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Chang-Guo Zhan: Using Computers to Battle Cocaine

by Jeff Worley

When Chang-Guo Zhan was 7 years old, his mother sat him down for a serious talk. "She was enthralled with science," says Zhan, a native of central China. "She told me that she hoped I would grow up to be a scientist or engineer."

Well, Mother knows best. Zhan says he was already bent toward science anyway, even at 7; his mother's encouragement merely fanned the flames of that small fire. Zhan earned both a bachelor's and master's degree in chemistry at Central China Normal University, a Ph.D. degree in physics at the Institute for Molecular Science in Japan, then a Ph.D. in chemistry at the University of Notre Dame. Talk about making your mother proud.

Zhan then went to the Columbia University Department of Medicine and also worked at Pacific Northwest National Laboratory as a visiting scientist. Last July he joined the UK faculty as an associate professor in the College of Pharmacy.

"I was attracted to UK because of all the possibilities for collaboration," says Zhan. "What I do is to design new drugs and new medicines through computational predictions, and what I can do here, that I couldn't anywhere else I've been, is also test these predictions—see how accurate they are." One of the many strengths of the UK College of Pharmacy, which has been ranked as the third-best pharmacy college in the nation by U.S. News & World Report for more than a decade, is that drug design, delivery and analysis can all be implemented in one location.

In his work to design new drugs for specific diseases, Zhan starts with some fundamental theories of physics, chemistry, and biology. Practical application of the theories usually requires some time-consuming mathematical manipulation, he says, which he does with the help of supercomputers. He uses the final computations to then predict new molecular structures that could turn out to be more effective drugs than what exist now.

"The computational approach is a starting point to develop drugs," Zhan says, adding that this approach is only possible because of developmental leaps in recent years of both computer science and computational science.

One of Zhan's current projects, funded by the NIH, is developing a drug to fight cocaine addiction, and his approach involves tinkering with a well-known cocaine-metabolizing protein that has a name long as a line of boxcars: butyrylcholinesterase (a plasma enzyme, hereafter referred to as "BChE" for convenience sake).

"First, with the help of a computer, we have to understand how this protein metabolizes cocaine through hydrolysis in the plasma of the human body. Then we will need to computationally predict how BChE's structure can be modified to hydrolyze cocaine more quickly." Zhan further explains that BChE is a structurally modified mutant. And if it can hydrolyze cocaine more quickly than the native BChE, and if this mutant could be injected into the human body, the protein would help decrease the half-life of the cocaine that exists in plasma. "In this way," Zhan says, "cocaine would have less of a chance to reach the central nervous system and therefore to be effective."

He adds that developing an effective pharmacological treatment, given the disastrous medical and social consequences of cocaine addiction, such as violent crime, illness and death, give this work a high priority.

So Zhan's job is to develop a computational model of a drug that is a good predictor for fighting addiction. But how does such a model on a computer screen get tested?

"After we predict that a specific BChE mutant can hydrolyze cocaine more quickly, an experimental biochemist can make the predicted mutant and measure the actual speed of cocaine hydrolysis in water. Then if that pans out—if the drug can hydrolyze cocaine quickly enough—we could propose performing preclinical tests with an animal model and, finally, clinical tests with human volunteers."

Zhan emphasizes that this work can be done only because of the many advances in the past half-dozen years in computer modeling. "I'm lucky here to have a large, dedicated supercomputer for this work at the UK Center for Computational Sciences, and lucky, too, that the staff there take good care of it. Also, I have access to a supercomputer in the Pacific Northwest National Laboratory, where I have worked off and on since 1998," Zhan says.

"I admit that I love this work," he adds. "I love the challenge. At the outset I may have no idea at all how to solve a particular problem; then eventually I make significant progress on it and finally solve it. It's this process of discovery I really enjoy."