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MIT made RNA’s fatty delivery shells change size on command

An automated process tunes lipid nanoparticle size and shape, but has yet to show better treatment outcomes.

AI-assisted, human-reviewed

Original editorial illustration of small and large lipid nanoparticles moving through a two-stage mixing channel.Science
Illustration: OddBrief

Key facts

Research
MIT reported an automated lipid nanoparticle process September 25
Control
timing and buffer composition affect particle size and shape
Use
research into RNA and DNA delivery
Limit
no patient benefit demonstrated

MIT chemical engineers reported on September 25 an automated method for making lipid nanoparticles in chosen sizes and shapes. The fatty particles carry RNA or DNA into cells, so faster, more controlled production could make it easier to compare candidate therapies, though the method has not shown a benefit in patients.

The delivery vehicle is part of the medicine

RNA molecules degrade quickly on their own. Lipid nanoparticles package them for delivery, as they do in some mRNA vaccines. The particles' size and shape affect where they tend to travel in the body, meaning two formulations with the same ingredients can behave differently.

Conventional production mixes lipid molecules in ethanol with RNA in an acidic buffer. MIT's team changed that mixing sequence. It first combines the streams at equal flow rates, then adds more buffer after a controlled delay. Longer delays let the forming particles grow larger. Changing the second buffer's concentration can alter their shape, including producing elongated particles the researchers compare with avocados.

The important distinction is that the team can vary size and shape without changing the particle ingredients. That makes comparisons more informative: a researcher can test whether dimensions alone change where a formulation goes or how well it works.

An automated feedback loop

The group combined the two-stage process with measurement and adjustment. A user specifies a target size, and the system generates particles, measures them and changes settings if the result misses the target. Data from those experiments also trained a machine-learning model to predict settings for particular particle dimensions.

MIT says the method can explore candidate formulations on a much shorter timescale than existing trial-and-error work. One researcher gave an example of particles measuring 150 nanometers versus 70 nanometers: despite identical chemistry, their behavior in the body could be very different. The method helps make such experiments more systematic, not necessarily more successful.

The researchers have filed a patent and are working to commercialize the approach through a company called BIZON Labs. The published result concerns particle engineering, not a new approved vaccine or therapeutic. Future studies would still need to establish where the particles go, what cargo they deliver, whether they are safe and whether any resulting treatment improves outcomes.

The next question is whether precise manufacturing control translates into a useful biological advantage. For now, the advance gives researchers a better way to ask that question.

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