Predicting drug side effects
It would certainly be nice for the pharmaceutical industry to identify potential side effects of a drug before it starts to be tested on humans. Now, a research team at the University of California, San Diego (UCSD) might have found a solution. They have developed a new computational technique to predict drug side effects. Besides identifying adverse effects of a new drug before human clinical trials, this method can also be used to explain the known side effects of drugs already on the market.
You can see above how "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." (Credits for images: Credit: Lei Xie, Jian Wang, and Philip Bourne,UCSD; credit for caption: Technology Review)
The UCSD team was led by Philip Bourne with the help of other members of his lab. The two other researchers involved are Lei Xie and Jian Wang. "Conventional test methods screen compounds in animal studies in advance of human trials in the hope of identifying the side effects of promising therapeutics. The UCSD team instead uses the power of computational modeling to screen specific drug molecules using a worldwide repository, the Protein Data Bank (PDB), containing tens of thousands of three-dimensional protein structures."
Here are additional details provided by the UCSD. "Drug molecules are designed to bind to targeted proteins in order to achieve a therapeutic affect, but if the small drug molecule that functions as a 'key' attaches to an off-target protein that has a similar binding site, or 'lock,' side effects can result. To identify which proteins might be unintended targets, the UCSD researchers take a single drug molecule and look for how it might bind to as many of the proteins encoded by the human proteome as possible. In this published case study, they looked at Select Estrogen Receptor Modulators (SERMs), a class of drug that includes tamoxifen, to illustrate the novel approach."
In "Calculating Drugs' Side Effects," Technology Review describes the results of the researchers. "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."
And her is a comment from Bourne: "'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.' 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."
Anyway, simulations cannot always replace experiments. So "the UCSD researchers are continuing their studies, which Bourne says can be applied to any drug on the market for which a structure of the drug bound to the receptor exists in the PDB. Bourne emphasized that results from this approach still needed to be tested experimentally."
Finally, here is the conclusion of the Technology Review article. "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."
This research work has been published by the PLoS Computational Biology journal in its November 2007 issue under the title "In Silico Elucidation of the Molecular Mechanism Defining the Adverse Effect of Selective Estrogen Receptor Modulators." Here are two links to this open access paper, in HTML format or in PDF format (9 pages, 700 KB). The above images have been extracted from this paper.
Sources: Debra Kain, University of California, San Diego (UCSD) news release, December 11, 2007; Jocelyn Rice, Technology Review, December 15, 2007; and various websites
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