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<p>Lipid nanoparticle (LNP) delivery of RNA therapeutics is constrained by poor tissue selectivity and off-target toxicity. Most high-throughput screening approaches have focused on single-target efficacy while overlooking off-target uptake. Here we report multiobjective LNP engineering with artificial intelligence (MOLEA), a system that integrates high-dimensional lipid representations, cell-type-resolved transfection data and multitask optimization to design ionizable lipids with both high potency and biological selectivity. MOLEA learns structure–function relationships across diverse cellular contexts to identify lipids that preferentially deliver mRNA to target tissue while minimizing hepatocyte transfection. Applying MOLEA to cartilage, we developed K9 LNPs, which achieve &gt;90% transfection efficiency in mouse joint chondrocytes and a 13.5-fold increase in knee-to-liver selectivity compared to the clinical benchmark SM-102. We demonstrate chondrocyte-specific <i>Mmp13</i> editing in osteoarthritis mouse models, leading to sustained cartilage protection and suppression of disease-associated immune and matrix remodeling. Our findings demonstrate how artificial-intelligence-guided multiobjective optimization can enable precision RNA delivery with potential applications to other tissues.</p>

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A multiobjective AI model for LNP engineering enhances tissue-selective mRNA delivery

  • 期刊论文

  • 发表日期：

    2026年4月28日

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  • Muye Zhou,
  • Yue Xu,
  • Gen Li,
  • Jingan Chen,
  • Margarita Savguira,
  • Breanna Seto,
  • Fanglin Gong,
  • Tyler Thomson,
  • Juan Chen,
  • Rick Xing Ze Lu,
  • Songtao Dong,
  • David Chen,
  • Christabel Eileen,
  • Shiqi Wu,
  • Gang Zheng,
  • Bowen Li

摘要

Lipid nanoparticle (LNP) delivery of RNA therapeutics is constrained by poor tissue selectivity and off-target toxicity. Most high-throughput screening approaches have focused on single-target efficacy while overlooking off-target uptake. Here we report multiobjective LNP engineering with artificial intelligence (MOLEA), a system that integrates high-dimensional lipid representations, cell-type-resolved transfection data and multitask optimization to design ionizable lipids with both high potency and biological selectivity. MOLEA learns structure–function relationships across diverse cellular contexts to identify lipids that preferentially deliver mRNA to target tissue while minimizing hepatocyte transfection. Applying MOLEA to cartilage, we developed K9 LNPs, which achieve >90% transfection efficiency in mouse joint chondrocytes and a 13.5-fold increase in knee-to-liver selectivity compared to the clinical benchmark SM-102. We demonstrate chondrocyte-specific Mmp13 editing in osteoarthritis mouse models, leading to sustained cartilage protection and suppression of disease-associated immune and matrix remodeling. Our findings demonstrate how artificial-intelligence-guided multiobjective optimization can enable precision RNA delivery with potential applications to other tissues.