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

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_BMC and log k_wRP, respectively). Based on the retention factor (log k_wRP) and slope of the linear curve (S) the isocratic parameter (φ₀) 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_BMC/log BB): 0.77), while for RP-HPLC system the correlations were lower (r(log k_wRP/log BB): 0.58; r(S/log BB): –0.50; r(φ₀/P_e): 0.61). Based on the log k_BMC 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.     

QSPR studies on the photoinduced-fluorescence behaviour of pharmaceuticals and pesticides

Fluorimetric analysis is still a growing line of research in the determination of a wide range of organic compounds, including pharmaceuticals and pesticides, which makes necessary the development of new strategies aimed at improving the performance of fluorescence determinations as well as the sensitivity and, especially, the selectivity of the newly developed analytical methods. In this paper are presented applications of a useful and growing tool suitable for fostering and improving research in the analytical field. Experimental screening, molecular connectivity and discriminant analysis are applied to organic compounds to predict their fluorescent behaviour after their photodegradation by UV irradiation in a continuous flow manifold (multicommutation flow assembly). The screening was based on online fluorimetric measurement and comprised pre-selected compounds with different molecular structures (pharmaceuticals and some pesticides with known ‘native’ fluorescent behaviour) to study their changes in fluorescent behaviour after UV irradiation. Theoretical predictions agree with the results from the experimental screening and could be used to develop selective analytical methods, as well as helping to reduce the need for expensive, time-consuming and trial-and-error screening procedures.     

氨基喹啉类生物碱抗疟原虫活性的理论研究

采用密度泛函理论(DFT),Becke三参数混合泛函方法(B3LYP),在6-311+G(d)基组水平,对21种氨基喹啉类化合物分子结构进行几何优化和量化计算.进而将量子化学参数作为基础参数,并结合物理化学参数作为独立变量,以抗疟原虫活性数据pIC50为应变量,运用多元线性回归,建立模型,并通过留一(LOO)交叉验证法对模型进行验证.结果表明C(8)与取代基R1之间的键长R(8,11)、最低空轨道与最高占据轨道之间的能量差εL-H、辛醇-水分配系数LogP、化合物的总能量ET、以及N(1)所带电荷Q(1)是影响氨基喹啉类化合物抗疟原虫活性的重要因素.本文所构建的模型具有良好的稳定性和预测能力,能用于对其抗疟原活性进行理论分析和预测,获得较好的结果.     

化合物膜水分配系数的QSPR研究和分子三维参数表征

利用多元线性回归和偏最小二乘分析方法,研究分子表面静电势参数,VolSurf参数与双十四酰基磷脂酰基胆碱(DMPC)-水分配系数之间的定量结构-性质关系(QSPR),均得到较好的结果.分子表面静电势参数分析表明化合物分子表面上负的静电势越分散,越倾向于分配在水相中;与水分子的作用越强,越有利于分配在水相中.此外,具有较大体积和偶极密度的分子倾向于分配在弱极性相中.VolSurf参数分析表明大体积的分子易于分配在DMPC中;分子表面亲水区域较大的分子则倾向于分配在水相中.两个方程中的重要参数之间有较高的相关性,且与DMPC-水分配系数相关性一致.     

Calculations of Hyperpolarizabilities for Para-disubstituted Benzenes with the QSPR

A quantitative structure-property relationship (QSPR) was made for the prediction of the hyperpolarizabilities(β) ofpara-disubstituted benzenes with the nonlinear optical properties, and the βcal2 calculated by this model accorded better with the experimental values (βexpt) compared with theβcal1 calculated at the CPHF/6-31G*//HF/STO-3G level of theory, especially whenβ was big.     

Modeling based on subspace orthogonal projections for QSAR and QSPR research

A novel projection modeling method for quantitative structure activity relationship (QSAR) and quantitative structure property relationship (QSPR) is developed in this paper. Orthogonalization of block variables is introduced to deal with the problem of variable selection. Projections based on least squares are used to construct the modeling space in order to search for the best regression directions for chemical modeling. A suitable prediction space for such a model is further defined to confine the usage range of the model. Three real data sets were analyzed to check the performance of the proposed modeling method. The results obtained from Monte‐Carlo cross‐validation (MCCV) showed that the proposed modeling method might provide better results for QSAR and QSPR modeling than PCR and PLS with respect to both fitting and prediction abilities. Copyright © 2007 John Wiley & Sons, Ltd.     

Application of a polarity parameter model to the separation of fat-soluble vitamins by reversed-phase HPLC

The retention behavior of a series of fat-soluble vitamins has been established on the basis of a polarity retention model: log k = (log k)(0) + p (P(m) (N) - P(s) (N)), with p being the polarity of the solute, P(m) (N) the mobile phase polarity, and (log k)(0) and P(m) (N) two parameters for the characterization of the stationary phase. To estimate the p-values of solutes, two approaches have been considered. The first one is based on the application of a QSPR model, derived from the molecular structure of solutes and their log P(o/w), while in the second one, the p-values are obtained from several experimental measurements. The quality of prediction of both approaches has also been evaluated, with the second one giving more accurate results for the most lipophilic vitamins. This model allows establishing the best conditions to separate and determine simultaneously some fat-soluble vitamins in dairy foods.     

Toward robust QSPR models: Synergistic utilization of robust regression and variable elimination

Widely used regression approaches in modeling quantitative structure-property relationships, such as PLS regression, are highly susceptible to outlying observations that will impair the prognostic value of a model. Our aim is to compile homogeneous datasets as the basis for regression modeling by removing outlying compounds and applying variable selection. We investigate different approaches to create robust, outlier-resistant regression models in the field of prediction of drug molecules' permeability. The objective is to join the strength of outlier detection and variable elimination increasing the predictive power of prognostic regression models. In conclusion, outlier detection is employed to identify multiple, homogeneous data subsets for regression modeling.