Use of biopartitioning micellar chromatography and RP-HPLC for the determination of blood–brain barrier penetration of α-adrenergic/imidazoline receptor ligands, and QSPR analysis

Abstract

For this study, 31 compounds, including 16 imidazoline/α-adrenergic receptor (IRs/α-ARs) ligands and 15 central nervous system (CNS) drugs, were characterized in terms of the retention factors (k) obtained using biopartitioning micellar and classical reversed phase chromatography (log k and log k, respectively). Based on the retention factor (log k) and slope of the linear curve (S) the isocratic parameter (φ) was calculated. Obtained retention factors were correlated with experimental log BB values for the group of examined compounds. High correlations were obtained between logarithm of biopartitioning micellar chromatography (BMC) retention factor and effective permeability (r(log k/log BB): 0.77), while for RP-HPLC system the correlations were lower (r(log k/log BB): 0.58; r(S/log BB): -0.50; r(φ/P): 0.61). Based on the log k retention data and calculated molecular parameters of the examined compounds, quantitative structure-permeability relationship (QSPR) models were developed using partial least squares, stepwise multiple linear regression, support vector machine and artificial neural network methodologies. A high degree of structural diversity of the analysed IRs/α-ARs ligands and CNS drugs provides wide applicability domain of the QSPR models for estimation of blood-brain barrier penetration of the related compounds.

High-performance liquid chromatography evaluation of lipophilicity and QSRR modeling of a series of dual DNA gyrase and topoisomerase IV inhibitors

“…SVM was developed as a binary classification tool [29]. However, in recent years, it has also been used as a nonlinear method in QSAR and QSPR modelling [30,31]. In this study, the optimal SVM(φ0) model was obtained using radial basis function (RBF) Kernel type and regression type 1.…”

Estimation of lipophilicity and design of new 17β-carboxamide glucocorticoids using RP-HPLC and quantitative structure-retention relationships analysis

“…SVM was initially developed as a binary classification tool [16]. It can also be used as a method in QSAR and QSPR modeling [17,18]. In this study, optimal SVM (logkw) model was obtained using radial basis function (RBF) Kernel type and regression type 1 was also selected.…”

Evaluation of the lipophilicity of chalcones by RP-TLC and computational methods

“…PLS modelling is useful when analysing data with collinear, noisy and numerous descriptors. Optimal number of PLS components was determined on the basis of each component's Although SVM was initially developed as a binary classification tool [21], it can also be used for the development of nonlinear QSAR and QSRR models [22,23]. In this study, SVM(C 0 ) model was created using radial basis function (RBF) Kernel type and regression type 1, while optimal gamma value was 0.333.…”