2026· The Innovation Drug Discovery· Vol 1, pp. 100006· 2 citations· 88 references
TL;DR
This review surveys how artificial intelligence is converting this nonlinear design space from empirical iteration into data-efficient, multi-objective optimization and outlines three priorities for translation: standardized data and metadata, mechanistic endpoints for escape and immunogenicity, and CMC-aware optimization.
Abstract
Lipid nanoparticles (LNPs) have made RNA therapeutics clinically viable, yet delivery remains the dominant constraint on efficacy and safety as applications expand beyond the liver. Performance is emergent: it reflects coupled choices in ionizable-lipid chemistry, multi-component formulation, and process history, and is further reshaped by biological interfaces including protein corona remodeling, tissue transport barriers, endocytic trafficking, and low-probability endosomal escape. This review surveys how artificial intelligence is converting this nonlinear design space from empirical iteration into data-efficient, multi-objective optimization. We highlight (i) structure-activity learning and synthesis-aware generative modeling for ionizable lipid discovery; (ii) formulation- and process-conditioned architectures that treat LNPs as composite, process-defined materials; and (iii) pooled in vivo barcoding and single-cell readouts that enable prediction and tuning of organ and cell-type tropism. We conclude by outlining three priorities for translation: standardized data and metadata, mechanistic endpoints for escape and immunogenicity, and CMC-aware optimization. Progress on these fronts will be necessary for potent, safe, and manufacturable mRNA-LNP medicines.
Lipid nanoparticles (LNPs) are now the leading delivery platform for nucleic acid therapeutics, but progress in the field is measured almost entirely by physicochemical and computational proxies rather than by functional properties that determine therapeutic outcomes. This review examines six interconnected areas of LNP development: microfluidic manufacturing, lyophilization, transdermal microneedle delivery, machine learning-guided formulation design, endosomal escape biology, and protein corona-mediated organ targeting. Although these areas are often discussed separately, they are linked by a common gap between routinely measured physicochemical or computational endpoints and the biological outcomes that determine therapeutic performance. A recently developed antifouling coating substantially reduced microfluidic channel fouling under the tested conditions, although its scalability remains to be validated. Lyophilization, by contrast, still requires formulation specific re-optimization for each new lipid composition, which remains an important barrier to clinical translation. In microneedle-based delivery, physicochemical integrity after fabrication is routinely treated as a proxy for therapeutic function, although, to our knowledge, no published study has directly compared endosomal escape capacity before and after microneedle fabrication. In machine learning, model accuracy is limited primarily by fragmented, outcome-biased training data rather than by algorithm design. Independent measurements of endosomal escape efficiency converge on a low ceiling whose biological origin, whether lipid-specific or inherent to the mechanism, remains unknown. For organ-selective targeting, one mechanistic account rests on a hypothesis tested in advance; another, equally prominent, has not been shown to have been anticipated rather than reconstructed after the fact. Closing this gap is now the field’s central methodologically priority.
Ognjen Milić, Sanela M. Savić, Melanija Zurković et al.· Pharmaceutics· 0 citations
ABSTRACT Despite the growing importance of lipid nanoparticles (LNPs) in mRNA therapeutics, current ionizable lipids, which comprise an ionizable head group, a linker, and hydrophobic tails that collectively govern the delivery behavior, exhibit dose‐limiting toxicity and suboptimal efficiency. Hydrophobic tail chemistry has been implicated in these limitations; however, a systematic structure–activity relationship (SAR) analysis is lacking. In this study, the authors synthesized a 56‐member library of tail‐modified ionizable lipids with biodegradable disulfide linkages and variations in chain length and branching. Systematic SAR evaluation revealed that hydrophobic tail architecture critically determines endosomal escape, biocompatibility, and mRNA translation efficiency. Optimized LNPs delivered mRNA with potent efficacy and markedly reduced cytotoxicity, lower cytokine induction, and balanced Th1/Th2 immune activation in a nonhuman primate model. The findings establish the first comprehensive SAR framework linking tail chemistry to ionizable lipid function, offering molecular design principles for next‐generation LNPs and enabling the development of safer and more effective mRNA‐based vaccines and therapeutics.
Seo-Hyeon Bae, Jisun Lee, J. Ahn et al.· Advancement of science· 0 citations
This work presents a metabolically targeted, bioorthogonal-activated delivery strategy to address the selectivity and efficiency limitations of current mRNA medicines, providing a promising platform for precision oncology.
Mingzhe Zhang, Chunhong Wang, Xiaohan Xu et al.· Journal of the American Chem...· 0 citations
The integration of computational prediction with programmable genome and RNA engineering may support more precise, adaptable, and clinically translatable mRNA therapeutics.
Haixing Shi, Shengbin Liu, Dan-Ling Dai et al.· MedComm – Biomaterials and A...· 0 citations
Lipid nanoparticles (LNPs) have emerged as the most widely used and clinically validated platform for delivering RNAs, including small interfering RNA (siRNA) and messenger RNA (mRNA), as exemplified by the FDA approvals of Onpattro, Comirnaty, Spikevax, and mRESVIA. LNPs offer various benefits for RNA delivery, such as protecting RNA from enzymatic degradation, increasing cellular uptake and endosomal escape, and improving pharmacokinetics. To further broaden their use across a wide spectrum of diseases, various LNPs have recently been developed with unique biological functions, such as immunomodulation, tissue microenvironmental modulation, endosomal pathway interference, pharmacological activity, and tissue and cell selectivity, giving rise to the emerging concept of bioactive lipid-derived nanoparticles (bioactive LNPs). In this review, we summarize bioactive LNPs incorporating naturally sourced bioactive lipids, bioactive agent-derived lipids, or novel bioactive lipids identified through combinatorial synthesis coupled with high-throughput screening. We also discuss the structure–activity relationship of these bioactive lipids and overview their corresponding LNPs for specific biomedical applications. We further provide perspectives on the challenges and opportunities in translating bioactive lipid-derived LNPs into next-generation RNA nanomedicines.
Kaiqi Long, Jeffrey Yuan, Yang Zhang et al.· Materials Today· 0 citations
This comprehensive review examines the molecular architecture and mechanisms of established platforms in the clinical setting, including mRNA, antisense oligonucleotides (ASOs), small interfering RNAs (siRNAs) and aptamers, alongside next-generation platforms, such as CRISPR-guided systems and circular RNAs (circRNAs).
Konstantina Athanasopoulou, Glykeria N. Daneva, V. Michalopoulou et al.· Current Issues in Molecular...· 0 citations
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