AGILE Platform: A Deep Learning-Powered Approach to Accelerate LNP Development for mRNA Delivery

Abstract Ionizable lipid nanoparticles (LNPs) have seen widespread use in mRNA delivery for clinical applications, notably in SARS-CoV-2 mRNA vaccines. Despite their successful use, expansion of mRNA therapies beyond COVID-19 is impeded by the absence of LNPs tailored to different target cell types. The traditional process of LNP development remains labor-intensive and cost-inefficient, relying heavily on trial and error. In this study, we present the A I- G uided I onizable L ipid E ngineering (AGILE) platform, a synergistic combination of deep learning and combinatorial chemistry. AGILE streamlines the iterative development of ionizable lipids, crucial components for LNP-mediated mRNA delivery. This approach brings forth three significant features: efficient design and synthesis of combinatorial lipid libraries, comprehensive in silico lipid screening employing deep neural networks, and adaptability to diverse cell lines. Using AGILE, we were able to rapidly design, synthesize, and evaluate new ionizable lipids for mRNA delivery in muscle and immune cells, selecting from a library of over 10,000 candidates. Importantly, AGILE has revealed cell-specific preferences for ionizable lipids, indicating the need for different tail lengths and head groups for optimal delivery to varying cell types. These results underscore the potential of AGILE in expediting the development of customized LNPs. This could significantly contribute to addressing the complex needs of mRNA delivery in clinical practice, thereby broadening the scope and efficacy of mRNA therapies. One Sentence Summary AI and combinatorial chemistry expedite ionizable lipid creation for mRNA delivery.