Abstract
Ionizable lipids are key components in lipid nanoparticles (LNPs) for nucleic acid therapeutics, primarily facilitating endosomal escape. Beyond this function, recent advances have demonstrated their broader roles, including immunogenicity modulation, toxicity reduction, and tissue-specific targeting. This review categorizes current approaches in ionizable lipid design, including functionalized lipid modifications, high-throughput combinatorial libraries, and machine learning-driven discovery methods. We explore how these strategies expand the chemical diversity of ionizable lipids, enabling the identification of novel structures with enhanced delivery properties and specialized biological functions. By integrating computational and experimental methodologies, this review provides a comprehensive framework for guiding the next generation of ionizable lipids in nanoparticle-based drug delivery systems.
| Original language | English |
|---|---|
| Pages (from-to) | 388-401 |
| Number of pages | 14 |
| Journal | Materials Today |
| Volume | 89 |
| DOIs | |
| State | Published - Oct 2025 |
Bibliographical note
Publisher Copyright:© 2025 Elsevier Ltd
Keywords
- High-throughput screening
- Ionizable lipids
- Lipid nanoparticles
- Machine learning
- RNA therapeutics
- Targeted delivery
- mRNA delivery
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