Integrative Biomedical Research
Integrative Biomedical Research (Journal of Angiotherapy) | Online ISSN 3068-6326
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Articles
Volume 10 Number 2 2026
REVIEWS (Open Access)
Engineering the Lipid Nanoparticle Through Formulation Chemistry, Analytical Characterization, and Machine-Learning-Guided Optimization of mRNA Vaccine Delivery Systems
Rajesh Thangaraja 1, Ong Khang Wei 2, Nurul Dayana Binti Mahizir 2, Gnanasekaran Ashok 3*
Integrative Biomedical Research 10 (2) 1-19 https://doi.org/10.25163/biomedical.10210918
Submitted: 18 July 2026 Revised: 06 September 2026 Accepted: 15 September 2026 Published: 17 September 2026
Abstract
Lipid nanoparticles (LNPs) remain the only clinically validated non-viral platform for messenger RNA (mRNA) delivery, yet their default liver tropism, reactogenicity, and cold-chain dependence continue to limit broader translation. This review synthesizes recent formulation, analytical, and computational literature to ask whether these three constraints can be engineered around rather than merely tolerated. We searched and evaluated primary research and review articles addressing LNP lipid composition, biodistribution, physicochemical characterization, storage stability, and artificial-intelligence-assisted formulation design, then organized findings around four questions: whether transfection can be uncoupled from inflammation, how protein corona composition dictates tissue homing, how muscle-cell transfection shapes immunogenicity, and whether machine learning can forecast biological outcomes rather than only physical ones. Across the reviewed studies, raising helper-phospholipid content, substituting linear PEG with polysorbate-80, and incorporating trehalose- or hyaluronic-acid-based hybrid shells consistently redirected particles away from hepatocytes and toward splenic or muscle-resident targets, often without sacrificing immunogenicity. Analytical advances, particularly two-dimensional liquid chromatography, revealed a previously hidden pool of surface-associated mRNA that bulk dye assays systematically misclassify as free cargo. Machine-learning frameworks, including self-validated ensemble models and the ELEVATE-LNP platform, predicted particle size and encapsulation efficiency with better than 97% accuracy from empty-particle screens alone. Collectively, the evidence suggests that LNP performance is tunable along largely independent axes of tropism, stability, and safety, provided formulation variables are chosen deliberately rather than inherited from first-generation platforms. We conclude by outlining where predictive modeling of immunological, rather than merely physical, endpoints represents the field’s next necessary step.
Keywords: lipid nanoparticles; mRNA vaccines; biodistribution; ionizable lipids; encapsulation efficiency; machine learning; formulation stability
References
Aebischer, M. K., Tsalmpouris, A., Rudaz, S., Malburet, C., Cotte, J. F., Guillarme, D., & Maurer, J. (2026). Two-dimensional AEX–IP-RPLC: New insights on mRNA integrity and encapsulation efficiency of mRNA-lipid nanoparticle formulations. Analytica Chimica Acta, 1394, 345215. https://doi.org/10.1016/j.aca.2026.345215
Anindita, J., Tanaka, H., Oyama, R., Matsumaru, A., Nakai, Y., Tange, K., Nagaoka, K., Nakanishi, H., Kawamura, T., Tanaka, T., Yamashita, T., Kuroda, A., Nomura, S., Hatakeyama, H., Itaka, K., Kodama, T., Kakimi, K., & Akita, H. (2026). Combination of ionizable lipids with oleic acid and vitamin E scaffolds for RNA cancer vaccine delivery. Journal of Controlled Release, 389, 114414. https://doi.org/10.1016/j.jconrel.2025.114414
Bae, S.-H., Yoo, S., Lee, J., Park, H.-J., Kwon, S. P., Jin, H., Park, S.-I., Lee, Y.-S., Bang, Y.-J., Roh, G., Lee, S., Youn, S. B., Kim, I. W., Oh, H. R., El-Damasy, A. K., Keum, G., Kim, H., Youn, H., Nam, J.-H., & Bang, E.-K. (2024). A lipid nanoparticle platform incorporating trehalose glycolipid for exceptional mRNA vaccine safety. Bioactive Materials, 38, 486–498. https://doi.org/10.1016/j.bioactmat.2024.05.012
Baghel, S., Lokras, A., Alves Dias, B., Landry, M., López Cerdá, S., Wadhwa, A., Herrera-Barrera, M., Thakur, A., Rades, T., Franzyk, H., Hopkins, C., Sun, C., Christensen, D., Pedersen, G. K., Sahay, G., & Foged, C. (2025). Design of messenger RNA vaccines based on lipid-polymer hybrid nanoparticles. Journal of Controlled Release, 388, 114385. https://doi.org/10.1016/j.jconrel.2025.114385
Eslami, M., Dowlat, B. F., Yaghmayee, S., Habibian, A., Keshavarzi, S., Oksenych, V., & Naderian, R. (2025). Next-generation vaccine platforms: Integrating synthetic biology, nanotechnology, and systems immunology for improved immunogenicity. Vaccines, 13(5), 588. https://doi.org/10.3390/vaccines13050588
Fatima, M., An, T., & Hong, K.-J. (2025). Revolutionizing mRNA vaccines through innovative formulation and delivery strategies. Biomolecules, 15(3), 359. https://doi.org/10.3390/biom15030359
Hirai, T., & Yoshioka, Y. (2026). Dissecting lipid nanoparticle adjuvanticity and reactogenicity to optimize vaccine formulations. Current Opinion in Virology, 76, 101540. https://doi.org/10.1016/j.coviro.2026.101540
Kim, B., Hosn, R. R., Remba, T., Yun, D., Li, N., Abraham, W., Melo, M. B., Cortes, M., Li, B., Zhang, Y., Dong, Y., & Irvine, D. J. (2023). Optimization of storage conditions for lipid nanoparticle-formulated self-replicating RNA vaccines. Journal of Controlled Release, 353, 241–253. https://doi.org/10.1016/j.jconrel.2022.11.022
Kumar, R., Kommineni, N., Aadil, K. R., Desai, N., Bunekar, N., Salave, S., Bulusu, R., Kumar, D., & Vora, L. K. (2026). Lipid nanoparticle-based mRNA therapeutics for infectious diseases. International Journal of Pharmaceutics, 687, 126420. https://doi.org/10.1016/j.ijpharm.2025.126420
Labonia, M. C. I., Escudé Martinez de Castilla, P., van der Kraak, P. H., Brans, M. A. D., Yang, Q., Lei, Z., de Jager, S. C. A., de Voogt, W. S., Schiffelers, R. M., Sluijter, J. P. G., & Vader, P. (2026). Efficiency and immunogenicity of lipid nanoparticle-mediated cardiac mRNA delivery are lipid composition-dependent. Molecular Therapy: Nucleic Acids, 37, 102939. https://doi.org/10.1016/j.omtn.2026.102939
Lamparelli, E. P., Ciaglia, E., Ciardulli, M. C., Lopardo, V., Montella, F., Puca, A. A., & Della Porta, G. (2025). Optimizing mRNA delivery: A microfluidic exploration of DOTMA vs. DOTAP lipid nanoparticles for GFP expression on human PBMCs and THP-1 cell line. International Journal of Pharmaceutics, 672, 125324. https://doi.org/10.1016/j.ijpharm.2025.125324
Lu, Y., Yang, Y., Yi, J., Hong, X., Lou, J., Li, M., & Zheng, A. (2025). Lyophilization optimization of LNP-mRNA using DOPE and beta-sitosterol helper lipids. Materials Today Bio, 32, 101813. https://doi.org/10.1016/j.mtbio.2025.101813
Maharjan, R., Kim, K. H., Lee, K., Han, H.-K., & Jeong, S. H. (2024). Machine learning-driven optimization of mRNA-lipid nanoparticle vaccine quality with XGBoost/Bayesian method and ensemble model approaches. Journal of Pharmaceutical Analysis, 14(11), 100996. https://doi.org/10.1016/j.jpa.2024.100996
Repellin, M., et al. (2026). PEG-free hyaluronic acid-coated lipid-polymer hybrid nanoparticles for efficient muscle-targeted mRNA vaccine delivery. Journal of Controlled Release, 397, 115128. https://doi.org/10.1016/j.jconrel.2026.115128
Roy, A. A., Jain, N., Pokale, R., Madhusoodanan, G., Mukharya, R., Farhana, S. A., Rao, B. S. S., Goda, J. S., & Mutalik, S. (2026). High-throughput characterization tools for quality control of lipid nanoparticle formulations. International Journal of Pharmaceutics, 697, 126862. https://doi.org/10.1016/j.ijpharm.2026.126862
Saraswat, A., Nomani, A., Yong, L.-K., Kuo, J. C.-T., Brown, H., Narayanareddygari, M., Peace, A., Fazily, R., Blake, T., Petro, C. D., Rayaprolu, B., Hansen, J., Bhalla, A. S., & Shameem, M. (2025). A polysorbate-based lipid nanoparticle vaccine formulation induces in vivo immune response against SARS-CoV-2. Pharmaceutics, 17(4), 441. https://doi.org/10.3390/pharmaceutics17040441
Sisk, C. K., Turner, L. M., Meraj, S., & Yusuf, N. (2026). Advances in mRNA-based melanoma vaccines: A narrative review of lipid nanoparticle and dendritic cell delivery platforms. Cells, 15(1), 99. https://doi.org/10.3390/cells15010099
Streiber, M., Schubert, U. S., & Traeger, A. (2026). Post-formation encapsulation strategies utilizing preformed vesicles for decentralized LNP assembly. Journal of Controlled Release, 395, 115023. https://doi.org/10.1016/j.jconrel.2026.115023
Suwanpitak, K., Sangnim, T., Srikoon, P., Panpipat, C., Wongkongkaew, N., Sriamornsak, P., & Huanbutta, K. (2026). Vortex-driven flow intensifies mixing for lipid nanoparticle assembly. Journal of Controlled Release [Preprint/In Press], 2352-9520, 26000393.
Suzuki, Y., Yakuwa, M., Sato, M., Samaridou, E., Beck-Broichsitter, E., Maeki, M., Tokeshi, M., Yamada, Y., & Sato, Y. (2025). Splenic B cell-targeting lipid nanoparticles for safe and effective mRNA vaccine delivery. Journal of Controlled Release, 382, 113687. https://doi.org/10.1016/j.jconrel.2025.113687
Weidensee, B., & Sahu, I. (2025). Decrypting the immune symphony for RNA vaccines. Vaccines, 13(8), 882. https://doi.org/10.3390/vaccines13080882
Yu, M., Wang, L., Elliott, T., Wang, X., Mathew, A., Qiu, B., Yao, L., Wang, W., & Zhang, N. (2026). ELEVATE-LNP: Empty lipid nanoparticle enabled validation and AI-guided therapeutic evaluation for efficient formulation optimization for mRNA delivery. Chemical Engineering Journal, 546, 180282. https://doi.org/10.1016/j.cej.2026.180282
Zhang, K., Liu, C., Ge, R., Zhang, Z., Liu, Q., Mo, O., Li, X., Liu, X., Li, H., & Hao, P. (2026). SM-102 based formulations for dual delivery of mRNA and ssDNA. Journal of Controlled Release, 392, 114740. https://doi.org/10.1016/j.jconrel.2026.114740
Zhou, J., Li, N., Liu, R., Kabanov, A. V., Polacheck, W. J., Nguyen, J., & Cao, Y. (2026). New approach methodologies (NAMs) for preclinical and translational evaluation of mRNA-lipid nanoparticle (LNP) therapeutics. Journal of Controlled Release, 397, 115157. https://doi.org/10.1016/j.jconrel.2026.115157