Prediction of high-performing spleen-targeted lipid nanoparticles using a deep learning model for robust anticancer immunotherapy

Abstract

Messenger RNA (mRNA) therapeutics hold significant potential across a wide range of medical applications. LNPs are the most clinically advanced mRNA delivery vehicles, but challenges such as off-target effects and liver accumulation limit their broader clinical use. While high-throughput screening is effective for identifying more efficient and selective ionizable lipids, the substantial experimental validation required limits its practical application. In this study, we developed a deep learning model to accelerate ionizable lipid optimization by virtually predicting high-performing ionizable cationic lipids. After applying this model to a series of bis-hydroxyethylamine derived lipids (BDLs), 24 promising candidates were synthesized for delivery efficiency and organ-selectivity validation. Among them, YK-407 exhibited superior in vitro transfection efficiency and in vivo organ-specific mRNA delivery. YK-407 LNPs predominantly targeted the spleen, particularly antigen-presenting cells (APCs). In a mouse OVA tumor model, YK-407 LNPs encapsulating OVA-mRNA almost completely inhibited tumor growth and induced a robust cytotoxic CD8⁺ T cell response in the spleen, outperforming clinically approved SM-102 and Dlin-MC3-DMA. Additionally, we demonstrated that YK-407 LNPs exhibited minimal toxicity for both the liver and spleen, with no significant inflammatory cytokine release. These findings highlight the potential of AI in LNP development and YK-407 holds great promise for applications in mRNA-based treatments.