High-Throughput Strategies for Streamlining Lipid Nanoparticle Development Pipeline

Area of Science:

• Biotechnology
• Nanomedicine
• Drug Delivery

Background:

• Lipid nanoparticles (LNPs) are clinically validated nanocarriers for nucleic acid delivery, crucial for mRNA vaccines and therapies targeting cancer, ocular, and infectious diseases.
• Traditional low-throughput methods for identifying optimal LNP formulations are resource-intensive and impractical for large-scale screening.

Purpose of the Study:

• To review integrated frameworks combining automation with high-throughput synthesis, characterization, and screening for LNP formulation development.
• To highlight strategies for rapid, multi-parametric evaluation of LNP properties and therapeutic delivery outcomes.

Main Methods:

• Leveraging automation and high-throughput platforms for lipid synthesis, characterization, and screening of LNP formulations.
• Utilizing advanced biomimetic models and in vivo multiplexed barcoding for tissue targeting and delivery insights.
• Implementing performance benchmarks at each stage to exclude suboptimal LNP candidates.

Main Results:

• High-throughput strategies enable rapid generation and evaluation of thousands of LNP formulations.
• Integrated pipelines systematically identify clinically viable LNP candidates by excluding suboptimal ones.
• Advanced screening tools provide deeper insights into biodistribution, cellular uptake, and intracellular trafficking.

Conclusions:

• Automation and high-throughput screening are transforming LNP formulation development, accelerating the identification of effective nanocarriers.
• Future directions include machine learning-guided design for next-generation LNP therapeutics.
• These advancements are crucial for translating LNP therapies from research to clinical application.