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Working Backwards to Fill the Drug Pipeline

The group also discovered evidence that the architecture and chemical signature in Celebrex, a popular prescription medicine for pain and inflammation, could allow it to bind the cell adhesion molecule cadherin-11, a molecule known to be important in both rheumatoid arthritis and in certain cancers with no known targeted therapies and poor prognoses. Stephen Byers, Ph.D., a researcher who studies the function of cadherins in breast and colon cancer, then proved through laboratory experimentation that Celebrex does indeed bind to cadherin-11, disabling its function. Byers is a co-author on the Journal of Medicinal Chemistry study.

(4)The scientists also predicted that tamoxifen, which is widely used to treat and prevent some breast cancers, would interact with multiple proteins related to various disorders, including Alzheimer’s disease, and cancers of the prostate and ovaries.

“(5)The safety of these drugs is already known, so once an old drug is repurposed to a new indication, it can directly enter clinical trials,” Dakshanamurthy says. “Based on past drug approvals, I would expect testing of approved agents could take between two to four years.”

Dakshanamurthy is not the only scientist working to repurpose old drugs, but he says the TMFS system offers the hope that the process can be computerized, which is relatively cheap, compared to traditional laboratory methods.

“We need to fill up the drug pipeline and get it flowing smoothly and quickly,” he says. “This method may help to pump up the volume — and that is very exciting.”

By Renee Twombly, GUMC Communications (Published August 22, 2012)


Repurposing drugs

Virtually there

Computers may give new life to old medicines Aug 11th 2012 | from the print edition

MOST drugs have side-effects. One reason is that they interact with proteins other than the one they are being aimed at to treat a particular disease. Viewed another way, however, this means some drugs can have more than one use if what is a side-effect of one treatment can be turned into the central effect of another. Existing medicines may thus be repurposed for new therapies, not only helping patients but also saving drug companies time and development money, since they do not have to retest the substances in question to show they are safe.

They do, however, have to work out what other proteins the drug in question is interacting with, and that itself is a costly business. But it may soon get cheaper if a method proposed by Sivanesan Dakshanamurthy, a molecular biologist at Georgetown University in Washington, DC, turns out to work. For Dr Dakshanamurthy plans to do the first part of this testing inside a computer.

Dr Dakshanamurthy’s initial study of the method, just published in Medicinal Chemistry, started from the observation that the shapes of most drug molecules—and particularly the dispositions of the groups of atoms within them that bind to specific proteins and thus make them potent—are well known and publicly available. Also publicly available are databases loaded with information about many of the proteins found in the human body, and what sorts of molecules they tend to interact with. It was just a question, he reasoned, of matching the two together.

Of course, “just” is a slippery word. But a lot of programming later he and his colleagues managed to build a model they hoped would be able to compare information on the structures of drugs with information on the structures of human proteins, in order to find the best fits between the two.

To test their model, the team fed it information on 3,671 drugs already approved by America’s Food and Drug Administration, together with data on the structures of 2,335 proteins found in the human body. And it worked. In 91% of cases it matched a drug to a protein known to be its target.

As a proof of principle, that is not bad. That the model missed some known interactions was to be expected, and the information thus gathered can be used to refine it. What Dr Dakshanamurthy was really looking for, though, were cases where the model predicted an interaction not yet observed in the real world. Given the model’s successful rate of matching drugs to known targets, he thinks some of these previously unknown connections might be worth investigating.

For example, the model suggests that a drug called mebendazole, currently used to combat hookworms, also interacts with a group of proteins known as tubulins. Since tubulins are associated with angiogenesis (the growth of new blood vessels from old ones), and angiogenesis is an important part of the development of cancers, it might behove someone to investigate whether mebendazole can be used to treat cancer as well as parasites.

The model also suggested that an anti-inflammatory drug called celecoxib should bind with CDH11, a protein which plays an important role in the development of both rheumatoid arthritis and metastatic breast cancer. That suggestion, too, would bear further scrutiny.

Mere interaction with a protein does not a drug make. The interaction has to have a useful effect—damping down the activity of a protein that is too abundant, perhaps, or stimulating that of one which is not abundant enough. But interaction of some sort is a prerequisite for the development of a new drug or a new use for an existing one. And the model offers lots of potential. Nearly 27,000 molecules are approved for pharmaceutical use, and the human genome project showed that there are at least 23,000 human proteins. Even though not all of those proteins are well-enough understood for the Dakshanamurthy treatment to work on them, that is still a lot of potential interactions—far more than can conveniently be investigated by screening in a laboratory. But screening in a computer is fast and cheap. And even if only a handful of the new interactions it throws up are real and useful, it will have been a worthwhile exercise.

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