Regulated PEGylation of cationic nanogels for enhanced siRNA delivery and orthotopic ovarian tumor therapy

Yifan Gao & Junyou Wang et al. · 2025-08-14

2Citations
Cationic nanogels represent an alternative yet promising class of polymeric vectors for siRNA delivery. However, their intrinsic surface charges inevitably lead to the formation of a protein corona, which often compromises delivery efficiency. In this study, we propose a regulated PEGylation strategy to address this obstacle. Based on a recently developed electrostatic templated polymerization (ETP) method, we prepared cationic nanogels with precisely regulated PEG length and density, and thoroughly investigated their impact on protein corona resistance and siRNA delivery efficiency. The identified structure-property relationship between PEGylation and delivery performance enabled the optimization of the shell PEG, ideally with a block length of 113 units and a fraction of 1 %, which eliminated the protein corona and simultaneously promoted the delivery capability. The nanogel core consisted of cationic poly(N-(4-Vinylbenzyl)-N, N-Dimethylamine) (PVBDMA) chains cross-linked by a reduction-responsive linker. This design facilitated siRNA endocytosis and endosomal escape while enabling cytoplasmic release through the dissociation of the nanogels triggered by intracellular GSH. We clarify that, the engineered PEGylated nanogel integrate multiple essential functions for efficient siRNA delivery, leading to significant gene silencing both in vitro and in an orthotopic ovarian tumor model. We believe that the proposed PEGylation strategy further enhances the delivery capacity of cationic nanogel vectors, thereby boosting their potential applications in the delivery of biotherapeutics.
TL;DR

The engineered PEGylated nanogel integrate multiple essential functions for efficient siRNA delivery, leading to significant gene silencing both in vitro and in an orthotopic ovarian tumor model, thereby boosting their potential applications in the delivery of biotherapeutics.

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Authors
Yifan Gao, Xiaodi Gong, Jing Chu, Yuening Qiu, Fan Du, Jichang Liu, Martien A. Cohen Stuart, Junyou Wang