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Quantitative structure-retention relationships studies of selected groups of compounds characterized by different pharmacological activity using multiple linear regression procedure

机译:使用多元线性回归程序研究以不同药理活性为特征的所选化合物组的定量结构-保留关系

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摘要

In this paper the quantitative structure-retention relationships (QSRR) studies for three different groups of drugs (cardiovascular system drugs, analgesic drugs and some compounds characterized by divergent pharmacological activity) and for chromatographic parameters (log k' or log k' w retention factors) determined with the use of the HPLC methods were performed. Molecular descriptors (over 4900 molecular modeling parameters) obtained using the HyperChem and the Dragon computer programs, and the Virtual Computational Chemistry Laboratory (VCCLAB) website were applied to derive the QSRR equations by means of multiple linear regression method with stepwise procedure. Several statistically significant QSRR equations as two-parameters for both cardiovascular and analgesic drugs, and sevenparameters for group of other drugs were built. Six QSRR equations with correlations R ranged from 0.9822 to 0.9957 were obtained for cardiovascular drugs, twenty-six QSRRs with R equal to 0.9120-0.9776 were derived for analgesics, and ten QSRRs with R from 0.9529 to 0.9924 were calculated for other drugs. Moreover, the proposed QSRR models give important information about physico-chemical properties of analyzed drugs, and indicated that descriptors characterized topology, geometry and lipophilicity of molecular structures of analyzed compounds are crucial for prediction of their retention parameters. Additionally, derived QSRR models can be helpful to search (to prediction) HPLC retention factor for the new drug candidates.
机译:在本文中,对三种不同类型的药物(心血管系统药物,止痛药和某些具有不同药理活性的化合物)和色谱参数(log k'或log k'w保留因子)进行了定量结构保留关系(QSRR)研究用HPLC方法测定)。使用HyperChem和Dragon计算机程序以及虚拟计算化学实验室(VCCLAB)网站获得的分子描述符(超过4900个分子建模参数)通过逐步回归的多元线性回归方法推导QSRR方程。建立了几个具有统计学意义的QSRR方程,将其作为心血管和止痛药的两个参数,以及其他药物组的七个参数。对于心血管药物,获得了六个相关系数为0.9822至0.9957的QSRR方程,为镇痛药得出了二十六个R等于0.9120-0.9776的QSRR,而对于其他药物则计算了十个相关系数为0.9529至0.9924的QSRR。此外,所提出的QSRR模型提供了有关被分析药物的理化性质的重要信息,并指出表征被分析化合物分子结构的拓扑结构,几何形状和亲脂性的描述符对于预测其保留参数至关重要。此外,派生的QSRR模型可能有助于搜索(预测)新药候选物的HPLC保留因子。

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