Combinatorial design of ionizable lipid nanoparticles for muscle-selective mRNA delivery with minimized off-target effects.

Ionizable lipid nanoparticles (LNPs) pivotal to the success of COVID-19 mRNA (messenger RNA) vaccines hold substantial promise for expanding the landscape of mRNA-based therapies. Nevertheless, the risk of mRNA delivery to off-target tissues highlights the necessity for LNPs with enhanced tissue selectivity. The intricate nature of biological systems and inadequate knowledge of lipid structure-activity relationships emphasize the significance of high-throughput methods to produce chemically diverse lipid libraries for mRNA delivery screening. Here, we introduce a streamlined approach for the rapid design and synthesis of combinatorial libraries of biodegradable ionizable lipids. This led to the identification of iso-A11B5C1, an ionizable lipid uniquely apt for muscle-specific mRNA delivery. It manifested high transfection efficiencies in muscle tissues, while significantly diminishing off-targeting in organs like the liver and spleen. Moreover, iso-A11B5C1 also exhibited reduced mRNA transfection potency in lymph nodes and antigen-presenting cells, prompting investigation into the influence of direct immune cell transfection via LNPs on mRNA vaccine effectiveness. In comparison with SM-102, while iso-A11B5C1's limited immune transfection attenuated its ability to elicit humoral immunity, it remained highly effective in triggering cellular immune responses after intramuscular administration, which is further corroborated by its strong therapeutic performance as cancer vaccine in a melanoma model. Collectively, our study not only enriches the high-throughput toolkit for generating tissue-specific ionizable lipids but also encourages a reassessment of prevailing paradigms in mRNA vaccine design. This study encourages rethinking of mRNA vaccine design principles, suggesting that achieving high immune cell transfection might not be the sole criterion for developing effective mRNA vaccines.

AGILE platform: a deep learning powered approach to accelerate LNP development for mRNA delivery.

Ionizable lipid nanoparticles (LNPs) are seeing widespread use in mRNA delivery, notably in SARS-CoV-2 mRNA vaccines. However, the expansion of mRNA therapies beyond COVID-19 is impeded by the absence of LNPs tailored for diverse cell types. In this study, we present the AI-Guided Ionizable Lipid Engineering (AGILE) platform, a synergistic combination of deep learning and combinatorial chemistry. AGILE streamlines ionizable lipid development with efficient library design, in silico lipid screening via deep neural networks, and adaptability to diverse cell lines. Using AGILE, we rapidly design, synthesize, and evaluate ionizable lipids for mRNA delivery, selecting from a vast library. Intriguingly, AGILE reveals cell-specific preferences for ionizable lipids, indicating tailoring for optimal delivery to varying cell types. These highlight AGILE's potential in expediting the development of customized LNPs, addressing the complex needs of mRNA delivery in clinical practice, thereby broadening the scope and efficacy of mRNA therapies.