A computer model can predict where in the body nanoparticles go

Nanoparticles are increasingly used in imaging, drug delivery, and other biomedical applications because of their unique properties. But nanoparticles that enter the body must end up somewhere after the therapy has run its course, and it’s been a challenge to predict which organs they will collect in—and therefore the therapy’s potential side effects.

A new study suggests that what’s on the surface of the nanoparticles may have the biggest influence on where the particles accumulate in mice (ACS Nano 2025, DOI: 10.1021/acsnano.5c03040).

Predicting how nanoparticles disperse in the body is like “finding patterns within the chaos,” says Mokshada Kumar, a pharmacokinetics PhD student at the University at Buffalo who was not involved in the study. There are plenty of data on nanoparticles in the body, but there is a lack of consensus on how researchers measure them chemically and biologically. For example, some researchers may assess the particle size using microscopy imaging; others may derive the same measurement from the way the particle scatters light. Likewise, some studies may report only the amount of the particles that made it into a single organ. These discrepancies make it difficult to generalize trends across systems, Kumar says.

To tackle this problem, researchers led by Bernd Nowack of the Swiss Federal Laboratories for Materials Science and Technology (Empa) scoured through studies that included similar measurement methods in mice. The result was a prediction model that links the particles’ physicochemical properties—including size, internal chemical makeup, and external coating—to their biological trajectory in healthy mice.

“The main goal is to find a way, using a simple test, to know how risky new materials will be before they are produced,” says Jimeng Wu of Empa, the lead author of the study. “We are trying to find a method that is capable to replace animal testing in the future.”

Wu and her colleagues’ analyses compared data on 18 nanoparticles made of five different materials: iron oxide, silicon dioxide, gold, graphene oxide, and titanium dioxide. These particles ranged from 5 to 3,000 nm in size and had varying surface coatings, shapes, electrical potentials, and dosing regimens. The researchers then fitted data on the particles’ distribution in four organs—the liver, kidneys, spleen and lungs—onto their model and figured out how likely it was for each type of particle to make it to those organs.

The researchers identified ζ potential, the electrical charge that forms on the surface of a particle in suspension, as the most influential predictor of where a nanoparticle settles in the body. Wu says the study proves that this “is a very important descriptor.” The two other properties that had the biggest effect were particle size and coating material.

While the sample size is limited, the new approach, “is a great step forward in the right direction,” Kumar says. This type of predictive approach could be used as a quick screening tool during particle design to eliminate unnecessary animal studies in the future. “It could save a lot of effort, resources and useful time,” she adds.