3998310

Copolymer blending and machine learning informs efficient terpolymer design for high performance nucleic acid delivery

Date
March 20, 2024
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Cationic polymers are promising for delivery of nucleic acids, yet, the development of high-performance vehicles is challenged by an infinite synthetic design space. Here, we describe a combinatorial set of 90 polymer blends, derived from 6 statistical copolymers, tailored to explore the role hydrophobicity/hydrophilicity, pKa, formulation (N/P) ratio, particle size, and payload binding affinity on performance and toxicity. SHapley Additive exPlanations (SHAP) machine learning was applied to identify polymer/polyplex attributes most impactful on transfection efficiency, viability, and overall effective efficiency. This allowed for the discovery of blended polyplexes with high levels of both delivery efficiency and cell viability relative to single copolymer formulations and the commercial control, JetPEI. From this data, we coupled composition with biological outputs to guide the synthesis and discovery of new single terpolymer compositions. The highest performing terpolymer composition matched that predicted by transfection with polymer blends. Data with the terpolymer library showed that traits such as larger particle size and low N/P were most beneficial to improving the overall effectiveness for delivery with kidney cells. Additionally, transfections with retinal epithelial cells produced different results, showing that other polymer formulations/compositions were top performers, which demonstrates cell type dependency to high performance transfection. Overall, our workflow for polymer vehicle discovery can be employed to expedite design space exploration and innovating high performance polymer delivery vehicles for nucleic acid therapies.

Presenter

Speaker Image for Theresa Reineke
University of Minnesota

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