A New Computational Technique Predicts Side-Effects Of A Major Cancer Drug

Main Category: Pharma Industry / Biotech Industry
Also Included In: Cancer / Oncology
Article Date: 28 Nov 2007 - 2:00 PDT

email to a friend   printer friendly   view / write opinions   rate article

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.

CITATION: Xie L, Wang J, Bourne PE (2007) In silico elucidation of the molecular mechanism defining the adverse effect of selective estrogen receptor modulators. PLoS Comput Biol 3(11): e217. doi:10.1371/journal.pcbi.0030217
Please click here

Disclaimer

This press release refers to an upcoming article in PLoS Computational Biology. The release is provided by the article authors. Any opinions expressed in this release or article are the personal views of the journal staff and/or article contributors, and do not necessarily represent the views or policies of PLoS. PLoS expressly disclaims any and all warranties and liability in connection with the information found in the releases and articles and your use of such information.

About PLoS Computational Biology

PLoS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales through the application of computational methods. All works published in PLoS Computational Biology are open access. Everything is immediately available subject only to the condition that the original authorship and source are properly attributed. Copyright is retained by the authors. The Public Library of Science uses the Creative Commons Attribution License.

http://www.ploscompbiol.org

About the Public Library of Science

The Public Library of Science (PLoS) is a non-profit organization of scientists and physicians committed to making the world's scientific and medical literature a freely available public resource.

http://www.plos.org