Ting Jeffrey M, Navale Tushar S, Bates Frank S, Reineke Theresa M
Departments of Chemistry and ‡Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, Minnesota 55455-0431, United States.
Departments of Chemistry and Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, Minnesota 55455-0431, United States.
ACS Macro Lett. 2013 Sep 17;2(9):770-774. doi: 10.1021/mz4003112. Epub 2013 Aug 15.
A comprehensive approach to target exact molecular weights and chemical compositions for multimonomeric statistical copolymers using a new controlled statistics method with reversible addition-fragmentation chain transfer free-radical (RAFT) polymerization is presented. The system chosen to illustrate this procedure is an acrylic quarterpolymer consisting of methyl acrylate, 2-carboxyethyl acrylate, 2-hydroxypropyl acrylate, and 2-propylacetyl acrylate, modeling a well-known macromolecule utilized to deliver poorly water-soluble drugs (hydroxypropyl methylcellulose acetate succinate, HPMCAS). The relative reactivities at 70 °C between monomer pairs were measured and employed to predict the feed ratio necessary for synthesizing well-defined compositions based on the Walling-Briggs model. Application of Skeist's equations addressed compositional drift and anticipated the general monomer incorporation distribution as a function of conversion, which was verified experimentally. This new and simple paradigm combining both predictive models provides complementary synthetic and predictive tools for designing macromolecular chemical architectures with hierarchical control over spatially dependent structure-property relationships for complex applications such as oral drug delivery.
本文提出了一种综合方法，使用具有可逆加成-断裂链转移自由基（RAFT）聚合的新型可控统计方法，来确定多单体统计共聚物的精确分子量和化学组成。用于说明该过程的体系是一种丙烯酸四元共聚物，由丙烯酸甲酯、丙烯酸2-羧乙酯、丙烯酸2-羟丙酯和丙烯酸2-丙酰乙酯组成，模拟一种用于递送难溶性药物的知名大分子（羟丙基甲基纤维素乙酸琥珀酸酯，HPMCAS）。测量了70℃下单体对之间的相对反应活性，并用于根据Walling-Briggs模型预测合成明确组成所需的进料比。应用Skeist方程解决了组成漂移问题，并预测了作为转化率函数的一般单体掺入分布，这通过实验得到了验证。这种结合了两种预测模型的新的简单范式，为设计具有层次控制的大分子化学结构提供了互补的合成和预测工具，用于复杂应用（如口服药物递送）中对空间依赖性结构-性质关系的控制。
