协同聚合物混合为高效三元共聚物的设计提供了依据,机器学习为 pDNA 输送的性能趋势提供了依据。

IF 4 2区 化学 Q1 BIOCHEMICAL RESEARCH METHODS Bioconjugate Chemistry Bioconjugate Pub Date : 2024-06-26 DOI:10.1021/acs.bioconjchem.4c00028
Michael C Leyden, Felipe Oviedo, Sonashree Saxena, Ramya Kumar, Ngoc Le, Theresa M Reineke
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引用次数: 0

摘要

阳离子聚合物为核酸递送提供了病毒载体的替代品。然而,开发转染效率高、毒性小的聚合物载体仍然遥遥无期,需要继续探索广阔的设计空间。传统的大单体单聚合物合成非常耗时,限制了新配方的发现速度。在这项工作中,我们提出了一种快速探测设计空间的实验方法,利用从 6 种统计共聚物中提取的 90 种聚合物混合物的组合集来递送 pDNA。这一工作流程有助于快速筛选多聚物成分,成功定制多聚物的疏水性、粒度和有效载荷结合亲和力。与单一共聚物对照组和商用 JetPEI 相比,该工作流程确定的混合多聚物具有较高的转染效率和细胞存活率,表明共聚物混合具有协同效益。多聚物组成与生物输出相结合,指导合成单一三元共聚物载体,其中高性能聚合物 P10 和 M20 可分别在无血清和含血清培养基中为 HEK293T 细胞提供卓越的转染效果。机器学习与 SHapley Additive exPlanations(SHAP)相结合,确定了对转染效率、存活率和总体有效率影响最大的聚合物/多聚物属性。随后在 ARPE-19 和 HDFn 细胞上进行的转染发现,P10 和 M20 的性能超过了 M10,这与 HEK293T 细胞的结果形成了鲜明对比。这种细胞类型依赖性增强了对多种细胞模型转染条件进行评估的必要性,从而有可能确定更有利于在某些组织中传递的分子。总之,所采用的工作流程可用于加快聚合物设计空间的探索,绕过大量合成,更容易地开发出用于核酸疗法的改良聚合物递送载体。
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Synergistic Polymer Blending Informs Efficient Terpolymer Design and Machine Learning Discerns Performance Trends for pDNA Delivery.

Cationic polymers offer an alternative to viral vectors in nucleic acid delivery. However, the development of polymer vehicles capable of high transfection efficiency and minimal toxicity has remained elusive, and continued exploration of the vast design space is required. Traditional single polymer syntheses with large monomer bases are very time-intensive, limiting the speed at which new formulations are identified. In this work, we present an experimental method for the quick probing of the design space, utilizing a combinatorial set of 90 polymer blends, derived from 6 statistical copolymers, to deliver pDNA. This workflow facilitated rapid screening of polyplex compositions, successfully tailoring polyplex hydrophobicity, particle size, and payload binding affinity. This workflow identified blended polyplexes with high levels of transfection efficiency and cell viability relative to single copolymer controls and commercial JetPEI, indicating synergistic benefits from copolymer blending. Polyplex composition was coupled with biological outputs to guide the synthesis of single terpolymer vehicles, with high-performing polymers P10 and M20, providing superior transfection of HEK293T cells in serum-free and serum-containing media, respectively. Machine learning coupled with SHapley Additive exPlanations (SHAP) was used to identify polymer/polyplex attributes that most impact transfection efficiency, viability, and overall effective efficiency. Subsequent transfections on ARPE-19 and HDFn cells found that P10 and M20 were surpassed in performance by M10, contrasting with results in HEK293T cells. This cell type dependency reinforced the need to evaluate transfection conditions with multiple cell models to potentially identify moieties more beneficial to delivery in certain tissues. Overall, the workflow employed can be used to expedite the exploration of the polymer design space, bypassing extensive synthesis, and to develop improved polymer delivery vehicles more readily for nucleic acid therapies.

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来源期刊
CiteScore
9.00
自引率
2.10%
发文量
236
审稿时长
1.4 months
期刊介绍: Bioconjugate Chemistry invites original contributions on all research at the interface between man-made and biological materials. The mission of the journal is to communicate to advances in fields including therapeutic delivery, imaging, bionanotechnology, and synthetic biology. Bioconjugate Chemistry is intended to provide a forum for presentation of research relevant to all aspects of bioconjugates, including the preparation, properties and applications of biomolecular conjugates.
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