Gene therapy has emerged as a significant advancement in medicine in recent years. However, the development of effective gene delivery vectors, particularly polymer vectors, remains a significant challenge. Limited understanding of the internal structure of polymer vectors has hindered efforts to enhance their efficiency. This work focuses on investigating the impact of polymer structure on gene delivery, using the well-known polymeric vector poly(β-amino ester) (PAE) as a case study. For the first time, we revealed the distinct characteristics of individual polymer components and their synergistic effects-the appropriate combination of different components within a polymer (high MW and low MW components) on gene delivery. Additionally, artificial intelligence (AI) analysis was employed to decipher the relationship between the polymer component distribution (PCD) and gene transfection performance. Guided by this analysis, a series of highly efficient polymer vectors that outperform current commercial reagents such as jetPEI and Lipo3000 were developed, among which the transfection efficiency of the PAE-B1-based polyplex was approximately 1.5 times that of Lipo3000 and 2 times that of jetPEI in U251 cells.
Keywords: artificial intelligence; gene delivery vectors; machine learning; pDNA delivery; poly(β-amino ester); polymer component distribution (PCD).