Volume 10 Number 2 2026
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) 1-8 https://doi.org/10.25163/biomedical.10210918
Submitted: 18 July 2026 Revised: 06 September 2026 Accepted: 15 September 2026 Published: 17 September 2026
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
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