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Lipid-based nanoparticles for nucleic acids delivery

  • Sonia Sarnelli , Manuel Cardamone , Ernesto Reverchon and Lucia Baldino ORCID logo EMAIL logo
Published/Copyright: March 5, 2025
Become an author with De Gruyter Brill

Abstract

This chapter highlights challenges and advancements in the production of lipid-based nanoparticles (LNPs) and their application in nucleic acid-based therapies. Recently, mRNA-based vaccines for COVID-19 immunization revealed that the use of nucleic acids is a promising strategy to develop treatments at high therapeutic efficiency and reduced side effects. In this context, LNPs emerged as favourable vehicles for nucleic acids delivery (like mRNA and DNA), due to their biocompatibility, bioavailability, and versatility. The four main components employed to produce LNPs loaded with mRNA are: cationic or ionizable lipids, helper lipids, cholesterol, and PEGylated lipids. Several conventional techniques have been proposed over the years to produce this kind of nanoparticles. However, they show many drawbacks that hinder the direct production of vesicles characterized by a nanometric size, high encapsulation efficiency of the active pharmaceutical ingredient, and prolonged stability. Processes assisted by supercritical fluids (in particular, supercritical CO2) can represent a sustainable and interesting alternative to produce LNPs without using post-processing steps for solvent removal and size reduction that are time-consuming procedures, lead to a large loss of nucleic acids, and negatively influence the general productivity of the process.


Corresponding author: Lucia Baldino, Department of Industrial Engineering, University of Salerno, Via Giovanni Paolo II, 132, 84084, Fisciano, SA, Italy, E-mail:

Acknowledgments

The authors would like to thank the editors David Bogle and Tomasz Sosnowski for their guidance and review of this article before its publication.

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: Conceptualization, L.B.; formal analysis, M.C. and S.S.; investigation, M.C. and S.S.; methodology, L.B.; supervision, L.B. and E.R.; validation, L.B.; visualization, E.R.; writing – original draft, M.C. and S.S.; writing – review & editing, L.B. and S.S.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The authors declare no conflicts of interest.

  6. Research funding: This research did not receive any financial support.

  7. Data availability: Not applicable.

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Received: 2025-01-24
Accepted: 2025-01-29
Published Online: 2025-03-05

© 2025 Walter de Gruyter GmbH, Berlin/Boston

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