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A New Computational Technique Predicts Side-Effects of a Major Cancer Drug

Researchers at the University of California San Diego have developed a novel computer technique to search for the side effects of major pharmaceuticals.

The study, reported November 30 in PLoS Computational Biology relates to a class of drugs known as Select Estrogen Receptor Modulators (SERMs), which includes tamoxifen, the most prescribed drug in the treatment of breast cancer.

Unexpected side effects account for one-third of all drug development failures and result in drugs being pulled from the market. Typically drugs are tested using an experimental method which aims to identify off-target proteins that cause side effects. The team in this study, led by Drs. Philip Bourne and Lei Xie, propose a computational modeling approach. If broadly successful the approach could shorten the drug development process and reduce costly recalls.

Rather than considering a single human protein to which a very large number of potential small molecule drugs can bind, Bourne et al. 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.

The team uses a case study focusing on SERMs to illustrate their technique. They report a previously unidentified protein target for SERMs which is supported by both biochemical and clinical data with known patient outcomes. The identification of a secondary binding site with adverse effects opens the door to changing the drug to maintain binding to the intended target, but to reduce binding to the off-target. This work is just the beginning of the process and experimental validation is continually needed.

By identifying new binding sites the computer analysis may also contribute to repositioning existing drugs to treat completely different diseases from those originally intended. Bourne and Xie are now working in this direction.

Andrew Hyde | alfa
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