Lipid Nanoparticle Database towards structure-function modeling and data-driven design for nucleic acid delivery

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Lipid Nanoparticle Database towards structure-function modeling and data-driven design for nucleic acid delivery

References

  1. Hou, X., Zaks, T., Langer, R. & Dong, Y. Lipid nanoparticles for mRNA delivery. Nat. Rev. Mater. 6, 1078–1094 (2021).

    Google Scholar 

  2. Baden, L. R. et al. Efficacy and safety of the mRNA-1273 SARS-CoV-2 vaccine. N. Engl. J. Med. 384, 403–416 (2021).

    Google Scholar 

  3. Mirkin, C. A., Mrksich, M. & Artzi, N. The emerging era of structural nanomedicine. Nat. Rev. Bioeng. 3, 526–528 (2025).

    Google Scholar 

  4. Albertsen, C. H. et al. The role of lipid components in lipid nanoparticles for vaccines and gene therapy. Adv. Drug Deliv. Rev. 188, 114416 (2022).

    Google Scholar 

  5. Han, X. et al. An ionizable lipid toolbox for RNA delivery. Nat. Commun. 12, 7233 (2021).

    Google Scholar 

  6. Akinc, A. et al. A combinatorial library of lipid-like materials for delivery of RNAi therapeutics. Nat. Biotechnol. 26, 561–569 (2008).

    Google Scholar 

  7. Miao, L. et al. Delivery of mRNA vaccines with heterocyclic lipids increases anti-tumor efficacy by STING-mediated immune cell activation. Nat. Biotechnol. 37, 1174–1185 (2019).

    Google Scholar 

  8. Cheng, Q. et al. Selective organ targeting (SORT) nanoparticles for tissue-specific mRNA delivery and CRISPR–Cas gene editing. Nat. Nanotechnol. 15, 313–320 (2020).

    Google Scholar 

  9. Chander, N., Basha, G., Yan Cheng, M. H., Witzigmann, D. & Cullis, P. R. Lipid nanoparticle mRNA systems containing high levels of sphingomyelin engender higher protein expression in hepatic and extra-hepatic tissues. Mol. Ther. Methods Clin. Dev. 30, 235–245 (2023).

    Google Scholar 

  10. Radmand, A. et al. Cationic cholesterol-dependent LNP delivery to lung stem cells, the liver, and heart. Proc. Natl. Acad. Sci. USA 121, e2307801120 (2024).

    Google Scholar 

  11. Zhu, Y. et al. Multi-step screening of DNA/lipid nanoparticles and co-delivery with siRNA to enhance and prolong gene expression. Nat. Commun. 13, 4282 (2022).

    Google Scholar 

  12. Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).

    Google Scholar 

  13. Abramson, J. et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630, 493–500 (2024).

    Google Scholar 

  14. Wang, H. et al. Scientific discovery in the age of artificial intelligence. Nature 620, 47–60 (2023).

    Google Scholar 

  15. Li, B. et al. Accelerating ionizable lipid discovery for mRNA delivery using machine learning and combinatorial chemistry. Nat. Mater. 23, 1002–1008 (2024).

    Google Scholar 

  16. Xu, Y. et al. AGILE platform: a deep learning powered approach to accelerate LNP development for mRNA delivery. Nat. Commun. 15, 6305 (2024).

    Google Scholar 

  17. Witten, J. et al. Artificial intelligence-guided design of lipid nanoparticles for pulmonary gene therapy. Nat. Biotechnol. 43, 1790–1799 (2025).

    Google Scholar 

  18. Kumar, G. & Ardekani, A. M. Machine-learning framework to predict the performance of lipid nanoparticles for nucleic acid delivery. ACS Appl. Bio Mater. 8, 3717–3727 (2025).

    Google Scholar 

  19. Suzuki, T. et al. PEG shedding-rate-dependent blood clearance of PEGylated lipid nanoparticles in mice: faster PEG shedding attenuates anti-PEG IgM production. Int. J. Pharm. 588, 119792 (2020).

    Google Scholar 

  20. Jiang, A. Y. et al. Combinatorial development of nebulized mRNA delivery formulations for the lungs. Nat. Nanotechnol. 19, 364–375 (2024).

    Google Scholar 

  21. Da Vela, S. & Svergun, D. I. Methods, development and applications of small-angle X-ray scattering to characterize biological macromolecules in solution. Curr. Res. Struct. Biol. 2, 164–170 (2020).

    Google Scholar 

  22. Zheng, L., Bandara, S. R., Tan, Z. & Leal, C. Lipid nanoparticle topology regulates endosomal escape and delivery of RNA to the cytoplasm. Proc. Natl. Acad. Sci. USA 120, e2301067120 (2023).

    Google Scholar 

  23. Wang, M. M. et al. Elucidation of lipid nanoparticle surface structure in mRNA vaccines. Sci. Rep. 13, 16744 (2023).

    Google Scholar 

  24. Brooks, B. R. et al. CHARMM: the biomolecular simulation program. J. Comput. Chem. 30, 1545–1614 (2009).

    Google Scholar 

  25. Tesei, G. et al. Lipid shape and packing are key for optimal design of pH-sensitive mRNA lipid nanoparticles. Proc. Natl. Acad. Sci. USA 121, e2311700120 (2024).

    Google Scholar 

  26. Philipp, J. et al. pH-dependent structural transitions in cationic ionizable lipid mesophases are critical for lipid nanoparticle function. Proc. Natl. Acad. Sci. USA 120, e2310491120 (2023).

    Google Scholar 

  27. Garaizar, A. et al. Toward understanding lipid reorganization in RNA lipid nanoparticles in acidic environments. Proc. Natl. Acad. Sci. USA 121, e2404555121 (2024).

    Google Scholar 

  28. Love, K. T. et al. Lipid-like materials for low-dose, in vivo gene silencing. Proc. Natl. Acad. Sci. USA 107, 1864–1869 (2010).

    Google Scholar 

  29. Li, L. et al. A biomimetic lipid library for gene delivery through thiol-yne click chemistry. Biomaterials 33, 8160–8166 (2012).

    Google Scholar 

  30. Whitehead, K. A. et al. Degradable lipid nanoparticles with predictable in vivo siRNA delivery activity. Nat. Commun. 5, 4277 (2014).

    Google Scholar 

  31. Miller, J. B. et al. Non-viral CRISPR/Cas gene editing in vitro and in vivo enabled by synthetic nanoparticle co-delivery of Cas9 mRNA and sgRNA. Angew. Chem. Int. Ed 56, 1059–1063 (2017).

    Google Scholar 

  32. Zhou, K. et al. Modular degradable dendrimers enable small RNAs to extend survival in an aggressive liver cancer model. Proc. Natl. Acad. Sci. USA 113, 520–525 (2016).

    Google Scholar 

  33. Lee, S. M. et al. A systematic study of unsaturation in lipid nanoparticles leads to improved mRNA transfection in vivo. Angew. Chem. Int. Ed. 60, 5848–5853 (2021).

    Google Scholar 

  34. Liu, S. et al. Membrane-destabilizing ionizable phospholipids for organ-selective mRNA delivery and CRISPR–Cas gene editing. Nat. Mater. 20, 701–710 (2021).

    Google Scholar 

  35. Li, Z. et al. Enzyme-catalyzed one-step synthesis of ionizable cationic lipids for lipid nanoparticle-based mRNA COVID-19 vaccines. ACS Nano 16, 18936–18950 (2022).

    Google Scholar 

  36. Li, B. et al. Combinatorial design of nanoparticles for pulmonary mRNA delivery and genome editing. Nat. Biotechnol. 41, 1410–1415 (2023).

    Google Scholar 

  37. Chen, J. et al. Combinatorial design of ionizable lipid nanoparticles for muscle-selective mRNA delivery with minimized off-target effects. Proc. Natl. Acad. Sci. USA 120, e2309472120 (2023).

    Google Scholar 

  38. Rhym, L. H., Manan, R. S., Koller, A., Stephanie, G. & Anderson, D. G. Peptide-encoding mRNA barcodes for the high-throughput in vivo screening of libraries of lipid nanoparticles for mRNA delivery. Nat. Biomed. Eng. 7, 901–910 (2023).

    Google Scholar 

  39. Goldman, R. L. et al. Understanding structure activity relationships of Good HEPES lipids for lipid nanoparticle mRNA vaccine applications. Biomaterials 301, 122243 (2023).

    Google Scholar 

  40. Xu, Y. et al. Delivery of mRNA vaccine with 1, 2-diesters-derived lipids elicits fast liver clearance for safe and effective cancer immunotherapy. Adv. Healthc. Mater. 13, 2302691 (2024).

    Google Scholar 

  41. Yan, Y. et al. Branched hydrophobic tails in lipid nanoparticles enhance mRNA delivery for cancer immunotherapy. Biomaterials 301, 122279 (2023).

    Google Scholar 

  42. Chen, Z. et al. Modular design of biodegradable ionizable lipids for improved mRNA delivery and precise cancer metastasis delineation in vivo. J. Am. Chem. Soc. 145, 24302–24314 (2023).

    Google Scholar 

  43. He, Z. et al. A multidimensional approach to modulating ionizable lipids for high-performing and organ-selective mRNA delivery. Angew. Chem. Int. Ed. 62, e202310401 (2023).

    Google Scholar 

  44. Su, K. et al. Reformulating lipid nanoparticles for organ-targeted mRNA accumulation and translation. Nat. Commun. 15, 5659 (2024).

    Google Scholar 

  45. Xue, L. et al. High-throughput barcoding of nanoparticles identifies cationic, degradable lipid-like materials for mRNA delivery to the lungs in female preclinical models. Nat. Commun. 15, 1884 (2024).

    Google Scholar 

  46. Chaudhary, N. et al. Lipid nanoparticle structure and delivery route during pregnancy dictate mRNA potency, immunogenicity, and maternal and fetal outcomes. Proc. Natl. Acad. Sci. USA 121, e2307810121 (2024).

    Google Scholar 

  47. Han, X. et al. In situ combinatorial synthesis of degradable branched lipidoids for systemic delivery of mRNA therapeutics and gene editors. Nat. Commun. 15, 1762 (2024).

    Google Scholar 

  48. Ren, Y. et al. Enhancing spleen-targeted mRNA delivery with branched biodegradable tails in lipid nanoparticles. J. Mater. Chem. B 12, 8062–8066 (2024).

    Google Scholar 

  49. Zhu, Y. et al. Screening for lipid nanoparticles that modulate the immune activity of helper T cells towards enhanced antitumour activity. Nat. Biomed. Eng. 8, 544–560 (2024).

    Google Scholar 

  50. Sabnis, S. et al. A novel amino lipid series for mRNA delivery: improved endosomal escape and sustained pharmacology and safety in non-human primates. Mol. Ther. 26, 1509–1519 (2018).

    Google Scholar 

  51. Patel, S. et al. Naturally-occurring cholesterol analogues in lipid nanoparticles induce polymorphic shape and enhance intracellular delivery of mRNA. Nat. Commun. 11, 983 (2020).

    Google Scholar 

  52. Bae, S.-H. et al. A lipid nanoparticle platform incorporating trehalose glycolipid for exceptional mRNA vaccine safety. Bioact. Mater. 38, 486–498 (2024).

    Google Scholar 

  53. Li, J. et al. High-throughput synthesis and optimization of ionizable lipids through A3 coupling for efficient mRNA delivery. J. Nanobiotechnol. 22, 672 (2024).

    Google Scholar 

  54. Xue, L. et al. Multiarm-assisted design of dendron-like degradable ionizable lipids facilitates systemic mRNA delivery to the spleen. J. Am. Chem. Soc. 147, 1542–1552 (2025).

    Google Scholar 

  55. Xue, L. et al. Combinatorial design of siloxane-incorporated lipid nanoparticles augments intracellular processing for tissue-specific mRNA therapeutic delivery. Nat. Nanotechnol. 20, 132–143 (2025).

    Google Scholar 

  56. Peña, Á et al. Multicomponent thiolactone-based ionizable lipid screening platform for efficient and tunable mRNA delivery to the lungs. Commun. Chem. 8, 1–12 (2025).

    Google Scholar 

  57. Yoo, S. et al. Novel less toxic, lymphoid tissue-targeted lipid nanoparticles containing a vitamin B5-derived ionizable lipid for mRNA vaccine delivery. Adv. Healthc. Mater. 14, 2403366 (2025).

    Google Scholar 

  58. Liu, L. et al. PEGylated lipid screening, composition optimization, and structure–activity relationship determination for lipid nanoparticle-mediated mRNA delivery. Nanoscale 17, 11329–11344 (2025).

    Google Scholar 

  59. Zhang, L. et al. Role of PEGylated lipid in lipid nanoparticle formulation for in vitro and in vivo delivery of mRNA vaccines. J. Control. Release 380, 108–124 (2025).

    Google Scholar 

  60. Xu, S. et al. In vivo genome editing of human haematopoietic stem cells for treatment of blood disorders using mRNA delivery. Nat. Biomed. Eng. https://doi.org/10.1038/s41551-025-01480-y (2025).

  61. Han, X. et al. Plug-and-play assembly of biodegradable ionizable lipids for potent mRNA delivery and gene editing in vivo. Preprint at https://doi.org/10.1101/2025.02.25.640222 (2025).

  62. Wu, S. et al. Paracyclophane-based ionizable lipids for efficient mRNA delivery in vivo. J. Control. Release 376, 395–401 (2024).

    Google Scholar 

  63. Han, X. et al. Optimization of the activity and biodegradability of ionizable lipids for mRNA delivery via directed chemical evolution. Nat. Biomed. Eng. 8, 1412–1424 (2024).

    Google Scholar 

  64. Weininger, D. SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J. Chem. Inf. Comput. Sci. 28, 31–36 (1988).

    Google Scholar 

  65. Yang, K. et al. Analyzing learned molecular representations for property prediction. J. Chem. Inf. Model. 59, 3370–3388 (2019).

    Google Scholar 

  66. Park, S., Choi, Y. K., Kim, S., Lee, J. & Im, W. CHARMM-GUI membrane builder for lipid nanoparticles with ionizable cationic lipids and PEGylated lipids. J. Chem. Inf. Model. 61, 5192–5202 (2021).

    Google Scholar 

  67. Mui, B. L. et al. Influence of polyethylene glycol lipid desorption rates on pharmacokinetics and pharmacodynamics of siRNA lipid nanoparticles. Mol. Ther. Nucleic Acids 2, e139 (2013).

    Google Scholar 

  68. Yanez Arteta, M. et al. Successful reprogramming of cellular protein production through mRNA delivered by functionalized lipid nanoparticles. Proc. Natl. Acad. Sci. USA 115, E3351–E3360 (2018).

  69. Chatterjee, S., Kon, E., Sharma, P. & Peer, D. Endosomal escape: a bottleneck for LNP-mediated therapeutics. Proc. Natl. Acad. Sci. USA 121, e2307800120 (2024).

    Google Scholar 

  70. Jayaraman, M. et al. Maximizing the potency of siRNA lipid nanoparticles for hepatic gene silencing in vivo. Angew. Chem. Int. Ed Engl. 51, 8529–8533 (2012).

    Google Scholar 

  71. Eastman, P. et al. OpenMM 7: rapid development of high performance algorithms for molecular dynamics. PLOS Comput. Biol. 13, e1005659 (2017).

    Google Scholar 

  72. Yu, H. et al. Real-time pH-dependent self-assembly of ionisable lipids from COVID-19 Vaccines and in situ nucleic acid complexation. Angew. Chem. 135, e202304977 (2023).

    Google Scholar 

  73. Adams, D. et al. Patisiran, an RNAi therapeutic, for hereditary transthyretin amyloidosis. N. Engl. J. Med. 379, 11–21 (2018).

    Google Scholar 

  74. Kjølbye, L. R. et al. Martini 3 Building Blocks for Lipid Nanoparticle Design. J. Chem. Theory Comput. https://doi.org/10.1021/acs.jctc.5c01207 (2025).

  75. Shi, D., Toyonaga, S. & Anderson, D. G. In vivo RNA delivery to hematopoietic stem and progenitor cells via targeted lipid nanoparticles. Nano Lett. 23, 2938–2944 (2023).

    Google Scholar 

  76. Jansen, A., Aho, N., Groenhof, G., Buslaev, P. & Hess, B. phbuilder: a tool for efficiently setting up constant pH molecular dynamics simulations in GROMACS. J. Chem. Inf. Model. 64, 567–574 (2024).

    Google Scholar 

  77. Vanommeslaeghe, K. et al. CHARMM general force field: A force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields. J. Comput. Chem. 31, 671–690 (2010).

    Google Scholar 

  78. Klauda, J. B. et al. Update of the CHARMM all-atom additive force field for lipids: validation on six lipid types. J. Phys. Chem. B 114, 7830–7843 (2010).

    Google Scholar 

  79. Lee, J. et al. CHARMM-GUI Membrane Builder for complex biological membrane simulations with glycolipids and lipoglycans. J. Chem. Theory Comput. 15, 775–786 (2019).

    Google Scholar 

  80. Jo, S., Kim, T., Iyer, V. G. & Im, W. CHARMM-GUI: a web-based graphical user interface for CHARMM. J. Comput. Chem. 29, 1859–1865 (2008).

    Google Scholar 

  81. Jorgensen, W. L., Chandrasekhar, J., Madura, J. D., Impey, R. W. & Klein, M. L. Comparison of simple potential functions for simulating liquid water. J. Chem. Phys. 79, 926–935 (1983).

    Google Scholar 

  82. Lee, J. et al. CHARMM-GUI input generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM simulations using the CHARMM36 additive force field. J. Chem. Theory Comput. 12, 405–413 (2016).

    Google Scholar 

  83. Hopkins, C. W., Le Grand, S., Walker, R. C. & Roitberg, A. E. Long-time-step molecular dynamics through hydrogen mass repartitioning. J. Chem. Theory Comput. 11, 1864–1874 (2015).

    Google Scholar 

  84. Gao, Y. et al. CHARMM-GUI supports hydrogen mass repartitioning and different protonation states of phosphates in lipopolysaccharides. J. Chem. Inf. Model. 61, 831–839 (2021).

    Google Scholar 

  85. Chow, K.-H. & Ferguson, D. M. Isothermal-isobaric molecular dynamics simulations with Monte Carlo volume sampling. Comput. Phys. Commun. 91, 283–289 (1995).

    Google Scholar 

  86. Åqvist, J., Wennerström, P., Nervall, M., Bjelic, S. & Brandsdal, B. O. Molecular dynamics simulations of water and biomolecules with a Monte Carlo constant pressure algorithm. Chem. Phys. Lett. 384, 288–294 (2004).

    Google Scholar 

  87. Ryckaert, J.-P., Ciccotti, G. & Berendsen, H. J. C. Numerical integration of the cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes. J. Comput. Phys. 23, 327–341 (1977).

    Google Scholar 

  88. Dion, M., Rydberg, H., Schröder, E., Langreth, D. C. & Lundqvist, B. I. Van der Waals density functional for general geometries. Phys. Rev. Lett. 92, 246401 (2004).

    Google Scholar 

  89. Essmann, U. et al. A smooth particle mesh Ewald method. J. Chem. Phys. 103, 8577–8593 (1995).

    Google Scholar 

  90. Rogers, D. & Hahn, M. Extended-connectivity fingerprints. J. Chem. Inf. Model. 50, 742–754 (2010).

    Google Scholar 

  91. Moriwaki, H., Tian, Y.-S., Kawashita, N. & Takagi, T. Mordred: a molecular descriptor calculator. J. Cheminform. 10, 4 (2018).

    Google Scholar 

  92. Kobierski, J., Wnętrzak, A., Chachaj-Brekiesz, A. & Dynarowicz-Latka, P. Predicting the packing parameter for lipids in monolayers with the use of molecular dynamics. Colloids Surf. B Biointerfaces 211, 112298 (2022).

    Google Scholar 

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