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机器学习揭示了聚合物微粒中的氨基类型确定了mRNA结合,In Vitro和In Vivo的性能,用于肺选择性输送

Sidharth Panda¹, Ella J Eaton^2,3,4,5, Praveen Muralikrishnan⁶

¹Department of Chemistry, University of Minnesota, Minneapolis, Minnesota 55455, United States.

|May 2, 2025

概括

研究人员通过改变氨基化学物质,优化了对mRNA输送的阴离子. 在体内,A7氨基结构表现出卓越的性能和向的肺部输送,突出了化学优化对有效的mRNA治疗的重要性.

科学领域:

生物材料科学生物材料科学
纳米技术纳米技术
分子生物学分子生物学

背景情况:

来自两类块共聚合物的阴离子细胞对mRNA输送具有前景.
可定制的多化冠冕允许定制纳米粒子属性.

研究的目的:

系统地研究氨基化学对mRNA输送的影响,使用cationic micelles.
确定关键的结构-性能关系,以优化mRNA递送效率和特异性.

主要方法:

配制了30个具有多种氨基功能的阴离子微粒纳米粒子 (MNP).
在多个细胞系中使用GFP+mRNA进行体外mRNA输送试验.
机器学习分析 (SHAP) 用于将氨基化学与性能指标相关联.
在体内输送研究和与体内模型的相关性 (多任务高斯过程).

主要成果:

氨基侧链量和化学结构极大地影响mRNA传递性能.
胺基特异性结合效率是有效性,细胞活力和GFP强度的关键决定因素.
A7两动物在体外表现出最高的GFP表达,在体内表现出最高的GFP表达和特定的肺部传递.
在体外和体外表现之间确立了强烈的相关性.

结论:

平衡mRNA结合强度对于最佳的mRNA递送至关重要.
胺功能的化学优化对于推进向mRNA传递至关重要.
在体外模型可以有效地预测mRNA传递系统的体内结果.

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