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New computational technique can predict drug side effects
Date:12/11/2007

Early identification of adverse effects of drugs before they are tested in humans is crucial in developing new therapeutics, as unexpected effects account for a third of all drug failures during the development process. Now researchers at the University of California, San Diego (UCSD) have developed a novel technique using computer modeling to identify potential side effects of pharmaceuticals, and have used the technique to study a class of drugs that includes tamoxifen, the most prescribed drug in the treatment of breast cancer. Their study is currently available on line at PLoS Computational Biology.

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 led by Philip Bourne, Ph.D., professor of pharmacology at UCSDs Skaggs School of Pharmacy and Pharmaceutical Sciences and Lei Xie, Ph.D., of the San Diego Supercomputer Center at UCSD 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.

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.

The computer procedure we developed starts with an existing three-dimensional model of a pharmaceutical, showing the structure of a drug mo
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Contact: Debra Kain
ddkain@ucsd.edu
619-543-6163
University of California - San Diego
Source:Eurekalert

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