2 of 0.618, which is comparable to the one from a QSPKR study using PLS to develop a model predicting systemic clearance of 12 adenosine A 1 receptor agonists in rats (20). More importantly, the predictability of the model was further verified by external validation when the model was tested with 18 compounds not present in the training set.…”

“…In the past, QSPKR has been successfully employed to predict a number of pharmacokinetic properties, including clearance, volume of distribution, bioavailability, and protein binding (13)(14)(15)(16)(17)(18)(19)(20)(21). To our knowledge, this study is the first to apply such a method to predict biliary excretion.…”

“…The concept can also be applied to pharmacokinetics by simply replacing the activities with pharmacokinetic parameters. Such quantitative structure-pharmacokinetic relationship (QSPKR) models have been successfully applied in several studies to predict clearance, volume of distribution, bioavailability and other pharmacokinetic parameters (13)(14)(15)(16)(17)(18)(19)(20)(21).…”

Quantitative Structure-Pharmacokinetic Relationships for the Prediction of Renal Clearance in Humans

“…Models that had Q 2 value of at least 0.5 (which translates to a P value of less than 0.05) were considered qualified. Moreover, the difference between values of the coefficient of determination R 2 and Q 2 was required to be ,0.3 to ensure model stability ( Van der Graaf et al, 1999).…”

Target and Tissue Selectivity Prediction by Integrated Mechanistic Pharmacokinetic-Target Binding and Quantitative Structure Activity Modeling

“…These QSARs enable the prediction of partitioning parameters from the molecular structure. While these QSARs are often used in PBPK modeling to predict non-specific tissue distribution parameters, the prediction of specific target binding parameters is currently not incorporated in PBPK modeling, based on the assumption that the amount of drug bound to its biological target is negligible relative to the total amount of drug in the body (14)(15)(16)(17).…”