PDF(Pubmed)
Abstract：
Successful therapeutic delivery of siRNA with polymeric nanoparticles seems to be a promising but not vastly understood and complicated goal to achieve. Despite years of research, no polymer-based delivery system has been approved for clinical use. Polymers, as a delivery system, exhibit considerable complexity and variability, making their consistent production a challenging endeavor. However, a better understanding of the polymerization process of polymer excipients may improve the reproducibility and material quality for more efficient use in drug products. Here, we present a combination of Design of Experiment and Python-scripted data science to establish a prediction model, from which important parameters can be extracted that influence the synthesis results of polybeta-amino esters (PBAEs), a common type of polymer used preclinically for nucleic acid delivery. We synthesized a library of 27 polymers, each one at different temperatures with different reaction times and educt ratios using an orthogonal central composite (CCO-) design. This design allowed a detailed characterization of factor importance and interactions using a very limited number of experiments. We characterized the polymers by analyzing the resulting composition by 1H-NMR and the size distribution by GPC measurements. To further understand the complex mechanism of block polymerization in a one-pot synthesis, we developed a Python script that helps us to understand possible step-growth steps. We successfully developed and validated a predictive response surface and gathered a deeper understanding of the synthesis of polyspermine-based amphiphilic PBAEs.
摘要：
使用聚合物纳米颗粒的siRNA的成功治疗性递送似乎是有希望但未被广泛理解和实现的复杂目标。尽管经过多年的研究,没有基于聚合物的递送系统被批准用于临床。聚合物,作为一个传递系统，表现出相当大的复杂性和可变性，使他们的一致生产是一项具有挑战性的努力。然而,更好地了解聚合物赋形剂的聚合过程可能会提高重现性和材料质量，从而更有效地用于药物产品。这里,我们提出了一种结合实验设计和Python脚本数据科学来建立预测模型，从中可以提取影响聚β-氨基酯（PBAE）合成结果的重要参数，临床前用于核酸递送的一种常见类型的聚合物。我们合成了一个由27个聚合物组成的库,使用正交中心复合材料（CCO-）设计，每个在不同的温度下具有不同的反应时间和离析物比率。该设计允许使用非常有限数量的实验来详细表征因子重要性和相互作用。我们通过1H-NMR分析所得组合物和GPC测量的尺寸分布来表征聚合物。为了进一步理解一锅法合成中嵌段聚合的复杂机理,我们开发了一个Python脚本，帮助我们理解可能的逐步增长步骤。我们成功开发并验证了预测响应面，并对基于多精胺的两亲性PBAE的合成有了更深入的了解。
