Monday, December 17, 2007

Calculating Drugs' Side Effects

A new computational technique could predict the toxicity of drug candidates.

By Jocelyn Rice

Double bind: Selective estrogen receptor modulators, such as the breast-cancer drug tamoxifen, bind to a specific pocket (top, shown with an unrelated molecule used to determine the pocket’s shape) in a protein called estrogen receptor alpha. The modulators may also bind to a similarly shaped pocket (bottom, again shown with a shape-gauging molecule) in another protein called SERCA. If they do, that could explain several of the side effects associated with tamoxifen. The blue and red coloring denotes electrostatic potential, which helps determine where and how a small molecule (like a drug) will bind to a protein. Credit: Lei Xie, Jian Wang, and Philip Bourne (Creative Commons Attribution License)

A new computational method that searches an enormous database of protein structures could allow researchers to predict a drug's potential side effects without breaking out a single test tube. The technique, developed by researchers at the University of California, San Diego (UCSD), could also be applied to existing drugs to explain known side effects or to identify additional uses.

"The approach that they're taking is different and certainly way more computationally intensive than other approaches," says Bryan Roth, a pharmacologist at the University of North Carolina, Chapel Hill, who was not involved with the work.

Drugs work by latching onto very specific receptor sites on protein targets, much like a key fitting into a lock. If a drug also happens to latch onto another, unintended target, it may produce unwanted side effects. The new method analyzes the shape of the intended lock and then combs through the structures of other proteins to look for similarly shaped locks. If such a lock is found, an algorithm rigorously determines whether the drug fits snugly into it. If it does, the technique has probably identified what's called an "off-target."

The researchers, led by UCSD pharmacologist Philip Bourne, didn't stop there. Once they had identified an off-target, they worked backward, looking for other drugs known to bind to it and then using their technique to determine whether those drugs could also bind to the original target. If so, the researchers had further evidence that the two proteins--the intended target and the unintended target--had highly similar drug receptor sites. Then, by assessing the biological function of the off-target, they could postulate possible side effects of the drug being tested.

In the November issue of PLoS Computational Biology, Bourne and his team described applying the new method to a class of drugs called selective estrogen receptor modulators. These drugs--including tamoxifen, the most widely prescribed drug for breast cancer--latch onto a protein called estrogen receptor alpha. Tamoxifen is known to cause side effects such as cardiac abnormalities, retinal degradation, and blood clots. All these side effects have one thing in common: they involve disruptions in the ways that calcium ions normally flow through cells, regulating the balance of electrical charges in various cell compartments.

"That would imply that maybe what's involved here is some protein that involves calcium, as part of the normal calcium flow in the cell," says Bourne. And sure enough, the team found that in addition to the lock on estrogen receptor alpha, tamoxifen could fit into a similar lock on a calcium-binding protein known as SERCA. Bourne speculates that when tamoxifen latches onto SERCA, it physically prevents calcium from doing so--potentially leading to the drug's calcium-related side effects.

"The prediction is certainly plausible and interesting," says Roth, but he cautions that it's purely computational and must be validated biochemically.

Bourne's new technique inverts the approach ordinarily taken to computational drug design. Most methods focus on the drug rather than on the protein that it binds to. Pharmaceutical researchers routinely sift through databases of small molecules looking for drugs to match a particular protein. Bourne's team, by contrast, is looking for proteins to match a particular drug.