Oct 2026· Journal of the American Chemical Society· 0 citations· 97 references
RNA Interference and Gene Delivery
Abstract
Circular RNA is a therapeutically promising RNA modality with superior stability and extended protein expression relative to linear RNA. While delivery systems for linear RNA have received much attention, less effort has been directed toward the delivery of circular RNA, whose closed topology and physicochemical properties differ from those of linear RNAs. Here, we disclose a class of ionizable lipids that enable efficient in vitro and in vivo delivery of circular RNA. These ionizable lipids are step-economically accessible, allowing facile assessment of their structure–delivery relationships. Our studies reveal key elements that enhance circular RNA delivery in vitro and in vivo, including aminoethoxy head units, doubly branched tails and linkers tuned for hydrogen bonding and biodegradability. The lead ionizable lipid for circular RNA delivery, iL-19, incorporating these features, achieves ∼28-fold higher protein expression in C2C12 myocytes than Moderna’s SM-102 formulation and 2.5-fold higher expression upon intramuscular administration in BALB/c mice. iL-19 also surpasses benchmark ionizable lipids ALC-0315 (7.8-fold) and DLin-MC3-DMA (33.4-fold) for intramuscular circular RNA delivery in vivo under otherwise identical conditions. This study establishes design principles and a step-economical synthesis for accessing ionizable lipids that enable highly efficient in vitro and in vivo circular RNA delivery.
The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.
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Anh Nguyen-Duc, Xiaofeng Wang, P. Abrahamsson· International Conference on...· 44 citations· ⚡5
It is demonstrated that linker-free PROTACs can outperform traditional designs, marking a paradigm shift in PROTAC development for targeted protein degradation.
Pinal, a 16-billion-parameter foundation model that produces protein candidates from natural-language functional descriptions, supports natural language as a high-level interface for candidate generation in protein design, enabling programmable exploration with reduced reliance on manually specified structural or seque...
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.