Abstract
To design a lipid nanoparticle (LNP) that effectively delivers nucleic acids to a specific cell or tissue type, multiple lipid components and their relative proportions must be decided on from a large number of options. As there is an incomplete understanding of the relationship between the molecular composition of a delivery vehicle, its structure and its activity, the decision is made by screening many formulations. Emerging technologies have rapidly accelerated the generation of large LNP libraries and the testing of their physicochemical properties and behaviour in vitro and in vivo. These screening tools are being increasingly integrated within artificial intelligence-driven discovery systems, wherein data obtained from the characterization and biological testing of LNPs are fed into machine learning models. These models can provide non-obvious relationships between composition and physical or biological outputs, or predict entirely new lipid structures. In this Perspective, we discuss advancements in the automation and parallelization of chemical synthesis, particle formulation, characterization and pharmacological screening that have improved the throughput of generating and testing large libraries of LNPs for nucleic acid delivery. We notably highlight the short-term potential of coupling these high-throughput platforms with machine learning to accelerate the prediction of optimal nucleic acid LNPs for new therapeutic targets.
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Acknowledgements
M.J.M. acknowledges support from the US National Institutes of Health (NICHD R01 HD115877), the Burroughs Wellcome Fund Career Award at the Scientific Interface (CASI), a US National Science Foundation (NSF) CAREER award (CBET-2145491), the American Cancer Society Research Scholar Grant (RSG-22-122-01-ET) and the Cystic Fibrosis Foundation (MITCHE24I0). D.A.I. acknowledges support from the Wellcome Leap R3 programme, a NSF Materials Research Science and Engineering Centers (MRSECs) grant (DMR-2309034), NSF Biofoundry and the Center for Precision Engineering for Health at University of Pennsylvania. A.R.H. is supported by the US National Science Foundation Graduate Research Fellowship.
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M.J.M. is named on patents describing the use of lipid nanoparticles and lipid compositions for nucleic acid delivery that are discussed in this article. D.A.I. is a founder and holds equity in InfiniFluidics. A.R.H. declares no competing interests.
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Hanna, A.R., Issadore, D.A. & Mitchell, M.J. High-throughput platforms for machine learning-guided lipid nanoparticle design. Nat Rev Mater (2025). https://doi.org/10.1038/s41578-025-00831-0
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DOI: https://doi.org/10.1038/s41578-025-00831-0
