Friday, February 10, 2017

Keeping Nanoparticles—and Treatments—on Target

We all know that the human body has weaknesses. Whether the cause is genetic, environmental, personal choices, pure dumb luck, or some combination of factors, it’s not uncommon for diseases to take hold and destroy a body cell-by-cell. In the fight against these diseases, one of the most promising approaches involves using tiny nanoparticles to carry toxic drugs to precisely the right place: the infected cells.

As this approach evolves, it is really important to make sure that the nanoparticles only target infected cells. If they latch on to healthy cells instead, they can cause serious side effects and make the treatment less effective. In research published this week in the journal Physical Review Letters, Stefano Angioletti-Uberti from Imperial College London and Beijing University of Chemical Technology demonstrates a way to design nanoparticles that are more selective about their targets.

The surface of every cell is covered in receptors, protein structures that bind to chemicals like hormones or neurotransmitters that are outside of the cell. These bonds can send signals into the cell and cause changes in its behavior.

In order to use nanoparticles for drug delivery, the first step is to identify receptors on the infected cells that are different from those on healthy cells. The next step is to find a molecule, or ligand, that binds strongly to that particular receptor. Then, the ligand can be attached to the outside of the nanoparticles carrying the drug.

With apologies to the biologists for this overly simple explanation, you might think about it this way. Imagine that a receptor is kind of like a locked door and ligands are keys. When the key fits perfectly inside the lock, the door opens and the drug is delivered. When a diseased cell has a different lock than a healthy cell, this technique seems straightforward -- the nanoparticle is outfitted with a key that only opens the door to a diseased cell.

Other scenarios, however, have to be considered. In the case of some diseases, including some cancers, a diseased cell will have the same receptor as a healthy cell, but lots more of them. The approach then is to attach several keys to the nanoparticle and design the nanoparticle to only release the drug when the binding energy between the nanoparticle and the cell is really high—higher than one key-in-lock combination will produce on its own. In other words, the door only opens when you have keys in multiple locks. This works most of the time, but not always.

Regardless of the targeting scenario, the problem is that ligands can still bind to receptors that aren’t an exact match—the bonds just aren’t as strong. In some cases, several weak bonds can add up to a high binding energy and fool a nanoparticle into thinking it’s found a diseased cell. This means more side effects and higher toxicity.

In considering this problem, Angioletti-Uberti wondered whether it would be possible to optimize the ligands so that they become more selective in this situation. He had an idea, but was in the middle of several other research projects so he filed it away in the ““Ideas_for_future_projects” folder on his computer. Fast-forward four years and, when time and opportunity aligned, Angioletti-Uberti opened the file and got to work.

The problem is one of statistics. There are several different receptors on a cell that can each bind to one, or none, of several ligands on a nanoparticle. For high selectivity, the targeted receptors should bind with the ligands so that their collective effect results in a high binding energy. In addition, they should bind a lot more weakly to all other possible receptors, so that even collectively these bonds do not contribute very much to the binding energy.

Working through a mathematical model for this, Angioletti-Uberti showed that as the number of ligands increases, the nanoparticles become more susceptible to attaching to healthy cells. This is because there is more potential for weak bonds to form between ligands and the other receptors on the surface of a cell. In other words, nanoparticles with more ligands have a harder time distinguishing between target cells and other cells.

While this is bad news, his work also suggests an easy and very general solution: before sending the nanoparticles out in search of diseased cells, coat them with “protecting” receptors that bind to the attached ligands.

This series of images shows a nanoparticle (blue) with several ligands (red) interacting with a cell surface that has orange and green receptors. Assume the orange receptors are overproduced if the cell is diseased, and green receptors have nothing to do with the health of the cell.
a)-b): Ideal scenario—Ligands only bind to targeted receptors, and nanoparticles attach to the cell when these are expressed above a certain threshold (indicated by 100% adsorption).
c)-d): Realistic scenario—Ligands see both targeted and untargeted receptors, binding to untargeted receptors more weakly. Multiple weak bonds can still lead to attachment even when targeted receptors are not over-expressed (c). This problem might be alleviated by using “protective" receptors (light green) directly coated on the nanoparticle (d).
Image Credit: Stefano Angioletti-Uberti.

It might seem counterintuitive, but here is the logic. You want the ligand to bind with only the targeted receptor, not anything else on the cell. A ligand can only bind to one receptor at a time, but the bonds in this system are constantly breaking and reforming. An unbound ligand will bind with whatever receptor is most favorable at the moment. If the “protecting” receptors are more favorable than random receptors on a cell but less favorable than the target receptor, an unbound ligand will bind first to a target receptor, if it’s available. If not, it will bind to a “protecting” receptor instead of a random receptor on a cell. This reduces the likelihood of weak bonds between a nanoparticle and an untargeted cell adding up to a high binding energy.

This work is theoretical, but Angioletti-Uberti is optimistic that it will soon be put to test in the lab. “I really hope to convince as many people as possible to try this design principle,” says Angioletti-Uberti. The benefit could be huge, he says, for drug-delivery systems, biosensors that look for diseases, and other biomedical applications that rely on targeting receptors.

Kendra Redmond

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