by Melissa Pappas
From COVID vaccines to cancer immunotherapies to the potential for correcting developmental disorders in utero, mRNA-based approaches are a promising tool in the fight against a wide range of diseases. These treatments all depend on providing a patient’s cells with genetic instructions for custom proteins and other small molecules, meaning that getting those instructions inside the target cells is of critical importance.
The current delivery method of choice uses lipid nanoparticles (LNPs). Thanks to surfaces customized with binding and signaling molecules, they encapsulate mRNA sequences and smuggle them through the cell membrane. But with a practically unlimited number of variables in the makeup of those surfaces and molecules, figuring out how to design the most effective LNP is a fundamental challenge.
Now, in a study featured on the cover of the journal Nano Letters, researchers from the University of Pennsylvania’s School of Engineering and Applied Science and Perelman School of Medicine have now shown how to computationally optimize the design of these delivery vehicles.
Using an established methodology for comparing a wide range of variables known as “orthogonal design of experiments,” the researchers simultaneously tested 256 candidate LNPs. They found the frontrunner was three times better at delivering mRNA sequences into T cells than the current standard LNP formulation for mRNA delivery.
The study was led by Michael Mitchell, Skirkanich Assistant Professor of Innovation in the Department of Bioengineering in Penn’s School of Engineering and Applied Science, and Margaret Billingsley, a graduate student in his lab.
Read the full story in Penn Engineering Today.