Penn researchers in the Mitchell Lab are modifying lipid nanoparticles, the delivery vehicles for mRNA therapies, to make them more potent, precise, and better tolerated.
Lipid nanoparticles (LNPs), the tiny delivery vehicles for mRNA therapies, already work extremely well, as demonstrated by the COVID-19 vaccines, which saved millions of lives.
Penn Engineering’s Michael Mitchell is the principal investigator at the Mitchell Lab.
(Image: Courtesy of Penn Engineering)
Made of different types of lipids, the same class of fatty molecules found in animal tissue and other natural substances like olive oil, LNPs have a unique ability to deliver delicate medicinal cargo. Because the membranes of animal cells are also made of lipids, the particles can easily pass through them.
But, as Michael J. Mitchell, associate professor of bioengineering, has found, there are still plenty of ways to improve LNPs, making them even more potent, precise and better tolerated.
Postdoctoral fellow Dongyoon Kim and doctoral student Amanda Murray found that subtle changes in lipid chemistry can influence immune cell metabolism and reduce inflammation. The results suggest that LNPs can actively shape how immune cells respond, opening new avenues for vaccines and immunotherapies.
Postdoctoral fellows Qiangqiang Shi and Jinjin Wang and doctoral student Hannah Geisler have helped develop prodrug-tethered LNPs (pLNPs), which could one day serve as a universal immunotherapy for cancers that form solid tumors. The particle delivers both mRNA that activates immune cells and a drug that blocks tumor-driven immune suppression.
Doctoral student Hannah Yamagata and postdoctoral fellow Marshall Padilla redesigned a key LNP component to steer mRNA toward lymph nodes while reducing off-target accumulation in the liver. The advance could enable more precise, lower-dose vaccines and expand the potential of mRNA therapies for cancer and autoimmune disease.
Doctoral student Andrew Hanna helped develop LIBRIS, an automated microfluidic platform that dramatically accelerates the process of synthesizing LNPs. By generating on the order of 1,000 distinct formulations per hour, the system could enable the large, systematic datasets needed to train predictive AI models, which could help researchers design particles with particular properties.
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