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协同聚合物混合告知高效的聚合物设计和机器学习识别pDNA交付的性能趋势

Michael C Leyden¹, Felipe Oviedo², Sonashree Saxena³

¹Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, Minnesota 55455, United States.

Bioconjugate chemistry

|June 26, 2024

概括

这项研究引入了一种快速方法,用于开发用于核酸输送的新型阴离子聚合物. 混合聚合物显示增强的转染效率和细胞活力,加速创建先进的基因治疗载体.

科学领域:

生物材料科学生物材料科学
基因传递系统是基因传递系统.
聚合物化学聚合物化学

背景情况:

阴离子聚合物是核酸输送的有希望的非病毒载体,但在低毒性下实现高转染效率仍然是一个挑战.
传统的聚合物合成是耗时的,阻碍了为最佳交付车辆快速探索聚合物设计空间.
开发高效和安全的聚合物基因传递系统对于推进核酸疗法至关重要.

研究的目的:

开发和验证一种加速的实验工作流程,用于探索用于核酸输送的化聚合物设计空间.
为了确定共聚合物混合对多重复性质和转化性能的协同效应.
利用机器学习来识别影响转染效率和细胞活性的关键聚合物和多重复性属性.

主要方法:

为了快速选,采用了90种来自6种统计共聚合物的聚合物混合物的组合方法.
多复合体的特点是疏水性,颗粒大小和DNA结合亲和力.
在多个细胞系 (HEK293T,ARPE-19,HDFn) 进行了转染效率和细胞活力测定,并使用机器学习 (SHAP) 引导分析.

主要成果:

与单个共聚合物和商业控制 (JetPEI) 相比,混合多重复合物显示出明显更高的转染效率和细胞活力.
特定的聚合物 (P10,M20) 在HEK293T细胞中表现优异,而M10在ARPE-19和HDFn细胞中表现出色,突出了细胞类型的依赖性.
机器学习确定了与生物结果相关的关键聚合物/多重复合属性,从而实现了数据驱动的合成.

结论:

开发的工作流程通过绕过广泛的合成,大大加快了有效的化聚合物输送载体的发现.
同聚合物混合提供了协同效应的好处,从而提高了转染效率和降低了毒性.
细胞特异性评估对于确定最佳的聚合物候选物,用于核酸输送中的多种治疗应用至关重要.

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