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Molecularly imprinted macroporous polymer monolithic layers for L-phenylalanine recognition in complex biological fluids

机译:用于L-苯丙氨酸识别的分子印迹大孔聚合物整体层在复杂的生物液中识别

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摘要

In present work, the development of macroporous monolithic layers bearing the artificial recognition sites toward L-phenylalanine has been carried out. The set of macroporous poly(2-aminoethyl methacrylate-co-2-hydroxyethyl methacrylate-co-ethylene glycol dimethacrylate) materials with average pore size ranged in 340-1200 nm was synthesized. The applicability of Hildebrand's and Hansen's theories for the prediction of polymer compatibility with porogenic solvents was evaluated. The dependences of average pore size on theoretically calculated parameters were plotted. The linear trend detected for Hansen's theory has indicated the high suitability of this approach to select appropriate porogens. The synthesized monolithic MIP layers were tested toward the ability to rebind phenylalanine-derivative in microarray format. The influence of such factors as average pore size of the material, the concentration of template molecule in polymerization mixture, interaction time of analyte with its imprinted sites on binding efficiency were studied. The developed materials demonstrated good analyte rebinding from buffer solution with recognition factors 2.5-3.4 depending on the MIP sample. The comparable rebinding efficiency was also detected when the analysis was carried using complex biological media. The selectivity of phenylalanine binding from the equimolar mixture of structural analogues was 81.9% for free amino acid and 91.2% for labeled one.
机译:在目前的工作中,已经进行了大孔整体层的开发,其中包含对L-苯丙氨酸的人工识别位点。合成了一组平均孔径为340-1200nm的大孔聚(2-甲基丙烯酸氨基乙酯-co-2-甲基丙烯酸羟乙酯-co-乙二醇二甲基丙烯酸酯)材料。评估了希尔德布兰德和汉森理论在预测聚合物与致孔溶剂相容性方面的适用性。绘制了平均孔径与理论计算参数的关系曲线。汉森理论的线性趋势表明,这种方法非常适合选择合适的成孔剂。对合成的整体MIP层进行了测试,测试其在微阵列格式下重新结合苯丙氨酸衍生物的能力。研究了材料的平均孔径、模板分子在聚合混合物中的浓度、分析物与其印迹位点的相互作用时间等因素对结合效率的影响。所开发的材料显示出良好的分析物从缓冲溶液中重新结合,识别因子为2.5-3.4,取决于MIP样品。当使用复杂的生物介质进行分析时,也检测到了类似的再结合效率。等摩尔结构类似物混合物中苯丙氨酸与游离氨基酸结合的选择性为81.9%,与标记氨基酸结合的选择性为91.2%。

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