--- title: "MIT Builds a Machine That Makes Drug-Carrying Particles to Order" description: "Automated control over the size and shape of lipid nanoparticles could cut the trial and error out of RNA drug design" author: "Arthur Wren" published: 2026-09-26T00:46:38.555Z modified: 2026-09-26T16:59:25Z url: https://rews.cc/a/mit-builds-a-machine-that-makes-drug-carrying-particles-to-o-6e3ae9 language: en tags: ["rna", "vaccines", "lipid-nanoparticles", "mit", "automation", "science"] publisher: "Rews (https://rews.cc)" --- # MIT Builds a Machine That Makes Drug-Carrying Particles to Order *Automated control over the size and shape of lipid nanoparticles could cut the trial and error out of RNA drug design* By Arthur Wren · September 26, 2026 · https://rews.cc/a/mit-builds-a-machine-that-makes-drug-carrying-particles-to-o-6e3ae9 ## In brief - MIT researchers automated production of lipid nanoparticles, the fatty shells that deliver RNA and DNA therapeutics - A two-step mixing method controls particle size via delay time and shape via buffer concentration - The system measures particles as they form, self-corrects, and trained a machine-learning model on the results - Particle size affects where in the body a drug ends up; the work appears in ACS Nano - Team filed a patent and is commercialising through a new company, BIZON Labs; FDA and NCI funded the research The mRNA vaccines that carried the world through the Covid-19 pandemic depend on a small piece of packaging. Inject mRNA naked and the body breaks it down at once; it has to ride inside a fatty shell called a lipid nanoparticle, or LNP, to reach the right cells. “These are really a revolutionary type of therapeutics, but they need some kind of delivery vehicle to bring them to the right cells in the body,” says Cedric Devos, an MIT postdoc. The trouble is that making those shells to a chosen size and shape has been slow, fiddly work. MIT researchers now say they have built a process that does it automatically, with no human hand involved, and they argue it could speed the development of new RNA and DNA therapeutics. The study appears in ACS Nano. Devos, fellow postdocs Aniket Udepurkar and Peter Sagmeister are lead authors, and Allan Myerson, a professor of the practice in MIT’s Department of Chemical Engineering, is senior author. LNPs usually have four components: an ionizable lipid, a phospholipid, cholesterol, and a lipid attached to polyethylene glycol, which stabilises the particle. To make them, two fluid streams are mixed at high speed — lipids suspended in ethanol in one, mRNA dissolved in an acidic buffer in the other. The streams do not mix at equal rates; there is about three times as much mRNA solution as lipid solution, a ratio that helps the particles form but gives little say over how large or what shape they end up. Designing new particles has meant long rounds of trial and error. Size is not a detail. It decides where in the body a particle is likely to land, and tuning size and shape could let developers aim drugs at particular organs and tissues. “If you make an LNP-based therapeutic with a target size of 150 nanometers, and one that is 70 nanometers, and everything else is the same, they will behave very differently,” Devos says. Myerson is blunter about the state of the art: “The size and shape of LNPs could not be reliably controlled by any previous production method. The problem may appear simple at first glance, but in reality it requires a deep understanding of lipid nanoparticle assembly.” The MIT group’s answer, set out in a paper in ACS Nano last year, was to split the mixing in two. First, mRNA and lipids meet at equal flow rates. Then, after a short delay, more buffer is added, which stops the particles growing. The longer the delay, the bigger the particle. “This gives you the ability to play around with the residence time, which is the time it takes between the first mixer and the second mixer,” Devos says. “It gives you a lever over lipid nanoparticle manufacturing that wasn’t available before.” Varying the buffer’s concentration at the second step changes shape too, turning spheres into elongated bodies the researchers compare to avocados. Neither change alters what the particles are made of — only how big they grow and what form they take. The new paper automates the whole thing. The team added a commercially available dynamic light scattering device that measures the particles as they form. A user specifies a size; the system makes it, checks it, and if the particles come out wrong, adjusts the delay time and other factors until they come out right. “The first paper really unlocked the new methodology to make lipid nanoparticles, to truly engineer them by size and shape,” Sagmeister says. “With the second study, we automate the whole process.” Shapes can be produced too, though measuring them still has to be done outside the automated system. Because the machine runs on its own, the researchers could test how each input changes the output far faster than before, then fed the results into a machine-learning model that predicts which combination of settings yields a given size or shape. “This method can help you determine what are the parameters that will generate specific size and shape attributes, before you take those particles and see which one will perform best,” Devos says. “It could be a quite powerful development tool.” Sagmeister credited MIT’s Undergraduate Research Opportunities Program, saying applied mathematics and computer science undergraduates Joy Ren, Sofiya Chubich and Dylan Nguyen learned “how advanced software engineering can be integrated with chemical engineering to develop an automated platform for LNP process development.” The researchers have filed a patent and are commercialising the technology through a new company called BIZON Labs, which has passed through the Martin Trust Center for MIT Entrepreneurship’s Researcher 2 Entrepreneur program and been accepted into MIT’s 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 at MIT.nano. Nothing here cures anything yet. What the machine removes is the drudgery between an idea for an RNA drug and the particle that might carry it — and in drug development, drudgery is measured in years.