AGILE platform: a deep learning powered approach to accelerate LNP development for mRNA delivery.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
26 Jul 2024
26 Jul 2024
Historique:
received:
29
01
2024
accepted:
09
07
2024
medline:
27
7
2024
pubmed:
27
7
2024
entrez:
26
7
2024
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 39060305
doi: 10.1038/s41467-024-50619-z
pii: 10.1038/s41467-024-50619-z
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
6305Informations de copyright
© 2024. The Author(s).
Références
Qin, S. et al. mRNA-based therapeutics: powerful and versatile tools to combat diseases. Signal Transduct. Target. Ther. 7, 166 (2022).
pubmed: 35597779
pmcid: 9123296
doi: 10.1038/s41392-022-01007-w
Hou, X., Zaks, T., Langer, R. & Dong, Y. Lipid nanoparticles for mRNA delivery. Nat. Rev. Mater. 6, 1078–1094 (2021).
pubmed: 34394960
pmcid: 8353930
doi: 10.1038/s41578-021-00358-0
Kim, Y.-K. RNA therapy: rich history, various applications and unlimited future prospects. Exp. Mol. Med. 54, 455–465 (2022).
pubmed: 35440755
pmcid: 9016686
doi: 10.1038/s12276-022-00757-5
Mendes, B. B. et al. Nanodelivery of nucleic acids. Nat. Rev. Methods Prim. 2, 24 (2022).
doi: 10.1038/s43586-022-00104-y
Mitchell, M. J. et al. Engineering precision nanoparticles for drug delivery. Nat. Rev. Drug Discov. 20, 101–124 (2021).
pubmed: 33277608
doi: 10.1038/s41573-020-0090-8
Nasreen, S. et al. Effectiveness of COVID-19 vaccines against symptomatic SARS-CoV-2 infection and severe outcomes with variants of concern in Ontario. Nat. Microbiol. 7, 379–385 (2022).
pubmed: 35132198
doi: 10.1038/s41564-021-01053-0
Patrignani, A. et al. Acute myocarditis following Comirnaty vaccination in a healthy man with previous SARS-CoV-2 infection. Radiol. Case Rep. 16, 3321–3325 (2021).
pubmed: 34367386
pmcid: 8326008
doi: 10.1016/j.radcr.2021.07.082
Akinc, A. et al. The Onpattro story and the clinical translation of nanomedicines containing nucleic acid-based drugs. Nat. Nanotechnol. 14, 1084–1087 (2019).
pubmed: 31802031
doi: 10.1038/s41565-019-0591-y
Rüger, J., Ioannou, S., Castanotto, D. & Stein, C. A. Oligonucleotides to the (gene) rescue: FDA approvals 2017–2019. Trends Pharmacol. Sci. 41, 27–41 (2020).
pubmed: 31836192
doi: 10.1016/j.tips.2019.10.009
Chaudhary, N., Weissman, D. & Whitehead, K. A. mRNA vaccines for infectious diseases: principles, delivery and clinical translation. Nat. Rev. Drug Discov. 20, 817–838 (2021).
pubmed: 34433919
pmcid: 8386155
doi: 10.1038/s41573-021-00283-5
Kim, M. et al. Engineered ionizable lipid nanoparticles for targeted delivery of RNA therapeutics into different types of cells in the liver. Sci. Adv. 7, eabf4398 (2021).
pubmed: 33637537
pmcid: 7909888
doi: 10.1126/sciadv.abf4398
Degors, I. M., Wang, C., Rehman, Z. U. & Zuhorn, I. S. Carriers break barriers in drug delivery: endocytosis and endosomal escape of gene delivery vectors. Acc. Chem. Res. 52, 1750–1760 (2019).
pubmed: 31243966
pmcid: 6639780
doi: 10.1021/acs.accounts.9b00177
Wittrup, A. et al. Visualizing lipid-formulated siRNA release from endosomes and target gene knockdown. Nat. Biotechnol. 33, 870–876 (2015).
pubmed: 26192320
pmcid: 4663660
doi: 10.1038/nbt.3298
Xu, E., Saltzman, W. M. & Piotrowski-Daspit, A. S. Escaping the endosome: assessing cellular trafficking mechanisms of non-viral vehicles. J. Control. Release 335, 465–480 (2021).
pubmed: 34077782
doi: 10.1016/j.jconrel.2021.05.038
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).
pubmed: 31570898
doi: 10.1038/s41587-019-0247-3
Li, B. et al. Combinatorial design of nanoparticles for pulmonary mRNA delivery and genome editing. Nat. Biotechnol. 41, 1410–1415 (2023).
pubmed: 36997680
pmcid: 10544676
doi: 10.1038/s41587-023-01679-x
Han, X. et al. An ionizable lipid toolbox for RNA delivery. Nat. Commun. 12, 7233 (2021).
pubmed: 34903741
pmcid: 8668901
doi: 10.1038/s41467-021-27493-0
Zador, A. et al. Catalyzing next-generation Artificial Intelligence through NeuroAI. Nat. Commun. 14, 1597 (2023).
pubmed: 36949048
pmcid: 10033876
doi: 10.1038/s41467-023-37180-x
Bhardwaj, G. et al. Accurate de novo design of membrane-traversing macrocycles. Cell 185, 3520–3532. e3526 (2022).
pubmed: 36041435
pmcid: 9490236
doi: 10.1016/j.cell.2022.07.019
Yeh, A. H.-W. et al. De novo design of luciferases using deep learning. Nature 614, 774–780 (2023).
pubmed: 36813896
pmcid: 9946828
doi: 10.1038/s41586-023-05696-3
Yamankurt, G. et al. Exploration of the nanomedicine-design space with high-throughput screening and machine learning. Nat. Biomed. Eng. 3, 318–327 (2019).
pubmed: 30952978
pmcid: 6452897
doi: 10.1038/s41551-019-0351-1
Paul, D. et al. Artificial intelligence in drug discovery and development. Drug Discov. Today 26, 80–93 (2021).
pubmed: 33099022
doi: 10.1016/j.drudis.2020.10.010
Melo, M. C. R. et al. Accelerating antibiotic discovery through artificial intelligence. Commun. Biol. 4, 1050 (2021).
pubmed: 34504303
pmcid: 8429579
doi: 10.1038/s42003-021-02586-0
Ma, Y. et al. Identification of antimicrobial peptides from the human gut microbiome using deep learning. Nat. Biotechnol. 40, 921–931 (2022).
pubmed: 35241840
doi: 10.1038/s41587-022-01226-0
McCloskey, K. et al. Machine learning on DNA-encoded libraries: a new paradigm for hit finding. J. Med. Chem. 63, 8857–8866 (2020).
pubmed: 32525674
doi: 10.1021/acs.jmedchem.0c00452
Stokes, J. M. et al. A deep learning approach to antibiotic discovery. Cell 180, 688–702.e613 (2020).
pubmed: 32084340
pmcid: 8349178
doi: 10.1016/j.cell.2020.01.021
Wang, W. et al. Prediction of lipid nanoparticles for mRNA vaccines by the machine learning algorithm. Acta Pharm. Sin. B 12, 2950–2962 (2022).
pubmed: 35755271
doi: 10.1016/j.apsb.2021.11.021
Huang, Y. et al. High-throughput microbial culturomics using automation and machine learning. Nat. Biotechnol. 41, 1–10 (2023).
Wang, Y., Wang, J., Cao, Z. & Barati Farimani, A. Molecular contrastive learning of representations via graph neural networks. Nat. Mach. Intell. 4, 279–287 (2022).
doi: 10.1038/s42256-022-00447-x
Chen, T., Kornblith, S., Norouzi, M. & Hinton, G. A simple framework for contrastive learning of visual representations. In International Conference on Machine Learning (PMLR, 2020).
Nazeri, M. T., Farhid, H., Mohammadian, R. & Shaabani, A. Cyclic imines in Ugi and Ugi-type reactions. ACS Comb. Sci. 22, 361–400 (2020).
pubmed: 32574488
doi: 10.1021/acscombsci.0c00046
Moriwaki, H., Tian, Y.-S., Kawashita, N. & Takagi, T. Mordred: a molecular descriptor calculator. J. Cheminform. 10, 4 (2018).
pubmed: 29411163
pmcid: 5801138
doi: 10.1186/s13321-018-0258-y
Yang, L. et al. Recent advances in lipid nanoparticles for delivery of mRNA. Pharmaceutics 14, 2682 (2022).
Barnard, J. M., Downs, G. M., von Scholley-Pfab, A. & Brown, R. D. Use of Markush structure analysis techniques for descriptor generation and clustering of large combinatorial libraries. J. Mol. Graph. Model. 18, 452–463 (2000).
pubmed: 11143562
doi: 10.1016/S1093-3263(00)00067-X
Kaczmarek, J. C. et al. Optimization of a degradable polymer–lipid nanoparticle for potent systemic delivery of mRNA to the lung endothelium and immune cells. Nano Lett. 18, 6449–6454 (2018).
pubmed: 30211557
pmcid: 6415675
doi: 10.1021/acs.nanolett.8b02917
Eygeris, Y., Gupta, M., Kim, J. & Sahay, G. Chemistry of lipid nanoparticles for RNA delivery. Acc. Chem. Res. 55, 2–12 (2022).
pubmed: 34850635
doi: 10.1021/acs.accounts.1c00544
Wang, X. et al. Preparation of selective organ-targeting (SORT) lipid nanoparticles (LNPs) using multiple technical methods for tissue-specific mRNA delivery. Nat. Protoc. 18, 265–291 (2023).
pubmed: 36316378
doi: 10.1038/s41596-022-00755-x
Hoerl, A. E. & Kennard, R. W. Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12, 55–67 (1970).
doi: 10.1080/00401706.1970.10488634
Ranstam, J. & Cook, J. LASSO regression. J. Br. Surg. 105, 1348–1348 (2018).
doi: 10.1002/bjs.10895
Natekin, A. & Knoll, A. Gradient boosting machines, a tutorial. Front. Neurorobot. 7, 21 (2013).
pubmed: 24409142
pmcid: 3885826
doi: 10.3389/fnbot.2013.00021
Hearst, M. A., Dumais, S. T., Osuna, E., Platt, J. & Scholkopf, B. Support vector machines. IEEE Intell. Syst. Appl. 13, 18–28 (1998).
doi: 10.1109/5254.708428
McInnes, L., Healy, J. & Melville, J. Umap: Uniform manifold approximation and projection for dimension reduction. Preprint at https://doi.org/10.48550/arXiv.1802.03426 (2018).
Lam, K. et al. Unsaturated, trialkyl ionizable lipids are versatile lipid-nanoparticle components for therapeutic and vaccine applications. Adv. Mater. 35, 2209624 (2023).
doi: 10.1002/adma.202209624
Lee, S. M. et al. A systematic study of unsaturation in lipid nanoparticles leads to improved mRNA transfection in vivo. Angew. Chem. 133, 5912–5917 (2021).
doi: 10.1002/ange.202013927
Whitehead, K. A. et al. Degradable lipid nanoparticles with predictable in vivo siRNA delivery activity. Nat. Commun. 5, 4277 (2014).
pubmed: 24969323
doi: 10.1038/ncomms5277
Li, Y. et al. Protein and mRNA delivery enabled by cholesteryl-based biodegradable lipidoid nanoparticles. Angew. Chem. Int. Ed. 59, 14957–14964 (2020).
doi: 10.1002/anie.202004994
Muzumdar, M. D., Tasic, B., Miyamichi, K., Li, L. & Luo, L. A global double-fluorescent Cre reporter mouse. genesis 45, 593–605 (2007).
pubmed: 17868096
doi: 10.1002/dvg.20335
Boettler, T. et al. SARS-CoV-2 vaccination can elicit a CD8 T-cell dominant hepatitis. J. Hepatol. 77, 653–659 (2022).
pubmed: 35461912
pmcid: 9021033
doi: 10.1016/j.jhep.2022.03.040
Musunuru, K. et al. In vivo CRISPR base editing of PCSK9 durably lowers cholesterol in primates. Nature 593, 429–434 (2021).
pubmed: 34012082
doi: 10.1038/s41586-021-03534-y
Rothgangl, T. et al. In vivo adenine base editing of PCSK9 in macaques reduces LDL cholesterol levels. Nat. Biotechnol. 39, 949–957 (2021).
pubmed: 34012094
pmcid: 8352781
doi: 10.1038/s41587-021-00933-4
Rampado, R. & Peer, D. Design of experiments in the optimization of nanoparticle-based drug delivery systems. J. Control. Release 358, 398–419 (2023).
pubmed: 37164240
doi: 10.1016/j.jconrel.2023.05.001
Labute, P. A widely applicable set of descriptors. J. Mol. Graph. Model. 18, 464–477 (2000).
pubmed: 11143563
doi: 10.1016/S1093-3263(00)00068-1
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).
Li, B. et al. Accelerating ionizable lipid discovery for mRNA delivery using machine learning and combinatorial chemistry. Nat. Mater. 23, 1–7 (2024).
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).
doi: 10.1002/anie.202310401
Liu, S. et al. Membrane-destabilizing ionizable phospholipids for organ-selective mRNA delivery and CRISPR–Cas gene editing. Nat. Mater. 20, 701–710 (2021).
pubmed: 33542471
pmcid: 8188687
doi: 10.1038/s41563-020-00886-0
Zhang, D. et al. Targeted delivery of mRNA with one-component ionizable amphiphilic Janus dendrimers. J. Am. Chem. Soc. 143, 17975–17982 (2021).
pubmed: 34672554
doi: 10.1021/jacs.1c09585
Boström, J., Brown, D. G., Young, R. J. & Keserü, G. M. Expanding the medicinal chemistry synthetic toolbox. Nat. Rev. Drug Discov. 17, 709–727 (2018).
pubmed: 30140018
doi: 10.1038/nrd.2018.116
Li, B. et al. Enhancing the immunogenicity of lipid-nanoparticle mRNA vaccines by adjuvanting the ionizable lipid and the mRNA. Nat. Biomed. Eng. 1–8 https://doi.org/10.1038/s41551-023-01082-6 (2023).
Zhang, M. et al. A survey on graph diffusion models: generative AI in science for molecule, protein and material. Preprint at https://doi.org/10.48550/arXiv.2304.01565 (2023).
Hoogeboom, E., Satorras, V. G., Vignac, C. & Welling, M. Equivariant diffusion for molecule generation in 3D. In International Conference on Machine Learning (PMLR, 2022).
Bemis, G. W. & Murcko, M. A. The properties of known drugs. 1. Molecular frameworks. J. Med. Chem. 39, 2887–2893 (1996).
pubmed: 8709122
doi: 10.1021/jm9602928
Landrum, G. Rdkit documentation. Release 1, 4 (2013).
Xu, K., Hu, W., Leskovec, J. & Jegelka, S. How powerful are graph neural networks? Preprint at https://doi.org/10.48550/arXiv.1810.00826 (2018).
Kingma, D. P. & Ba, J. Adam: a method for stochastic optimization. Preprint at https://doi.org/10.48550/arXiv.1412.6980 (2014).
Sundararajan, M., Taly, A. & Yan, Q. Axiomatic attribution for deep networks. In International Conference on Machine Learning (PMLR, 2017).
Kokhlikyan, N. et al. Captum: a unified and generic model interpretability library for PyTorch. Preprint at https://doi.org/10.48550/arXiv.2009.07896 (2020).
Wellawatte, G. P., Seshadri, A. & White, A. D. Model agnostic generation of counterfactual explanations for molecules. Chem. Sci. 13, 3697–3705 (2022).
pubmed: 35432902
pmcid: 8966631
doi: 10.1039/D1SC05259D
Kauffman, K. J. et al. Optimization of lipid nanoparticle formulations for mRNA delivery in vivo with fractional factorial and definitive screening designs. Nano Lett. 15, 7300–7306 (2015).
pubmed: 26469188
doi: 10.1021/acs.nanolett.5b02497
Mo, Y. et al. Light-activated siRNA endosomal release (LASER) by porphyrin lipid nanoparticles. ACS Nano 17, 4688–4703 (2023).
pubmed: 36853331
doi: 10.1021/acsnano.2c10936
Ma, S., Xu, Y. & Cui, H. Dataset for AGILE platform: a deep learning-powered approach to accelerate LNP development for mRNA delivery. Zenodo. Preprint at bioRxiv https://doi.org/10.1101/2023.06.01.543345 (2024).