Monday, December 17, 2007

Calculating Drugs' Side Effects

Continued from page 1

By Jocelyn Rice

While other groups have also used computational methods to identify off-targets of known drugs, the technique has never before been deployed on such a colossal scale. The new method searches the entire known "druggable genome," a set of proteins with the potential to bind to drugs that were culled from the Protein Data Bank, a database containing the structures of more than 10,000 proteins. Crunching through so many structures required a major dedication of computing resources. Lei Xie, senior scientist on the project, originally planned to develop the technique for a pharmaceutical company. When he was not granted the resources, he brought the project to Bourne's lab.

Previous work on off-targets was limited to small clusters of proteins grouped by functional or structural similarity. Searching the whole known druggable genome opens the door to the discovery of unexpected drug-protein relationships that narrower searches would miss. Indeed, that seems to be what's happened with tamoxifen and the calcium-related SERCA protein.

But although the Protein Data Bank holds an enormous number of protein structures, it is by no means comprehensive. Bourne estimates that the set of proteins his team worked with represents approximately 40 percent of the true druggable genome. Many drug receptor proteins either haven't yet had their structures elucidated or are not amenable to current methods of determining protein structure. "There are a lot of very interesting targets that we have no structural information about, and this approach is not going to be useful for those," says Roth. However, he adds, "if you have a three-dimensional structure for a target, then it's a great way to go."

Bourne hopes that this kind of computational screening will be adopted by the pharmaceutical industry. By screening in silico--using computers--for potential harmful side effects, companies may be able to eliminate drug candidates before they undergo expensive animal testing and clinical trials. In addition, as Bourne demonstrated with the selective estrogen receptor modulators, a drug can be modified so that it binds more tightly with its target protein and more loosely with off-target proteins, increasing its effectiveness and reducing its side effects.

"In an ideal situation, this would become part of the drug-discovery process," Bourne says. Roth agrees that drug design would benefit from such an approach.

The technique could also be used to identify ways to repurpose existing drugs, an application that Bourne's group is currently exploring. Not all side effects are bad: just as the antidepressant bupropion is also used as an aid in smoking cessation, many drugs could have more than one beneficial use. Computational screening for off-targets could identify such alternative uses of known drugs.