Automated lipid nanoparticle platform promises faster RNA therapy design

The revolutionary success of mRNA vaccines against Covid-19 rested on an unsung piece of engineering: the lipid nanoparticle, or LNP, a fatty bubble that ferries fragile genetic instructions safely through the bloodstream and into cells. Now researchers at the Massachusetts Institute of Technology have unveiled an automated platform that can manufacture these delivery vehicles far faster than before, and with unprecedented control over their size and shape — two properties that determine where in the body the particles travel and how well a therapy works. The new study, published in ACS Nano, could dramatically shorten the trial-and-error process that currently slows the development of RNA and DNA medicines.

Lipid nanoparticles are the workhorses of a rapidly growing class of therapeutics. mRNA injected on its own would be shredded within minutes by enzymes in the body, so it must be wrapped in a protective shell of lipids that both shields the genetic cargo and encourages cells to take it up. These are, as the researchers describe them, a revolutionary type of medicine, but one that depends entirely on a delivery vehicle capable of reaching the right cells. Getting that vehicle right has long been as much art as science.

A typical LNP contains four ingredients: an ionizable lipid that binds the genetic payload, a phospholipid, cholesterol, and a PEG-lipid that stabilizes the particle. To assemble them, manufacturers rapidly mix two streams of fluid — one carrying lipids dissolved in ethanol, the other carrying mRNA in an acidic buffer. Conventional mixing uses roughly three times more of the mRNA solution than the lipid solution, a ratio that promotes particle formation but offers little command over the dimensions of the resulting particles. Because size governs biodistribution, that blind spot has been a persistent bottleneck.

Size matters enormously. A particle destined for the liver may fail entirely if it is too large or too small to be taken up by the intended tissue, and tuning dimensions can open the door to targeting other organs. As senior author Allan Myerson, a professor of the practice in MIT’s Department of Chemical Engineering, notes, the problem may look simple at first glance but in reality requires a deep understanding of how lipid nanoparticles assemble. Previous production methods, he explains, simply could not reliably control particle size and shape.

The foundation for the new platform was laid in a study the same team published last year, also in ACS Nano. There, the researchers split the mixing process into two steps. In the first, mRNA and lipids are combined at equal flow rates, allowing particles to begin nucleating and growing. Then, after a precisely timed delay, additional buffer is injected, which halts growth. By adjusting the residence time — the interval between the two mixers — the team gained a direct lever on particle size. Long delays let particles grow large; short delays keep them small. Changing the composition of the buffer added in the second step, meanwhile, allowed them to morph the particles from spheres into elongated forms resembling avocados.

Crucially, both interventions adjust size and shape without altering the chemical composition of the nanoparticles themselves, meaning developers can explore the design space of a fixed formulation. Cedric Devos, an MIT postdoc and one of the lead authors of the study, compares the residence time to a lever over LNP manufacturing that simply was not available before. The approach gives formulators a way to ask, systematically, which combinations of parameters generate specific size and shape attributes before testing which particles perform best in biological experiments — a powerful development tool, in his words.

The new paper takes that methodology a decisive step further: automation. The team connected their two-step mixing system to a commercially available dynamic light scattering device that measures particle sizes in real time as they form. The entire process runs without any human intervention. A researcher simply specifies a target size, and the system produces particles, measures them, and — if they miss the mark — automatically adjusts the delay time and other operating parameters until the output matches the specification. The platform can also generate particles of different shapes, though shape measurements currently must be performed outside the automated loop.

With the first study, the team unlocked a new methodology for truly engineering lipid nanoparticles by size and shape, says Peter Sagmeister, an MIT postdoc and lead author. With the second, they automated the whole process. The project also drew on MIT’s Undergraduate Research Opportunities Program, through which applied mathematics and computer science undergraduates Joy Ren, Sofiya Chubich, and Dylan Nguyen helped integrate advanced software engineering with chemical engineering to build the automated platform — a collaboration Sagmeister credits with giving the students hands-on experience at the intersection of the two disciplines.

The payoff of automation is speed, and speed enables something deeper: data. Working on a timescale far faster than conventional experiments allow, the researchers systematically varied the inputs to the system and recorded how particle size and shape responded. They then used this dataset to train a machine-learning model capable of predicting the combination of factors needed to generate a particular size or shape on demand. In effect, the platform converts LNP formulation from a craft into an engineering discipline, where desired properties can be specified up front and achieved predictably rather than discovered through weeks of iterative tinkering.

The implications reach across the entire pipeline of nucleic acid drug development. Because a 150-nanometer particle and a 70-nanometer particle built from identical ingredients will behave very differently in the body, as Devos points out, the ability to rapidly generate matched particles differing only in size gives developers a rigorous way to identify the optimal formulation for a given application — whether a vaccine, a cancer therapy, or a treatment aimed at a tissue that current LNPs struggle to reach. The researchers have filed for a patent on the technology and are commercializing it through a startup called BIZON Labs, which has received support from MIT’s Researcher 2 Entrepreneur program and acceptance into the Institute’s flagship delta v accelerator. The work was funded by the U.S. Food and Drug Administration and a Koch Institute Support grant from the National Cancer Institute, and carried out in part using MIT.nano facilities. If the platform performs as promised outside the lab, the slow, guesswork-laden search for the perfect nanoparticle may soon give way to something resembling an assembly line for the next generation of genetic medicines.

Subject of Research: Automated two-step microfluidic manufacturing and machine-learning optimization of lipid nanoparticles for RNA and DNA therapeutics

Article Title: A new technique could accelerate the development of RNA therapies

Article References: A new technique could accelerate the development of RNA therapies. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: lipid nanoparticles, RNA therapeutics, mRNA vaccines, microfluidics, drug delivery, machine learning, automation, nanotechnology, chemical engineering, MIT, ACS Nano, particle size control