Dr. Michael Karimov (Research Scientist at Bayer AG) and Dr. Ryan Karongo (Scientific Lab Head at
Bayer AG) have presented an automated high-throughput method utilizing the Andrew+ pipetting robot aimed
at addressing the pain points of LNP preparation.
Dr. Ryan Karongo, Scientific Lab Head at Bayer AG
Dr. Michael Karimov, Research Scientist at Bayer AG
What are LNPs and why automate their preparation?
Advancements in RNA-based therapies, including the COVID-19 vaccines, highlight the significance of lipid nanoparticle (LNP) formulations. LNP formulation technology is essential for these RNA-based therapies, but has historically been particularly time-consuming and costly. The reasons behind these limitations are that LNPs (comprised of cationic, ionizable lipids and other constituents) notably require precise mixing for optimal effectiveness. This requirement notably highlights the fact that microfluidic devices seem indispensable in order to achieve successful formulations, adding complexity to the preparation methods and considerably hindering potential scalability.
Looking closer at the results and benefits of automating LNP formulations with Andrew+
LNP Characterization and Comparisons Across Preparation Methods
Microfluidics | Manual Pipetting | Automated Pipetting (Andrew+) | |
|---|---|---|---|
Size (nm) | 90 +/- 2.3 | 176.1 +/- 1.5 | 207.08 +/- 18.88 |
PDI | 0.08 +/-
0.01 | 0.11 +/- 0.01 | 0.17 +/- 0.04 |
mRNA encapsulation efficiency
(%) | >97 | >97 | >97 |
Automated LNP preparation with Andrew+ shows slightly larger particle sizes compared to other methods but maintains consistent encapsulation efficiency and transfection efficacy into HepG2 cells.
Andrew+ provides comparable results to manual pipetting and microfluidics-based methods regarding LNP characterization
Automated preparation of LNP
formulations for high throughput screening
purposes allow the preparation of up to
96 formulations in two hours
Automated pipetting with Andrew+
shows adequate well-to-well
repeatability (n=8) for LNP preparation
The content from this page is reformatted from the webinar and application note linked above
