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There are two articles on the same subject. Here’s the task:

  1. Read both articles

  2. Look at the facts underlined in Discovering New Uses for Old Drugs. Are they mentioned in the second article? If yes, underline them in the second article and write the number of the fact in the margin

  3. Find and underline at least 3 more facts that were mentioned in both articles (please number them so that it’s clear what corresponds to what).

  4. Find and underline at least 4 facts in each article that are not mentioned in the second one (please underline them in a different way, e.g. with a wavy line).

  5. Imagine that you need to write a short article for a technology website summarizing this new research. Write the summary of these two articles. It has to be between 70 and 120 words long.

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Discovering New Uses for Old Drugs

(1) It’s widely estimated that it takes at least $1 billion to develop a new drug. More than 100,000 compounds are screened to find about 10,000 that are worthy of studying in preclinical laboratory cell and animal studies. Of these, only around 10 ever make it as far as clinical trials. One of these experimental agents is eventually proven effective and approved for use by the U.S. Food and Drug Administration (FDA) — eight to 14 years later.

Given that drug discovery and testing is prohibitively expensive, time consuming and prone to failure, the number of agents approved for use in the U.S. by the FDA has fallen from 50 in 2000 to just 24 in 2011. Despite the odds, a team of scientists at Georgetown University Medical Center has found two new potential anti-cancer drugs for the small price of several federal research grants. And they did it, quickly, using mostly computers.

The team, led by Sivanesan Dakshanamurthy, Ph.D., has developed a novel method to uncover new uses for existing drugs, of which there are thousands. The latest count puts the number of clinically active agents, already approved and available worldwide, at about 27,000.

And, given the emerging realization that identical biological pathways can be active in different ways in different diseases, “the possibilities for repositioning existing drugs for new indications seems limitless,” says Dakshanamurthy, an assistant professor of oncology who works in the Experimental Therapeutics Program at Georgetown Lombardi Comprehensive Cancer Center.

Working backwards — taking a known drug and finding new uses — to discover potential new therapies could radically reduce drug testing and approval time, Dakshanamurthy says.

In the August 1 issue of the Journal of Medicinal Chemistry, Dakshanamurthy and seven other GUMC researchers described how (2) they found that a drug used to treat hookworm has unexpected anticancer properties, and that a popular anti-inflammatory drug is active in both rheumatoid arthritis and hard-to-treat cancers.

“This is just so much fun — it’s like being a kid in a candy store,” he says. For this grown-up computational chemist/biochemist, the sweet reward will be to efficiently identify new and unexpected treatments for patients using drugs already on the market.

Molecule of Best Fit”

What Dakshanamurthy and his colleagues developed is a novel rapid computerized system that maps the crystal structure of an approved drug and tests whether it fits into human protein crystal structures. “Drugs work on a lock and key system. The lock is the protein and the drug is the key that turns it on or off,” he says.

To find these potential keys, Dakshanamurthy taps into the National Institutes of Health (NIH) Chemical Genomics Center (NCGC) database of 27,000 pharmaceutical structures, which the federal government has made available to help researchers pursue drug “repositioning.” To find the potential locks, he mines the protein database maintained by the NIH’s National Center for Biotechnology Information.

The scientists developed a comprehensive computerized prediction method called “train, match, fit, streamline,” or TMFS, to map new drug-target interactions and predict new uses. It measures 11 different variables, including the shape and topology of the drug and ligand, the contact points of the ligand and the target protein, chemical similarity, and the tightness by which the agent docks and binds to the protein. “This allows us to better predict the ‘molecule of best fit’,” Dakshanamurthy says.

Using TMFS, the researchers screened 3,671 FDA-approved and investigational drugs across 2,335 protein structures. (3) The method turned up known lock-and-key, drug-ligand interactions with 91 percent accuracy, as measured by published studies, and it also uncovered several new uses for old drugs.

For example, TMFS predicted that the anti-hookworm drug mebendazole can inhibit the vascular endothelial growth factor receptor 2 (VEGFR-2) which is a protein that binds to and activates VEGF. VEGF promotes angiogenesis, or the creation of new blood vessels — a process necessary for tumor growth. Mebendazole could therefore be a possible VEGF inhibitor, joining other experimental anti-VEGF agents being tested for various cancers. Dakshanamurthy demonstrated that the drug does indeed affect the function of VEGFR-2.

“It may be now possible to repurpose this anti-parasitic drug to cut off the blood supply that enables many forms of cancer to grow and spread,” Dakshanamurthy says.

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