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首页> 外文期刊>Journal of liquid chromatography and related technologies >NP TLC data in structure-activity relationship study of selected compounds with activity on dopaminergic, serotoninergic, and muscarinic receptors
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NP TLC data in structure-activity relationship study of selected compounds with activity on dopaminergic, serotoninergic, and muscarinic receptors

机译:NP TLC数据在对多巴胺能,5-羟色胺能和毒蕈碱受体具有活性的所选化合物的构效关系研究中

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摘要

The available information on ligand-binding sites within the metabotropic receptors allows us to choose and apply chemical elements of the biological environment in the chromatographic analysis, which are responsible for formation of the drug-receptor complex. This information provides the opportunity to form analytical and statistical models of interactions between the drugs studied and dopaminergic, serotoninergic, and muscarinic receptors. Simple analytical models for predicting the direction of potential activity within these receptors with the use of chromatographic data and calculated physicochemical parameters were presented. The statistical tool used in this study was Stepwise Discriminant Analysis (SDA). The joint element of simulated environment interactions between various compounds and a biological goal is aspartic acid. The NP TLC plates, impregnated with a solution of aspartic acid (L-Asp), were used in two developing solvents as metabotropic receptors interaction models. The 33 selected drugs were divided into three groups of activity. The following group codes were assigned to them a priori: D, 5HT, and M (for compounds with activity on dopaminergic, serotoninergic, and muscarinic receptors, respectively). The presented discriminant models based on biochromatographic studies and physicochemical data are an efficient tool in the SAR analysis for initial prediction of compound activity direction within selected receptors.
机译:代谢亲和性受体内配体结合位点的可用信息使我们能够选择并应用色谱分析中生物环境的化学元素,这些化学元素负责形成药物受体复合物。这些信息提供了形成研究药物与多巴胺能,5-羟色胺能和毒蕈碱受体之间相互作用的分析和统计模型的机会。提出了使用色谱数据和计算的理化参数预测这些受体内潜在活性方向的简单分析模型。本研究中使用的统计工具是逐步判别分析(SDA)。各种化合物与生物学目标之间模拟环境相互作用的共同要素是天冬氨酸。 NP TLC板浸有天冬氨酸(L-Asp)溶液,被用于两种正在发展的溶剂中作为代谢型受体相互作用模型。选定的33种药物分为三组。将以下组代码优先分配给他们:D,5HT和M(分别针对对多巴胺能,5-羟色胺能和毒蕈碱受体具有活性的化合物)。提出的基于生物色谱研究和理化数据的判别模型是SAR分析中用于初步预测所选受体内化合物活性方向的有效工具。

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