Development of Quantitative Structure-Pharmacokinetic Relationships

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

“…In the case of Vd SS , the high value of the regression coefficient for log P is consistent with many previously published reports that high lipophilicity is associated with large Vd SS . 7 However, from Figure 5B it can be seen that 3DPP3 and HEFO are also important factors that are positively correlated with Vd SS , whereas increased values of 3DPP1, IOPO, and DIPO will reduce Vd SS . Figure 5C shows that many factors contribute to the model for f U , which would be expected on the basis of the high number of significant correlations found previously (see Results).…”

“…This result confirms the conclusion made by others that lipophilicity is a key determinant of protein binding but that other physicochemical features, in particular electronic properties, also play an important role. 7 To date, MLR has been used successfully as a regression technique in many QSPKR studies. The main disadvantage of MLR compared with PLS is that it performs very poorly in the case of collinearities among descriptor variables and that it can only be applied when the number of compounds exceeds the number of descriptors by at least a factor 3-5.…”

“…In recent years, sophisticated alternatives for traditional "Hansch-type" QSAR methods have been developed, in particular in the area of three-dimensional (3D) molecular characterization and multivariate data analysis. [2][3][4] In contrast to these developments in the QSAR field, the vast majority of quantitative structure-pharmacokinetic relationships (QSPKR 5 ) studies have only focused on univariate correlations of individual pharmacokinetic parameters with lipophilicity and ionization, and it has been well established that log P and pK A are important determinants of drug absorption, distribution, protein binding and elimination (for example 3,[6][7][8][9][10]. A major drawback of such QSPKR models is that they provide no insight into the influence of other physicochemical properties and therefore their conceptual and predictive value is rather limited.…”

Renal clearance–lipophilicity relationships of some organic acids in rabbits, rats and mice

“…For quantification of the effect of lipophilicity, an equation was employed, where CLR represents renal clearance of the parent drug, ERPF is effective renal plasma flow, D is the partition coefficient of the acids between octanol and water, and a and b are constants. In interspecies comparison, the values of parameters a and b are similar indicating no significant interspecies differences in this route of elimination.An attractive approach to the correlation of drug pharmacokinetics to physicochemical or structural characteristics is the development of quantitative relationships which allow a prediction of pharmacokinetic properties before synthesis (Seydel 1984;Mayer & Van de Waterbeemd 1985). We have shown previously that total plasma clearance of some acidic compounds (iododerivatives of benzoic, phenylacetic and hippuric acids) is predominantly dependent on their molecular structure in three animal species (LazniEek et al 1990).…”

“…An attractive approach to the correlation of drug pharmacokinetics to physicochemical or structural characteristics is the development of quantitative relationships which allow a prediction of pharmacokinetic properties before synthesis (Seydel 1984;Mayer & Van de Waterbeemd 1985). We have shown previously that total plasma clearance of some acidic compounds (iododerivatives of benzoic, phenylacetic and hippuric acids) is predominantly dependent on their molecular structure in three animal species (LazniEek et al 1990).…”