Optimization of polymeric nanoparticle systems for the delivery of siRNA using design of experiments and machine learning
Loading...
Date
2024-06-18
Authors
Farfan Benito, Johan
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
The ability of siRNA to silence disease-related genes has shown a promising therapeutic potential. However, their application is still hampered by the lack of safe and efficient delivery vehicles. Poly(β-amino ester)s (PBAEs) are a group of biocompatible and biodegradable cationic polymers widely studied as siRNA carriers but the factors directing the polymerization process are not fully understood, particularly for synthesizing amphiphilic copolymers. Here, a Design of Experiment (DoE) approach was employed to synthesize a library of 27 amphiphilic PBAEs copolymers based on spermine, oleylamine and 1,4-butanediol diacrylate and study the influence of the reaction conditions on their final characteristics. Additionally, the in vitro toxicity of the polymer library was assessed on 16HBE14o- cells revealing the importance of hydrophobic modification and quality of the polymers on the biocompatibility. Moreover, their capacity to deliver siRNA was evaluated by preparing polyplexes that completely encapsulate siRNA at an N/P ratio of 10 with optimal hydrodynamic size and PDI. Finally, their capacity to mediate gene silencing in enhanced green fluorescence protein expressing H1299 cells was assessed by flow cytometry. The results suggested the critical importance of polymer hydrophobicity to increase transfection efficiency. Further analysis using machine learning and SHapley Additive exPlanations revealed the importance of the stability of the polyplexes on the transfection efficiency. This study therefore provides a strategy based on DoE and machine learning for studying complex polymerization mechanisms and finding structure/property relationships governing polymeric nucleic acid delivery.
Description
Keywords
Polymeric vehicles, Poly(β-amino ester)s, siRNA delivery, Machine learning