Muye Zhou

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.

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.

The ionizable lipid component of lipid nanoparticle (LNP) formulations is essential for mRNA delivery by facilitating endosomal escape. Conventionally, these lipids are synthesized through complex, multistep chemical processes that are both time-consuming and require significant engineering. Furthermore, the development of new ionizable lipids is hindered by a limited understanding of the structure-activity relationships essential for effective mRNA delivery. In this work, we have developed a modular platform utilizing the Passerini reaction to rapidly generate large, chemically diverse libraries of biodegradable ionizable lipids. This high-throughput approach enables the systematic exploration of various lipid components-head groups, tails, and spacers-and their impacts on mRNA delivery efficiency. By investigating the hydrogen bonding potential between the lipid's head groups and the mRNA's ribose phosphate complex, we found that optimizing the methylene units between the lipid's head groups and linkages could enhance endosomal escape and, consequently, mRNA delivery efficiencies. Leveraging this insight, our platform has led to the identification of the biodegradable ionizable lipid A4B4-S3, which outperforms the current clinical benchmark, SM-102, in gene editing efficacy in mouse liver following systemic administration and demonstrates the promise for repeat-dose protein replacement treatments. This work not only offers a rapid, scalable method for ionizable lipid synthesis but also deepens our understanding of their structure-activity relationships, paving the way for more effective mRNA therapeutics.